rafmacalaba/data-use-mentions-extended huggingface dataset:
of the POS installations during the program as well as the time they received it. Extracting from the information, we can thus identify treatment group, control group, as well as intervention time for our analysis purpose.

Given the rudimentary stage of the POS distribution program’s scalability, the number of POS recipients is relatively small compared to those who have not received one–as the Data section shows. That results in small-sized treatment groups in our estimations, which may compromise the statistical power, and either mute or exaggerate the estimated effects. This could be a caveat, however with the near-complete administrative data on such a program, such a limitation is rather natural and leads to a minimum bias in the results. Therefore, the policy insights that this study offers remain valid.

8
consistent with political economy explanations of how banking systems in low-income countries serve the interests of the elite, rather than providing broad-based access to financial services.

We acknowledge that our approach comes with caveats and highlights gaps in our knowledge about the effects of financial structure. For example, our inability to find a suitable instrument for stock market development makes it difficult to study whether market capitalization enables firms to grow more quickly in high-income countries. Though again, our results using the RZ methodology also show no evidence of a positive link between stock market development and labor growth of firms in industries that depend heavily on external finance.

Perhaps more importantly, though the enterprise surveys that we use offer rich information across a wide range of countries, our labor growth rates can only be computed over a two-year window. We are therefore relying on a snapshot of firm growth to draw inferences. Case studies of firm financing patterns and financial development in low income countries over longer periods are likely to offer a more complete picture of how firms grow. To the extent that some low-income countries have been better able to achieve financial inclusion, those case studies could shed light on how banks can foster growth across a broader spectrum of firms during the early stages of development. However, to the extent that the political economic incentives that limit the inclusiveness of financial systems in low-income countries cannot be overcome, it would be wise to explore how alternative policies and intermediaries could better achieve that aim.

27
<!-- Start of picture text --> Figure 1: Under-5 mortality rate Figure 2: Maternal mortality ratio<br>250 1,600<br>Low income Low income<br>1,400<br>Lower middle income Lower middle income<br>200<br>Upper middle income 1,200 Upper middle income<br>Congo<br>1,000<br>150<br>Congo 800<br>100 600<br>400<br>50<br>200<br>0<br>0<br>0 5,000 10,000 15,000 20,000<br>0 5,000 10,000 15,000 20,000<br>GNI per capita (PPP, current USD) GNI per capita (PPP, current USD)<br>Source: WDI Source: WDI<br>Under-5 mortality rate (per 1,000 live births)<br>Maternal mortality ratio (per 100,000 live births)<br><!-- End of picture text --> According to the latest household survey from 2005 (ECOM, Republic of Congo 2006), about half of the population lives below the poverty line and meeting most MDGs is a challenge. Health indicators are particularly low and infant and under-5 mortality have even increased in recent years (Table 2). In the education sector, school enrolment rates have increased significantly during the last decade in all school segments, following the introduction of free schooling in 2007/08, but qualitative indicators remain low. In addition, educational progress is highly unequal, with which much better outcomes in urban areas and for high-income households.

**Table 2:** Key social indicators, 1990 - 2009

|**Indicator**|**1990**|**2000**|**2006**|**2007**|**2008**|**
.0|8.2|164|
|8. Wood, paper, publishing, and<br>printing|26.8|17.5|14|
|Total|19.0|18.1|484|

Sources: LFS 2019, CFUWBES1, WBES 2013 and 2019. Notes: Model-predicted estimates are based on projected jobs lost using a multi-stage model and the employment structure in LMPS. Survey-measured jobs lost are based on directly computed sector-average job losses. In both cases, to estimate the allocation of jobs lost to production and non-production workers, sector-specific Tobit polynomial models are estimated using the 2013 and 2019 WBES. All calculations are survey-weighted.

have faced fewer job losses (6 percent of workers). In almost all cases, non- production workers experienced lower job losses in the summer than production workers in all the sectors except for garments (See Appendix 34
intervention (Knauer, Kariger, Jakiela, Ozier and Fernald 2019b).

We assessed children’s fine motor skills using a subset of items from the Malawi Developmental Assessment Test (Gladstone et al. 2010). Specifically, the survey included six questions from the MDAT fine motor sub-scale that showed high predictive power (in terms of other development outcomes) in a pilot study (Knauer et al. 2019a). The items measure young children’s ability to build simple structures (e.g. a tower) with blocks and to use a pencil to make elementary drawings (e.g. a circle). Both vocabulary and fine motor indices were converted into age-normalized z-scores. We then average the individual (z-score) components to construct an overall measure of child development.

