rafmacalaba/data-use-mentions-extended huggingface dataset:
_|
|1st Stage F-Stat|208_._63|104_._26|208_._63|104_._26|208_._63|104_._26|84_._51|42_._55|84_._51|42_._55|84_._51|42_._55|
|Observations<br>|1,271,082|1,271,082<br>|1,271,094|1,271,094|1,271,094|1,271,094|10,220|10,220|10,220|10,220|10,220|10,220|
|Adjusted R<sup>2</sup>|0.003|0.005|0.0001|-0.001|-0.001|-0.001|-0.01|0.01|0.001|0.01|-0.003|-0.004|

Standard errors are clustered on Woredas. The models include a control for the log distance to the nearest targeted area. “Imp Rd.” indicates that a unit was near (within 5km of) an improved road. _NTL_ 96 Lit indicates that the Woredas had some positive nighttime lights at baseline. The excluded group is Woredas where the maximum value of nighttime lights at baseline was 0. When using 1x1km pixels as the unit of analysis, NTL is the inverse hyperbolic sine of nighttime lights of the 1x1km pixel, which has a similar interpretation as logs, and Urban and Cropland are binary variables indicating whether the pixel contains urban or cropland. When using Kebeles as the unit of analysis, NTL is the average nighttime lights within Kebeles, Urban is the total urban area within Kebeles, and Cropland is the total cropland area within Kebeles; we use the inverse hyperbolic sine transformation on all outcome variables.<sup>_∗_</sup> p _<_ 0.1;<sup>_∗
upper portion of the matrix contains a set of K exogenous variables (which may include dummy variables for place) that affect the level of treatment c among those with choice. The bottom portion of this matrix is zero, as is the bottom portion of the vector of errors , consistent with the deterministic nature of c=ε=0 of those without choice.

> to Finance” authored by Morduch and Dean Karlan in the widely respected _Handbook of Development Economics_ (Karlan and Morduch, 2009):

> “Roodman and Morduch (2009) attempt to find closure to the issue by returning to the data and rebuilding the analysis from scratch. They are unable to replicate results from Pitt and Khandker (1998) or Khandker (2005). In fact, their estimates carry the opposite sign. Rather than concluding that microcredit harms borrowers, however, they unearth a raft of identification issues which are not solved with panel data. Their revised analysis casts doubt on all of the findings from the related set of papers, including Morduch (1998)’s finding on consumption smoothing. “

> 8 More generally, we require only that the offer of treatment be random conditional on a set of exogenous covariates that might include location fixed-effects, the assumption actually made in PK.

> 9 Thus, elements of c can be stochastically or deterministically zero depending on if individuals are offered treatment.

6
<br>head female|0.28|0.08|0.11|0.75|
|aesize|Adult equivalent household size|3.90|0.55|2.16|8.09|
|pchild<br>*Note: all o<br>exception of|Percentage of children in<br>household<br>bservations are sub-county means of t<br>the Inequality measures. No. of obse|0.44<br>he household<br>rvations: 719|0.06<br>values of th<br>|0.12<br>e variables me|0.60<br>ntioned, with the|

# **4. Estimation**

The properties of estimators obtained from downstream<sup>3</sup> regressions using imputed values for welfare indicators are investigated in Elbers et al. (2005). Their main proposition is that coefficients from regressions involving imputed welfare indicators which have been derived with small area estimation techniques, either on the LHS or on the RHS, do not differ systematically from regressions with ‘real data’. The 10
Policy Research Working Paper 7573

# **Abstract**

This paper examines whether an expansion in the supply of public preschool crowds out private enrollment. The paper uses rich data for municipalities in Brazil from 2000 to 2006, where federal transfers to local governments change discontinuously with given population thresholds. The results from a regression-discontinuity design reveal that larger federal transfers lead to a significant expansion of local public preschool services, but show no evidence of crowding out of private enrollment. This finding is consistent with a theory in which households differ in their willingness to pay for preschool services, and private suppliers optimally adjust prices in response to an expansion of lower-quality, free-of-charge public supply.

This paper is a product of the Trade and International Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at pbastos@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
034|0.515|208|
|Grantee’s years of education|9.722|9.946|0.224|0.724|207|
|Partner’s years of education|13.196|14.048|0.851|0.524|175|
|Years of marriage|13.328|13.297|-0.031|0.983|208|
|<br>Recommended window is [-202|1.9; 2021.9]|with 208 observ|ations (134 bel|ow, 74 abov|e).|

_Note:_ Recommended window for local randomization tests based on Cattaneo et al. (2018)

# <mark>6.2.3 Effect on Placebo Outcomes</mark>

Finally, we conduct a falsification test to investigate whether the CCT program had any effect on placebo outcomes. Considering that CCT programs can have an overarching effect on a multitude of outcomes, it is rather difficult to identify suitable placebo outcomes. Nonetheless, after careful consideration we identified a few placebo outcomes via utilizing a list randomization measurement experiment that was embedded as a part of the IE study to measure GBV.

