rafmacalaba/data-use-mentions-extended huggingface dataset:
- Levine, R. (2005). “Finance and Growth: Theory and Evidence,” in P. Aghion and S.N. Durlauf (eds.) _Handbook of Economic Growth_ , Vol 1A, p. 865-934

- Nenova, T., C. T. Niang, and A. Ahmad (2009). _Bringing Finance to Pakistan’s Poor: Access to Finance for Small Enterprises and the Underserved._ Washington, DC: World Bank.

- Schiffer, M., and B. Weder (2001). “Firm Size and the Business Environment: Worldwide Survey Results,” Discussion Paper 43, International Finance Corporation, Washington, DC.

- Tejerina, L., and G.D. Westley. (2007). “Financial Services for the Poor: Household Survey Sources and Gaps in Borrowing and Saving.” Sustainable Development Department Technical Papers Series POV-117. Inter-American Development Bank, Washington, DC.

- World Bank (2008). _Banking the Poor: Measuring Banking Access in 54 Economies_ . Washington, DC: World Bank.

- World Bank. Various years. _Living Standard Measurement Survey._ Washington, DC: World Bank. http://iresearch.worldbank.org/lsms/ lsmssurveyFinder.htm.

20
results not shown). This suggests that potential migrants find information about migrant demand in Malaysia and the Persian Gulf countries from channels other than the existing local migrant network. The increased demand may be transmitted though recruitment companies and the local agents who seek workers more aggressively when migrant demand abroad is higher.

# **6 Conclusion**

Workers want to move towards better economic opportunities and away from disamenities such as conflict. The 2011 Gallup polls estimates that over 1 billion individuals desire to migrate abroad, at least for temporary work (Esipova, Ray, and Publiese, 2011). But the fact that only 3 percent of the global population are international migrants suggests that many who want to migrate do not, in fact, migrate. Lack of global mobility of workers has been suggested as one of the biggest distortions in the global economy (Clemens, 2011). Workers typically cannot migrate to places where the returns to their skills are highest for two key reasons. First, liquidity constraints may prevent them from being able to afford the costs of migration. And, second, the lack of demand for migrant workers, either economic or institutional, lowers the expected benefit from migration.

In this paper, I investigate how ‘push’ and ‘pull’ shocks affects the migration of Nepali workers to destinations that vary in their costs of, and returns to, migration. First, I find that income shocks, arising from positive rainfall shocks, increases migration to a low-cost lowreturn destination (India), but these shocks do not affect the migration to high-cost high-return

> 19These results are consistent with what I find in Shrestha (2017a) for migrant outflows from Nepal to Malaysia and the Persian Gulf countries. In an exercise in that study, I find that when relative exchange rates between destination changes, causing a shift in the wages that they earn, migrants respond by choosing the destination with a favorable exchange rate. I cannot use the same variation as a measure of a shock to destination wages in this study because Nepal
levels of academic performance.<sup>7</sup> Although education policy can make a large difference in the long run, socio-economic circumstances continue to play a role in student learning in many middle-income EAP countries, in contrast to the average perceptions of EAP officials.

_Figure 4: Poverty and malnutrition are not considered critical for learning by policy makers in EAP._ <!-- Start of picture text --> 80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>LAO PHL IDN VNM MNG Non-EAP<br>Average learning poverty guess<br>Share of respondants attributing low learning to school factors<br>Share of respondants attributing low learning to poverty & malnutrition<br>Shares<br><!-- End of picture text --> Sources: CGD–World Bank survey, 2022. Factors were pooled together depending on whether they refer to external determinants such as poverty, malnutrition, lack of internet or others. School quality factors include teaching quality, school facilities and lack of learning materials. The share of officials attributing low learning to poverty and malnutrition decreases with per capita GDP, as expected.

EAP officials’ low estimations of the influence of poverty and malnutrition on learning relative to other factors is reflected in the low priority given to programs that address poverty or malnutrition issues. As part of our survey, respondents were presented with several potential policy interventions randomly selected from a pre-determined set of options, including: (i) giving teachers structured early grade reading lesson plans with linked materials and training; (ii) in-service training for teachers; (iii) delivering universal, free school lunch; (iv) removing fees for secondary school; (v) laptops for schools; (vi) hiring extra specialist teachers for students with disabilities; and (vii) building hospitals. In this discrete
# **2. FINCA and the DRC Context**

Even within Africa, which lags other developing regions in financial development (Allen et al., 2014, 2015: Beck and Cull, 2014, 2015), the Democratic Republic of Congo (DRC) has stood out as one of the least banked countries in the region. In 2014, 17% of adults in the DRC had an account with a formal financial institution. In comparison, 34% of adults in Sub-Saharan Africa, 54% of adults in developing economies, and 62% of adults worldwide had formal accounts.<sup>14</sup> Moreover, the 17% figure represents a substantial gain in financial inclusion in the DRC, since the 2011 figure stood at less than 5% (Demirguc-Kunt and Klapper, 2012a,b).

