An Assessment of the Impact of Exchange Rate Deregulation and Structural Adjustment Programme on Cotton Production and Utilization in Nigeria
Aliyu A. Ammani
At Nigerias independence, agriculture was the mainstay of the economy. It provided employment, food, raw materials for industry and foreign exchange. However, within 20 years of Independence the country became unable to cope with the overall needs of its food and raw materials. Increased foreign exchange earnings from the export of crude oil were implicated as leading to the neglect and subsequent decline in the performance of the Nigerian agricultural sector. The Structural Adjustment Programme SAP was adopted to restructure and diversify the productive base of the economy in such a way as to reduce dependency on the oil sector and imports. One of the key policy strategies designed to achieve the Nigeria's SAP goals was the adoption of a market-determined exchange rate. This paper set out to assess the impact of exchange rate deregulation and SAP on cotton production and utilization in Nigeria. Time series data on aggregate cotton production, Nairas average cross exchange rates with the US dollar and average capacity utilization rate of textile manufacturers in Nigeria for the period 1973-2007 were collected and analysed using Multiple-regression and the students t test technique. Findings includes: exchange rate deregulation per se has no significant effect on cotton production in Nigeria; more cotton was produced in Nigeria during the post-SAP period; the average capacity utilization of domestic textile industry in Nigeria during the pre-SAP period was higher than during the post-SAP period. Based on the findings of the study some noteworthy lessons were highlighted.
Received: October 27, 2011;
Accepted: November 17, 2011;
Published: January 07, 2012
At Nigerias independence in 1960, agriculture was the mainstay of the
Nigerian economy. According to Ilugbuhi (1968), peasant
agricultural production for export provided the stimulus to Nigerias overall
economic growth. Agriculture provided employment to over 75% of the population
and accounted for over 70% of total food consumption. It also provided raw materials
for industry, export earnings to finance imports and foreign exchange (Alamu,
The importance of the cotton crop to the Nigerian economy cannot be over-emphasised
(Chikwendu, 1993; Adeniji et
al., 2007). The lint removed from the seed is used as raw material for
the textile industry. The cotton-seed provide edible vegetable oil for human
consumption (Kudi et al., 2007). The cotton-seed
cake is used as an important raw material for livestock feeds (Barje
et al., 2008; Udo and Umoren, 2011). About
80% of total cotton production in Nigeria was carried out by peasant farmers
(Adeniji, 2007). The Nigerian textile industry was the
second leading employer of labour after the public sector (Idem,
1999; Manyong et al., 2005). Cotton was thus
an important source of food for man, feed for animals, raw material for the
textile industry, direct employment to cotton farmers as well as an indirect
source of employment to workers employed by agro-based industries that relied
on cotton as raw material i.e., textiles, edible oil and animal feed manufacturers.
About 21 years after Independence, Abdullahi (1981)
observed: Nigeria can no-longer produce enough food for its fast growing population
neither could the agricultural system cope with the increasing demands of the
agricultural raw materials to keep the countrys oilmills, textile and
other agro-based industries operating at full capacity let alone have surplusses
for export. In fact many of the agro-based industries which once depended on
locally produced raw materials are closing down unless of course they are allowed
to import part or all of these raw materials from abroad. Numerous other parameters
point to the obvious and undeniable fact that the country is progressively becoming
unable to cope with the overall needs of its food and raw materials.
