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The performance of exports depend on the interaction of the demand and supply-side factors. The demand side factors are often external to the country in question and most often are related to tariffs, or rules and regulations of the importing countries. Other non-price factors such as Non-Technical Barriers to trade such as SPS measure and other quality control measures may also be considered as demand side factors affecting export supply. Among the factors in the macro-economic environment that impinges on export supply response capacity at the

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aggregate economy are the investment regime, the level of investment – both domestic and private investment; real effective exchange rates; and foreign direct investment (FDI) flows. Investment in the exporting sectors is critical for sustenance of export growth and ensuring technological capability. There are compelling reasons for expecting exports to benefit differentially from public and private investment since they all tend to target different types of capital (i.e. public investment might target some infrastructural capital such as roads, electricity, water etc. while private investment might target technological capability).

This infinite elasticity of export demand allows for the estimation of a single equation for export supply function. Therefore in modelling horticultural exports trade, a supply function derived from the assumption of profit maximization on the part of producers and exporters is specified. Since the exports are supply constrained, an increase in the production capacity of the economy is likely to have appositive effect on the exports and vice versa

Both supply side and demand side constraints may affect export supply. In this case demand side constraints from developing countries are mainly standards and regulations and preferential market access. The supply side constraints are economic variables within the exporting country that may affect or constrain exports.

An export supply response model of horticulture exports is estimated using co-integration and ECM techniques applied to secondary quarterly data for the period 1995–2012 collected from various sources including the FAO database, and various volumes of the government of Kenya Economic Survey and Horticultural Crops Development Agency (HCDA). Johansen’s approach is used to test for co-integration between horticultural exports and its explanatory variables, and the ECM is used to estimate short- and long-run elasticities. The Granger causality test is conducted to determine the direction of causation between variables to draw policy conclusions.

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Variable Description

1. Real Effective Exchange Rates (REER)

Over the past several decades, Kenya has moved towards increasingly market-determined trade and exchange rate regimes. Kenya’s fixed exchange rate regime was replaced by a crawling peg which, in turn, was eventually replaced by a floating regime. Figure 4.2 below show the trends in exchange rates between 1995 to 2012 and there is a general steady increase with some fluctuations.

Figure 4.2: Trends in exchange rates

Source: KNBS

REER is defined as the measure of nominal exchange rate adjusted from for price differentials between Kenya and its trading partners. The real exchange rate is an important price transmission instrument for exports decisions. REER is computed using nominal the exchange rate data relative to price differentials between Kenya and its trading partners (mainly the EU). The annual data on REER was obtained from UNCTAD and interpolated into quarterly basis using E-views version nine.

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is the effective relative price indices or the weighted wholesale price index of trading partners and the consumer price index for the home country. The subscripts j, i and t represent country, trading partner and period respectively. P*_it is the total trade weighted wholesale price index of the trading partners representing the price of tradable, and P_jt is the CPI of the domestic country used as a proxy for price of non-tradable.

On the other hand, the individual trading partners (exchange rate in the period under review divided by exchange rate in the base period) raised to the power of their weights in the base period.

R

^it=nominal exchange rate of shilling per unit of the currency of the i-th trading partner in period t.

S

^it= 1/nominal exchange rate of the shilling per unit of the currency of the i-th trading partner

S

^*it =index calculated as a share of the exchange rate of the currency of the i-th trading partner per 1 shilling in period t divided by exchange rate of the foreign currency of the i-th trading partner in the base period

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w

ⁱ= sum of the standardized weights of the shares of the foreign trade of the individual trading partners equals 1

W

ⁱ=weighted average of the share of the i-th trading partner’s exports and imports in the exports and imports of the Kenya

W

^xi= share of the exports of the i-th trading partner in the total exports of Kenya

V

^x = share of the exports of Kenya in the total turnover of Kenya

W

^im share of the imports of the i-th trading partner in the total imports of Kenya

V

^m= share of the imports of Kenya in the total turnover of Kenya Evidence already adduced provides strong indications in the literature which link exchange rate policy to export performance.

