Modelos de cálculo para el par de apriete

Unrestricted vector auto regressions (VARs) allow feedback and dynamic interrelationship across all the variables in the system and it’s highly competitive with the large-scale macro econometric models in forecasting and policy analysis. The unrestricted VARs model assumes that each and every variable in the system is endogenous and does not impose any a-priori causality restrictions among the variables.

A VAR model allows the variables to interact with each other and themselves too without imposing a theoretical structure on the estimates. Variance decompositions (VDCs) and impulse response functions (IRFs) are the major tools found in VAR tool kit. VDCs reflect the portion of the variance in the forecast error for each variable due to innovations to all variables in the system while IRFs show the response of each variable in the system to shock from system variables.

It traces the effect of a one-time shock to one of the innovations on current and future values of the endogenous variables

The bench mark reduced VAR model can be written as

X _t = α₀+ A₁ X_t-1 + B^-1 e t………(6)

However the reduced form disturbances are generally known to be correlated hence it is necessary to transform the reduced form model into a structural form model. This is known as VAR identification process. This study relied on recursive identification scheme which entails that the ordering of the variables according to contemporaneousness. The VAR model is a vector natural logarithm of total Horticultural exports (LNt_HORT_EXP), Gross domestic product (LN GDP), export of flowers (LNE_flowers), effective exchange rate (LN_NEER), consumer price index(LNCPI) ,foreign direct investments (LNFDI) and total horticultural rejections(LNT REJECTIONS).

Impulse Responses

Impulse responses trace out the response of current and future values of each of the variables to a one-unit increase (or to a one-standard deviation increase, when the scale matters) in the current value of one of the VAR errors, assuming that this error returns to zero in subsequent periods and that all other errors are equal to zero. Impulse response functions (IRFs) show the dynamic behaviour of a variable as

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given by its time path in response to exogenous random shocks given to this and other variables. This implies that it is possible to identify the pass through effects of shocks on variables. Panel A, B and C illustrates the impulse response functions (IRFs) of the VAR model for a period of 20 quarters forecast horizon. The vertical axis shows the magnitude of a shock while the horizontal axis shows the time path of the responding variable.

Each panel depicts the dynamic effect of a one standard deviation innovation on each of the variables of interest. The broken red line shows the 95% confidence interval generated by 1000 Monte Carlo simulations while the bold blue line shows the impulse response function of one variable due to innovation in other variable. A response is considered significant if it doesn’t contain the zero line or the horizontal bench mark line within its confidence bands.

In panel a shock in flower production (a sudden raise in flower production) remains significant in the market for four quarters before the stability is resumed. However a shock in economic performance marked by sudden raise in GDP seems not to have any significant effect on flowers production. This can be explained by the fact that agricultural production of flowers seems to be driven by exogenous factors rather than domestic economic performance. On the other hand, most of the flowers produced are for exports hence domestic economic performance may not have an effect on flowers production.

A similar argument is supported by the evidence that foreign direct inflows have no effect on flower production.

Notably, a raise in flower production has an appreciating effect on exchange rate with the effect lasting for three quarters. Kenya majorly being an exporter of agricultural products, exchange rate seems to fluctuate in tandem with agricultural exports. On the other hand, consumer price index shock weakens exchange rate for 10 quarters.

Domestic inflation raises the level of imports hence the exchange rate depreciates. However, depreciation has a positive but lagged effect on flower production since it is associated with high returns for exporters.

The lagged effect is explained by the gestation period required for agricultural sector to respond to market dynamics.

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PANEL A

In panel B below, foreign direct investments has a significant effect on consumer price index. These can be explained by the fact that investments are a component of aggregate demand. Based on aggregate demand- aggregate supply model, it is expected that a raise in

-.4

Response of LNE_FLOWERS to LNGDP

-.4

Response of LNE_FLOWERS to LNE_FLOWERS

-.4

Response of LNE_FLOWERS to LNCPI

-.4

Response of LNE_FLOWERS to LNNEER

-.04

Response of LNCPI to LNFDI

-.04

Response of LNNEER to LNGDP -.04

Response of LNNEER to LNE_FLOWERS

-.04

Response of LNNEER to LNCPI

-.04

Response of LNNEER to LNFDI AB

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investment will raise price level. However, FDI flows have no effect on the total export of horticultural products. It is also notable that total export of horticultural makes the exchange rate to appreciate.

Depreciation lowers agricultural output in the first quarter perhaps due to raise in imported inputs but the output rises in the longer horizon.

Again, depreciation raises GDP significantly in the 4^th quarter perhaps through the exports channel but the effect is not permanent because the shock dies in the 8^th quarter.

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PANEL B

In panel c below, rejection of horticultural exports does not reduce production. On the other hand, increase in horticultural production does not increase the level of rejected exports.

