rafmacalaba/datause-encoder-data huggingface dataset:
principal component analysis (PCA) was first conducted and components with eigenvalues greater than or equal to 1 were retained. Then, the most important variables in each component were identified, including all variables within 10% of the weight of the most important, if the correlation with the most important variable was less than or equal to 0.6. When two or more properties were retained from the same component (where they are weakly correlated and within 10% of the highest weighted property), each property received the same weight.

The index with the greatest predictive power with respect to crop cut yield was composed of organic carbon (%), soil electrical conductivity (an indicator of soil salinity), and pH. These variable values were transformed to a range from 0 to 1, where 1 represents the most optimal value in the sample (e.g., highest value for OC, intermediate values for pH), and 0 represents the lowest value in the sample. A composite index was then generated by weighting each variable by the fraction of total variance explained by its corresponding component. The relative weights for organic carbon, soil electrical conductivity, and pH are 68.3, 68.3, 31.7, respectively.<sup>7</sup> Given data limitations, the constructed index focuses on nutrient storage capacity but ignores the other two components of soil quality identified by Mukherjee and Lal (2014) related to root development and water storage.<sup>8</sup> Although these soil samples were acquired in MAPS I, they still provide a useful measure of soil quality to compare with the various yield measures. Importantly, the selected maize plot for most households (n = 312) was part of the same parcel as in the previous year, so that the soil sample was from the same part of their farm. Concerning the remaining sample of households that had a MAPS II plot selected from a non‐MAPS I parcel, the median distance between the MAPS II and the MAPS I plot locations was 0.56 kilometers, lending support to likely similarity in soil profiles of nearby plot locations. More importantly, the regression results using soil quality showed very little sensitivity to excluding those households where the parcel moved between years.

# **2.3.3. Ground‐Based Maize Yield Measurement**

# **2.3.3.1. Farmer Estimation**

Plot managers were asked to report their estimate of maize harvest at the parcel‐plot‐level during the post‐harvest visit, replicating the design of the Uganda National Panel Survey (UNPS)

> 7 Organic carbon and soil electrical conductivity were both retained from the first component and, therefore, hold the same weight.

> 8 The PCA‐based soil quality index was constructed for the full MAPS 1 sample, and therefore, analyzes the correlation of soil properties and crop cutting yields on a larger sample than MAPS 2.

8
ment (Bongartz and Chambers, 2009), implemented in the context of the government’s TSC. Alok (2010), in his memoirs as an administrative officer responsible for the TSC, describes Maharashtra as an early and rapid adopter of the TSC. Among Indian states, Maharashta has the most villages which have won the clean village prize for eliminating open defecation. Our data come from a study done early in the implementation of the TSC in that state.

## **1.3 Overview**

The next section outlines our empirical strategy: analysis of a randomized, controlled experiment. Although the original decision of the Maharashtra government was to conduct an experiment in three districts, the experiment that occurred was confined to one district. Nevertheless, in all three districts there were three rounds of survey data collected, one before the experimental intervention and two after.<sup>6</sup> Section 3 presents and analyzes the results of the experiment. Section 4 discusses policy implications, considering treatment heterogeneity and the consequences of the government’s original decision to experiment. Section 5 concludes.

# **2 Method: A randomized field experiment**

The timeline of this experiment contained four events: the experimental intervention in early 2004 and three survey rounds.

- February 2004: baseline survey data collection

- shortly thereafter: community level sanitation “triggering” intervention

- August 2004: midline survey data collection

- August 2005: endline survey data collection

> 6This unusual circumstance of data collected beyond the experimental panel was due to funding rigidities of large bureaucracies, in this case both the World Bank and the Government of Maharashtra.