Several reasons were advanced for the decline in the performance of the Nigerian
agricultural sector, prominent among which is the increased foreign exchange
earnings from the export of crude oil between 1972 and 1980 that led to the
neglect of the Nigerian agricultural sector (Asiabaka and
Owens, 2002; Walkenhorst, 2007; Sekumade,
The international oil market plunged in 1982, reducing significantly Nigerias
ability to finance imports, including food and persistent current account deficits
began to emerge while unpaid trade bills began to accumulate (Osuntogun
et al., 1997). Signs of economic decline: trade deficits, budget
deficits, inflation and balance of payments problems became seriously manifest
(Osaghae, 1995). Experts pointed at structural adjustment
as the panacea to Nigerias persistent economic doldrums. The key argument
of the structural adjustment framework for economic policy reform was that state
and state interventionism were the sources of economic distortions to African
economies since independence (Colclough and Manor, 1991;
Structural adjustment, according to Ahmed and Lipton (1997)
is: A set of measures that seek to permit renewed, or accelerated, economic
development by correcting structural disequilibrium in the foreign and public
balances. Often, such measures are required as conditions for receiving World
Bank and IMF loans. These reforms attempt to eliminate distortions such as an
overvalued exchange rate, high fiscal deficits and restrictions on trade and
inefficient public services that often prevent an efficient allocation of resources
in the economy.
The broad objective of Nigeria's SAP was to restructure and diversify the productive
base of the economy in such a way as to reduce dependency on the oil sector
and imports. One of the key policy strategies designed to achieve the Nigeria's
SAP goals was the adoption of a market-determined exchange rate (Moser
et al., 1997). This is based on the argument in literature that overvalued
exchange rates makes domestic products, including agricultural products, not
only less competitive with imports but also less profitable as export (Mamingi,
1997). Exchange rate depreciation lowers the foreign currency price of exports
and tends to increase the quantity of exports and export revenue in domestic
currency (Fang et al., 2005; Hadiwibowo,
2010; Azgun, 2011). Empirical studies by Bahmani-Oskooee
and Kara (2003) and Abolagba et al. (2010)
reported that currency devaluation increases exports.
Another side of Exchange Rate deregulation is its effect on a countrys
Real Exchange Rate (RER). A crucial component in evaluating a countrys
macroeconomic performance and the sustainability of its policies is competitiveness
assessment, which routinely starts from an assessment of the RER level (Di
Bella et al., 2007). Many developing countries adopted real exchange
rate devaluation as an effective strategy to boosts exports (Haddad
and Pancaro, 2010). Hausmann et al. (2005) and
Adeniyi et al. (2011) reported a significant
relationship between RER depreciation and rapid economic growth. Real overvaluation
hampers exports with a consequent decline in economic growth (Easterly,
2005; Johnson et al., 2007). According to
Okonjo-Iweala and Osafo-Kwaako (2007), volatile fiscal
spending contributes to real exchange rate volatility. Barnett
and Ossowski (2002), domestic currency appreciation and reduction in competitiveness
of the non-oil sectors of the economy are likely consequences of fiscal expansions
funded by oil revenue. There is considerable theoretical and empirical evidence
on the adverse effects of volatility for growth (Fatas and
Mihov, 2003; Serven, 2003; Bleaney
and Greenaway, 2001).
The main objective of this paper is to assess the impact of exchange rate deregulation and SAP on cotton production in Nigeria. Specifically the study seeks to:
||Determine relationship between the Real Exchange Rate (RER)
of the Naira and the average cross exchage of the Naira to the US$ over
the study period (1973-2007)
||Investigate the effects of exchange rates of the Naira to the US$ on the
production of cotton in Nigeria (1973-2007)
||Assess the impact of SAP on the production and utilization of cotton in
To achieve the objectives of this study, the following hypotheses were formulated and tested:
||The SAP measures have no significant effects on the level
of cotton production in Nigeria during the study period
||The exchange rates of the Naira to the US$ has no significant effects
on the level of cotton production in Nigeria during the study period
||There is no significant difference in aggregate cotton production in Nigeria
before and after SAP in Nigeria
||There is no significant difference in average annual capacity utilization
of domestic textile industry in Nigeria before and after SAP in Nigeria
MATERIALS AND METHODS
This study utilised time series data on aggregate cotton production, Nairas
average cross exchange rates with the US dollar and average capacity utilization
rate of textile manufacturers in Nigeria for the period 1973-2007 to achieve
its stated objectives (Appendix Table A1).