To understand the mechanisms through which sibling gender impacts child development, we collected data on early childhood stimulation using an expanded version of the Family Care Indicators (FCI) questionnaire (Hamadani et al. 2010, Kariger et al. 2012). The FCI asks about six types of stimulating activities: for example, reading, singing, storytelling, and physical play. We expanded this set to include additional stimulating activities more appropriate for slightly older children: for instance, teaching a child letters or English words (Knauer et al. 2019a). Based on extensive piloting, we also expanded the questionnaire to better capture the full range of family members who engage in early childhood stimulation. While the original instrument asks about stimulation by a child’s mother, father, and by other adults aged 15 and over, we also ask about stimulating activities by older sisters, older brothers, and grandparents. Summary statistics on who engages in early childhood stimulation are shown in Appendix Figure A1. On average, older sisters engage in more stimulating activities with young children than any other household members. Though older sisters do the most, even older brothers play an important role: older brothers do more than fathers or grandparents.

19
in nighttime light growth to assess the effect of the 2016 Indian banknote demonetization. Second, using nighttime lights is particularly appropriate for this study since this measure is available at high spatial granularity at monthly frequency.<sup>2</sup> This allows us to match nighttime lights to the relevant district-level zone classification to determine economic activity in the pre-period in March and April, and to compare it to activity in the post-period in May, June, and July. In contrast, official quarterly GDP estimates and other measures of overall economic activity like electricity consumption are not disaggregated at the district level. Finally, nighttime lights from satellites represent an objective measure of economic activity, which is also immune to biases due to survey non-response which might be correlated with lockdown policies.

We find that nighttime light intensity in India dimmed during the strict national lockdown in March and April and recovered in May and subsequent months. In May, districts with the most severe restrictions witnessed a 12.4% (0.06 standard deviation) lower recovery in nighttime light intensity compared to those with the least restrictions. The recovery for districts with intermediate restrictions was 1.7% (0.01 standard deviation) lower. While remaining in the same direction, the impact of the zone classification tapered off in June and July. Our findings are robust to several logically orthogonal robustness checks. These include a test of the impact of the zonal classification on the pre-period, where we reassuringly find no effect. We restrict the sample to only those districts that border a differently classified zone, to non-metropolitan districts, and those that were not subsequently reclassified in mid-May and find no qualitative change in our results. Finally, a test with placebo zone classifications yields coefficients that are close to zero and insignificant. Facebook mobility data from cellphone locations indicates that restricted mobility is a plausible channel. Monthly household data point to lower household income but not consumption as an important channel for these findings. More developed districts with above median population density, share of employment in
institutions);

- Insurance companies premia (life and non-life);

- Mutual funds and pension funds assets;

- Public and private debt securities (domestic and external capitalization);

- Domestic equity market capitalization.

- Domestic equity market turnover

- The share of fee-based income in total banking income

- Banks’ net interest margin;

To reflect cross-border finance, we use two financial globalization indicators:

- Gross country external debt (claims and liabilities);

> 15 The importance of mood swings for financial bubbles and panics finds its roots in Keynes’s animal spirits and Hyman Minsky’s writings on financial crises (see Minsky 1975). More recently, it was popularized by Kindleberger (1989) and Shiller (2006).

> 16 The data are from FinStats 2009, a world-wide financial database put together by the World Bank, which covers 40 key financial indicators for the period 1980-2008 (coverage quality varies between variables). The data come from a variety of sources including IFS, BIS, WDI, S&P, Bankscope, Axco, and national sources.

8
Our main result—AP management increases bird abundances—is robust to using corrected data that holds constant the share of observations in bird-abundant locations post-transference. The regression coefficients are quite similar to those from our primary specification (Figure S3b). The average effect remains large and statistically significant (ATT = 0.277 with a standard error of .072), which represents an increase in bird abundances of 32% (compared to 37% in our primary specification).

# **A.2.2 Robustness to Changes in Birder Seasonality (Figure S4)**

The second concern parallels the first, except rather than studying whether AP shifts _where_ birding trips occur we study whether AP shifts _when_ birding trips occur. Bird abundances naturally fluctuate over the course of a year due to seasonal bird migration. If AP increases the share of birding trips that occur in bird-abundant months of the year, then we would overestimate the increase in bird abundances due to AP management. To assess this possibility and its implications for our main result, we implement the identical procedure described in SI A.2.1, except now month of year is the dimension of interest rather than a 0.1<sup>_◦_</sup> grid cell. Month of year refers to the same month every year (e.g., “January”, as opposed to “January 2018”).

Prior to transference, there is little trend in the probability that the proportion of observations in bird-abundant months of the year is above the median (Figure S4a). However, there is a statistically significant increase on average over the eleven years of AP management we consider (ATT = 0.046 with a standard error of 0.015). It appears that part of the post-transference increase in bird abundances is due to a greater share of observations occurring in protected areas’ more bird-abundant months of the year.