Maintaining realistic exchange rates is one of the key components of a rational export regime. Allowing exchange rates to adjust to more realistic levels could lead to significant increases in the production and export of such items as high-value horticultural products for which many African countries may have an underlying comparative advantage.

2. Income Per Capita

A single variable – income per capita - cannot by itself explain the structure of the exports trade. However, per capita income reflects the effects of economic processes and is usually regarded as an indicator of country’s level of development. If the predictions of the Hecksher-Ohlin theory are correct, we expect to find (a) a positive relationship between income per capita and the share in the total industrial exports of (human and physical) capital intensive goods and (b) a negative relationship for labour intensive goods.

3. Inflation (CPI)

Inflation triggers a host of other things such as a rise in the prices of inputs and consequently this increases the cost of production and may be a disincentive for exports (Biggs Tyler, 2007; Oyejide, 2007). Data on inflation was obtained from KNBS in quarterly form.

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4. Foreign Direct Investment

Foreign direct investment (FDI) can play a significant role in promoting economic development in low-income countries by serving as a mechanism through which superior technology and managerial know-how can be transferred to such countries. The sign and magnitude of the net incentive generated by the public and private investment will be different over various time periods and across a number of export product groups. An important part of this study is to draw inferences on the net incentive regarding investment policy (reflected by FDI flows, private, and private investment) on horticultural exports.

FDI flows can play a significant role in promoting economic development by serving as a mechanism through which superior technology and managerial know-how are transferred to the country.

FDI capital flows are virtually everywhere subject to a mix of restrictions and incentives. An export incentive framework is embedded in the incentive regime of any typical country which has implications not only for the allocation of savings but also for exporting activities generated by the FDI flows (Oyejide, 2007).

5. Standards

Standards affecting Kenya’s horticulture sector are classified according to process and product standards as well as private labelling and traceability and based on SPS WTO notifications. A further classification is based on the private voluntary standards that key players in the horticulture industry have to comply with on order to access the EU market. The classification in this paper is therefore based on the WTO notifications and information related to standards (chapter 2 above) and classified into 2 main categories: WTO sanitary and phyto-sanitary (SPS) measures and TBT measures. These are then further classified into product measures, process measures, conformity assessment, and traceability requirements (Appendix 4.1). Data on WTO notifications was obtained from WTO database. The other data used is rejections data which is a proxy for stringency. The rejections data is obtained for both flowers and fresh fruits and vegetables and is obtained from RASFF data base.

Border rejections of Kenya’s exports intensified since the introduction of EUREPGAP and the revision and introduction of EU regulations already discussed in the above sub-sections. KEPHIS has

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pointed out that 52% of interceptions are attributed to documentation while 48% is due to quality parameters. Of the 48 per cent attributed to quality about half are attributed to harmful micro-organisms ie bacteria, fungi and viruses while 23.8 per cent can be attributed to pesticide residues. The pesticides exceeding EU Maximum Residue Level (MRLs) in these checks included acephate, chlorpyrifos-ethyl, diafenthiuron, dimethoate, indoxacarb, methomyl, methamidophos and omethoate. In a few cases, the concentrations detected presented a possible acute health risk to consumers. For instance in the second quarter of 2013, the non-compliance rate for beans from Kenya was 2.8 % and for peas it was 9.6 %. Figure 4.2 below gives trends in rejections from 2011-2014 due to micro-organisms and maximum residue limits for pesticides.

Figure 4.3(a): Trends in Export Rejections for Cut Flowers and Fruits and Vegetables Due to SPS measures (2011-2014)

Source: EU commission data base of food safety and plant health and RASSF http://ec.europa.eu/food/plant/plant_health_biosafety/europhyt/i nterceptions_en.htm

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Figure 4.3 (b): Trends in Rejections Due to TBT Measures

Source: RASFF

As already discussed in previous sections, the role of standards and other NTBs cannot be underestimated. These Standards not only act as barriers to trade but also increase transaction costs thereby impeding exports. Considering that Kenya’s exports constitute a small proportion of the world’s exports, Kenya therefore takes the demand conditions of the importing countries as given and is infinitely price elastic.

In document Cálculo de uniones atornilladas en el diseño de maquinaria (página 87-94)