rafmacalaba/datause-encoder-data huggingface dataset:
world markets, but also for less widely traded substitutes such as cassava and matooke. As noted by Benson et al. (2008), increased demand from regional trading partners such as Kenya and southern Sudan has exerted upward pressure on food prices in Uganda. Although prices dipped somewhat following the harvest in mid-2008, prices for staple foods in Uganda started rising again in late 2008 and early 2009.

This paper is organized as follows. Section 2 provides background information on staple food consumption patterns in Uganda, and on the magnitude and timing of food price increases. Section 3 describes the calculation of the net benefit ratio (NBR), the analytical methods used to estimate changes in poverty, and the underlying household survey data. Section 4 presents results on which income groups and regions are most affected by food price increases. Section 5 reports the impact of food price increases on poverty levels in Uganda. Section 6 summarizes and offers concluding remarks.

# **2. Background**

Uganda is a predominantly rural country, with only 15% of its 27 million inhabitants living in urban areas (UBOS 2006). Almost three-quarters (73%) of the country’s work force is employed in agriculture (UBOS 2006), which accounts for 29% of GDP (World Bank 2008b). Although most rural households are engaged in food crop production, most (57%) are also net buyers of staple foods; in urban areas 92% of households are net buyers of staple foods (Benson et al. 2008).

Unlike many countries in Asia or southern Africa where a single commodity is the dominant food energy source (rice and maize, respectively), Uganda has a relatively diverse mix of staple foods, including maize, cassava, sweet potatoes, and matooke. Table 1 shows the average budget shares in Uganda, disaggregated by region and rural/urban area of residence. The primary staple has a pronounced regional pattern, corresponding with production environments. Matooke figures prominently in Central and Western regions, whereas maize and cassava are the dominant staples in the Eastern and Northern regions. On average, slightly more than one-half of the household budget goes to food in Uganda (including valuation of home-produced food).

As a landlocked country, Uganda is not well-integrated with international markets, especially for low value commodities such as cereals. Thus price trends within Uganda deviate somewhat from the global price trends shown in Figure 1. Local market conditions 4
# IV. **Exchange Rates and Economic Performance in Sub-Saharan Africa**

# **Introduction**

While the countries of post-colonial Africa represent a remarkable diversity of people and geography, their economic policies leading up to the 1980s bore a striking similarity (Foroutan, 1997). They established tight control over economic activity using import and export quotas and prohibitions, price controls, foreign exchange controls, interest rate ceilings, state-owned enterprises, and marketing monopolies. This produced a layering of policies with a strong bias against trade (Nash, 1997). By the early 1980s, many countries faced balance of payments crises and economic decline.

Terms of trade shocks played a large role in triggering these problems (Bouton, Jones, and Kiguel, 1994). Commodity booms in the second half of the 1970s led to higher government revenues and the rapid expansion of government spending. In the early and mid-1980s, commodity prices fell, but governments did not adjust. Continuing to spend, they financed their mounting deficits with external debt and inflation. For example, Madagascar's budget deficit grew from four percent of GDP in 1978 to 17 percent in 1982, inducing inflation. Kenya's foreign debt, very low at the beginning of the 1970s, reached 50 percent of GDP a decade later. The foreign borrowing appreciated the equilibrium real exchange rate. Inflation and a lack of adjustment of nominal exchange rates led to significantly overvalued real exchange rates throughout Africa.

In response to the real appreciation, countries imposed exchange controls and import restrictions rather than devalue, and this triggered a downward spiral. The overvalued rate reduced incentives for exporters and the trade balance worsened, forcing officials to impose even tighter restrictions. Many countries ended up with negative growth. Ghana's real per capita income, for example, declined 30 percent between 1970 and 1983, and real export earnings declined 52 percent. Throughout Africa, real per capita income fell by 15 percent between 1977 and 1985 and export perfornance collapsed from about 10 percent annual growth in the early 1970s to declines in the early 1980s (Jones and Kiguel, 1994). These problems resulted in a period of painful adjustment programs.

The valuation of the real exchange rate proved a key to successful reforms. Comparing three areas of policy - exchange rate, fiscal, and monetary - Bouton, Jones, and Kiguel conclude that exchange rate reforns had the biggest effect on growth.

