rafmacalaba/data-use-mentions-extended huggingface dataset:
conflict and improve economic security and employment opportunities for individuals in conflict-affected regions.<sup>4</sup> Increasingly, the focus of public works programs is to improve medium-run employability, assets, and resilience to future shocks (Gehrke and Hartwig, 2018). Besides providing short-term employment or income support, these programs also aim to open pathways to productive employment after participants exit the program. This is often done by combining the public works experience with additional interventions, such as vocational training or micro-entrepreneurship support.<sup>5</sup> This paper reviews experimental evidence on public works programs, including a wave of recent studies, and provides directions for future research. We use a meta-analysis model to estimate the average impact of public works programs on individuals’ earnings and employment. We use data from all countries for which a published paper, working paper, or project report was either publicly available or made available by the authors as of December 2021. As such, the review is based on 11 randomized control trials across 9 countries––Comoros (Gazeaud et al., 2019), Côte d’Ivoire (Bertrand et al., 2021), DRC <mark>(Brandily-Snyers et al., 2021;</mark> Mani and Mvukiyehe, 2021), Djibouti (Devoto et al., 2017), the Arab Republic of Egypt (World Bank, DECRG; Croke et al., 2023), Ethiopia (Abebe et al., 2021), the Lao People’s Democratic Republic (World Bank, EAPGIL, 2020), Sierra Leone (Rosas and Sabharwal 2016), and Tunisia (Leight and Mvukiyehe, 2022) – see Appendix Table A1 for details.<sup>6</sup> We complement earlier work by Subbarao et al. (2013) and Gehrke and Hartwig (2018) in several important ways. First, we focus on public works experiments. Recent experimental studies provide causal evidence on the impacts of public works programs on individual participants
geographical heterogeneity in protest intensity across states in Egypt. Using a similar method that exploits variation in conflict intensity, this paper seeks to understand the role of exposure to violent events in changing household dynamics for women in an ongoing conflict.

Equally important in the context of this paper is a study conducted in Yemen using household welfare data from 2014. Tandon (2018) compares household welfare before and after the Houthi take-over of Sana’a in 2014 and finds a nearly universal and immediate drop women’s decisionmaking ability which preceded the worsening of household welfare. This impact is explained by a worsening of perceptions of safety, resulting in women being less likely to leave the household and make decisions related to spending. The author was able to exploit the fact that the survey was uninterrupted despite the unexpected capture of the capital, and that the sample was designed to randomize respondents over space and time.

This paper contributes to literature in other conflict affected settings that have explored changes in various indicators of female empowerment. A study from Egypt finds that female labor force participation increased in districts with higher intensity of protests, despite worsening conditions for men (El-Mallakh, Maurel, and Speciale 2018). Similarly, evidence from Nepal shows an increase in women’s likelihood of employment as a consequence of conflict (Menon and van der Meulen Rodgers 2015), as this becomes an important livelihood option during times of crisis. Qualitative evidence from Gaza and Liberia points to an increase in women’s economic agency in towns more affected by conflict, but in Gaza this is combined with men feeling disempowered and limited changes to underlying social norms (Petesch 2018). On the other hand, studying the long term impacts of the 1994 Rwandan genocide, La Mattina (2017) finds an increase in spousal abuse and reduced decision-making power for women, while Schindler (2010) finds that young, unmarried women are more likely to conform to gender roles when there is a shortage of men resulting from the genocide. These conflicting results from different contexts indicate that
institutions relative to the GDP to control for the financial development of a country and to account for the overall size of the financial institutions sector. Further, when studying the role of foreign banks, we control for the degree of competition in the banking sector using an index of banking sector concentration (the Herfindahl-Hirschman index of bank assets in the country).

