rafmacalaba/data-use-mentions-extended huggingface dataset:
is an omitted variable, positively correlated with GDP per capita. Thus, with the addition of this economic model, we have another alternative to the luxury good hypothesis:

**_Selection: The selective reporting hypothesis._** _Richer countries tend to have more complete SP spending data which imparts an upward bias to the estimated income slope of the SPEC._ We test this hypothesis.

Lastly, in the context of the pandemic, we consider a further hypothesis:

**_The COVID hypothesis:_** _Richer countries were impacted more by the pandemic and responded accordingly with their own SP spending._ By this view, rich countries had more of a shock to protect themselves from, reflecting higher levels of social and economic interaction that helped spread the infection (prior to vaccine availability). We can use COVID mortality data in measuring the severity of the pandemic shock.<sup>21</sup> These data do suggest a greater COVID impact in countries with higher GDP per capita; in our data set, the correlation coefficient between COVID deaths per capita and GDP per capita is 0.439. We test this as an extra covariate for SP spending during the pandemic.

# **3. Data and descriptive statistics**

The main dependent variable in our analysis is the share of national income devoted to public SP expenditure. In our first set of data, the coverage is across countries and over time up to (but not including) the pandemic, while the second is for the designated SP responses to the pandemic.

## **3.1 Pre-pandemic data on social protection**

Data on public spending at the country level were assembled for the purpose of this paper from the records kept by multiple agencies, notably Eurostat, OECD, ECLAC, IMF, and the World Bank. We have drawn on all these sources to provide as complete a data set as feasible. The Addendum provides details on the specific sources for every country and year, covering 142 countries over 1995-2020 (
(q-value = 0.03), which represents a 24% decrease in food assistance relative to control. This is a nontrivial proportion of households’ budgets: it is equivalent to over 10% of households’ total monthly labor income pre-pandemic (as control households reported 218 USD PPP in average monthly food assistance and pre-pandemic monthly labor income of 503 USD PPP). It is worth noting that this measure of assistance includes both formal assistance, such as from the UN High Commissioner for Refugees and the World Food Program, as well as informal assistance from local organizations and community members. Regardless of the channel, this substantial decrease in the household’s food budget seems likely to have contributed to worse household food security. This is in line with conversations that the research team had with several humanitarian aid organizations during the pandemic, in which some implementers sought to allocate emergency assistance to households that were not already benefiting from large forms of assistance, such as the HSP.

The midline survey data also indicates that there were several other unexpected short-run changes in household outcomes. For instance, it appears that changes in household composition may have further contributed to household hunger (Panel C, Table 5). Treated households at midline report significantly more children than control households (point estimate 0.3 children, q- value=0.03), and this effect is driven by an increase of 0.16 more adolescent boys (q-value=0.33). This movement between households can be understood as a form of redistribution within the Syrian refugee community, by which treated households shared some of the program benefits of the HSP but at the potential cost of food security to the full household. Despite the possible negative effect on household food security, the study cannot rule out that recomposition might have improved the well-being of the new household members who moved in (and the households they came from), if those adolescents came from relatively worse-off households. (Similarly, the targeting of food assistance away from HSP treatment households noted
2018), 73% of firms report experiencing regular power outages, with an outage frequency of 85 times per month and a duration of 70 minutes on average. In addition, a significant share of the population still does not have access to electricity. In 2016, about 76% of the Bangladeshi population were estimated to be connected to the grid, though access rates differ considerably across divisions (World Bank, 2016). The Household and Income Expenditure Survey conducted in 2010 by the Bangladesh Bureau of Statistics constitutes one of the few sources of sub24
The identifying assumption in equation (2) is that credit bureau reforms are exogenous shocks to the supply of credit.<sup>16</sup> The primary role of credit bureaus is to facilitate exchange of information among banks and financial institutions and thus it is reasonable to assume that the effect of CB reform on job growth as estimated in the specification above is exogenous and operates only through the effect of CB reform on credit markets. This approach takes advantage of cross-country variation in the supply of credit due to some countries introducing credit bureaus while others did not. With country fixed effects in the difference-in-difference setting, we are measuring the within country effect of CB reform. As such, this controls for any omitted variable bias from time-invarying country level variables. Introducing year fixed effects controls for macro variations across time periods.

