rafmacalaba/data-use-mentions-extended huggingface dataset:
||**Table 1. L**|**abor force partici**|**pation rates**||
|---|---|---|---|---|
||Abidian|Other Cities<br>|I Rural Areas|C8te d'Ivoire|
|||1985|||
|Very poor|29.5|47.1|67.9|63.7|
|Mid-poor|28.1|40.5|64.7|59.6|
|Non-poor|33.8|40.0|64.9|50.3|
|AI|1<br>33.6|..<br>40.6<br>1988|65.21_|<br>53.4|
|Very poor|_|43.8|69.0|66.4|
|Mid-poor|21.9|43.3|66.9|58.0|
|Non-poor|33.5|37.9|64.2|49.7|
|All|32.0|40.0|66.1|54.4|

Table 1 shows the labor force participation rates in 1985 and 1988, by poverty status. Labor force participation is defined here as any form of economic activity for wages or own account, whether paid or not. Labor force participants thus consist of employees, employers, self-employed, and unpaid family workers. Two conclusions stand out from Table 1. First and foremost, labor force participation is _inversely_ related to welfare level: the very poor have the highest rate of participation. This immediately dispenses with the hypothesis that the poor are poor because they do not participate in the labor force. Second, participation is much higher in rural than urban areas. This is consistent with the previous finding, since poverty in Cote d'Ivoire is higher in rural areas
pattern of attendance gains for Cohort 1 Busia girls in 2001 is similar to the pattern in test score gains for that subsample, with large gains near the winning test score threshold and at the bottom of the test score distribution (Figure 7). The pre-program attendance gains might be due to anticipation of the future scholarship opportunity. Attendance impacts were not significantly different between school terms 1, 2 and 3 in 2002 (not shown), so there is no evidence that gains were largest in the period immediately preceding exams, due to cramming, for instance. Busia district boys in scholarship schools show similar effects, with larger gains in 2001 than in 2002 (Table 7, Panel B, regression 2). It is unclear why attendance gains are considerably larger in 2001 than in 2002.

Teachers in program schools were five percentage points more likely to be present at school than comparison school teachers during 2002, reducing overall teacher absenteeism by approximately onethird (Table 7, Panel C), and this large effect is robust to alternative specifications (not shown).<sup>20</sup> The 2002 Student Questionnaire collected information on educational inputs, study habits, and attitudes that may have affected school performance, to capture other dimensions of effort at least in part. Because the questionnaire was administered in mid-2002, cohort 1 girls had already competed for the scholarship when they filled out the questionnaire, while cohort 2 girls had not yet competed.

There is a significant increase in textbook use among program girls in cohort 1 (Table 8, Panel A): girls in program schools report having used textbooks at home 6 percentage points more than in comparison schools, suggesting that the program led to more intensive studying among the girls (although there is no effect for cohort 2). However, there is no impact on the likelihood that program school students sought out extra school coaching, handed in homework, were called on by the teacher in class, or did chores at home (Panel A). In the case
)|0.24|0.42|0|1|10,432|
|Agricultural work (Y=1)|0.22|0.41|0|1|10,432|
|Work outside the home (Y=1)|0.05|0.22|0|1|10,432|
|Hrs. total worked|11.57|19.34|0|105|6,611|
|Hrs. own-account HH-enterprise work|6.31|15.63|0|105|6,611|
|Hrs. agricultural work|3.53|9.93|0|70|6,611|
|Hrs. work outside the home|1.74|8.40|0|96|6,611|

_Source_ : Authors’ tabulations of individual-level GHS data. All panels include observations that are observed in the 2018-2019 GHS round and also at least one previous round; the sample is restricted to those that were at least 5 years old as of their first appearance in the data (when the labor module is first applicable). Borno state is excluded.