Conceptual framework: The conceptual framework for this study is based on the following arguments: First, to achieve SAPs lofty goal of restructuring and diversifying the productive base of the Nigerian economy so as to reduce dependency on the oil-sector and import, agriculture is expected to play a significant role. Second, two important indicators to monitor the attainment of these objectives are: (i) increase yield in agricultural export crops to reduce dependency on oil as source of foreign revenue, (ii) increase capacity utilization of agro-based industries that utilizes industrial crops as raw material to reduce importation. Third, a significant change in the levels of these indicators will signify the level of impact of SAP.
Estimation of RER: The Real Exchange Rate (RER) was captured using the
following proxy (Mamingi, 1997):
||Official nominal exchange rate measured in Naira per US$
|| The US Wholesale Price Index
|| Domestic consumer price index
The Nigerian cotton WPI was taken as proxy for the US WPI following Harberger
(1986), Bautista (1987) and Fosu
(1992). The CPI for cotton was assumed constant because cotton is purely
an intermediate good.
Model specification and estimation: The impact of exchange rate deregulation on the production of an export/industrial crop such as cotton is assumed its impact on changes in the level of cotton production resulting from changes in exchange rate. The impact of the SAP measures is assumed as its direct effect on the level of cotton production in Nigeria as captured by the dummy variable D.
Consider a typical cotton farm with a production function:
where, Y is output, X represent variable inputs and Z represent fixed and other shifter variables of the function. Ignoring the fixed costs, the production function becomes:
Based on the economics of production outlined above, an empirical aggregate model is developed for cotton production in Nigeria, leaving out variables of less interest to this study, as follows:
where, Yt is cotton production in year t (measured in MT), X1t is exchange rate of the Naira to the US dollar in year t (expressed as a ratio of the Naira to the US dollar), X2t is average capacity utilization of domestic textile industry in year t (expressed in percent) and D is the dummy variable that takes a value of 0 for the years 1973-1985 and 1 for the years 1986-2007.
As noted in various literature, empirical analysis of time series data pose
several challenges as empirical work, including causality tests of Granger and
Sims based on time series data assumed that the underlying time series is stationary
(Seddighi et al., 2000; Enders,
1995; Patterson, 2002). Mercifully, as Gujarati
(2003) noted, by simply establishing stationarity of the residuals from
regression equation, the traditional regression methodology can be conveniently
applied to data involving non stationary time series.
Cointegration was tested on the data collected for this study using the Cointegrating
Regression Durbin-Watson (CRDW) Test method as expounded by Gujarati
Our regression model:
was estimated and the residuals obtained.
The DW d was computed using the following relation:
In CRDW Test, the Durbin-Watson d obtained from the cointegrating regression (6) is used, with a proviso that the null hypothesis is d = 0 rather than the standard d = 2 in the conventional DW test for autocorrellation.
The computed DW d (0.856) obtained from the cointegrating regression (5) is
greater than the critical value of 0.386 at the 5% level, thus it was concluded
that the regression residuals are stationary. However, the estimated DW d value
of 0.856 is lower than the critical DW dL value of 1.283, indicating
an evidence of positive first order serial correlation (Appendix
The first-order difference transformation method was not used to remedy the
detected autocorrelation problem because it is not appropriate for our case
despite its other advantages. This decision is guided by Maddala
(1992) rule of thumb on the appropriateness of using the first-order difference
method: use the first difference transformation method whenever d<R2.
It will be recalled that our computed d and R2 from Eq.
5 are 0.856 and 0.505, respectively i.e., d>R2.