6
Policy Research Working Paper 10912

# **Abstract**

While cash transfers have emerged as an attractive option to minimize negative long-term impacts of conflict, the scope for targeting and assessing their impact in such settings is often challenging. This paper shows how a digital farmer registry in Ukraine (the State Agrarian Register) helped to target and evaluate such a program, using the country’s $50 million Producer Support Grant in a way that largely avoided mis-targeting. The analysis applies a difference-in-differences design with panel data from 2019–23 on crop cover at the parcel/farm level for the universe of eligible farmers registered in the State Agrarian Register. The findings suggest that the program significantly increased area cultivated, although the effect size remained modest. Impacts were most pronounced near the frontline and for the smallest farmers. The paper discusses the implications in terms of a more diversified menu of support options and the scope of using the State Agrarian Register to help to implement these options, as well as lessons beyond Ukraine.

This paper is a product of the Development Research Group, Development Economics. 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 kdeininger@worldbank.org and dali1@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
|Firm Provides Training Y:1 N:0|0.001*|0.002***|0.001*|-0.000|-0.002|0.008|0.000|-0.013|
|---|---|---|---|---|---|---|---|---|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.012)|(0.012)|(0.013)|(0.013)|
|Courts Obstacle Y:1 N:0|-0.010|0.001|-0.018|0.050|-1.169|-0.908|-1.193|-1.014|
||(0.060)|(0.059)|(0.065)|(0.089)|(1.179)|(1.215)|(1.257)|(1.135)|
|Total hours of power outages|-0.001|-0.001|-0.000|-0.002**|0.005|0.005|0.008|-0.005|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.008)|(0.008)|(0.009)|(0.009)|
|Firm Purchased Fixed Assets Y:1 N:0|0.163**|0.194***|0.125*|0.326***|0.773|1.334|0.722|0.336|
||(0.064)|(0.063)|(0.066)|(0.104)|(1.006)|(1.029)|(1.070)|(1.545)|
|Time Tax|-0.000|0.000|0.000|0.001|-0.046|-0.046|-0.049|-0.010|
||(0.002)|(0.002)|(0.002)|(0.004)|(0.054)|(0.059)|(0.055)|(0.066)|
|Population (logs)|-0.012|-0.004|-0.023|0.114*|-1.509|-1.389|-1.740|-0.804|
||(0.076)|(0.081)|(0.078)|(0.061)|(1.114)|(1.071)|(1.209)|(0.836)|
|Primary Education|-0.011|-0.011**|-0.010|-0.016*|-0.025|-0.035|-0.018|-0.031|
||(0.007)|(0.005)|(0.007)|(0.009)|(0.121)|(0.091)|(0.124)|(0.107)|
|Freedom from Business Regulations|0.038|0.049|0.049|-0.101|0.015|0.229|0.079|-0.840|
||(0.097)|(0.114)|(0.096)|(0.109)|(1.099)|(1.157)|(1.163)|(1.082)|
|Merchandize Trade to GDP ratio|-0.003|-0.003|-0.003|-0.003|-0.033|-0.036|-0.030|-0.063*|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.039)|(0.034)|(0.042)|(0.034)|
|Growth rate of GDP per capita (%, annual)|0.020|0.020|0.017|0.058**|-0.233|-0.232|-0.282|0.704*|
||(0.021)|(0.021)|(0.022)|(0.023)|(0.237)|(0.203)|(0.249)|(0.414)|
|Inflation (GDP deflator)|0.014|0.014|0.015|0.010|-0.287**|-0.300**|-0.301**|-0.255*|
||(0.013)|(0.015)|(0.012)|(0.015)|(0.144)|(0.128)|(0.151)|(0.153)|
|Rule of Law|0.057|0.084|-0.063|0.596*|-3.839|-3.392|-5.272|3.421|
||(0.304)|(0.311)|(0.303)|(0.324)|(5.259)|(4.756)|(5.585)|(3.428)|
|Control of Corruption|0.019|-0.029|0.068|-0.266|1.814|0.973|2.045|0.290|
||(0.354)|(0.339)|(0.369)|(0.352)|(5.004)|(4.710)|(5.445)|(3.347)|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|5.775**|7.438*|5.327*|7.953**|81.460*|90.235*|79.382*|83.072**|
||(2.734)|(3.810)|(2.788)|(3.096)|(42.713)|(47.735)|(45.844)|(34.514)|

62
through until the end of December 2018, which corresponds to three years after the group interventions and more than four years after the individual intervention ended. The potential drawback is that it only covers formal employment. Appendix 8 discusses this data in more detail, and compares it to the firm survey data, finding a correlation of 0.93 (Figure A8.1), and that few firms appear to have large numbers of informal workers.