The country’s long history of conflict has no doubt led to institutional and infrastructural deficiencies that impede economic growth, and thus also impede demand for financial services. However, evidence from financial diaries in other developing countries (Collins et al., 2009) suggests strongly that poor clients have many financial needs and, because their incomes are often irregular and emergencies arise, their financial lives are unpredictable. The low financial usage ratios reported above therefore make it highly likely that demands for financial services for large segments of the Congolese population go unmet by its formal financial services providers such as commercial banks.

FINCA DRC is part of FINCA, an international microfinance institution present in 23 countries worldwide, focused on offering financial services and products to small scale businesses and households. Founded in 1985 in Bolivia, FINCA opened its first program in Africa in 1992 in Uganda. In 2009 FINCA introduced branchless banking – ATMs, computerized points-of-sale transactions, and cellular banking – in an effort to expand its outreach while keeping associated costs low. In late 2016, 2016 FINCA had over 643,000 savers in Africa, an average disbursed loan

> 14 The figures for account ownership are from Demirguc-Kunt, Klapper, Singer,
-- Start of picture text --> Non-innovative firms Innovative firms Adoption Invention<br>100<br>95<br>90<br>85<br>80<br>75<br>70<br>65<br>60<br>55<br>50<br>All countries Low income Lower middle Upper middle High income<br>income income<br><!-- End of picture text --> Source: Own elaboration using data from the World Bank – Enterprise Surveys.

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# **1. Introduction**

Did the financial sectors of developing countries that received assistance from the World Bank perform better than those in countries that did not? And did the countries that received World Bank assistance enjoy more financial sector development than they otherwise would have? Based on financial sector data in the 1990s, the answer to the first question appears to be yes. Despite severe financial crises in multiple countries that received World Bank assistance to support financial sector reform (such as Argentina, Brazil, Mexico, and Russia), the growth rates for borrowing countries for multiple standard measures of financial sector development were greater than for countries that did not receive such support.

A skeptic could argue that such results are driven by selection – aside from a few crisis countries that received loans, borrowers tended to be countries that were most apt to improve their financial sectors on their own. To answer our second question, whether borrowing countries developed their financial sectors more quickly than they otherwise would have, we use both treatment effects regressions that explicitly account for selfselection and propensity score matching techniques. We also offer robustness checks that examine whether our findings are specific to a region(s) and whether improvements in financial indicators preceded or followed lending projects – if improvement preceded those projects, it would seem unlikely that they had a large causal impact in borrowing countries. However, these multiple estimation techniques provide evidence that reinforces our main findings. Borrowing countries tended to experience substantial postloan improvement in their financial indicators, significantly more than the typical improvement in non-borrowing countries, even after accounting for self-selection.

This paper contributes to a broader literature on the effects of economic reform programs in developing countries. Much of that literature focuses on the impact of IMF and World Bank adjustment lending on broad macroeconomic aggregates, most notably real per capita growth.<sup>1</sup> Much has been made of selection problems in that literature, and

> 1 Easterly (2005) describes this literature as including Barro and Lee (2002), Conway
female entrepreneurship. The government and/or international organizations could use the more nuanced findings to target firms of specific sizes if they aim at enabling women entrepreneurs. To shed light on this question, in Table 8, we separate the sample firms into two groups: those hiring 50 or fewer permanent employees three years ago (i.e., three years before the firm survey); and the other larger firms.

The general FDI effects on _Female Entrep_ are much larger for small firms than for medium-and-large firms. The coefficient of is more than twice larger for small firms than for medium-and-large firms. Interestingly, the within-industry FDI crowding out cc FF effect on _Female Entrep_ does not differ significantly between these two types of firms. These results support the _FDI-size-bias hypothesis._

# **Firm sector affiliation and FDI effects on female entrepreneurship**

Given women’s general lack of comparative advantage (relative to men) in brawn, and women’s advantage in handling interpersonal relationships, it is generally believed that women tend to do better in service-sector jobs than manufacturing jobs (Buera et al., 2013; Ngai and Petrongolo, 2017). Indeed, women’s job growth in service sectors tends to outpace that in other sectors (Lan and Shepherd, 2018). Given the higher share of women being employed in services, and the consistency between gender norms and job requirements in services than in other sectors, we now examine whether the FDI effects on female entrepreneurship differ by sectors. To this end, in Table 9 we allow the FDI effects to differ by sectors.