The Praise-Winsten transformation method, as expounded by Gujarati
(2003) was used to transform the model, using ñ estimated based on
the Durbin-Watson d statistic. This is done, based on the following assumptions:
(a) that the error term in Eq. 5 follows the AR (1) scheme
and (b) that if Eq. 5 holds true at time t, it also holds
true at time (t-1), thus:
Multiplying Eq. 7 by p:
Subtracting Eq. 7 from Eq. 5:
Where, βt = (μt- ρμt-1)
Equation 10 was then expressed as follows:
Where, β*1 = β1 (1-ρ), = (Yt-ρYt-1), = (X1t-ρX1t-1), = (X2t-ρX2t-1), β*2= β2 and β*3= β3.
OLS was then applied to the transformed variables to obtain the usual optimum properties of the OLS coefficients asymptotically.
RESULTS AND DISCUSSION
Relationship between Real Exchange Rate (RER) and the average cross exchange
of the Naira to the US$: Fig. 1 depicts a graphic representation
of how average exchange rate and the values of RER estimated in this study compare
over the period 1973-2007.
|| Results of regression analysis with transformed variables
|| Results of regression analysis with transformed variables
|R2: 0.287, Adjusted R2: 0.218, R: 0.536,
F(model): 4.167, p-value for F(model): 0.014, DW
d: 0.566, aStatistically significant statistics at both β:
5 and β: 1%
It is clear that the 2 sets of data have almost the same values up to 1985,
the pre-SAP period. But beginning from 1986, the SAP period, when the country's
exchange rate was first determined officially through a public auction in the
foreign exchange market, a slight difference in values between ER and RER could
be noticed. From 1989 when the autonomous market and the official foreign exchange
market were merged to form a single Inter-bank Foreign Exchange Market (IFEM),
the difference in values between ER and RER became pronounced. From 1991 the
difference between the two values became glaring, indicating a case of RER volatility.
The ER and RER values deviate extensively from one another, indicating distortions
in resource allocation. This finding is in agreement with that of Adubi
and Okunmadewa (1999), that exchange rate volatility has a direct negative
effect on the level of agricultural export trade in Nigeria by causing a decline
in export production.
The effects of exchange rates of the Naira to the US$ and SAP on cotton production in Nigeria: The specified aggregate cotton production model is estimated using the transformed time series data for the period 1973-2007 with SPSS 16.0 supported with Microsoft Excel 2007.
The R2 and F values of obtained from our transformed regression
Eq. 10 (Table 1), are considearbly smaller
than those obtained from our level form regression Eq. 4 (Appendix
Table A2). As noted by Gujarati (2003), this is because
by taking the first-difference in the course of transforming our regression
model, we are essentially studying the behavior of variables around their (linear)
trend values. In such case, as observed by Maddala (1992),
we cannot compare the R2 and F values of Eq. 7
and 10 because the dependent variables in the 2 models are
different. This explains our decision to use the R2 and F values
of our level form model (7) in discussing the fitness of our model.
The F value of 10.535 computed for Eq. 7 is highly significant. This implies that the included explanatory variables (annual average exchange rate of Naira to the US$, average annual capacity utilization of domestic textile industry and the effects of the SAP measures on cotton production as represented by the dummy variable) together significantly explain the variation in aggregate cotton production. The R2 value obtained from the equation is 0.505. This indicates that the explanatory variables included in the model explained, on the average, more than 50% of the variation in the total aggregate cotton production over the study period. The unexplained variation, less than 50%, in the model is attributable to other factors not specified in the model due to difficulties in quantification and availability of relevant data.
All estimated parameters of our regression Eq. 10, with
the exception of that of the dummy variable D, were statistically not significant
at the 5% level. The t value of 2.986 obtained for the coefficient of the dummy
variable D from the regression equation is found to be significant when viewed
in relation to its computed p-value of 0.005, hence the formulated null hypothesis
is rejected. The statistically significant coefficient of the dummy variable
indicates that the SAP measures have a positive impact on cotton production
in Nigeria. This finding is in agreement with Adubi and Okunmadewa
(1999) that increased in production of export crops followed the adoption
of SAP in Nigeria.