The identification strategy relies on the approach originally proposed by Rajan and Zingales (1998), which exploits exogenous variation in the financial dependence of industries due to the technological features of production processes. As pointed out by Rajan and Zingales (1998), what matters is the ranking of the financial dependence of the industries. This ranking can be expected to largely reflect technological features, while financial dependence does not vary substantially across firms within an industry. The measure of industry-level degree of external financial dependence is constructed using data from Maskus et al. (2012) and Klapper et al. (2006).<sup>7</sup> Similar to Rajan and Zingales (1998), external financial dependence is defined as the median of the ratio of capital expenditures minus cash flow from operations divided by capital expenditures across U.S. firms in a given industry over the

> 7Industries are defined at the 2-digit level in ISIC Rev.3.

8
Policy Research Working Paper 5502

# **Abstract**

This paper uses data on publicly listed companies in Jordan to evaluate corporate vulnerability and perform corporate stress testing. The exercise finds that both earnings and interest rate shocks have significant impact on corporate vulnerability. Because different banks have different sector exposures, and different sectors exhibit different vulnerability at different times, the stress tests that include corporate exposure will provide a more precise evaluation of bank soundness.

This paper is a product of the Finance and Private Sector Development 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 author may be contacted at ilove@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
previous results from more stable settings.

# **7 Benchmarking TUP Households’ Socioeconomic Conditions against Those of Non-Ultra-Poor Households**

How do the improvements in UP treatment households’ outcomes influence their economic and social standing relative to the rest of the population in their communities? To answer this question, we leverage data collected at baseline, midline and endline on a random representative sample of non-UP households (excluding the census of UP that was randomized into treatment and control) across the 80 villages in the study. Bedoya et al. (2019) show that (i) all households, including non-UP, are vulnerable; and (ii) UP households participating in this study were considerably worse off than non-UP households at baseline across all dimensions studied, including consumption, poverty, food security, assets, and psychological well-being. Here, we extend this analysis by exploring where UP treatment households have been able to catch up with non-UP households across multiple socioeconomic dimensions. Table 10 shows how socioeconomic gaps

> 34The control group doubled their consumption between baseline and the 10-year follow-up, while earnings multiplied by 3 between the 18-month and the 10-year follow-ups (Banerjee et al., 2021).

36
As described below, this is highly correlated with measures of rule of law and contract enforcement at the country-level. By controlling for country-level unobservables and region characteristics (such as GDP per capita and the share of college-educated workers), the correlation between residual variation in trust and region-level unobservables is likely less severe than at the countrylevel. Relative to the small number of papers that use regional variation to examine the relation between institutions and management (Amit et. al. 2015, Akcigit et. al. 2016), our large sample spanning 134 regions allows to cluster standard errors at the region-level (at which the explanatory variable of interest varies) for correct inference and examine heterogeneous effects across countries.<sup>8</sup> We find that in regions characterized by a lower level of trust, firms are more likely to be family-managed and family-managed firms are relatively larger. This is consistent with the sorting patterns predicted by our model. Highlighting the importance of our approach that leverages regional variation within countries, we find that cross-country specifications overestimate trust’s effect on family management by about 50%.<sup>9</sup> In further support of the mechanism behind our findings, we employ the Levchenko (2007) measure of product complexity at the industry-level and show that our results are stronger within more complex industries which are likely to be particularly sensitive to the external contracting environment as predicted by our framework.

While La Porta, Lopez-de-Silanes and Shleifer (1999) and Aminadav and Papaioannou (2018) focus on ownership structure, as in Villalonga and Amit (2006) we are able to break apart family ownership from management and explore which drives differences in firm performance. We find that (i) founder firms (i.e. young, family firms) perform better than their older counterparts and that (ii) family-owned but professionally-managed firms perform better than those which are both family-owned and family-managed. This reinforces the Bloom and Van Reenen (2007) findings that primogeniture is especially harmful for management practices, and
# _Within-country_

We complement this cross-country analysis with within country analysis of the relationship between teacher reported salaries and student test scores using PASEC microdata from 10 African countries.<sup>39</sup> Specifically, for student _i_ with teacher _t_ we estimate a model that pools across countries and included fixed effects.