# **The Exchange Rate: Cross-Country Studies**

Any analysis of the continent's experience must differentiate between the CFA franc countries, which peg their currency to the French franc, and the countries of the rest of Sub-Saharan Africa, which independently determine their exchange rate policies. 19

> **19** The West and Central African Monetary Unions, which together with Comoros, form the CFA (Communaute Financiere Africaine) zone have convertible currencies pegged at a fixed rate against the

15
even in some of the most challenging environments in the world in different fields of development. The key here is to have the appropriate mindset and to have a much closer interaction between policy makers, project implementers, and researchers from the very beginning of the design of a particular IC program or policy intervention, so that all the options and possibilities for rigorously measuring the impact of such intervention can be thought through before the start of the project implementation, as opposed to the evaluation becoming an afterthought.

**Third,** the ability of tracking jobs in different locations and sectors over time is a necessary pre-condition to be able to detect the job effect of any intervention or policy change. This implies the importance of good quality data both from surveys and administrative sources in developing countries. A key reason why most of the quasi-experimental evaluations of business incentives are from the United States or other developed countries is the absence of good quality data on relevant variables of interest in many developing countries. Increasingly, the use of information and communication technologies (ICT) is becoming an integral part of IC and PSD projects which include online registration and tax filing, online applications for various government services, and automation of different government agencies, such as business registry, tax authority, and investment promotion agency. Similarly in different countries efforts are underway to maintain a database of businesses that tracks different incentive schemes. All these initiatives would significantly reduce the cost of collection and maintenance of good quality administrative data, which could be leveraged for rigorous analysis. The World Bank Group in coordination with development partners  should discuss up front with the relevant government counterparts the possibility of granting the use of this type of administrative data for research purposes, observing, of course appropriate confidentiality.<sup>14</sup> At the same time, efforts are also needed to improve the coverage and quality of relevant surveys related to jobs such as, household, labor force, and business surveys.

> 14 Currently, a team from the investment climate department has proactively engaged with business registration and tax authorities of different countries to use administrative data to analyze firm dynamics and job creation in these countries.

33
Then, we analyze whether allowing or not for past export status to affect productivity has any impact in the analysis of the self-selection and LBE hypotheses. To the best of our knowledge De Loecker (2013) and Manjón _et al._ (2013), for Slovenian and Spanish manufacturing firms, respectively, are the only two papers that use quite a similar method to the one in this paper.

However, although closely related, the estimation method in our study differs at some points to that in De Loecker (2013). De Loecker (2013) relies for estimation in Ackerberg _et al._ (2006) while we implement the GMM framework proposed by Wooldridge (2009). Wooldridge (2009) argues that both OP, Levinsohn and Petrin (2003, LP hereafter) or Ackerberg _et al._ (2006) two–step estimation methods can be reconsidered as consisting of two equations which can be jointly estimated by GMM in a one-step procedure. This joint estimation strategy has the advantages of increasing efficiency relatively to two-step procedures, making unnecessary bootstrapping for the calculus of standard errors, and also solving the identification problem of the labor coefficient in the first estimation step (noted by Ackerberg _et al._ , 2006).

We also differ from De Loecker (2013) in that he uses investment as a proxy variable (and so does Van Biesebroeck, 2005) whereas we use intermediate materials. In this way we avoid possible concerns about zero-investment observations (LP, 2003) and the invertibility of the investment function (Van Biesebroeck, 2005).

However, our methodological procedure in this paper is closer to the one in Manjón _et al._ (2013) for Spanish firms. Although Van Biesebroeck (2005) and De Loecker (2013) also provide evidence from Sub-Saharan and Slovenian manufacturing, Manjón _et al._ (2013) do provide evidence from Spain.

To anticipate our results, we find, in general, that both exporters are more productive than non-exporters and that the most productive firms self-select into export markets (evidence on the self-selection hypothesis). We also find certain evidence on learning-by-exporting: the productivity of firms that start exporting grows more than that of non-exporters the first year they sell in 6
25 Finally, the Grameen Bank's **experience** also suggests that loans made to rural poor are more easily recovered if they **are** collected in regular (even weekly) small amounts suitable to the circumstances under which these people earn and live. This approach may, however, have to be adapted to constraints imposed by production cycles. **The** provedure is also likely to increase transaction costs for both borrowers and lenders, but the advantages of significantly higher recovery rates must be considered against these drawbacks.

