rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6754

# **Abstract**

In May 2013, Federal Reserve officials first began to talk of the possibility of tapering their security purchases. This tapering talk had a sharp negative impact on emerging markets. Different countries, however, were affected very differently. This paper uses data on exchange rates, foreign reserves and equity prices between April and August 2013 to analyze who was hit and why. It finds that emerging markets that allowed the real exchange rate to appreciate and the current account deficit to widen during the prior period of quantitative easing saw the sharpest impact. Better fundamentals (the budget deficit, the public debt, the level of reserves, or the rate of economic growth) did not provide insulation. A more important determinant of the differential impact was the size of the country’s financial market: countries with larger markets experienced more pressure on the exchange rate, foreign reserves, and equity prices. This is interpreted as showing that investors are better able to rebalance their portfolios when the target country has a relatively large and liquid financial market.

This paper is a product of the Operations and Strategy Unit, Development Economics Vice Presidency. 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 pgupta5@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
finance institutions, and there was no deadline for enrollment.<sup>8</sup> Upon enrolling, the insurance took effect the first day of the following month. Government ID numbers were collected to match respondents to health insurance enrollment data provided by the INSS. One year after the baseline survey, respondents were approached for a follow-up survey. Overall, 93 percent of the adult respondents were re-interviewed (N=2608). Of the 2608 adults who were in both rounds of the initial evaluation, 62 percent had at least one child under the age of 16 (N=1614).

The overall take-up rate of insurance for respondents with insurance-eligible children was 35 percent among those who were offered the six-month subsidy and 2.22 percent among those who were not.<sup>9</sup> The primary sample consists of 2,996 children in 1614 households in both waves of the survey; this sample is 74 percent of all children at baseline. As with the full adult sample in Thornton et al. (2010), there was no differential sample attrition between those who were offered the subsidy and those who were not (available upon request).

Baseline statistics of households (Panel A), parents (adult respondents with at least one child under 16, Panel B), and children (Panel C) are presented in Table 1. Households have on average 4.8 members, including 1.3 children age 11 and under. Average monthly household income is 5,399 Córdobas (US$298). Parents have 9.3 years

> 7 Although the 6-month subsidy is large relative to median income, household disposable income should be unaffected by receiving the subsidy since most households do not purchase the insurance without a subsidy. 8 Thornton et al. (2010) also measures whether the location of enrollment (at a micro-finance facility or the standard INSS enrollment location) affects the enrollment decisions of informal-sector workers, and whether MFI clients have different utilization patterns than non-MFI clients.

> 9 These take-up rates of health insurance are comparable to the existing literature. The Oregon Health Insurance Experiment found that low-income individuals who won a lottery for Medicaid in the United States increased the probability of enrolling by 25 percentage points compared to similar individuals who had also enrolled in the lottery (Finkelstein et al 2012).  Enrollment in the SKY health insurance program in Cambodia peaked after 6 months at 44 percent in the most heavily subsidized treatment group (Levine et al. 2016), and a study in Kenya found a 17 percent take-up rate of formal health insurance (Chemin 2014).

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Dhaka and Sylhet leads to multiple, relatively short power outages while Rangpur experience longer but fewer electricity disruptions. Outages in Dhaka tend to affect the largest number of consumers on average, which is explained by the high population density in this area<sup>21</sup> .

Figure 3.8 displays a large difference between the number of days of outage and the number of outages. For example, in Dhaka, a storm seems to be causing on average 17 outages while in Chittagong, a storm is reported to be responsible for barely more than one power disruption. While this could be the sign of discrepancies between the quality of the grid at the division level, this difference could be due to reporting practices being inconsistent across districts and divisions. Therefore, it is important to bear in mind that the analysis is not meant to be representative at the country level. In what follows, we will favor the use of dated storms inducing outages when matching power disruptions with climate data, rather than the number of outages associated to each event. The magnitude of a blackout will be inferred from information on duration and number of consumers affected.

**Figure 3.8: Spatial distribution of outages**

<!-- Start of picture text --> 300 400 358.4<br>260<br>350<br>250<br>300<br>200<br>250<br>150 200<br>109 115<br>150<br>100<br>55 100<br>50 5 18 50 11.4 0.4 9.7 7.3 10.8<br>0 0<br>Total number of storms that caused outages over the period 2000-2017 Average number of outages per year<br>Chittagong Dhaka Khulna Rajshahi Rangpur Sylhet Chittagong Dhaka Khulna Rajshahi Rangpur Sylhet<br><!-- End of picture text -->

> 21 For the purpose of validation, newspaper coverage was sought for outages having affected more than 1,000,000 people. When such evidence could not be found, those observations were excluded of the analysis. Overall, we deleted 13 outage reports, five of which were issued in Khulna.

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living in her father‘s household, a woman will work on the family land and eventually obtain use rights on a plot. As a wife, she‘ll work on the land of her husband‘s family and might also have use rights on a particular plot. If she is in neither of these positions, she simply won‘t have access to land. This in part explains the high remarriage rate following widowhood or divorce. Whether this fact contributes to gender inequality in terms of well-being is a question we want to explore in this paper.

The data used here come from an original survey entitled Pauvreté et Structure Familiale (Poverty and Family Structure, henceforth PSF) conducted in Senegal in 2006/2007. The PSF survey stems from the cooperation between a team of French researchers and the National Statistical Agency of Senegal.<sup>13</sup> The survey is described in detail in De Vreyer et al. (2008).