Under the list randomization (LR) method, we divided the study sample into two groups, i.e., treatment and control. From the control group, we asked questions on innocuous topics (i.e., on topics that are unrelated to the CCT program and exposure to GBV), whereas from the treatment group, we asked the same innocuous questions and a few questions on GBV. Notably, both the treatment and control group included equal number of respondents that were assigned beneficiary and non-beneficiary status. Each respondent, in both LR treatment and control group, was asked 8 set of questions - one corresponding to the 8 different types of GBV, namely physical IPV, emotional IPV, economic IPV, sexual IPV, physical NPV, emotional NPV, sexual NPV and economic
This can happen if the economy is not dynamic. Or when higher education is overexpanded (Gonzalez and Uwaifo Oyelere 2011).

In scenarios 3a and 3b, the rates of return to university education remain unchanged because the crisis has a similar impact on secondary-educated and university-educated workers. The reduction in earnings among both groups of workers is such that the rate of return is the same before and during the crisis.

# **3. Data and Stylized Facts from Past Crises**

We use data on economic growth rates from the World Development Indicators <u>(http://datatopics.worldbank.org/world-development-indicators/) and estimates of the</u> returns to university education obtained from the following sources:

- Indonesia: 1990, 1992, 1994, 1996, 1998, 2000, 2002: Psacharopoulos and Patrinos (2018); 1999, 2003, 2004, 2006, 2008, 2009, 2010: Montenegro and Patrinos (2014).

- Pakistan: 1991 and 1995: Psacharopoulos and Patrinos (2018); 1992, 1999, 2004, 2005, 2006, 2007, 2008, 2010: Montenegro and Patrinos (2014); 2012, 2014, and 2018: authors’ calculations.<sup>2</sup>

- South Africa: 1993: Psacharopoulos and Patrinos (2018); 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, and 2011: Montenegro and Patrinos (2014).

Figures 1, 2, and 3 present economic growth rates and the returns to university education in Indonesia, Pakistan, and South Africa, respectively. Despite the different social and political circumstances, we find two patterns across the three countries. First, the returns to university education steadily increased in all three countries. With globalization and economic development, including the expansion of the service sector, automation (reduces the earnings of secondary-educated workers) and technological revolution (increases the earnings of university-educated workers), those with higher education experienced higher
In all tables and analysis, we use sampling weights provided in the ES COVID-19 Follow-up Surveys to correct for unequal probability of selection as well as ineligibility for all reported statistics. We also rescale the sampling weights to give equal weight to all countries.

# **2.1 How many firms received government support and when?**

Our sample includes 3,647 firms across 15 countries that reported whether they received government support in Rounds 1, 2, and 3 and reported changes in performance in Round 3. Table 1 reports the share of firms that received government support by survey round. About 4 in 10 firms reported receiving support in Round 1; in Round 2 and Round 3 the share of firms reported doing so drops to about a quarter. Cumulatively, by Round 3 just over half of firms (55 percent) reported having received support. The support measure reaching by far the largest share of firms was wage subsidies, with 44 percent of firms – or 8 out of 10 firms that received any type of support – receiving such subsides. The second most wide-reaching measure was cash transfers (22 percent), followed by fiscal relief and payment deferrals (12 percent each). New credit was the least commonly reported support measure (8 percent). To minimize reverse causality in examining the relationship between firm performance in Round 3 and government support, we will focus on support in Rounds 1 and 2 in our analysis below. The relative ordering of the most common support measures is similar.

Table 2 also reports the share of firms that received government support but does so by the share of firms that received government support in each combination of rounds. Considering all three rounds (Panel A), firms could receive up to 3 rounds of support in 8 possible combinations: from receiving support in each of the three rounds to none. Most firms which received any government support did so only in Round 1: 20 percent of firms or about one-third of firms receiving support. This was followed by firms receiving support
Figure B11: Comparison of urban and rural poverty rates <!-- Start of picture text --> (A) Official definition (B) DOU (C) DB<br>80 80 80<br>60 60 MWI 60<br>40 40 40 MWI<br>TZA NER<br>UGA NER TZA<br>TZA ETH ETH<br>20 UGA MWI 20 20<br>GABMRTSENCIVGIN GNBGHAETHBFATCDAGOLSO NER GABMRTGINCIVSENGHA GNBTCDBFALSO AGO GABMRT SENCIVGHAGINGNBTCDBFA LSO AGOUGA<br>20 40 60 80 20 40 60 80 20 40 60 80<br>Poverty rate in rural areas (%) Poverty rate in rural areas (%) Poverty rate in rural areas (%)<br>Poverty rate in urban areas (%) Poverty rate in urban areas (%) Poverty rate in urban areas (%)<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: GHSPOP 1km is used for the DOU and DB methods. Dashed lines are 45-degree lines. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method and the categories “Core” and “Suburb” for the DB method. Dashed lines are 45-degree lines. Poverty is measured using the $2.15 poverty line.