We assess the magnitude of this upward bias in the same manner as in SI A.2.
1992 RPED survey have been given in Table 3.4 above together with, the minimum wage level prevailing at this date.

It is seen that the minimum wage was more or less at par with the average earnings in micro enterprises, and as such could be considered to have provided a floor to the wage level. It might be argued that wages of the least paid or unskilled worker in an enterprise would be less than the average earnings. If this were the case the minimum wage could conceivably fall between the levels of the bottom wage in micro and small firms. On the further assumption that minimum wages are at least enforceable in small firms, but difficult to enforce in micro enterprises, we could at a stretch ascribe the differential between micro and small firms to the minimum wage laws. But this explanation leaves untouched the reasons for the continuous increase in earnings, after controlling for measurable worker quality, with further increases in firm size.

Other possible routes of institutional influence are public sector wage policy and unionization. The levels of wages established for public sector enterprises, particularly in the state owned enterprises could have a disproportionately strong effect on the larger firms. But this argument has the same difficulty as the minimum wage hypothesis-it 23
0.0|0.1|0.1|
||||_0.90_|_0.08_|_1.00_|
|Vans|0.3|0.0|**-0.5**|**0.1**|**-0.4**|
||||_0.00_|_0.00_|_0.00_|

Note: The coefficients measure the time fixed effects relative to 2007:Q1. The figures in italics are p-values for a test for whether there is a difference between the fixed effects between that year and the previous year. Values are shown in the prices of 2007. Boldfaced numbers are statistically different from the previous year at the 5% level; yellow cells indicate lower real expenditure per capita in 2009 than in 2008. Number of observations: 176,141 individuals.

# **Are Survey Data Essential?**

Thailand is unusual in the quality and frequency with which it collects household survey data. Could one have determined the effects of the 2008 recession in a cheaper way, for instance using Losers and Winners in Recession: Thailand 2008-09 24
# **Introduction**

Although the COVID-19 pandemic started as a health crisis, the economic shutdowns and downturns that it entailed have had catastrophic economic consequences. The shutdowns also implied a halt to much of the data collection done by national statistical offices, which has led to a lack of information on the magnitude of the economic consequences (United Nations and World Bank 2020). At the country level, it is unclear which parts of the distribution were most hurt by the pandemic. This lack of distributional evidence has made it difficult to understand whether broad-based growth policies should be preferred over policies prioritizing the worst off. At the global level, it is unclear where the economic consequences of the pandemic have been most severe, which matters for targeting of emergency funds across countries for international organizations, international NGOs, and development agencies.

In this paper, we triangulate various data sources available to get a global picture of the impact of the pandemic on inequality and poverty in 2020. We use welfare distributions for 2019 covering 168 countries comprising more than 97% of the world’s population from the World Bank’s Poverty and Inequality Platform (PIP) as our starting point. To obtain estimates for 2020 we use published traditional household surveys available for 20 countries and tabulated household income statistics available from national statistical offices for a further 8 countries. We complement them with a simulation exercise using High-Frequency Phone Surveys (HFPS) for 37 countries. The HFPSs were conducted in a large number of developing countries to provide a real-time picture on the pandemic. While these surveys do not contain information on households’ level of income, they do include information on whether households gained or lost income. We use this information together with household characteristics and sectoral national account data to estimate the pandemic’s distributional impact. For 26 countries that do not have any of the above three sources of data, we rely on estimates from simulations published in the literature or provided to us by World Bank teams. For the rest of the world, covering roughly
Financial Regulation and Development banks, which contributes to a neat identification; a long-term branch map covering installations in the last 15 years; and annual report digitization for five years around the policy. In the Antebellum USA study, we combine the big-versus-small bank cross-sectional variation to the non-simultaneous introduction of taxes at state level. In this case, the number of available banks is much larger, 1,280, but the time span, though longer, comprises only yearly observations. Both cases support our theoretical predictions: banks exposed to this policy do not lose profits, accumulate more safe assets, and increase their deposits and loans.

We believe our results send two important messages. First, with regards to the economic literature on financial development, we highlight that banks, mostly neglected in the literature, should be considered of primary importance in the provision of financial development. Governments have important responsibilities to play in providing an optimal level of regulation, and striking a balance between securing profitability and the provision of extensive access. This is a field that needs further research and our paper provides some substantial reference for this. Second, we also aim to challenge the widespread idea that banking-sector deregulation in low-income countries is an optimal policy (IMF 2013a). A long history of regulation (Glaeser and Shleifer 2003; Shleifer 2005), and especially financial regulation (Barth, Caprio, and Levine 2004, 2008), in western economies shows the opposite, and our paper provides strong quantitative evidence that a moderate degree of regulation should actually be encouraged by national and international actors.