# **5. Impact on Management Practices**

The interventions aimed to improve specific management practices covered under the 141 practices that comprise Anexo K. These practices were measured for all firms during the diagnostic phase in 2013, and then measured monthly during the implementation periods of the individual and group interventions, and again one-year post-intervention. The control group had these measured towards the end of the individual treatment intervention, and again at the time of the one-year follow-up.

Figure 1 shows the trajectory of impacts on management practices for the overall Anexo K management score, and for the scores under the five separate areas of finances, human resources, logistics, marketing and sales, and production practices. We see that the individual treatment group sharply improves practices overall, and in all five areas, during the implementation phase, while the control group improves by much less. The group treatment likewise sharply improves practices for this treatment group during the implementation phase, and end up with practices at or above where the individual treatment group ended. This improvement in management then persists for the following year for both groups. Figure 2 compares the distributions of management practices at baseline, and at the last follow-up, for the three groups. Kolmogorov-Smirnov tests show we cannot reject equality of distributions at baseline, but at the endline, both the individual and group treatments are significantly different from the control group (p-values 0.004 and 0.003 respectively), although are not significantly different from each other (p-value 0.643).

For our regression analysis, we therefore classify our data into three periods: baseline, during the intervention (measured at the end of implementation for the individual and group treatments, and the first follow-up for the control group), and post-intervention (measured at the one-year followup post-intervention for the individual and group treatments, and the second follow-up for the control group). This time-shifts the data for the group treatment to account for the delay in implementation, which meant that its follow-ups took place a year later than the other two groups.

14
# III. RESULTS

We build on the specification of Aviat and Coeurdacier (2007), who studied a similar regression without the migration variable. We include in the regression source and destination fixed-effects as advocated by Okawa and van Wincoop (2012). Table 1 presents the first set of results. Column (1) reports a standard gravity equation without migration, and column (2) introduces the migration variable. Country fixed-effects are included in columns (3) and (4). The migration variable is highly significant, implying a strong correlation of migration with international loans.<sup>9</sup> Following Okawa and van Wincoop (2012), column (5) drops the fiscal variables and stock market correlation. This does not affect much the results. On the other hand, we gain a much larger sample with 17 lending countries, 175 borrowing countries and a total of 1,628 observations (country pairs), given missing data and zeros, which is twice the original sample size. The migration coefficient remains highly significant in the Poisson regression proposed by Santos Silva and Tenreyro (2006), which is reported in column (6).<sup>10</sup> These results are robust to different sets of fixed-effects and further control variables, i.e., remittances and political institutions (appendix table S.5).

We now turn to heterogeneous effects along a number of dimensions.

# _Skilled Versus Unskilled Migration_

The significance of migration in the gravity regressions of foreign loans suggests that migrants alleviate informational frictions between their host and home countries. Arguably, to be able to perform such a role, migrants should have the required skills and connections that allow them to communicate efficiently with the financial markets in their host country and connect them with their home countries. Hence, one would expect the migration effect to work mainly through skilled migrants. We test

> 9. The coefficients of the gravity variables change to some extent (compare column 1 with column 2), because the migration variable is correlated with the gravity variables (e.g., see Artuc et al. [2015]). In fact, Artuc et al. (2015), which is our source of migration data, use gravity regressions to interpolate missing data.

> 10. The Poisson estimator increases the sample size by adding observations with zero loans. However, the rise in the sample size is moderate for two reasons. First, we include only country pairs that have positive migration flows. Second, countries that do not borrow from at least two lending countries are dropped due to the fixed effects. Hence, conditional on having positive migration and at least two lending countries per each borrowing country, the share of country pairs with zero loans is roughly 11 percent of the sample. In addition, the Poisson estimator imposes adding-up constraints and gives a larger weight to larger observations (Fally 2015).