# **_B. Orbis sample_**

Even with the use of a difference-in-difference estimation strategy for the impact of CB reform, arguably our analysis may not fully account for the endogeneity in the decision by countries to introduce CBs. For example, firm-level factors may drive countries to introduce CBs and also impact job growth. In such a scenario, our specification may suffer from selection bias due to omitted variables as the effects on job growth from these unobserved factors are attributed to CB reform. To address these concerns we use panel data from Orbis that allows us to include firm fixed effects to control for any firm-level unobserved heterogeneity. We also use two distinct methods to address causality issues that may result from country-level omitted factors or firm demand for finance. First, we empirically match the treatment and control countries to produce a more aligned sample of countries. In the specification (2) above, any country without a CB is placed in the control group. To provide a better counterfactual and to mitigate the selection

> 16 We relax the assumption of exogeneity in the following section.

16
rainfall datasets produced by long-running Earth-observing satellites are often lower resolution, have spatial or temporal gaps in data coverage, and exhibit variation in the fidelity and methodology of measurements over time.

In our empirical analysis, our preferred measure of precipitation shocks is a variable we call `dry days’, which varies at the annual and secondary sub-national administrative unit (ADM2) level. To construct this variable, we start with the daily total precipitation values observed in all ERA5 grid cells that are contained within the ADM2 units where we observe at least one firm in the ES data. Then, to focus on contemporary climate and match the temporal coverage of the ES data, we restrict the daily data to the period from 1990 to 2021. We then compute the long-run (1990-2021) mean and standard deviation for each day of the year in each grid cell. Using these moments of the local climate distribution, we then classify whether the precipitation observed on each day is more than 1 standard deviation below the long-run average for that particular day of the year. Days that satisfy this condition are considered `dry’ days. Finally, we sum across days within grid-years and average across ERA5 grid cells within ADM2 units to arrive at the final measure of dry days that we use in our primary empirical analysis. In many of our specifications, we also include an analogous measure of `wet’ days, which captures the number of days in a year where rainfall was more than 1 standard deviation above the long-run day-of-year average.

Previous studies have often measured rainfall shocks in terms of (normalized) deviations of total cumulative annual or seasonal rainfall from location-specific long-run averages. Standardized drought indices, such as the SPEI or PDSI, have also been widely used. Relative to these standards, our dry (wet) days measure has two key advantages. First, our measure captures extreme dryness (wetness) events even when they occur over a period of just a few days (or even just a single day). This helps us identify nonlinear effects
0.208|1.450|1.992|2.605|0.840|0.428|9.241|
|Mean C 2017|0.200|1.354|1.793|2.522|0.793|0.361|9.203|

This table shows the long-term effects of the program on a set of variables that result from aggregating groups of durable assets and household characteristics. The reweighting strategy employed is the inverse probability weight (DiNardo et al., 1996). In Panel A observations are restricted to households included in 1997 ENCASEH (original households), while in Panel B observations are restricted to newly formed households in 2017 (new households) that were identified by searching for members no longer present in their original 1997 households. The reweighting strategy seeks to reproduce the original sample distribution. Two different target distributions are set following the attrition analysis done in _Table 3_ . _Original Distribution_ means the 1997 sample, which corresponds to the original households in the 506 localities. The purpose is to make our results comparable to the vast literature that focuses on this group. _Localities 2017 distribution_ is based on the 334 localities chosen for the sample and assumes that all the original 1997 households in those localities would have been found. The outcomes employed are: (1) _Transportation_ , which adds the number of cars and vans; (2) _Entertainment equipment_ , which gathers under entertainment equipment number of TV, radio and sound equipment; (3) _Kitchen supplies_ , which sums the following appliances: blender, refrigerator, electric grill, gas stove or microwave oven; (4) _Basic services_ , which consists of the availability of the following services: firm floor, electricity, drainage and water access in the property; (5) _IT items_ , which refer to the number of computers and cellphones; and (6) _IT services_ , which sums the home phone, internet and pay-TV; (7) _ln(imp cons)_ , is the
’ (Cramer _et al._ , 2006; Dijkstra, 2002), the model has spread to other development agencies like DFID and UNDP.