The FDI spillover effects are 70 percent larger for firms in services than in manufacturing (i.e., 0.70 vs 0.41). Furthermore, once controlling for country fixed effects, the within-industry FDI premium has further positive spillover effects only in service sectors. In contrast, though statistically insignificant, the negative sign of c in manufacturing is consistent with the existence of within-industry FDI crowding out effects FF on female entrepreneurship in
securities issued by all issuers as a percentage of GDP, and has a mean of 0.105. Finally, Total capitalization is the sum of Capitalization and Bonds with a mean of 1.900. Data on credit aggregates and overall stock market valuation are from the World Development Indicators database, while data for the bonds variable are from the debt securities database of the Bank of International Settlements (BIS).

As additional control variables, in some specifications we also interact the financial development proxies with a measure of firms’ dependence on external finance. Analogously to Maturity, we construct the desired dependence on external finance variable, DEF, for each US sector as the share of financing of capital expenditures that cannot be covered by the operating cash flow, i.e. as (capital expenditures – operating cash flow)/capital expenditures, on the assumption that firms in the US are not constrained in their external financing. Specifically, DEF is the sectoral median of the firm-level average value of (capital expenditure - operating cashflow)/capital expenditure over the 1995-2013 period for US firms in a three-digit SIC sector.<sup>8</sup> In Table 1 we see that DEF has a mean of 0.094.

In addition, we examine how the relationship between the firm volatility variables and Maturity depends on several indices of institutional quality. These institutional indices measure the ease and cost of credit transactions in a certain country, and therefore might influence refinancing risk.

Accounting is a measure of accounting standards and captures the quality of information available on the firm. Such transparency may matter, as it enables debt holders to monitor firms better. Also, better quality information may reduce the perceived need for creditors to refuse to roll over their credits due to limited information on the firm. Accounting

> 8 To calculate DEF, we exclude firms with fewer than ten years of observed book value of assets to ensure that we calculate this variable over a relatively extended period of capital expenditures and operating cash flows.

10
# **A Supplementary Methods**

Each subsection details the construction of an outcome variable or set of outcome variables, the regression(s) on that outcome variable presented in the main text, and robustness checks for that outcome variable.

## **A.1 Elephant Poaching (Figure S1 and Table S6)**

The Monitoring the Illegal Killing of Elephants (MIKE) program records elephant deaths each year at sites across Africa and Asia. As of January 2023 when we downloaded the data, there were 96 sites, with data spanning from 2000 to 2021 [57]. Among the 66 African MIKE sites, 8 are currently managed by AP (treatment group), and 35 were among those identified by AP as “anchor areas” meeting their criteria for future management (control group). Our regression analysis includes 578 site-year observations from these 43 distinct sites (Figure S1). Table S6 displays Average Treatment effects on the Treated (ATTs) by “cohort”, or the calendar year in which protected areas are transferred to AP management.

MIKE data contain the total number of elephant carcasses discovered at a site in a year, as well the number of those carcasses that were killed illegally (poached). Dividing the number of poached carcasses by the total number of carcasses yields the Proportion of Illegally Killed Elephants (PIKE). This normalization controls for fluctuating survey efforts and elephant population sizes across sites and years under the following assumption: conditional on the number of poached and non-poached carcasses available for discovery, the probability of finding a poached carcass equals the probability of finding a non-poached one [102, 103]. Studies of MIKE sites corroborate that PIKE accurately reflects mortality patterns in the sense that PIKE is not confounded by changes in survey effort or in the underlying elephant population level [104, 105].

Even if the conditional probabilities of finding poached and non-poached carcasses differ, bias in our estimated impact of AP management on PIKE would only arise if AP management changes the probability of discovering a
and lender countries. The subsidiary loan share is positively related to capital stringency and the strength of official supervisory power in lender countries consistent with regulatory arbitrage. Regulatory arbitrage appears to have been more important during the first part of our sample period in the years 1995-2005.

The estimation using bilateral loan data also suggests a strong role for the quality of institutions to affect the location of loan origination. In particular, we find that stronger creditor rights and speedier contract enforcement in borrower countries, and weaker monitoring incentives and slower contract enforcement in lender countries, lead to a higher foreign subsidiary share in syndicated loan origination.

International regulatory and institutional differences potentially also affect the determination of the lead bank of the loan syndicate among the participating banks. The lead bank performs the main tasks of borrower selection and monitoring on behalf of the entire syndicate, and regulation and institutions could affect a bank’s comparative advantage and incentives to perform these tasks well. We find that stricter capital regulation and greater supervisory power discourage a lead bank role consistent with regulatory arbitrage, while such a role is facilitated by a greater rule of law.