We also estimate a model that includes a vector of controls ( **_X_** ) for teacher characteristics: gender, experience and its square, years of schooling, highest education degree obtained, duration of preservice training, whether the teacher is also the principal, and contract status.

Student and teacher data are collected in Grades 2 and 6 in the study countries. We estimate models that pool the data across grades (and in those models include a dummy variable for being a Grade 6 observation) as well as grade-specific models. Note that salaries are reported in the survey in intervals (e.g., “Between CFAF 15,000 and 29,000” “Between CFAF 30,000 and 59,000” ...)—with a total of 13 possible intervals. We assign the value of the midpoint of the reported interval to the teacher as their salary.<sup>40</sup> The estimates derived from this analysis are reported in Table A12.

> 39 The mean and standard deviation of test scores for each grade level are reported in Table A4.

> 40 For the lowest interval “Less than CFAF 14,000” we assign a value of 14,000.

46
for future temperatures come from data created by Rasmussen et al. (2016) and used in Hsiang et al. (2017a). These data collect the output of between 28 and 44 global circulation models (GCM) for each of the RCP scenarios and measure the number of days the maximum temperature is in each of the 1<sup>_◦_</sup> C bins for every county in the continental United States from 1981 to 2100. We use data on temperatures in Texas from 2000 to 2050 to examine how future climate change will change arrests.

23
0.094*|
||(0.039)|(0.021)|(0.007)|(0.007)|(0.057)|
|Dummy year 2016|0.678***|-0.106***|0.041***|0.005|0.127**|
||(0.041)|(0.021)|(0.009)|(0.009)|(0.061)|
|Constant|9.316***|0.323***|-0.008|0.014|4.193***|
||(0.101)|(0.053)|(0.022)|(0.021)|(0.153)|
|Observations|3,863|3,863|3,863|3,863|3,863|

Heteroskedasticity-robust standard errors in parentheses. Standard errors are corrected for sampling weights and cluster correlation at the commune level. *** p<0.01, ** p<0.05, * p<0.1.

Source: Estimation from VHLSS 2010, 2012, 2014 and 2016.

37
the years 1991-1996 that were projected in the Spring and in the Fall.<sup>19</sup> The Spring forecast of inflation and GDP growth is used as the forecast for the second and third quarters while the Fall forecast is taken as the forecast for the fourth and first quarter. In the spirit of Romer and Romer (2004), the change in the policy rate for each country is regressed on two lags of inflation, GDP growth, the policy rate, and on (2-quarter) forecasted values for inflation, the growth rate as well as changes in lags and in forecasted values of these variables. The residuals from this regression represent the exogenous shocks to the policy rate

> 19The WEO historical data used in this paper corresponds to the April 17, 2018 update. This version does not have inflation forecast for Argentina in 2014.

24
engagement in reproductive and maternal health and violence prevention in Rwanda: Findings from a randomized controlled trial.” _PLOS ONE_ 13(4): e0192756.

- Dupas, Pascaline, Karlan, Dean, Robinson, Jonathan, and Diego Ubfal. 2018. “Banking the Unbanked? Evidence from three countries.” _American Economic Journal: Applied Economics_ 10(2): 257-97.

- Dupas, Pascaline and Jonathan Robinson. 2013. “Savings Constraints and Microenterprise Development:

- Evidence from a Field Experiment in Kenya _." American Economic Journal: Applied Economics_ 5: 163-192.

- Elam, Amanda, Hughes, Karen, Guerrero, Maribel, Hill, Stephen, and Catharina Nawangpalupi. 2021. “Women’s Entrepreneurship 2020/21 - Thriving Through Crisis.” Global Entrepreneurship Monitor. Global Entrepreneurship Research Association, London Business School.