# **7. Moving Beyond Failure: Evidence and Explanations**

I turn now to the evidence of failure which I touched on at the start of the paper. Since I will rely on World Bank project evaluation reports, we should recognize that World Bank projects are a skewed 14
are included in growth models, the ability of such models to explain economic growth is substantially enhanced (see Hanushek and Kimko (2000), Hanushek and Woessmann (2020, 2012)). From a macroeconomic perspective, there are several mechanisms through which education affects economic growth. In neoclassical growth theory, education increases human capital and labor productivity moving countries towards a higher equilibrium level of output. In endogenous growth theory, education increases innovation, new technologies, products and processes.

The next section develops a “Skills Index” that ties together many of the underlying concepts in this literature. Our objective is to use this Skills Index to analyze the relationship between human capital, skills and economic growth and to provide estimates of what skills enhancement can mean for economic growth.

# **III. THE CONCEPT OF A SKILLS INDEX**

An index combines several variables into a single metric. The objective of a wellconstructed index is to combine variables into a single metric such that the multidimensional nature of the data is preserved while highlighting the general directional movement of the whole. A well-constructed index can summarize complex, multi-dimensional realities to provide a useful guide for policy making, facilitate communication and promote accountability. Greco et al. (2019) point to the U.N.’s Human Development Index (HDI) as an example. The HDI was initially criticized for its methodology. However, Nobel laureate Amartya Sen, one of its early critics, changed his 11
overall picture that emerges is consistent with the interpretation that forbearance offers extended by the lender highlight the value of the banking relationship and create incentives to engage with the lender in an effort to preserve the option of doing business with the lender in the future.

If this interpretation is correct, one additional implication is that borrowers with fewer outside options should have higher demand for continued interactions with the lender. To test this, we examine whether borrowers with a shorter credit history and borrowers who live in areas with fewer banks—two proxies for more limited outside options—respond differently to the product offer. Results by number of banks are shown in Table B.13, and for credit histories in Table 6. We find suggestive evidence that supports this conclusion: point estimates indicate that borrowers without a formal credit history and borrowers in areas with fewer banks are more likely to repay their loans and more likely to accept the non-credit product offer in the follow-up experiment, though standard errors around these estimates are large so that they should be interpreted as merely suggestive.

Taken together, the results from our two complementary experiments suggest that, rather than damaging credit discipline, repayment flexibility offered by one’s lender can improve loan repayment in the short term and foster the establishment of banking relationships in the longer term. Importantly, we show that the demand for future engagement with the lender is not explained by motives that could create moral hazard in future interactions with the lender. Hence, repayment flexibility offered by a bank can be interpreted as an _investment_ in the banking relationship that can be profitable because it helps the bank identify customers with the highest willingness and ability to repay and ties them into longer term banking relationships. This is analogous to studies on relational contracting (Fehr and List, 2004) that have shown that investments into relationships by one contracting party can generate information about the contracting party and promote trust and trustworthy behavior.

# **5 Conclusion**

This paper uses a nationwide randomized experiment in India to estimate the effects of debt
Measurements of Poverty in Indonesia:

# 1996, 1999, and Beyond*

Menno Pradhan, Free University Asep Suryahadi, SMERU Sudamo Sumarto, SMERU Lant Pritchett, World Bank# Social Monitoring & Early Response Unit

## Jakarta

The findings and interpretations in this report are those of the authors, and should not be attributed to the World Bank Group or to any agencies providing financial support to SMERU activities and reports.

' This report is based on the full SUSENAS sample of 65,000 households. We update the same methodology previously applied to the accelerated sample of 10,000 households. We thank Wenefrida Dwi Widyanti and Yusuf Suharso for their research assistance. We are grateful to BPS for providing access to the data.
main effect,” _Stat Modeling Blog_ .

- Gelman, A., J. B. Carlin, H. S. Stern, and D. B. Rubin (1995): _Bayesian data analysis_ , Chapman and Hall/CRC.

- Girma, S., Y. Gong, H. Görg, and Z. Yu (2009): “Can Production Subsidies Explain China’s Export Performance? Evidence from Firm-level Data,” _Scandinavian Journal of Economics_ , 111, 863–891.