26
- 29  Das J, Holla A, Das V, et al. In Urban And Rural India, A Standardized Patient Study Showed Low Levels Of Provider Training And Huge Quality Gaps. Health Affairs 2012;31:2774–84. doi:10.1377/hlthaff.2011.1356

- 30  Gough EK, Moodie EEM, Prendergast AJ, _et al._ The impact of antibiotics on growth in children in low and middle income countries: systematic review and meta-analysis of randomised controlled trials. _BMJ_ 2014;348. doi:10.1136/bmj.g2267

- 31  Rowe AK, Rowe SY, Holloway KA, _et al._ Does shortening the training on Integrated Management of Childhood Illness guidelines reduce its effectiveness? A systematic review. _Health Policy Plan_ 2012;27:179–93. doi:10.1093/heapol/czr033

- 32  Rowe AK, Labadie G, Jackson D, _et al._ Improving health worker performance: an ongoing challenge for meeting the sustainable development goals. _BMJ_ 2018;362. doi:10.1136/bmj.k2813

- 33  Free C, Phillips G, Galli L, _et al._ The Effectiveness of Mobile-Health Technology-Based Health Behaviour Change or Disease Management Interventions for Health Care Consumers: A Systematic Review. _PLOS Medicine_ 2013;10:e1001362. doi:10.1371/journal.pmed.1001362

23
countries and time. It currently covers more than 150 countries and has more than 1,000 surveys. The time coverage goes from 1960 until 2016, but it varies by country. Appendix 1 shows the country and time coverage of the sample used in this paper, which excludes the pre-1990 samples. Finally, we use several variables from the World Development Indicators (WDI), including GDP per capita

> 2 Azerbaijan, Bosnia-Herzegovina, Kenya, Kosovo and Lao People’s Democratic Republic are also covered by a STEP survey but, since we do not have harmonized repeated cross-sections with the required variables for these countries, they are excluded from the sample.

5
0|
|North Central Coast|-0.7|-1.6|0.2|1.2|0.3|2.1|0.9|0.0|1.8|12.2|11.9|12.5|
|Central Highlands|2.1|2.9|1.3|5.2|5.9|4.6|5.3|6.1|4.5|2.7|-5.6|14.6|
|South East|7.3|7.1|7.5|9.6|9.4|9.8|9.2|8.9|9.4|24.3|23.0|25.5|
|Cuu Long River Delta|-1.2|-2.0|-0.3|-0.1|-1.2|0.8|-0.8|-2.2|0.5|25.9|33.4|16.6|

_Source: LFS, 2007; LFS, 2009_ There is evidence of a dichotomy between men and women: men left rural areas to go to urban areas; women increased participation everywhere, in particular in urban areas. The decrease in the working age population in rural areas was mostly due to a decrease in male working age population in rural areas, suggesting that men are leaving these areas. Except in Central highlands, the rate of annual growth of working age population was greater for women than for men. At the same time, the labor force of women increased in 9
price puzzle. The transmission of a monetary policy shock to output and prices is statistically different from estimates obtained when excluding the amplification mechanism through exchange rates.

Existing studies attribute the exchange rate puzzle in EMEs to various underlying factors—including concerns over debt financing, habit persistence of exchange rates, disconnection between bond and currency markets, and the response of liquidity problems following monetary policy shocks. The latter, often referred to as “UIP premium puzzle,” has been considered one of the most compelling reasons for the disconnection between interest rates and exchange rates. Kohlscheen (2014) argues that it is difficult to attribute the exchange rate puzzle solely to fiscal dominance, as unexpected rate increases are not associated with increases in risk premia. Similar results are obtained in the case of Chile—a country with the highest possible short-term credit rating since 1995 and a debt/GDP ratio below 10%. Instead, De Leo et al. (2023) find, by examining quarterly data from the mid-1990s to 2019 across a large set of EMEs, that monetary policies in EMEs tend to adopt Taylor-rule-like approaches but still exhibit a procyclical nature. They argue that one of the primary reasons for this phenomenon is the disconnect between policy rates and short-term market rates in EMEs.

Blanchard (2004) argues that if an increase in the real interest rate also raises the likelihood of default on debt, it may diminish the attractiveness of domestic government debt, resulting in a real depreciation. This scenario is more likely with higher initial debt levels, a larger proportion of foreign-currencydenominated debt, and heightened risk premiums. In such a context, inflation targeting can yield counterintuitive effects: a rise in the real interest driven by monetary policy responding to higher inflation

> 41 In the case of EMEs, earlier studies based on event studies with _daily_ data show that the currency response to monetary policy is low or nonexistent (Aktas et al. 2005; Duran et al. 2012; Pennings et al. 2015).