- Elefante, Marina, Hasan, Tazeen, Hyland, Marie, Mazoni Silva, Martins, Natalia, and Tea Trumbic. 2023. “Accelerating Gender Equality Through Reforming Legal Frameworks.” World Bank Group Gender Thematic Policy Notes Series: Evidence and Practice Note.

- Ellis, Mia, Cynthia Kinnan, Margaret McMillan, and Sarah Shaukat. 2022. “What Predicts the Growth of Small Firms? Evidence from Tanzanian Commercial Loan Data.” Working Paper.

- Evans, David, Jakiela, Pamela, and Heather Knauer. 2021. “The Impact of Early Childhood Interventions on Mothers.” _Science_ 372 (6544): 794–96.

- Fafchamps, Marcel, McKenzie, David, Quinn, Simon, and Christopher Woodruff. 2014. “Microenterprise Growth and the Fly-paper Effect: Evidence from a Randomized Experiment in Ghana.” _Journal of Development Economics_ 106: 211-226, 2014.

- Fang, Sheng, Goh, Chorching, Roberts, Mark, Xu, Lixin Colin, and Albert Zeufack. 2022. “Female entrepreneurs and productivity around the world: Rule of law, network, culture and gender equality.” _World Development_ 154: 105846.

- Field, Erica
specificity reduces the influence of forgetting. Anchoring the reporting to the previous interview reduced the possibility of telescoping.

Table 1 presents descriptive statistics on household characteristics across the four survey designs, drawing on the end line survey (postharvest). For these set of traits, households are well balanced across the different survey designs.

To be sure that the differences across the four arms of the experiment are not plagued by confounding factors, we consider some identification concerns. First, households are randomized within villages to account for micro agroecological patterns affecting household labor (which we may not capture through data sources). This raises the possibility of intracluster contamination, whereby one person’s response is influenced by another’s design status. We opt for within-village randomization because we believe that such contamination is unlikely because villages are relatively large and diffuse.

Second, the weekly visits themselves could have caused differential labor (akin to Hawthorne effects). We cannot rule this out, but note two points. There was a general increase in hours as the season progresses (which might occur if respondents started work at a more intensive pace or overreported work as a result of frequent interviews), nor do we find a decline in hours over the season in the weekly interviews because of respondent fatigue. There was also little difference between the face-to-face and phone interviews, whereas one would expect Hawthorne effects to be stronger in in-person visits.

Third, we note that self-reporting rates were similar across survey designs. In the weekly visit group, interviewers were instructed to collect information directly from respondents, where possible, to avoid proxy reporting. Meanwhile, in the weekly phone interviews, one household member typically reported on his/herself as well as on other household members, although the possibility exists that people may have self-reported by turn. In both recall survey designs and consistent with current common practice, interviewers were instructed to ask the most knowledgeable person in the household to report on family farm labor. Despite differences in the instructions given to enumerators and in the feasibility of
Expenditure Data.” _Review of Income and Wealth_ 49: 463–489.

Sahn, David and Stephen Younger. 2009. “Measuring Intra-Household Health Inequality: Explorations Using the Body Mass Index.” _Health Economics_ 18: S13-S36.

Smith, Lisa and Lawrence Haddad. 2015. “Reducing Child Undernutrition: Past Drivers and Priorities for the Post-MDG Era,” _World Development_ 68: 180-204.

van de Walle, Dominique. 2013. “Lasting Welfare Effects of Widowhood in Mali.” _World Development_ 51: 1-19.

Victora, Cesar. 1992. “The Association between Wasting and Stunting: An International Perspective.” _Journal of Nutrition_ 122(5): 1105–10.

Wagstaff, Adam. 2003. “Child Health on a Dollar a Day: Some Tentative Cross-country Comparisons.” _Social Science and Medicine_ 57(9): 1529-38.