10
# **Colombia**

- World Bank. 2003. Implementation Completion Report on a Loan in the Amount of US$ 225 million to The Government of Colombia for he Earthquake Recovery Project. Washington, D.C.

- “ECLAC Study on Impact of January Earthquake in Colombia to be Presented to President Andres Pastrana”.

<u>http://www.scienceblog.com/community/older/archives/L/1999/A/un990625.html</u>

# **East Timor**

- Cliffe, Sarah and Rohland, Klaus. 2002. The East Timor Reconstruction Program: Successes, Problems, Tradeoffs. Working Paper. World Bank, Washington D.C.

- Alonso, Alvaro and Brugha, Ruairi. 2006. Rehabilitating the Health System after Conflict in East Timor: A Shift from NGO to Government Leadership. Oxford University Press, London.

# **Afghanistan**

- Scanteam. 2005. Assessment, Afghanistan Reconstruction Trust Fund. Oslo, Norway.

- Suhrke Astri et al. 2002. Peacebuilding: Lessons for Afghanistan. Chr. Michelsen Institute. Norway.

- World Bank. 2005f. Afghanistan: Managing Public Finances for Development. Main Report (Vol. 1). Poverty Reduction and Economic Management Sector Unit South Asia Region, Washington, D.C.

- Mckechnie, Alastair. 2003. Humanitarian Assistance, Reconstruction and Development in Afghanistan: A Pratitioner’s View. CPR Working Paper. Washington, D.C.

- Asian Development Bank, United Nations Development Programme, and World Bank (ADB, UNDP, and WB). 2002. Afghanistan: Preliminary Needs Assessment for Recovery and Reconstruction.

# **South Sudan**

- Joint Assessment Mission (JAM) Sudan. 2005a. Framework for Sustained Peace, Development and Poverty Eradication.Volume 1: Synthesis. http://www.unsudanig.org/JAM/

- Joint Assessment Mission (JAM) Sudan. 2005b. Framework for Sustained Peace, Development and Poverty Eradication.Volume 2: Cluster Costings and Matrices. <u>http://www.unsudanig.org/JAM/</u>

- Joint Assessment Mission (JAM) Sudan. 2005c. Framework for Sustained Peace, Development and Poverty Eradication.Volume 3: Cluster Reports. http://www.unsudanig.org/JAM/

# **Haiti**

34
being restricted to only disaster-affected regions, by the robustness of the results to any comparison region, and at least some evidence that the change in attitudes resulted in a decline in IPV. As discussed in the Introduction, the large decline in the acceptability of IPV is novel and contrasts with several other contexts in which the change in IPV prevalence following a disaster was unable to be statistically discernable from no change. These results also illustrate that the severity of the disaster is important to the baseline empirical pattern, and that other regions in a state of emergency but less badly affected did not have a similar change.

# **Section 7. Why Is the Change in IPV Incidents Reported by Women Less Precise Than the Change in IPV Attitudes of Men?**

We investigate why the change in the prevalence of IPV was less robust and less strong than the change in the acceptability of IPV reported by men. First, one possibility that could explain why the change in the prevalence of IPV was imprecisely estimated and potentially small is the possibility that instances of IPV could be a low-frequency event, and that the potential for IPV decreased even if the actual prevalence did not change. However, we illustrate that the frequency of the event is likely not the sole cause of the difference.

Figure 9a investigates changes in the intensity of IPV among women who experienced IPV in the past year. As described in the Data Section, for respondents that have ever experienced each of the 13 IPV incidents, the respondent is also given the opportunity to answer whether the event occurred either often, sometimes, within the past year, or not within the past year. But when we restrict the sample to the 2,451 women who have experienced IPV in the past year (out of 3,945 total), and analyze the change in the share that experience any of the IPV events often, the estimates are small in magnitude, imprecisely estimated, and change signs between specifications.<sup>34</sup> Thus, even among women that experience multiple IPV incidents each year, there was little change in the severity of the IPV.