The statistically not significant estimated exchange rate parameter (-0.574)
with a p-value of 0.570 at the 5% level indicates that exchange rate deregulation
per se has no significant effect on cotton production in Nigeria. This finding
contradicts Abolagba et al. (2010) that exchange
rate deregulation leads to increase production of export crops. However, this
contradiction could be explained away as resulting from the double-edged sword
characteristic of exchange rate deregulation, one of the lessons enumerate from
this study as highlighted in the conclusion section of this paper.
The statistically not significant estimated average capacity utilization of domestic textile industry parameter (0.982) with a p-value of 0.334 at the 5% level indicates that domestic utilization has no significant effect on cotton production in Nigeria.
Impact of the structural adjustment programme on the production and utilization
of cotton: As mentioned earlier and for the reasons advanced, the transformed
regression Eq. 10 yielded parameters with considerably lower
values. This informed our decision to utilize the Students t test technique
for comparison of means of independent samples, at the 5% level of significance
to test hypotheses (iii) and (iv). For a description of the Students t
test technique, by Lehmann (1991), Hogg
and Craig (1995) and Keller and Warrack (2003). Sulaiman
and Jaafar-Furo (2010) demonstrated the application of the Students
t test technique in socio-economic research.
From the results of the Students t test (Table 2),
the calculated t value of -3.400 is found to be highly significant when viewed
in relation to the computed p-value of 0.002, hence the null hypothesis is rejected
and it is thus concluded that there is a highly significant difference in mean
aggregate cotton production between the pre-SAP period (1973-1985) and post-SAP
|| Results of students t test
The mean difference of -173.38 indicates that the mean aggregate cotton production
in Nigeria in the post-SAP period is higher than the mean aggregate cotton production
of the pre-SAP period. The aggregate mean of the pre-SAP period is 167.308 while
that of the post-SAP period is 340.691. Thus, more cotton was produced in Nigeria
during the post-SAP period. This finding is in agreement with our earlier finding
that increased cotton production followed the adoption of SAP in Nigeria.
From the results of the Students t-test, the calculated t value of 5.846 is found to be highly significant when viewed in relation to the computed p-value of 0.000, hence the null hypothesis is rejected and it is thus concluded that there is a highly significant difference in average annual capacity utilization of domestic textile industry between the pre-SAP period (1973-1985) and post-SAP period (1985-2006). The mean difference of 26.45% indicates that the average annual capacity utilization of domestic textile industry in Nigeria during the pre-SAP period is higher than that of the post-SAP period. The mean of average annual capacity utilization of domestic textile industry of the pre-SAP period is 71.14% while that of the post-SAP period is 44.69%. Thus, the average capacity utilization of domestic textile industry in Nigeria during the pre-SAP period was higher that during the post-SAP period.
This study set out to assess the impact of exchange rate deregulation and SAP on cotton production in Nigeria. The main findings of the study are:
||The ER deregulation that follows the adoption of SAP measures
resulted in a pronounced deviation between the ER and RER values indicating
distortions in resource allocation
||SAP measures have a positive impact on cotton production in Nigeria
||Exchange rate deregulation per se has no significant effect on cotton
production in Nigeria
||Domestic utilization by textile manufacturers has no significant effect
on cotton production in Nigeria
||More cotton was produced in Nigeria during the post-SAP period
||The average capacity utilization of domestic textile industry in Nigeria
during the pre-SAP period was higher that during the post-SAP period
Based on the findings of this study, the following lessons are noteworthy: Exchange rate deregulation is a double edge sword. On the one hand, successful exchange rate devaluation could lead to increase in producer price which will increase producer incentives. On the other hand, devaluation could lead to increase in prices of agricultural inputs produced outside the economy like fertilizer, pesticides and machinery which as imported goods have their prices raised by exchange rate devaluation, which in-turn increases costs of input and decreases producer incentive to produce more. Thus, in a mineral resource dominated economy like Nigeria, exchange rate has little or no effect on export crop production.