27
factor productivity, prices or both. Apart from labor market regulations, pertinent regulatory activities include those aimed at financial markets/institutions, environmental externalities and zoning, industrial licensing, bankruptcy laws, the regulation of utilities including power, telecom or transport companies, law and contract enforcement institutions, customs regulation, and domestic and foreign trade taxes.

4 Many other aspects are either far more difficult to observe (e.g. the quality of law or contract enforcement institutions), or do not seem to vary a great deal sub nationally (e.g. industrial licensing and the regulation of financial markets). Besley and Burgess (2004) make the latter point with respect to labor regulation. 5 Our finding of association between business environment and spatial productivity gaps is also consistent with Escribano and Guasch (2004), Latin American data, Eifert et al (2005) for a number of African economies, and Haltiwanger and Schweiger (2005) on a much larger set of developing economies. Each of these papers analyzes international productivity gaps based on the World Bank Investment Climate Surveys but from perspectives slightly different from Dollar et al (2004). Dollar et al (2002) and HallwardDriemeier et al (2003) link in country variation in productivity to business environment using the World Bank survey data for India and China respectively.

6 Based on an analysis of time series of aggregate (state level) data, Besley and Burgess conclude that labor law reforms that some states carried out over the period 1958-1992 to reduce compliance costs led to faster growth in manufacturing employment and productivity than in states that, either did not amend the labor law at all, or did so, but in ways that increased the cost of regulation to businesses.

4
underreporting of specific categories of goods such as tobacco and alcohol. Further, the undersimulation for expenditures also reflects the fact that not all final consumption is conducted by private households. VAT is also paid by public institutions and by the corporate sector for which the final sales are exempt from VAT. Additionally, some taxable incomes and a high proportion of some consumption items, like accommodation, are received and paid by visiting foreigners. Indirect subsidies (in this case the electricity subsidy) are simulated with an accuracy of 67.2 percent when compared to official statistics. However, the scale of the electricity subsidy is very small when compared to indirect taxes; according to government statistics, 134.9 billion KHR in 2019 was spent on the electricity subsidy in 2019, as opposed to 6,259.9 billion KHR in revenues from VAT alone.

> 16 See the appendixes for more details.

> 17 The total value of household consumption in the CSES 2019/20 is KHR 107,335.6 billion. According to the World Bank, GDP in 2019 was US$ 27.09 billion at official exchange rate and household consumption was 74% of GDP. This means: 27.09*4070*0.74 = KHR 81,590 billion; so less than observed in the data. So, with more consumption we catch less VAT. Usually this can be explained by government, NGO spending, intermediate VAT in zero-rated final goods. However, all of those are low in Cambodia. The only reasonable explanation seems to be that we miss consumption of very rich households which is likely to be subject to VAT. This would suggest though that household consumption in the national statistics would also be too low. Note that we assume that VAT is not paid on food in rural areas – this seems reasonable, but perhaps it is too restrictive.

15
**Figure 2. Distributional incidence of subsidies (by decile) in five African countries**

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail. _Note:_ All figures are calculated using sample weights. Total expenditure is total household expenditure in all categories. The distribution of expenditure refers to the countrywide distribution of expenditure per capita, i.e., households are ranked according to their expenditure per capita.

**Figure 3. Distributional incidence of subsidy beneficiaries (by decile) in five African countries**

_Source:_ Original calculations using data from household surveys, IBNET, and national utilities and programs. See the data and methodology section, and the appendix, for more detail.

13
spend on private education and tutoring for their children. As long as these are not payments or benefits from the public system, they are not part of the exercise.

In what follows, we provide an overview of each of the main fiscal tools and illustrate the policies that are part of the CEQ analysis. Given data limitations, the analysis is based on a snapshot of the Egyptian policy landscape as of 2015. This is because the latest available HIECS is from calendar year 2015, and the crux of the exercise is to be able to map the information from administrative data (government accounts) to the distribution of Egyptian households observed in the survey and estimate the benefits (payments) they receive (make).

5