rafmacalaba/datause-encoder-data huggingface dataset:
lanced view of the role project interventions played in improving farmers’ productivity, income among others. The present paper cites the most relevant findings obtained from the field work; for a comprehensive picture of the qualitative work, as well as a detailed description of the analytical methodology and data, see Behrman (2009).

# **QUANTITATIVE DATA**

The quantitative analysis makes use of a panel survey specifically designed to measure the impact of the program. The survey was designed and implemented in 2006 by a research firm under the direct supervision of the World Bank. The baseline survey was administered to 857 households immediately following their relocation. Fifty beneficiary groups were selected, and 5-10 households from each of these were surveyed. Households were asked about their assets, agricultural production during the 2005-2006 season, current activities, and average (monthly and yearly) expenditures on various categories of goods. The same survey was administered to households in districts untouched by the program in the same time period, both at baseline and at follow-up.

While data collection was conducted after relocation, a number of variables are retrospective and can therefore be interpreted as true baseline values. These include variables related to agricultural production and livestock holdings, for which all questions referred the previous agricultural cycle ending in June 2006. The survey includes a question on the year in which year each durable asset owned by the household was obtained, which is used to construct values of productive assets and consumer durables acquired in 2005 or earlier. While most questions on expenditures and food security refer to the current period and are therefore unusable as baseline variables, data on the number of meals taken in the lean season (or time in the year before crops are ready to harvest, January – February) refers to years preceding relocation. All 12
- Beine, Michel, Simone Bertoli and Fernández-Huertas Moraga, Jesús (2014). “A Practitioners’ Guide to Gravity Models of International Migration.” The World Economy, forthcoming.

- Black, D., S. G. Sanders, E. J. Taylor and L. J. Taylor (2015). "The Impact of the Great Migration on Mortality of African Americans: Evidence from the Deep South." _American Economic Review_ , 105(2): 477-503.

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- Borjas, George (2015). “The Wage Impact of the Marielitos: A Reappraisal,” NBER Working Paper No. 21588.

- Borjas, George (2016). “The Wage Impact of the Marielitos: Additional Evidence,” NBER Working Paper No. 21850.

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- Card, David. (1995). "Using Geographic Variation in College Proximity to Estimate the Return to Schooling." In L.N. Christofides, E.K. Grant, and R. Swidinsky, editors, Aspects of Labor Market Behaviour: Essays in Honour of John Vanderkamp Toronto: University of Toronto Press.

- Carrington, W. J., and P. J. De Lima (1996). “The impact of 1970s repatriates from Africa on the Portuguese labor market,” _Industrial and Labor Relations Review_ , pp. 330-347.

- Ceritoglu, E., H. B. G. Yunculer, H. Torun, and S. Tumen (2015). “The Impact of Syrian Refugees on Natives' Labor Market Outcomes in Turkey: Evidence from a QuasiExperimental Design.” MPRA Paper 61503.

- Del Carpio, X., C. Ӧzden, M. Testaverde, and M. Wagner (2015). “Local Labor Supply Responses to Immigration.” _The Scandinavian Journal of Economics_ 117 (2): 493– 521.

- Dustmann, Christian and Ian Preston (2012). “Estimating the Effect of Immigration on Wages,” _Journal of the European Economic Association_ , Vol. 10 (1), 216-223.

- Dustmann, Schönberg and Stuhler (2015). “Labor Supply Shocks and the Dynamics of Local Wages and Employment,” mimeo.

- Erdogan, M.(2014). _Syrians in Turkey: Social Acceptance and Integration Research_ . Migration and Politics Research Centre, Haceteppe University.

Gibson, J., D. McKenzie and S. Stillman (2010). “How Important is Selection? Experimental Vs Non-experimental Measures of the Income Gains from Migration,” _Journal of the European Economic Association_ , 8(4): 913-45.

30
conduct activities in mainland China. Also, FDI from third countries may be channeled through Hong Kong even prior to 1997 (Fung 1997).

To deal with these potential issues, we summed China and Hong Kong FCS and repeated the econometric analyses. The results remain unchanged and for the sake of brevity are not reported here.<sup>13</sup> There is still no evidence of a substitution effect between China-Hong Kong and other countries, including LAC, and there is some evidence of complementarities.