52
a sense of obligation to use those funds to purchase insurance, even though there was no requirement or pressure that they do so. While we cannot rule this possibility out entirely, we find evidence, as noted above, that the sensitivity of insurance demand to liquidity shocks is largest amongst poor households. This matches with the liquidity constraints explanation, since these households are more likely to face financial constraints and limited access to financial services. In contrast, we believe that the reciprocity explanation would be more likely to hold amongst wealthy households, for whom the cash gift is less valuable.

# _C. Trust_

Our Andhra Pradesh results suggest that the farmer’s level of trust in the ICRISAT insurance educator significantly influences insurance demand. An endorsement of this educator by a local BASIX LSA significantly increases insurance demand. Importantly, this only holds amongst households familiar with BASIX and thus for whom the word of the LSA is credible. For this subgroup, LSA endorsement increases the probability of insurance purchase by 10.1 percentage points, equivalent to 36% of the sample average purchase rate. In contrast, amongst households unfamiliar with BASIX, LSA endorsement has no effect on demand (the point estimate is actually negative, although not statistically significant).

Evidence from the Gujarat flyer experiments may also be interpreted in terms of a trust effect. These results show that for a subset of flyer treatments, insurance demand is significantly lower when the flyer emphasizes religious cues of a religion different to the treated household.

While trust has long been posited as an important determinant of demand for financial products (Neil Doherty and Harris Schlesinger, 1990, and Guiso et al., 2008), these results provide the first experimental evidence that trust matters. Trust is likely to be particularly important for financial services demand in environments like the one we study where formal legal protections are relatively weak, and household financial literacy and education is low. _D. Financial literacy and education_ The education and financial literacy statistics in Table 3 document that a significant fraction of households in
DRC are part of a multi-establishment firm, 2 percent engage in exporting activity and 3 percent are foreign owned. Other firm characteristics of note include total female employment, as well as female participation in management and ownership: the average firm employs only one woman (although the standard deviation is high), while approximately 23 percent of firms have a top female manager and 27 percent have some degree of female participation in ownership. The data show that women have greater representation in the capital city (Kinshasa), where the average firm employs two women, 28 percent of firms have a female top manager and 32 percent have female participation in ownership.<sup>13</sup> The DRC survey contains the standard ES questions on firm characteristics, performance, and business environment. The survey also contains several country-specific questions included to ascertain women’s autonomy, equality of economic opportunity, and gender discrimination in the DRC. These specific questions were built based on the legal gender discriminations identified by the WBL data and discussed in the previous section.<sup>14</sup>

> 13 A breakdown of Table 3 by firm location is not provided here but is available upon request.

> 14 The specific questions are presented in Table A1 in the Annex; in each case the question was only posed where relevant, as explained in the second column of Table A1.

8
3.1. Investing in infrastructure is important, but what matters is to spend it well Data on infrastructure investment and maintenance are difficult to obtain. Few fiscal authorities disclose granular expenditure data and if so, they often lack common or harmonized public accounting standards, such that specific infrastructure expenditure items can be mixed with other expenditure types. Especially in developing countries the level of transparency varies, and more generally the definition of – for instance – “infrastructure maintenance” spending differs. Moreover, the sectoral disaggregation of public spending data is difficult due to the organization of public budgets – including distribution across federal and regional entities -- thus making it difficult to distinguish public spending on water, electricity or transport infrastructure respectively. Rather than relying on nationally published budget figures, we tap two international sources of investment data in order to compile a consistent panel of public transport expenditure.<sup>2</sup> From the OECD International Transport Forum database we obtain transport infrastructure investment spending for 57 middle and high income countries, covering the time from 1995 to 2016 (OECD, 2018). This is complemented by public infrastructure investment data from the World Bank’s BOOST initiative, available through the Open Budgets Portal (World Bank, 2018b). Together, these yield a panel of 603 individual country-year observations from 85 countries, covering all income groups.<sup>3</sup> In addition, information on the reliability of the transport infrastructure is proxied by a measure of the quality of logistic services provided by the Logistics Performance Index (LPI), a benchmarking tool created to help countries identify the challenges and opportunities they face in their performance on trade logistics (World Bank, 2018a). The LPI 2018 allows for comparisons across 160 countries, and offers country specific scores along six dimensions: (i) customs, (ii) infrastructure, (iii) international shipments, (iv) logistics competence, (v) tracking and tracing, and (vi) timeliness. The infrastructure sub-indicator aggregates a quality
While we know of no studies that systematically explore firm-level externalities in agricultural production, a number of studies have done so in an industrial context. Henderson (2003) uses panel data for machinery and high-tech industries to estimate firm-level production functions that allow for scale externalities from other local plants in the same industry and from the diversity of local economic activity, finding strong evidence of information spillovers for high tech industries only and little evidence of benefits from diversity beyond the own industry. Moretti (2004) looks at human capital externalities, finding that in a city spillovers will be larger between industries that are economically closer. Currie _et al._ (2015) link firm-level with other data to assess the impact of opening or closing of 1,600 U.S. industrial plants in the 1990-2002 period, finding impacts on toxic air emissions, housing values, and the incidence of low birthweight in the vicinity of these plants. Applying this to mines suggests that openings and closings have different effects (ChuhanPole _et al._ 2015) and that mining can empower women (Kotsadam and Tolonen 2015).