45
variables; (ii) using the same survey instrument for treated and controls; and (iii) comparing participants and nonparticipants from the same local market.

Our data satisfies all three of these conditions. First, we have a rich set of control variables. As we describe below, the data come from detailed household surveys and provide ample individual and household characteristics, which we use to control for observable factors affecting participation in microfinance. Specifically, we use characteristics of eligible female (i.e., aged 18– study the impact of agricultural extension program in Peru, Chen et al. (2009) study the effects of the World Bankfinanced Southwest China Poverty Reduction Project.

6. First, we compare MFI borrowers to two distinct groups of controls: those without any loans and those with loans from other sources such as family and friends and money lenders. Second, we have a larger and richer dataset, which allows us to use a larger number of control variables in PSM estimation. Specifically, Floro and Swain’s (2012) control sample includes only 51 observations for nonparticipants, relative to nearly 700 participants. This implies that, on average, the same control observation has to be matched to nearly 14 treatment observations. In our sample, the treatment and control groups are much more balanced, which suggests likely smaller bias and variance. Third, Floro and Swain (2012) focus primarily on an indicator of vulnerability, which they measure as the variance of consumption and average food expenditures. We have a much wider set of outcomes, including purchases of durables, education, health expenditures, home repairs, and other expenditure categories, which allows for broader focus. Fourth, we use the data that has been used in an RCT evaluation, which allows us to compare the performance of these two methods, evaluate the presence of spillovers, and the magnitude of entrepreneurial bias. Finally, Floro and Swain (2012) study the impact of bank-connected Self Help Groups (SHG) rather than loans from a specialized
the standard errors of the small area poverty estimates (i.e. an over-estimation of precision). The reason for this is that the random offsets partially destroy the spatial correlation structure in the data and error term.

The decision to restrict attention to publicly available remote sensing data is a deliberate one. This ensures that the data are available at no cost in any given country, and so the approach can easily be replicated and readily repeated as new survey data become available. By the same token, our exercise arguably provides a conservative assessment, i.e. a lower bound of success as higher resolution remote sensing data that come at a cost will allow for more precise estimates. It should be noted that with the rapid technological advancements in this field, what is costly today will in all likelihood become low-cost or publicly-available in the near future.

The paper is organized as follows. An overview of the two small area estimation 5
did not deliver long-run growth in Costa Rica. The latter considers the effects of trade liberalization in four CENAMs and finds no evidence of reform-led growth in any except Nicaragua.

> 4 The exclusion of other CENAMs is not because of their growth performance. Instead, the unavailability of data restricts their inclusion.

> 5 I follow the World Bank’s income definitions (The World Bank, 2020). For the fiscal year 2020, the World Bank classifies countries with GNI per capita of USD 12,376 or more in 2018 (as per the World Bank Atlas method) as highincome.

2
interest rates are then reported using an average weighted by loan volume. Doing so puts greater weight on larger loans and ensures that our results are economically meaningful.

The sixth data set consists of the monthly balance sheet information of 22 commercial banks representing more than 98 percent of commercial bank-lending in the country.<sup>23</sup> Data are provided by the Bank of Mexico (Banxico) and variables in this data set include the total credit volume of banks, both to private firms and public entities, as well as their respective interest rates.

Finally, the seventh main data set is Orbis, a _firm-year_ level data set compiled by Bureau van Dijk, containing information on the balance sheets and income statements of a large set of Mexican firms. The data set reports information on assets and revenues of firms as well as their total and bank-specific liabilities by type of financing. As shown in Morais et al. (2019) this sample of firms is representative of the universe of sectors and locations in Mexico, albeit skewed towards larger firms.