- Groh, M., N. Krishnan, D. McKenzie, and T. Vishwanath (2016): “The Impact of Soft Skill Training on Female Youth Employment: Evidence from a Randomized Experiment in Jordan,” _IZA Journal of Labor and Development_ , 5.

- Hastie, T., R. Tibshirani, J. H. Friedman, and J. H. Friedman (2009): _The elements of statistical learning: data mining, inference, and prediction_ , vol. 2, Springer.

- Higuchi, Y., V. H. Nam, and T. Sonobe (2017): “Management practices, product upgrading, and enterprise survival: Evidence from randomized experiments and repeated surveys in Vietnam,” _GRIPS Working Paper_ .

- Hirschleifer, S., D. McKenzie, R. Almeida, and C. Ridao-Cano (2016): “The Impact of Vocational Training for the Unemployed: Experimental Evidence from Turkey,” _Economic Journal_ , 126, 2115–2146.

- Hussam, R., N. Rigol, and B. Roth (2022): “Targeting High Ability Entrepreneurs Using Community Information: Mechanism Design in the Field,” _American Economic Review_ , 112, 861–98.

- Iacovone, L., W. Maloney, and D. McKenzie (2022): “Improving Management with Individual and Group-Based Consulting: Results from a Randomized Experiment in Colombia,” _Review of Economic Studies_ , 89, 346–71.

- Imbens, G. and
to which mobile money is capturing the nonbanked market clearly differs across economies. This may reflect the varied and quickly evolving public policies surrounding mobile money. When M-PESA began in Kenya, it had no association with the formal banking sector and mobile banking customers there were exempt from the documentation requirements imposed by banks.<sup>27</sup> But governments increasingly are favoring bank-led models in which mobile money providers have partnerships with or are formed directly through banks. In India the government introduced regulations in 2008 requiring that mobile money schemes be operated by banks, making it difficult for an M-PESA–type market entrant to lead the nascent mobile money movement.<sup>28</sup> This has probably contributed to the slow growth of mobile money in India, where only 4 percent of adults in the Global Findex sample report having used a mobile phone in the past 12 months to pay bills or send or receive money.

**27**

MEASURING FINANCIAL INCLUSION
**Figure 1. Tradable Consumption Shares across Income Deciles in Brazil, 2003**

<!-- Start of picture text --> 50.0%<br>45.0%<br>40.0%<br>35.0%<br>30.0%<br>25.0%<br>20.0%<br>15.0%<br>10.0%<br>5.0%<br>0.0%<br>1 2 3 4 5 6 7 8 9 10<br>definition 1 definition 2 definition 3<br><!-- End of picture text --> Source: Lederman, Lichand and Fajnzylber (2011), based on data from the Brazilian POF survey of 2003.

27
Table 4 is the regression equivalent of the figures which shows that the tendencies in the graphs are typically not statistically significant. For construction permits for instance the lowest slope is 1.22 and falls to .010 and -.018 in the upper ranges of the DB data. However, the F-test on the spline terms fails to reject that 1.22 and .010 are different because the standard errors on each coefficient are so large—the data cannot reject that 1.22 is equal to zero (t-stat is only 1.84) so cannot reject it is equal to .01.

**Table 4:  The spread of “favored” and “disfavored” firms (90**<sup>**th**</sup> **less 10**<sup>**th**</sup> **or 75**<sup>**th**</sup> **less 25**<sup>**th**</sup> **) is associated with increases in the DB days at best at low levels of DB**

||Imp|orts|Constr<br>Per|uction<br>mit|Operati|ng License|
|---|---|---|---|---|---|---|
||90-10|75-25|90-10|75-25|90-10|75-25|
|Constant|7.887|1.221|-50.446|27.821|48.182|22.750|
|(t-stat)|1.056|0.350|-0.515|0.766|2.712|3.630|
|Slope over lowest tercile of DB|0.616|0.422|1.228|0.119|0.743|-0.005|
|(t-stat)|1.
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# **5.3.2 Wallsten (2002)**

Wallsten (2002) concentrates more on regulation per se. The concern of this paper is whether or not the establishment of a regulator _before_ privatization (defined as partial or total sale of shares of the incumbent telecom company) provided benefits in terms of higher investment and larger privatisation revenues.