The availability of adequate and functional infra-structure is critical for the success of SAP vis-α-vis the enhancement of the productivity of domestic agro-based industries is such a way as to reduce dependency on imports. The absence of supporting infra-structure, especially electric power supply in Nigeria, increased the operational costs of domestic textile manufacturers thereby making their products less competative both for export and against imported textile materials. As a consequence, most textile manufacturers were sent out of business, and their teeming employees rendered jobless. It has been mentioned earlier, that the textile industry was the second largest employer of labour in Nigeria.
Effective policies to protect domestic agro-based industries in an oil dependent country like Nigeria are a pre-requisite to the adoption of SAP. The absence of such policies in Nigeria, due to its membership of the WTO, makes domestic textile manufacturers vulnerable to the excessive and aggressive competition and probably dumping, from established foreign textile manufacturers.
|Appendix Table A1:
||Time series data on aggregate cotton production, Nairas
average cross exchange rates with the US dollar and average capacity utilization
rate of textile manufacturers in Nigeria for the period 1973-2007
|Source: CBN (2009)
|Appendix Table A2:
||Results of regression analysis of level Eq.
|R2: 0.505, Adjusted R2: 0.457, R:0.711,
F (model):10.535, p-value for F(model):0.000, DW d:0.856,
aStatistically significant statistics at both β: 5% and
Abdullahi, A., 1981.
The problems and prospects of the green revolution for agricultural and rural development of Nigeria: Technical and environmental perspectives. Proceedings of the National Seminar The Green Revolution in Nigeria, September 19-24, 1981, Ahmadu Bello University, pp: 1-11
Abolagba, E.O., N.C. Onyekwere, B.N. Agbonkpolor and H.Y. Umar, 2010.
Determinants of agricultural exports. J. Hum. Ecol., 29: 181-184.Direct Link |
Adeniji, O.B., 2007.
Constraints to improved cotton production in katsina state, Nigeria. J. Applied Sci., 7: 1647-1651.CrossRef | Direct Link |
Adeniji, O.B., J.P. Voh, T.K. Atala and A.O. Ogungbile, 2007.
Adoption of improved cotton production technologies in katsina state, Nigeria. J. Applied Sci., 7: 397-401.CrossRef | Direct Link |
Adubi, A.A. and F. Okunmadewa, 1999.
Price, exchange rate volatility and Nigeria's agricultural trade flows: A dynamic analysis. AERC Research Paper 87. African Economic Research Consortium, Nairobi, pp: 1-47.
Ahmed, I.I. and M. Lipton, 1997.
Impact of structural adjustment on sustainable rural livelihoods: A review of the literature IDS working paper No. 62. University of Sussex, Sussex.
Alamu, J.F., 1981.
Small-scale mechanised farming: The only hope of the current green revolution in Nigeria. Proceedings of the National Seminar on The Green Revolution in Nigeria, September 24, 1981, Nigeria, pp: 132-139
Asiabaka, C.C. and M. Owens, 2002.
Determinants of adoptive behaviors of rural farmers in Nigeria. Proceedings of the 18th AIAEE Annual Conference Durban, (AIAEE'02), South Africa, pp: 13-20
Bahmani-Oskooee, M. and O. Kara, 2003.
Relative Responsiveness of trade flows to a change in prices and exchange rate. Int. Rev. Applied Econ., 17: 293-308.
Barje, P.P., O.W. Ehoche, L.O. Eduvie, A.A. Voh Jr, G.N. Akpa and O.S. Lamidi, 2008.
Effect of varying levels of whole cottonseed supplementation on concentrate intake, weight gain and blood parameters in friesianxbunaji and bunaji heifers. Asian J. Anim. Vet. Adv., 3: 1-8.CrossRef | Direct Link |
Barnett, S. and R. Ossowski, 2002.
Operational aspects of fiscal policy in oil producing countries: Working paper No. WP/02/177. International Monetary Fund, Washington DC. USA.
Bautista, R.M., 1987.