# **2.3 Data sources and definitions**

Before developing the econometric approach, we discuss data. For large farms, we use the commercial farm survey regularly conducted by Ethiopia’s Central Statistical Agency (CSA). This survey covers a sample of 10-50 ha farms and the universe of operational farms cultivating 50 ha or more. Farm level information is provided on input use, output, and year of establishment. GPS coordinates taken for every field allow us to map all farms above 50 ha that were operational in 2014.<sup>6</sup> For any small farm and year, this allows us to compute distance to the nearest large farm or the nearest large farm growing the same crop and the total area cultivated by large farms or devoted to large farm cultivation of a specific crop within a certain radius.

Information on smallholders comes from 11 years (2004-14) from CSA’s smallholder survey
consumption or expenditures on non-food items such as health, education and clothing, or to reduce purchases of agricultural or livestock inputs than actions that reduced asset holdings or increased indebtedness. Third, borrowing money to buy food was used by more than half of households reporting that income was much less than usual. Fourth, few households reported selling consumer durables, possibly because these are relatively illiquid. A larger proportion (21 percent) reported selling productive assets but note that this category includes livestock which also serves as a store of value.

# **_3.3. PSNP participation_**

We define PSNP participation in three different ways. First, we define access to PSNP based on self-reported receipt of any payments in the August 2019 survey. PSNP participation could be endogenous to the pandemic if, for example, the government expanded the program in response to the spread of the coronavirus; using pre-COVID-19 access to PSNP transfers to define participation in the program addresses this concern. This approach assumes that program participation remains stable during our study period. We compared the share of households receiving PSNP transfers in June 2020 and we find that 89 percent were also PSNP beneficiaries in August 2019.

Second, we use information on actual PSNP transfers made in the six months prior to the August 2019 survey and generate an indicator variable assuming a value of 1 for households who received transfers exceeding 100 birr (about 3.5 USD) and 0 for households receiving less than 100 birr or not at all. This definition reduces potential measurement errors due to misreporting of program participation. Third, we generate aggregate district level access to PSNP transfers. Among the 88 woredas (districts) which were supposed to be included in the PSNP, there were six woredas that did not make any payments because of delays in implementation and related logistical problems. This provides a relatively more exogenous variation in households’ access to PSNP transfers.

# **4. Estimation strategy**

To assess the impact of PSNP participation on household food security as well as maternal and child
.03|0.00|0.00|0.04|0.01|0.11|
|If yes, fertilizer|0.87|0.57|0.78|0.91|0.89|0.85|
|If yes, seed|0.33|0.29|0.34|0.34|0.46|0.49|
|If yes, other|0.11|0.24|0.08|0.11|0.03|0.04|
|<br>**Panel C: PSG benefits & its use**|||||||
|Received PSG benefit|1.00|1.00|1.00|1.00|0.00|0.00|
|if yes, amount received (US$)|4,718.2|737.2|3,235.5|6,426.0|||
|<br>crop prod. input incl harvest|<br>0.87|0.85|<br>0.93|<br>0.86|||
|<br>crop prod. investment|0.01|0.04|0.02|0.00|||
|<br>livestock|0.11|0.10|0.04|0.14|||
|consumption/other|0.01|0.00|0.01|0.00|||
|Area (ha) supported by PSG|55.66|8.70|38.17|75.81|||
|No. of obs.|514|110|79|325|345|347|

_Source:_ Own computation based on PSG records. _Note:_ The types of support other than PSG was a multi response question and