# C. Reduced Lenders* Costs

While improved loan recovery rates are the crucial factor In cutting down lenders' costs and risks, reduced administrative costs due to scale economies are further expected to limit lender transaction costs. If functions such as loan application, information gathering on potential borrowers, loan supervision and collection can be passed from the lender to the borrowing group the lenders' administrative costs are likely to be reduced. This in turn would enhance more lenders to make credit available under similar conditions and hence, the number of small farmers with access to credit would increase. Review of a sample of 15 group lending projects does not provide a clear-cut answer in this regard. From the reports which provided information on transaction costs, it can be concluded that lenders in the Philippines, India, Nepal, Bolivia and the Dominican Republic benefitted from scale economies in administrative costs when making loans to groups. However, in most cases this was only true because lenders were not required to carry group formation costs. In the Dominican Republic these were borne by the refinancing agency, while in
proficiency levels, the threshold of 400 points is used as the lowest bound for a basic level of science literacy (Organisation for Economic Co-operation and Development (2004), p. 292). In general, a level of 400 points means performance at one standard deviation below the OECD mean. The share of students achieving this level ranges from 18% in Peru to 97% in the Netherlands and Japan, with an international median of 86% in our sample. The threshold of 600 points captures the notion of very high performers, performing at more than one standard deviation above the OECD mean. The share of students achieving this level ranges from below 0.1% in Colombia and Morocco to 18% in Singapore and Korea and 22% in Taiwan, with an international median of 5% in our sample.

When we enter the share of students above the two thresholds jointly in our growth model (see column (9) of Table 4.3), both turn out to be separately significantly related to economic growth. That is, both education for all and the share of absolutely top performers seem to exert separately identifiable effects on economic growth. These initial results should be viewed as suggestive rather than definite, not the least because of issues of multicollinearity between the two measures of educational quality which have a bivariate correlation of 0.73. Importantly, the relative size of the effects of performance at the bottom and at the top of the distribution depends on the specific specification used, and further research is needed to yield more detailed predictions. Nonetheless, the evidence strongly suggests that both dimensions of educational performance count for the growth potential of an economy (see Hanushek and Wößmann (in process) for greater detail).<sup>51</sup> Additional specifications using different points of the distribution of test scores support this general view. The positive effect of educational quality is highly robust to measuring test-score performance at different percentiles of the distribution, such as the 25<sup>th</sup> , 75<sup>th</sup> , and 95<sup>th</sup> percentile, rather than at the mean. When including two measures at a time, they are always jointly, and often separately, significant. When including two percentile measures, it is not always clear whether the higher ones tend to dominate the lower ones or vice versa in different specifications, which are again hampered by strong multicollinearity of the test-score measures. The highest bivariate correlation among the different test-score measures is between mean performance and the share of students in a country reaching the threshold of 400 test-score points, at 0.99. Thus, mean performance seems to capture mainly the pattern of basic literacy across the countries in our sample. The specifications including two

> 51 In contrast to the result of Castelló and Doménech (2002) based on years of schooling, we do not find a significant effect of inequality of educational quality per se on economic growth, but rather significant effects at different points of the distribution.

39
Policy Research Working Paper 6651

# **Abstract**

While there is a consensus that the 2008–2009 crisis was triggered by financial market disruptions in the United States, there is little agreement on whether the transmission of the crisis and the subsequent prolonged recession are due to credit factors or to a collapse of demand for goods and services. This paper assesses whether the primary effect of the global crisis on Eastern European firms took the form of an adverse demand shock or a credit crunch. Using a unique firm survey conducted by the World Bank in six Eastern European countries during the 2008–2009 financial crisis, the paper shows that the drop in demand for firms’ products and services was overwhelmingly reported as the most damaging adverse effect of the crisis. Other “usual suspects,” such as rising debt or reduced access to credit, are reported as minor. The paper also finds that the changes in firms’ sales and installed capacity are significantly and robustly correlated with the demand sensitivity of the sector in which the firms operate. However, they are not robustly correlated with various proxies for firms’ credit needs.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hanguyen@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
15 where, _IMP_ 12 _t_<sup>+</sup> _EXP_ 12 _t_ is the total annual volume of trade between every two countries 1 and 2. Both _TRD_ 1 and _TRD_ 2 are fractions, with 0 ≤ _TRD_ ,1 _TRD_ 2 < 1 . We will refer to TRD1 as _Trade importance_ and to TRD2 as _Trade dependency_ . The data sets will be identified in parentheses to the right of the name of the variable.