In spite of this evidence, there is still the possibility that the emergence of China and India may have taken foreign investment in some sectors from other developing economies. Cravino, Lederman, and Olarreaga (2006) highlighted trends in FCS in the manufacturing sector in China, India, and Latin America that may differ from trends in aggregate FCS. As a robustness check, the following section thus discusses results based on U.S. foreign investment data across industries.

# _5.1. U.S. foreign investment across sectors_

To deal with the potential heterogeneity across sectors, with an emphasis on manufacturing industries, here we repeat the analysis using U.S. sector FCS data. Our benchmark specification is again the KCM in logs. We include all the previously discussed explanatory variables, and also an interaction of all these variables with a dummy for the manufacturing sector. Again, we include time dummies and fixed effects in the estimations.<sup>14</sup>

> 13 These results are available from the authors upon request.

> 14 In the conditional-mean Negative Binomial specification we also include a dummy for the manufacturing sector. This is possible because the Conditional Mean estimator proposed by Hausman, Hall, and Griliches (1984) does not control for all stable covariates -- see Alison and Waterman (2002).

15
48

# NOTES

1. Until the crisis of the 1980s, the economywide approach to income distribution analysis relied heavily on the long-run objectives treated in the influential work of Chenery, Robinson, and Syrquin (1987).

2. Cornia, Jolly, and Stewart (1987) advocate a combination of expansionary macro policies and targeted micro policies designed to increase equity and efficiency. In an IMF study, Heller et al. (1988) suggest a descriptive approach that relies on a classification of the poor by meaningful socioeconomic groups so as to be able to speculate on how the poor fared during adjustment.

3. The country studies in this symposium are syntheses of longer, single-volume country studies published by the OECD. For a fuller discussion of each country's experience, the reader is referred to the volumes referenced in the foreword to this symposium.

4. This section draws on Bourguignon (1991).

_5._ As discussed below, for economies inside their production possibility frontier, adjustment may not conflict with poverty alleviation, if adjustment also entails moving closer to the frontier, as would occur, for example, with effective structural reforms that improve resource allocation.

6. This analysis neglects the political-economy reasons that make redistributive policies difficult to implement (e.g., interest group coalitions that oppose the policies). This issue is discussed in further detail in section 5 below.

7. One could, of course, redistribute current income at B2. This would lead to position B above B2 (not shown in the figure). The evidence suggests that in economies with well-functioning tax and admimistrative systems (e.g. the Nordic European countries), substantial current income transfers can be sustained at low distortionary costs. In developing countries, by and large, static and dynamic costs of current income transfers are likely to be large.

8. Evaluating the outcome of a capital transfer strategy is likely to be even more complex as it involves carrying out a cost-benefit analysis of investment in human capital of the poor. Since the poor are not assumed to receive any current income under this strategy, both the poor and non-poor lose from this strategy in period 1, and the economy moves to Cl. As depicted here, the asset transfer strategy which leads to C2, dominates the current transfer strategy, but is shown to be less productive than investment in the rest of the economy because Cl and C2 lie on ISO-lines below Al and Az. However, once all considerations are taken into accovant, including the difficulty of measuring the economic efficiency benefits resulting from less social conflict, it is likely that if the capital transfer strategy is well implemented, the economy could move to C;, and the equity-efficiency conflict suggested by a capital transfer would then disappear.

9. For example, the framework outlined in figure 1 would be useful for analyzing the tradeoffs involved in Malaysia's New Economic Policy (NEP) of 1975.
”trained” we then analyze how well they predict the test data set.<sup>13</sup> Our dependent variable, historical defaults, is sourced from The Bank of Canada (Beers and Mavalwalla (2017)). A default is defined as a late interest payment, an outright payment default, or when creditors incur losses owing to an extension of maturities or a reduction in rates. Using historical defaults helps us avoid having to define a vulnerability indicator and then trying to predict that. We are interested in predicting when a default occurs in time , i.e., when the 0 flips to a 1 in the sample. We test two cases for the timing of defaults, one where default occurs in period _T_ and data are expressed in period _T −_ 1, and the other where both default and data are contemporaneous. The results do not change much when we vary the timing. Admittedly the variables that lead to default might be different than the variables that keep a country in default. We are not separating out these effects and instead code the default data as equal to 1 when in default and 0 otherwise, and not only 1’s when the default occurs. This is partially related to the default data, where default states tend to be very persistent in the sample of countries.