The summary statistics of our data set are shown in Table 1, with the definitions of all variables listed in Table A1 in the Appendix. In Panel A, we display summary statistics of macrolevel data from states and municipalities. We start by presenting the ex-ante indebtedness of state governments, measured by the main indicator _DebtStates,16Q1_ , which is the ratio of states’ public debt to freely disposable income one month prior to the introduction of the FD Law.<sup>24</sup> This ratio is on average 86 percent. However, there is large variation across states (Figure 1). While states in the bottom decile have a ratio of less than 20 percent, the indebtedness ratio of states in the top decile is 225 percent.<sup>25</sup> Furthermore, the average maturity of debt to states is of 14.6 years, with bottom and top deciles having a maturity of 8.3 and 19.3
joint test that none of the jj ββ treatments had any effect ( 1 = 2 = 3 = 4 = 0), and that all four treatments had equal effects ( 1 = 2 = 3 = 4). ββ ββ ββ ββ ββ ββ ββ ββ As noted, our sample size is smaller than originally planned, which reduces our statistical power. This is less of an issue for binary outcomes like use of business practices, but is more of a challenge for outcomes like sales, profits and employment, which are heterogeneous and heavily skewed. We therefore use two approaches to help potentially improve power. The first is to employ the post-double selection lasso approach of Belloni et al. (2014). This uses a disciplined way of selecting baseline control variables to add to equation (1) to the extent that they are strong predictors of future outcomes. Moreover, this method also selects variables that predict treatment status, which can help account for chance imbalances and any imbalances caused by selective attrition. Second, while our main interest lies in examining the trajectory of treatment impacts in order to determine whether impacts persist well after the interventions end, we also attempt to boost power by pooling both rounds of data on firm growth (sales, profits, employees) and examining average impacts over the two years (McKenzie 2012).

17
the restrictiveness labor regulation which enables us to control for a potential gap between legislation and enforcement. The indicator we use is the plant manager’s response to the World Bank Investment Climate Survey question on whether or not managers considered labor regulation to be a major or severe obstacle to the operations or growth of their businesses. As can be seen from Table 2, there is sharp contrast in responses across states. While between one-third and 70 per cent of managers consider labor regulation as a major to severe obstacle in states such as

> 15 This too applies for establishments with more than 100 employees, but Zagha (1999) notes that some states have made the provisions mandatory to firms with 50 or more workers while other states have abolished the employment size limit altogether.

11
The discussion that follows focuses on two types of measures: export restrictions and import liberalization-cum-facilitation. These measures cover most of the measures in the data set. Details on the set of policy instruments included in these two categories are reported in Appendix Table A1.<sup>5</sup> The data set encompasses significantly more measures than reported by the two international organizations that have engaged in monitoring exercises. The first of these is the International Trade Centre (ITC), which launched a COVID-19 dashboard in early 2020 that provides information on trade measures taken by countries. The focus of this effort is not limited to food and medical products. As of October 9, 2020, the ITC repository comprised 338 measures implemented by 145 countries, custom unions and autonomous territories, substantially fewer than what is reported by the GTA.<sup>6</sup> The second is the WTO, which reports on COVID-19 related measures notified by its members and maintains a separate data set on export and import-related measures compiled by the WTO Secretariat that is drawn from official sources. The latter includes only measures that are verified by the WTO members concerned. Transparency is a fundamental dimension of WTO membership. This also applies to emergency measures. WTO members must notify quantitative restrictions that are motivated by domestic emergencies, including public health. The relevant 2012 Decision on Notification Procedures for Quantitative Restrictions (WTO G/L/59/Rev.1) stipulates that notifications must occur at two yearly intervals, and more salient to the present context, report changes as soon as possible, and no later than six months from their entry into force (Hoekman, 2020).<sup>7</sup> WTO members may engage in so-called reverse notifications as well, reporting on measures imposed by other countries and customs territories.