# **Appendix A: Deposit Decision with Different States and Remunerations**

As in Section 1.3, the depositor problem can be described by Plugging the constraint in the maximand, we obtain The conditions are and 42
- (a) What are the scale (stocks, flows and locations) and nature of the socio-economic vulnerabilities, welfare and needs of displaced people (including those that are unique to displaced people as well as those shared with host communities), and how are these evolving over time?

- (b) What are the scale (numbers and locations) and nature of impacts (both positive and negative) on host communities, e.g. in terms of service delivery, welfare, jobs and social cohesion, and how are these evolving over time?

- (c) What are the fiscal and macroeconomic impacts on host countries, and how are these changing over time?

- (d) What has been the scale of returns (numbers and locations), how successful have these returns been, and what are the impediments to sustainable reintegration including the socioeconomic vulnerabilities, welfare and needs of returnees (both those that are unique to returnees as well as those shared with communities in return areas)?

- (e) What have been the impacts of previous interventions in various contexts to address the development challenges posed by forced displacement, and what lessons can inform the design of future interventions?

These analytical questions require data collection efforts that go beyond the information requirements to support humanitarian responses in the areas of protection and assistance. Very broadly, the additional data requirements include: (a) estimates of the numbers of returnees that have yet to be successfully/sustainably reintegrated and data on their locations; (b) estimates of the size of affected host populations and data on the impact of displacement on host countries and communities; (c) data on the socio-economic vulnerabilities, welfare and needs of displaced and returnee populations; and (d) rigorous impact assessments of interventions in various contexts to address the development impacts of forced displacement.

# **Returnees**

Data on returns of refugees and IDPs are incomplete. UNHCR provides data on refugees who have returned to their host countries (39 countries during 2015) and
by the 2001 population rather than the population.

The rest of the paper is organized as follows. The next section describes the data. Section 3 takes a first look at the pattern of changes in self-rated welfare and power between 1999 and 2001. Section 4 investigates the determinants of welfare and power in 2001, and Section 5 analyzes the changes in welfare and power. The final section concludes.

4 The terminology is adopted from Lokshin and Ravallion (2002) who analyze subjective welfare and power in Russia, focusing on the differential impacts of individual income, household income and income inequality. Frey and Stutzer (2000) argue that direct democracy and federal structure raise self-reported well-being in Switzerland and ? finds that the quality of goverment matter for happiness. These two papers do not separate between welfare and power. > In the following, I will use ‘1999’ to refer to the date before the violence of September 1999, and ‘2001’ to end 2001, the time of the fielding of the TLSS.
24,000 disaster events across the globe from 1900 to the present and contains data on the number people affected, injured, and killed in these events as well as estimates of property damage and loss of livestock. A less frequently used but similar source is NatCatSERVICE.

> 6This so-called “creative destruction” effect of natural disasters has been probed further in the literature. For example Cuaresma, Hlouskova, and Oberstriner (2008) examine if trade facilitates such a process following natural disasters but find that only richer countries benefit from capital upgrading while disasters tend to reduce technology spillovers between industrialized and developing countries.

5
9<br>12.2<br>15<br>5 0.90<br>-5<br>-15 -8.30<br>Medium (between 51 and 250)<br>Large (more than 250 employees)<br>% difference<br><!-- End of picture text --> _Source:_ World Bank staff calculations based on SBS data set. _Note:_ Numbers reflect estimated coefficients (multiplied by 100) for size dummy categories—relative to small firms (1– 50 employees), the omitted category—in a fixed-effect regression model controlling for firm covariates plus year, county, and NACE 4-digit sector fixed effects, as displayed in equation (11). All bars reflect statistically significant results. See table A1.5 in annex 1 for the complete set of results.

**By the same token, export intensity also influences markup performance, with firms that**

**export earning higher markups.** Being an exporter is a relevant firm feature to be analyzed when it comes to understanding markup performance. Recent models of international trade predict that exporting firms earn higher markups than non-exporting firms, either because they are relatively more productive and can thus undercut their rivals (see Melitz and Ottaviano 2008), or because they produce higher-quality goods and rely on higher-quality inputs, which allow them to charge higher prices (see Hallak and Sivadasan 2009). In this regard, the results summarized in figure 10 are consistent with the literature and suggest that exporting firms— defined as those whose direct exports represent more than 10 percent of total revenue—earn higher markups than non-exporting firms that operate in the same product market (NACE 4- digit sector); the difference ranges from 12 percent to 41.6 percent, depending on how data are clustered across large sectors (manufacturing, services, and mining).

> 33 See footnote 32 on how to interpret results in table A1.6 in annex 1 (