Several papers have considered how regulatory arbitrage affects the structure of international banks. Considering international bank M&As, Karolyi and Taboada (2015) find evidence of regulatory arbitrage whereby acquisition flows involve acquirers from countries with stronger regulations than their targets, while abnormal returns are larger when acquirers come from more restrictive banking environments. This suggests that stringent regulations provide banks with a comparative advantage to expand abroad. Consistent with this, Frame, Mihov, and Sanz (2016) show that U.S. Bank Holding Companies (BHCs) are more likely to operate subsidiaries in countries with weak regulation and supervision, and that financial institutions’ decisions to operate in environments with lax environments are associated with 4
# 2. Literature: Collecting and Estimating House Prices

Despite their importance, private property price data are not readily available for most countries and are particularly scarce for EMDEs. Further, the lack of standardization of property prices and the heavy reliance on indices to monitor changes over time make comparisons across countries cumbersome. Addressing this gap, the paper places itself at the crossing of three streams of literature: existing efforts to collect or collate data on residential property prices and to construct residential property price indices (Section 2.1); studies on determinants of house prices (Section 2.2); and a relatively new area of big data and machine learning approaches to estimate (determinants of) house prices (Section 2.3). Finally, we summarize the existing gap within this line of research and outline how we address it (Section 2.4).

## 2.1 Current Efforts to Collect House Price Data and Existing House Price Indices

Since housing plays a key role in the growth of many aspects of a country’s economy including the development of the construction industry, job creation, and improving the living conditions of many HHs, governments have a great interest in understanding property prices and their developments over time. Most advanced economies’ statistical offices or central banks, therefore, started collecting data on residential property prices in the 1970s (Knoll et al. 2017). Also, tax authorities, land registries, or real estate associations collect, hold, and sometimes even publish data on residential property prices in many advanced economies.<sup>3</sup> With the 2008-2009 global recession, which many scholars attributed to misalignments in housing and housing-related asset prices, the interest in dynamics in housing markets rose significantly (e.g., Goodhart & Hofmann 2008; Del Negro & Otrok 2007). Since then, several international organizations and central banks have increased efforts to develop global property price indices and collated real property price data for various predominantly developed economies to monitor macro-financial stability and price
from 82 developing countries during the period 2002-2019; while De Loecker (2013) study focuses on 7,915 manufacturing firms in Slovenia during the period 1994-2000. Data for WBES come from the World Bank, while data from De Loecker (2013) come from the Slovenian Central Statistical Office. The correlation coefficient is 0.36.

# **6 Nonparametric Estimates of the Digital-Technology Adoption Effect (DAE) on TFPR**

This section presents the semi-parametric estimates of the digital adoption effects (e.g., email and website), using the approach presented in section 4. Table 3 reports the median effects, the percentage of the estimation sample with positive marginal effects, and the F-test associated with each variable of interest. Column (1) displays the results from estimating an endogenous TFPR process that is a function of learning by exporting, as in De Loecker (2013). Column (2) reports the results from estimating an endogenous TFPR process that is a function of the adoption of digital business solutions, namely email and website. Column 21
sector, they are driven by a reduction in export volumes. Although we did not observe the product quality upgrading effects of NTMs in our sample, we find that initial product quality moderates the trade-reducing effects of NTMs. The trade-reducing effect of NTMs at the intensive margin is reversed for firms with higher initial product quality in the manufacturing sector and is less pronounced for firms with relatively high initial product quality in the agriculture sector.

Our work contributes to the literature on how standards and regulations shape firms’ export strategies in developing countries in three main ways. First, we examine the relationship between technical measures and trade using firm-level data and focussing on both the agriculture and manufacturing sectors. Standards and regulations are more likely to affect the trade of firms in low-income countries, especially those in Africa because they tend to be more standard-takers rather than standard-makers. Yet, much of this research has focused on relatively more developed and emerging economies, leaving a gap in understanding the specific challenges and opportunities facing African countries.<sup>3</sup> Most of the existing literature on the trade effects of NTMs for African exports have predominantly done this at the country level for the agriculture sector (Otsuki et al., 2001; Czubala et al., 2009; Kareem et al., 2017; Tchakounte and Fiankor, 2021; Sanjuán López et al., 2021; Traoré and Tamini, 2022), mainly due to lack of firm-level data for these countries.<sup>4</sup> It is firms and not countries that largely engage in international trade (Bernard et al., 2007). Thus, our study builds upon Fernandes

> 2Going forward, we will use the terms standard-like NTMs, SPS and TBT measures, and standards and technical regulations interchangeably.

> 3See, for example case studies from France (Fontagné et al., 2015; Schmidt and Steingress, 2022; Disdier et al., 2023), China (Beestermöller et al., 2018), Colombia (Rosenow, 2024)