Since our unit of observation is the river, we construct the trade variable for the entire dyad (the two riparians). As was indicated in our analytical framework, one riparian may be more interested in signing a treaty than the other. However, the outcome (as we measure it) does not reveal which riparian initiated the water treaty and, thus, our trade variables measure the dyadic trade volume rather than that of each riparian state.

# **Geography**

The 13 geography configurations were re-categorized into three groups, capturing the rivers that fall under the through-border geography and the rivers that fall under the border-creator geography. The remaining rivers that fall under the other 11 configurations were included under _other_ geography, whereby this category served as a benchmark. The reasons for this regrouping are as follows: (1) the distorted distribution of the 13 categories doesn’t allow the estimated regression model to be fully ranked, and (2) we are mostly interested in the impact of the two extreme geographies that have been identified by Dinar S. (2007, 2006b) and their ability to explain interactions between riparian states. The two dummies that are included in the regressions are _Through-border_ , and _Border-creator_ , for the through-border and border-creator configurations, respectively.

# **FUNCTIONAL FORMS AND ESTIMATION ISSUES**

The empirical specifications of the various expressions to be estimated<sup>xviii</sup> are as follows: _Treaty/no-treaty_ =f1(.) _Number of treaties_ =f2(.) _Share of water allocation issues_ =f3(.)
_stability_ , and _rule of law_ . _Finance_ and b _usiness environment_ have high intercepts which make up for their negative slopes. While it is encouraging to find that there is aid that is flowing into these two areas (high intercepts), it seems like donor countries are not targeting the right countries (negative slopes), especially when it comes to _finance_ related aid. The _labor_ area seems to be the most neglected; although it has a positive slope there is very low levels of aid that flow into any country with the objective of educating and training the workforce and/or to improve labor regulations.

# **Does More Aid Lead to Better Firm Perceptions?**

In the previous section we found that aid indeed targets those areas which firms in recipient countries have identified as obstacles for their growth. Now the question is whether aid actually improves those areas so that fewer firms find them to be obstacles for their growth. Once the aid disbursement has been received and the project has been implemented we expect that the firms’ perceptions for that area would be better than if no aid has been received. In this section we attempt to answer the question: are firm perceptions better in those areas that have received more aid in the past?<sup>10</sup> With this objective in mind we run regressions of the following form: _Perception_ , the dependent variable in this case, is the natural logarithm of the percentage of firms in a recipient country _r_ that have identified the area _j_ as an obstacle in year _s_ . _Aid_ is the natural logarithm of the amount disbursed in millions of USD dollars from donor country _d_ to recipient country _r_ , for obstacle _j_ , and year _t_ . Since we want to identify

> 10 We would like to analyze whether aid _improves_ aid perceptions. For this we need to analyze the _change_ in firms’ perceptions; however, there is no panel data available for this specific question on the Enterprise Survey. We are left we the second best analysis which is identifying whether firms’ perceptions are better for those areas that have received more aid in the past than for those areas that did not receive as much aid.

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TABLE 4: Composition of the Oplimal Intemational Poitfolio

|Rateof<br>Retumn<br>(Rc)|USA|UK<br>I|A|GER|SWZ|FRA|HKG|TAI|KOR|MEX|111|BR|IND|GSCi|Sumof<br>Weights|Potfolio<br>Standard<br>Deviation|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|0.75|||.858||.056|||||||||.086|1|5.76|
|1.0|||.474||.187|||||||||.339|1|3.76|
|1.25|.175||.228||.131||||||||.055|.410|1|2.84|
|1.4727*|.254*||.123*||.056*||.017*|.024*|.006*||.021*||.084*|.415*|1*|2.71*|
|1.50|.253||.115||.048||.023|.026|.010||.025||.084|.415|l|2.72|
|1.75|.225||.034|||.002|.079|.045|.051||.060|.002|.087|.415|1|2.82|
|2.00|.126|||||0o6|.128|.053|.073|.030|.073|.010|.097|.403|l|3.11|
|2.25|.009||||||.181|.057|.088|.074|.078|.017|.111|.385|1|3.56|
|2.50|||||||.205|.061|.076|.138|.060|.027|.131|.301|l|4.16<br>S|
|2.75|||||||.227|.066|.061|.204|.040|.037|.152|.213|1|4.92|
|3.00|||||||.250|.071|.047|.269|.020|.048|.173|.124|l|578|

NOTE: Stnmed values represent retum. weights and standar **d** eviation of the ninimwm-risk portfolio. Date Sources: See Tables I and 2.