Second, it is possible that the time period covered by the questionnaire- one year- is too long and might also capture events from before the disaster, which would contribute to noise in the measure and the less precise estimates. However, this is unlikely to be the case. The surveys in disaster-affected regions were delayed until November and December of 2018, which already is six to seven months after the disaster and limits the time period covered that is prior to the disaster. Additionally illustrating that this is likely not the entire explanation, the food security questions

> 34The result is the same when estimated with the full sample of women, where the indicator for experiencing IPV often for the excluded part of the sample is zero.

18
32

# **V.   ROBUSTNESS CHECKS**

So far, the results are consistent with our hypothesis that reforms in developing countries increase firm-level productivity with specific effects for firms facing high levels of distortions, financially included firms and small firms. In the following, we perform a variety of sensitivity analyses to check the robustness of the impacts of reforms on productivity. First, we account for economic crisis context in order to distinguish the effects of reforms from the effects of economic recovery. Second, we add additional control variables. Third, we check the sensitivity of the findings using an alternative way of calculating the indexes of reforms. Fourth, we check whether our results are robust using alternative measures of productivity. Fifth, we investigate whether our results are robust to the use of an alternative methodology, especially the Difference and Difference approach from Rajan and Zingales (1998) following among others by Aghion _et al._ (2014). Finally, we use unsuccessful reforms as a counterfactual experiment to validate the hypothesis of the positive impact of successful structural reforms on productivity. Finally, we check the sensitivity of the findings using an alternative way of calculating the indexes of reforms.

**_Crisis and economic recovery._** First, the MONA database includes both reforms implemented in a context of crisis (pre- and post-crisis reforms) and typical structural reforms, i.e., reforms implemented outside a crisis context. In the crisis context, the increase in productivity might be due to economic recovery and non-directly related to reforms implemented. From being sluggish in a crisis context characterized by weak economic growth, the domestic demand might be naturally restored and drive the increase in productivity. To ensure that the increase in productivity is due to reforms and not to economic recovery, we assess the robustness of the findings accounting for economic crisis context. The approach consists of identifying countries that were officially in crisis before and after the period of the reforms based on Laeven and Valencia’s (2012) database of crisis.<sup>26</sup> Following Reinhart and Rogoff (2014), we consider that economic recovery can influence productivity growth within eight years following the crisis. 27 The crisis context analysis shows that only 7 countries of 30 were officially in crisis within

> 26 The database documents systemic banking crisis, currency crisis, and sovereign debt crisis (default and restructuring).

> 27 Reinhart and Rogoff (2014) show that it takes on average about eight years to reach the pre-crisis level of income.
# _II. METHODOLOGY_

Public expenditure at a regional level is determined by factors that may simultaneously affect the nutritional levels of children. Maternal characteristics that induce mothers to choose to live in a specific location with a specific level of public expenditure may also affect their ability to care for their children. Moreover, the government may decide to spend more in an area where there is a greater need for public intervention (Rosenzweig and Wolpin 1986). This incentive creates reverse causality<sup>4</sup> . I address this endogeneity problem using an instrumental variable approach. My exogenous variation in public expenditures comes from unanticipated regional mining revenues.

# _Source of Exogenous Variation: Unanticipated Mining Revenues_

Unanticipated mining revenues are composed of revenues from the exploitation of minerals (gold, zinc, lead, silver, iron tin, copper, and molybdenum), oil, and gas. These three are lumped together as “mining resources” in Peruvian national accounts. The Mining Canon Regulations (Supreme Decree No 005-2002-EF) approved in 2002 state that regional and local governments are entitled to all of the mining royalties collected in their region (these are equivalent to 50% of the income and rents obtained by the central government from the exploitation of mining activities in their jurisdictions)<sup>5</sup> . At the national level, royalties

> 4 I conducted a Wu-Hausman F test statistic to test whether public spending was, in fact, exogenous. I was able to reject the null hypothesis (p-value = 0.053). This means that the endogeneity among the regressors would have deleterious effects on OLS estimates.

5 According to the law, the amount of royalties generated in a year is distributed with a delay of a year and a half. This results in a disassociation between the time at which these amounts are generated and when they are spent.

6