In principle, transparency through notification and reverse notification supports discussion in the WTO of measures taken. In practice many WTO members have not lived up to their transparency obligations, notwithstanding commitments by G20 Trade Ministers to notify
model of ...scal adjustment, the presence of a lagged dependent variable introduces new sources of endogeneity. First, without controlling for the ...xed e¤ects, the model in equation (5) has a built-in positive bias in the ...rst lag of the dependent variable.<sup>26</sup> Second, the di¤erenced version of the equation eliminates this bias, but has a built-in negative bias due to the fact that yi;t<sup>�=</sup> �yi;t is negatively correlated with yi;t<sup>�</sup> �1<sup>= �yi;t�1:Thusanunbiasedestimateofthelagged</sup>

> 24Most studies have not been able to use this method because either budget institutions do not change much over time or because changes are di¢ cult to measure. When it has been implemented with U.S. statelevel data (e.g., Knight and Levinson 2000) the results are typically di¤erent, indicating that the problem of omitted variables is relevant.

> 25Additional omitted variables could include political institutions. Evidence from Europe shows that institutional design responds to political factors and events (Hallerberg, Rolf, and von Hagen 2009).

> 26Nickell (1981) has shown that this "dynamic panel bias" disappears only when T approaches in...nity.

14
# **Table 1**

# **Individual Characteristics**

||**School Sta**<br>**Sur**<br>|**keholder**<br>**vey**<br>|**2008**<br>|**LSMS**<br>|
|---|---|---|---|---|
||**Mean**|**SD**|**Mean**|**SD**|
|**Demographics**|||||
|Average years of education|10.98|3.25|10.73|3.36|
|Average age|40.17|6.39|44.22|17.58|
|Wealth indicator|0.00|0.92|n.a.|n.a.|
|Householdyearlyincome(in 000LEK)|n.a.|n.a.|385|1,894|
|**Survey-based measures of social capital**|||||
|Belongs to an organization (church, political group, sport etc.)|0.02|0.15|0.24|0.75|
|Participated in any community activity in the past year|0.15|0.35|0.11|0.31|
|Thinks village members would cooperate in the case of water shortage|0.72|0.45|0.64|0.48|
|Think that “most people can be trusted”|0.13|0.34|0.27|0.17|
|% of relatives or friends amongotherparents|0.06|0.90|n.a.|n.a.|
|**Involvement with the pupil’s education:**|
was no clearinghouse for trades in most of the new derivative instruments. Moreover, the Commodity Futures Modernization Act of 2000 ensured that derivatives remained unregulated. In such conditions, derivatives trading expanded quickly, increasing from a total outstanding nominal value of $106 trillion in 2001 (1039 percent of U.S. GDP, and 331 percent of world GDP), to a value of $531 trillion in 2008 (3688 percent of U.S. GDP and 868 percent of world GDP).<sup>49</sup> However, the legal and technological infrastructure of the industry was inadequate to handle such an explosion of the volume of derivatives, which worsened the impact of the lack of regulation of derivatives.<sup>50</sup> 76. The development of mortgage-backed securities was largely driven by the shadow banking system (Figure 45), which experienced dramatic growth in the run-up to the crisis. The growth of the shadow banking system was partly the result of the perceived decline in the profit-

> 47 Roubini and Mihm (2010): p.74.

> 48 CDO’s essentially bundled high-risk subprime mortgages and then sliced them into tranches (equity, mezzanine and senior). Purchasers of the equity tranche had the highest return and highest risk, while purchasers of mezzanine and senior tranches had correspondingly less return and less risk.  If any homeowner in the underlying pool defaulted, the holders of equity tranches would see the highest loss. 49 Bank of International Settlements, Semiannual OTC derivatives statistics at end-June 2011.

> 50 Goodman, Peter S.: “Taking hard new look at a Greenspan legacy,” New York Times, October 8,  2008.

54
Policy Research Working Paper 8710

# **Abstract**

This paper combines local election results and geo-referenced road construction data over 1993–2012 to investigate political bias in road infrastructure investment in a democratic setting, focusing on the case of Mexico. Using a regression discontinuity design, the paper finds strong evidence of partisan allocation of federally-funded highways to municipalities that voted for the president’s party in legislative races, nearly doubling the stock of highways compared to opposition municipalities. The extent of political favoritism in highway provision is stronger under divided government when the president has no majority in the legislature, suggesting political efforts to control the Congress.

This paper is a product of the Development Research Group, Development Economics. 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://www.worldbank.org/research. The authors may be contacted at hselod@worldbank.org and .ssoumahoro@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