Production incentives in Philippine agriculture: Effects of trade and exchange rate policies: IFPRI research report, No. 59. International Food Policy Research Institute, Washington DC., USA.
Bleaney, M. and D. Greenaway, 2001.
The impact of terms of trade and real exchange rate volatility on investment and growth in sub-Saharan Africa. J. Dev. Econ., 65: 491-500.CrossRef |
Central Bank of Nigeria Statistical Bulletin. Vol. 20, CBN Press, Abuja, Nigeria
Chikwendu, D.O., 1993.
Marketing of cotton in a deregulated economy: The Nigerian experience. Nig. J. Agric. Extension, 8: 55-55.
Colclough, C. and J. Manor, 1991.
States or Markets? Neoliberalism and the Development Debate. Oxford University Press, Oxford
Di Bella, G., M. Lewis and A. Martin, 2007.
Assessing competitiveness and real exchange rate misalignment inlow-income countries. IMF Working Paper. WP/07/201. Washington, DC., http://www.imf.org/external/pubs/ft/wp/2007/wp07201.pdf.
Easterly, W., 2005.
National Policies and Economic Growth: A Reappraisal. In: Handbook of Economic Growth, Aghion, P. and S. Durlauf (Eds.). Elsevier, Amsterdam, The Netherlands, pp: 1015-1056
Enders, W., 1995.
Applied Econometric Time Series. John Wiley and Sons, New York, ISBN-13: 9780471039419, Pages: 433
Fang, W.S., Y.H. Lai and S.M. Miller, 2005.
Export promotion through exchange rate policy: Exchange rate depreciation or stabilization? Economics Working Papers, Paper 200507. http://digitalcommons.uconn.edu/econ_wpapers/200507.
Fatas, A. and I. Mihov, 2003.
The case for restricting fiscal policy discretion. Q. J. Econ., 118: 1419-1447.Direct Link |
Fosu, K.Y., 1992.
The real exchangerate and Ghana's agricultural exports. AERC Research Paper 9. Centre for the Study of African Economies, Oxford, http://www.aercafrica.org/documents/RP9.pdf.
Gujarati, D.N., 2003.
Basic Econometrics. 4th Edn., Tata McGraw-Hill, New Delhi
Haddad, M. and C. Pancaro, 2010.
Can real exchange rate undervaluation boost exports and growth in developing countries? Yes, But not for long. Economic Premise No 20. Washington DC., World Bank, http://socionet.ru/publication.xml?h=repec:wbk:prmecp:ep20&l=en.
Harberger, A.C., 1986.
Economic Adjustment and the Real Exchange Rate. In: Economic Adjustment and Exchange Rates in Developing Countries, Edwards, S. and L. Ahamed (Eds.). University of Chicago Press, Chicago and London
Hausmann, R., L. Pritchett and D. Rodrik, 2005.
Growth accelerations. J. Econ. Growth, 10: 303-329.
Hogg, R.V. and A.T. Craig, 1995.
Introduction to Mathematical Statistics. 5th Edn., Prentice Hall, Englewood Cliffs, NJ
Idem, N.U.A., 1999.
Cotton Production in Nigeria. Baraka Press and Publishers Ltd., Kaduna
Ilugbuhi, T.O., 1968.
Nigeria's experience in domestic financing of development. Institute of Administration, Research Memo Series, Zaria, Nigeria.
Johnson, S., J.D. Ostry and A. Subramanian, 2007.
The prospects for sustained growth in Africa: Benchmarking the constraints: Working paper No. 07/52. International Monetary Fund, Washington, DC., USA.
Keller, G. and B. Warrack, 2003.
Statistics for Management and Economics. 6th Edn., Brooks/Cole, Pacific Grove, California, United States, ISBN: 9780534421946, Pages: 715
Kudi, T.M., J.G. Akpoko and Z. Abdulsalam, 2007.