Furthermore, all our data are stacked. We thus have _t_ annual observations from 2008 until 2020 for _k_ = 65 market access countries. We control for within country specific fixed effects via demeaning.

# **4.1 Econometric specification**

The standard logistic regression (with binary response equal to default (1) and no default (0)) can be expressed as: where _x_ is the input vector (i.e., the four indices from the DVA, or the three alternative models).

The parameters are estimated via maximizing the log-likelihood: An additional constraint is added for the Lasso. Lasso regressions are useful for model selection by shrinking coefficients to zero. As an example, it is possible that the subjective probability estimate tracks the fundamental index over time. Lasso will penalize this and shrink one of the coefficients to zero. The general goal of Lasso is to minimize the sum of squared errors by including a penalty function: where _j_ is the _j_ th variable in the list of _K_ , _t_ is the time index, _y_ is default, and _x_ are the four components of debt in a matrix. The tuning parameter _λ_ controls the amount of

> 13We also run these tests over an in-sample period and a pseudo out of sample period where 2019 is used as the out of sample year.

22
to recover efficient costs, monitoring and enforcing quality of service standards, and overseeing market entry.

**Figure 3.1: Definition of regulatory reform**

|**Model**<br>**classification**|**Defining characteristics**|
|---|---|
|1. No regulator|There is no entity separate from the line ministry responsible for<br>regulatory oversight of the power utility (‐ies)|
|2. Regulatory entity|A regulatory entity has been created that exists outside of the line<br>ministry and has clearly defined regulatory responsibilities and some<br>degree of decision‐making autonomy|

Regulatory entities have diffused rapidly across the developing world, yet are still not as prevalent as in the developed world. Regulatory entities existed only in a handful of countries up until the year 1995 (Figure 3.2). During the decade 1995‐2005, there was remarkably rapid diffusion of regulatory entities with about 40 percent of developing countries adopting the reform during this period. Uptake continued during the subsequent decade 2005‐2015 though at a much slower pace, with an additional 20 percent of developing countries establishing a regulatory entity. As of 2015, 72 percent of developing countries have a regulatory entity, making this by far the most popular and widely adopted of the four major policy recommendations of the Washington consensus. Nevertheless, this still falls considerably short of the 91 percent prevalence of regulatory entities in the developed world (Figure 3.3).

**Figure 3.2: Percentage of developing countries establishing regulatory entities over time**

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014<br><!-- End of picture text --> Source: Own elaboration based on RISE 2015 database and independent research

**Figure 3.3: Comparison of developed and developing countries on extent of regulatory reform, 2015**

|Percentage of countries|Developed countries|Developing countries|
|---|---|---|
|With regulatory entity|91|72|
|Without regulatory entity|9|28|
||100|100|

8
O.A.M.D.G.

Colombia, such as Rubio (1998) and Londoño and Guerrero (1999) do not make this distinction. The most recent cost estimate by Pinto et al. (2005) concentrates on the armed conflict only. Further, our paper highlights an indirect cost of violence to be reckoned with: households are deviated from their optimal savings and settling behaviors.

# **References**

**Alessie R, Hochguertel S, van Soest A (2002):** “Household Portfolios in the Netherlands”, in Guiso L, Haliassos M and T Jappelli (ed) _“Household Portfolios”_ , MIT Press, Cambridge London 341-398

**Apodaca C (1998):** “Human Rights abuses: Precursor to Refugee Flight?”, in _Journal of Refugee Studies_ 11 (1): 80-93

**Arriagada I, Godoy L (1999)** : “Seguridad ciudadana y violencia en América Latina: diagnóstico y políticas en los años noventa.”, _CEPAL_ , División de Desarrollo Social

**Attanasio O, Gómez LC, Gómez A, Meghir C, Murgeítio C et al** . **(2004):** “Baseline report on the evaluation of Familias en Acción”, _IFS Report_ , Centre for the Evaluation of Development Policies

**Banks J, Tanner S (2002):** “Household Portfolios in the United Kingdom”, in _Guiso L, Haliassos M and T Jappelli, (ed.), “Household Portfolios”_ , MIT Press, Cambridge London 219251

**Basu K (1999)** : “Child Labour: Cause, Consequence and Cure, with Remarks on International Labour Standards”, _Journal of Economic Literature, Vol 37_ , September 1999, 1083-1119

**Bauer T, Sinning M (2005):** „The Savings Behaviour of Temporary and Permanent Migrants in Germany“, _CEPR Discussion Paper Series, DP 5102_
Policy Research Working Paper 11004