Like Fink et al, the paper estimates fixed effects models on a data set of 200 countries over the period 1985-99. The models estimated date the establishment of the regulator using ITU data – many were only established after 1995. Unlike Fink et al or Pargal, there are no data on competition or liberalization so that the effects of privatization and regulation may be exaggerated.

One other feature of the Wallsten data is that the sample included major OECD countries (including the US, the UK, France, Germany, etc) and there are no estimates reported for developing countries only and there are no dummies for international regions. Hence, the results reported may be unreliable to the extent that different factors are relevant (or more important) in rich than in poor countries (e.g. on quality of regulation)<sup>53</sup> . The estimated equations included the standard control variables of per capita GDP and population.

One interesting feature is the use of self-reported answers on whether regulators were independent. The specific question to which countries sent replies to the ITU was whether “the Regulatory Authority [is] independent from political power”<sup>54</sup> . As Wallsten points out, this is a very ambiguous question with clear incentives to provide an answer of independence - and virtually no penalties against providing such an answer. Wallsten rightly points outs that this is emphatically not an appropriate way to collect reliable data on the quality of regulation<sup>55</sup> .

Bearing these qualifications in mind, the estimation results are nevertheless supportive of a positive impact of regulation on investment. The key results are:

-
--|---|---|---|---|---|---|
|Amenity|Tap<br>Water|house<br>Tap<br>Water|In-house<br>Latrine|Water<br>Closet|Tap<br>Water|In-house<br>Tap Water|In-house<br>Latrine|Water<br>Closet|
|Mean value in<br>Statutory Towns|57.9%|35.9%|59.0%|28.2%|**_5.7%_**|**_5.4%_**|8.0%|28.4%|
|Mean value in|||||||||
|Proximate Census<br>Towns|47.87%|27.96%|66.34%|32.46%|8.15%|7.77%|11.96%|29.38%|
|Mean value in|||||||||
|Other Census<br>Towns|49.20%|32.64%|64.69%|37.72%|4.85%|4.34%|8.84%|23.18%|

Source: Mukhopadhyay (2017),. Proximate CTs refer to CTs that are close to larger towns.

There is an emerging, but still very limited, literature that focuses on some selected CTs, and it comes either from field research or from journalistic coverage.<sup>6</sup> These works provide insights into the low level of services, even in CTs located in the periphery of large metropolitan cities and into some of the governance issues that impact service delivery. The cases of Molachur (50 km from Chennai) and Chakan (30 km from Pune)<sup>7</sup> that
increases in wages and productivity (Clemens (2011)). Migration driven by natural or man-made disasters, such as the Venezuelan exodus, is much more likely to be of the South-South type and, as a result, this type of productivity gain may be less relevant.

An important feature of the EPEC survey is that it contains retrospective information on the last job held by migrants prior to leaving Venezuela. The goal in the remainder of this section is to compare the changes in occupation experienced by Venezuelan migrants 14
005)|(0.016)|
|MAS_×_Ln(Duration)|0.273***|0.155***|-0.008|0.002|-0.044|
||(0.055)|(0.031)|(0.014)|(0.014)|(0.041)|
|INFRA_×_GDP growth|-0.544|-0.286|-0.641|-0.663|-1.271|
||(1.728)|(0.979)|(0.435)|(0.445)|(1.304)|
|INFRA_×_RER|1.621**|-0.449|-1.283***|-0.662***|-0.980|
||(0.796)|(0.451)|(0.200)|(0.205)|(0.601)|
|INFRA_×_Ln(Size)|0.040*|0.033**|0.003|-0.017***|-0.061***|
||(0.023)|(0.013)|(0.006)|(0.006)|(0.018)|
|INFRA_×_Ln(Duration)|0.149**|0.193***|0.025|-0.032*|-0.078|
||(0.065)|(0.037)|(0.016)|(0.017)|(0.049)|
|_R_<sup>2</sup>|0.194|0.082|0.025|0.045|0.118|
|Observations|1668|