Assessment of the cotton industry using the global commodity chain analysis approach in katsina state, Nigeria. J. Applied Sci., 7: 3557-3561.CrossRef | Direct Link |
Lehmann, E.L., 1991.
Testing Statistical Hypotheses. 2nd Edn., Chapman and Hall, New York.
Maddala, G., 1992.
Introduction to Econometrics. 2nd Edn., Macmillan, New York, USA., Pages: 663
Manyong, V.M., A. Ikpi, J.K. Olayemi, S.A. Yusuf, B.T. Omonona, V. Okaruwa and F.S. Idachaba, 2005.
Agriculture in Nigeria: Identifying Opportunities for Increased Commercialization and Investment. IITA, Ibadan, Nigeria, pp: 159
Mamingi, N., 1997.
The impact of prices and macroeconomic policies on agricultural supply: A synthesis of available results. Agric. Econ., 16: 17-34.
Moser, G.G., S. Rogers and R. van Til, 1997.
Nigeria: Experience with Structural Adjustment. International Monetary Fund, Washington DC., Pages: 106
Okonjo-Iweala, N. and P. Osafo-Kwaako, 2007.
Nigeria's Economic Reforms: Progress and Challenges. Brookings Global Economy and Development, Washington, DC., ISBN: 9780979037658, Pages: 28
Olukoshi, A.O., 2004.
Democratization, Globalization and Effective Policy Making in Africa. In: The Politics of Trade and Industrial Policy in Africa: Forced Consensus? Soludo, C., O. Ogbu and H. Chang (Eds.). Africa World Press/IDCR, United States
Adeniyi, O., O. Omisakin and A. Oyinlola, 2011.
Exchange rate and trade balance in west african monetary zone: Is there a j-curve? The Int. J. Applied Econ. Fin., 5: 167-176.CrossRef | Direct Link |
Osaghae, E.E., 1995.
Structural Adjustment and Ethnicity in Nigeria. Nordic Africa Institute, Uppsala, ISBN: 9789171063731, Pages: 66
Osuntogun, C.A., C.C. Edordu and B.O. Oramah, 1997.
Potentials for Diversifying Nigeria's Non-Oil Exports to Non-Traditional Markets. African Economic Research Consortium, Nairobi, ISBN: 9789966900432, Pages: 36
Patterson, K., 2002.
An Introduction to Applied Econometrics: A Time Series Approach. Palgrave Macmillan, New York
Azgun, S., 2011.
Determinants of foreign trade deficits in the Turkish economy. Int. J. Applied Econ. Finance, 5: 149-156.CrossRef |
Seddighi, H., K. Lawler and A. Katos, 2000.
Econometrics: A Practical Approach. 1st Edn., Routledge, London, ISBN: 0415156440, pp: 396
Sekumade, A.B., 2009.
The effects of petroleum dependency on agricultural trade in Nigeria: An error correlation modeling (ECM) approach. Sci. Res. Essay, 4: 1385-1391.Direct Link |
Serven, L., 2003.
Real exchange-rate uncertainty and private investment in LDCS. Rev. Econ. Stat., 85: 212-218.Direct Link |
Sulaiman, A. and M.R. Ja'afar-Furo, 2010.
Economic effects of farmer-grazier conflicts in nigeria: A case study of bauchi state. Trends Agric. Econ., 3: 147-157.CrossRef | Direct Link |
Udo, I.U. and U.E. Umoren, 2011.
Nutritional evaluation of some locally available ingredients use for least-cost ration formulation for African catfish (Clarias gariepinus
) in Nigeria. Asian J. Agric. Res., 5: 164-175.CrossRef |
Walkenhorst, P., 2007.
Distortions to Agricultural Incentives in Nigeria. Agricultural Distortions Working, World Bank, Washington DC.
Hadiwibowo, Y., 2010.
Capital inflows and investment in developing countries: The case of Indonesia. Int. J. Applied Econ. Finance, 4: 220-229.CrossRef | Direct Link |