# **Abstract**

The Middle East and North Africa is the most water scarce region in the world. Although studies have explored the effect of extreme weather events on agriculture, much less is known about the effect on businesses. Using geocoded firm-level data from the World Bank’s Enterprise Surveys across the Middle East and North Africa region, this study analyzes the effects of precipitation shocks on firm performance. The findings show that firms in areas that experience negative precipitation shocks have lower sales, labor productivity, and investment. The study tests a number of channels identified in the literature. Poor infrastructure, such as water and electricity outages, and lower access to finance that occur as a result of negative precipitation shocks are found to be key channels. Negative precipitation increases the share of temporary workers, possibly explaining the drop in labor productivity. A new channel of governance is also uncovered—negative precipitation shocks increase the time spent by senior management in dealing with regulations and expectations of solicitations of bribes. The results also show that firms respond to precipitation shocks by adopting greener practices, suggesting scope for adaptation in the region.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region and the Office of the Chief Economist, Planet Vice Presidency. 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://www.worldbank.org/prwp. The authors may be contacted at aislam@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
number of variables in each model as the number of observations decreases. No models were established for Harari, Dire Dawa, and Addis Ababa, as all woredas (or equivalent locations) were sampled in the survey. For those areas, the sample from EMDHS 2014 is used.

The Ethiopian census has both a short and a long form, and to increase model fit, the models use the long form, including enumeration area (EA) averages of all variables from the census whether found in the survey or not. Separate models for the long and short form are theoretically possible. Unfortunately, this is not possible using the PovMap software, as the program cannot handle the likely correlation between two such models, which would result in inaccurate estimates of undernutrition standard errors.

To mitigate issues arising from the time interval between the 2007 Population Census and EMDHS 2014, only variables whose distributions did not change much between the two are used in the z-score models. A detailed examination of the distribution of all variables found in both survey and census ensured that only similar variables were utilized in the models. Appendix Table A1 shows the survey and census means for variables included in the models. All models include variables that are based on EA or higher-level census means; these are not included in Table A1, as they are comparable by definition.

Variable selection into the estimation models was based on contribution to adjusted r-square, but also considered variable skewness (very skewed variables were excluded as it could result in overfitting to the sample) and correlation between variables (highly correlated variables were excluded). Further, all models include variables generated at levels above the household level, as too high spatial correlation in the error term relative to overall error term can jeopardize accuracy of estimated standard of error of undernutrition indicators [28,29] . The models also include variables as number of children in household and children close in range, which capture within household correlation. See also Jones and Haslett [22], as well as Fujii [16], for alternative ad hoc modeling solutions for within household correlation.

The models have adjusted r-squares similar to those of other undernutrition regressions, particular those used for undernutrition maps (0.06 to 0.27 for stunting and between 0.08 and 0.14 for underweight). Some studies have 7
19 representing the down side risk insurance provided through the MAT), there is no a priori presumption on the rate at which the marginal impact of the MAT should respond to higher uncertainty. We find it to be a declining rate.

Fifth, it remains true however, that the increase in MERT an introduction of the MAT leads to is higher at higher levels of uncertainty. This establishes a presumption that the MAT tends to penalize high risk firms more than low risk firms for given rate of return. However un standard asset pricing principles one would expect high risk firms also to offer higher average rate of returns; since the latter tend to diminish the impact of a MAT of a given rate, it is not necessarily true that a MAT unduly falls on high risk firms. This is in the end an empirical matter, to which we return in our analysis of the Brazilian data (cf Section 4.3).

# b. Loss Carry-Forward Provisions and Uncertainty

The previous section shows that minimum taxes are likely to penalize new firms facing a high degree of uncertainty on their profits. This effect can to some extent be offset by allowing losses to be carried forward, to be applied against taxable income in periods where the latter is positive. However carry-forward provisions are no substitute for full loss provision unless there is no limit on the number of years losses can be carried forward and unless they are indexed by the nominal interest rate.<sup>1</sup> 01 Table 3 shows that in an uncertain world, as illustrated by a standard deviation of 1, the lower the number of years losses can be carried forward, the higher the effective tax rate. Granting infinite carry forward--or 50 years, which is as

> **IO/ A recent overview of the** issues **raised by tax losses** is **found in Mintz, J. (1989).**