-.4

Response of LNGDP to LNNEER

-.4

Response of LNGDP to LNT_HORT_EXP

-.02

Response of LNCPI to LNFDI

-.02

Response of LNCPI to LNNEER

-.04

Response of LNNEER to LNCPI

-.04

Response of LNNEER to LNFDI

-.04

Response of LNNEER to LNT_HORT_EXP

-.2

Response of LNT_HORT_EXP to LNGDP

-.2

Response of LNT_HORT_EXP to LNCPI

-.2

Response of LNT_HORT_EXP to LNFDI

-.2

Response of LNT_HORT_EXP to LNNEER

-.2

Response of LNT_HORT_EXP to LNT_HORT_EXP

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PANEL C

-.4 -.2 .0 .2 .4 .6

2 4 6 8 10 12 14 16 18 20

Response of LNTREJECTIONS to LNT_HORT_EXP

-.2 -.1 .0 .1 .2 .3 .4

2 4 6 8 10 12 14 16 18 20

Response of LNT_HORT_EXP to LNNEER

-.2 -.1 .0 .1 .2 .3 .4

2 4 6 8 10 12 14 16 18 20

Response of LNT_HORT_EXP to LNTREJECTIONS

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4.6 Conclusion

The horticulture sector is one of the ‘success stories’ of the Kenyan export economy. It represents over 33 per cent of total exports and 15 per cent of agricultural GDP. This sector is therefore critical for the country’s growth and development. This paper analyses the effects of standards on exports in view of other factors that affect export supply;

it attempts to answer the fundamental question, to what extent do standards affect export supply? Findings from the study contribute to a very important policy debate on standards and developing country exports.

An empirical model is specified along the standard trade models that incorporate real exchange rate, per capita income, CPI all which appear to impact on exports. An error correction formulation is used to distinguish between the long run and short-run elasticity. Results from this study indicate that the error correction term in the model is found to be statistically significant, confirming the validity of the long-run equilibrium relationship. In the short-run, real exchange rate and per capita income have profound influences on export performance including flower and fresh fruits and vegetables exports.

The structural break is observed to have a negative influence on horticulture exports and those of fresh fruits and vegetables but not those of flowers. The structural breaks occurred in the years after various rules and regulations we introduced or made tighter for instance the most significant break in 2004 was mainly due to the introduction of EUREPGAP and stricter EU regulations including harmonization of EU standards with WTO standards in the year 2003.

Standards do affect exports in a negative way. The effect of rejections is pronounced both in the long and short run and for the three categories i.e. total horticultural exports, cut flowers and fresh fruits and vegetable exports. SPS measures have a negative and significant relationship for horticulture exports and for both cut flower and fruits and vegetable exports in the short run. In the long run, the effects of SPS measures are not significant. TBT measures are also found not to be significant to any of the export categories in the long and short run. In conclusion, the study shows that supply response for horticulture is mainly influenced by variables such as GDP per capita, exchange rates

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and FDI. Standards, specifically SPS measures also affect supply response of horticultural produce and in the short and long run.

Looking

at the impulse response functions we can conclude that exchange rates do affect export volumes as depreciation lowers agricultural output ie exports in the first quarter.

From this chapter we can therefore conclude that as much as standards may affect exports in the long and short run, other macro-economic variables such as real exchange rate, inflation and per capita income are also significant in determining the supply response of exports. From a development perspective, it is important to have sound macro-economic policy for export growth and for these exports to contribute to meaningful development. The next sections of this research dissertation will examine the specific value chain effects of standards on different actors to determine who are the losers and the winners in a globalized value chain and the last section will examine the participation of smallholders in these value chains as direct beneficiaries of these exports.

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5.1 Introduction

The high value fresh produce sector in many developing countries is a recently emerging non-traditional export commodity sector consisting of a number of key players including producers, farmers (large and small-scale), exporters and distributors, traders and retailers. These retailers are governed by standards and have reorganized their supply/value chains around notions of traceability, food safety, and quality assurance. Some authors, particularly in the economic geography

‘school of thought’, have established a link between the roles of standards in shaping the governance structures of high value chains (Graffham et al. 2007, Nielsen 2008, Lazaro et al. 2010, Ouma 2010).

The governance of global chains is a crucial element for efficiency and distributional effects - including for growth and food security. The chain governance itself is endogenous in an environment of weak contract enforcement and imperfect markets, and importantly, depends on the value in the chain (and on other commodity characteristics). The supply chain governance – or the way economic transactions in supply chains are coordinated (Gereffi et al. 2005) – are crucial in determining how economic surpluses are generated and distributed along the chain.

There is large variation in how food and agricultural commodity chains are governed, with the involvement of the public sector and/or different private agents and the varying levels of vertical coordination between those actors. It has been argued and empirically demonstrated that the degree of vertical coordination in supply chains indeed influences economic outcomes, in particular efficiency and equity (Swinnen & Maertens 2007) this is often because large and often multinational companies are extracting the entire surplus through their bargaining power within the chains.

There is an emerging body of literature that analyzes the distribution of gains along value chains. This literature can be broadly divided into 3 strands - the first focuses on the uneven geographical incidence of price

5 Understanding the Distributional