The PSF is a nationally representative survey covering a sample of over 1800 households spread over 150 clusters drawn randomly from the census districts so as to insure a geographically representative sample. About 1,750 household records can be exploited, covering 14,450 individuals. The survey describes a population of which the majority (57.1%) live in rural areas, 48% is male and 95% is Muslim—statistics that accord well with other sources (World Bank, 2009). Urbanization over time is evident in that, amongst those adults who had a father in rural areas, 22% now live in urban areas. A similar percentage of those whose mother lived in rural areas also do so.

Senegalese households are large, with slightly more than eight members on average in the PSF. The families are typically multigenerational. Polygamous unions are common, with 24% of married men and 37% of married women engaged in such unions, which mostly comprise a husband and two wives (only 20% of polygamous unions have more than two wives). We find that 31% of polygamous men have non-cohabiting wives. Among those, in only half of the cases are the two parts headed by the same person or by the husband for one and a wife for the other i.e., for the other half, a married polygamous women is living in a household headed by a relative (mainly her father, brother or son). Those large households are extended both horizontally and vertically, with 36% of household members that are neither the head, nor one of his wives or children. Two thirds of households include such "extended" family members.

> 13 Momar Sylla and Matar Gueye of the Agence Nationale de la Statistique et de la Démographie of Senegal (ANSD) on the one hand and Philippe De Vreyer (University of Paris-Dauphine and IRD-DIAL) Sylvie Lambert (PSE) and Abla Safir (now with the World Bank) designed the survey. The data collection was conducted by the ANSD thanks to the funding of the IDRC (International Development Research Center), INRA Paris and CEPREMAP.

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Policy ReseaRch WoRking PaPeR 4654

# **Abstract**

This paper reports on the latest update of the Worldwide Governance Indicators (WGI) research project, covering 212 countries and territories and measuring six dimensions of governance between 1996 and 2007: Voice and Accountability, Political Stability and Absence of Violence/Terrorism, Government Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption. The latest aggregate indicators are based on hundreds of specific and disaggregated individual variables measuring various dimensions of governance, taken from 35 data sources provided by 32 different organizations. The data reflect the views on governance of public sector, private sector and NGO experts, as well as thousands of citizen and firm survey respondents worldwide. The authors also explicitly report the margins of error accompanying each country estimate. These reflect the inherent difficulties in measuring governance using any kind of data. The authors also briefly describe the evolution of the WGI since its inception, and show that the margins of error on the aggregate governance indicators have declined over the years, even though they still remain non-trivial. The authors find that even after taking margins of error into account, the WGI permit meaningful cross-country comparisons as well as monitoring progress over time. In less than a decade, a substantial number of countries exhibit statistically significant improvements in at least one dimension of governance, while other countries exhibit deterioration in some dimensions. These aggregate indicators, spanning more than a decade, together with the disaggregated individual indicators, are available at www.govindicators.org.

This paper—a product of the Growth and the Macroeconomics Team, Development Research Group, in collaboration with the Global Governance Program of the World Bank Institute—is part of a larger effort in the department to study the causes and consequences of good governance. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The authors may be contacted at akraay@worldbank.org, dkaufmann@worldbank.org, and mmastruzzi@ 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
of incentive effects in transferring money would be much higher in Vidzeme, notwithstanding the marginal decline in the poverty incidence.<sup>27</sup> In addition to Riga, Kurzeme is the other region that benefited the most from the growth process, with over 32% decline in poverty incidence and about a 50% fall in the poverty gap.

**Figure 2: Over Time Poverty Incidence Curves for Distributions of Urban Per Capita Household Expenditure in LVL (1997 - 2000)**

<!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>1997<br>30<br>2000<br>20<br>10<br>0<br>2 32 62 92 122 152 183 217 276 393<br>Monthly per capita expenditure<br>cumulative distribution of population<br><!-- End of picture text --> Source: Latvia Household Budget Surveys (HBS 1997, 2000).

**Figure 3: Over time Poverty Incidence Curves  of Rural Per Capita Household Expenditure in LVL (1997 – 2000)**

> 27 Under the assumption of perfect targeting, the total amount required to eradicate poverty is a function of the poverty gap, the poverty line and the total population and can be expressed  as _y_ = _nzp_ 1 . For instance, in this particular case, where the region of Vidzeme accounts for about 12% of total population, the amount of resources required to eradicate poverty over a month period under the best and optimal allocation scenario would be about 617,000 LVL.

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to 30% of their production in the domestic market, and the related profits are taxed at the general rate.

Figure 1: Timeline of reform.

<!-- Start of picture text --> 0% CIT 10% CIT<br><!-- End of picture text --> <!-- Start of picture text --> Announcement<br>10% CIT if incorporated after 2007<br>or incorporated before 2008<br>and older than 10 years<br><!-- End of picture text --> <!-- Start of picture text --> Enforcement<br><!-- End of picture text --> Note: This timeline depicts the subsequent budget law postponements of the enforcement of the Corporate Income Tax (CIT) reform for Offshore firms, thus leaving the full exemption in place until 2013, when the law 2006-80 was finally applied.

# **3 Data and Descriptive Statistics**

The main source of data we use are administrative records on Tunisian registered firms, the Repertoire National des Entreprises (RNE). This data spans the universe of all private and public sector firms and includes information such as firms’ 4-digit economic activity sector, location, age and whether owners are foreign.<sup>9</sup> In joint collaboration with the Tunisian Statistics Institute (INS), the RNE microdata has been merged with tax returns to include yearly measures of firms’ declared turnover, profits, and revenue from exports; social security data on total employment and wage bill; and customs data on total value of exports and imports.<sup>10</sup> We clean the data in a number of ways. In Figure A1 in the Appendix, we provide the key steps we take to arrive at our analysis sample. We start with a full panel of almost 16 million observations and almost 1.5 million firms between 1995 and 2021. First, we focus

> from the Ministry of Finance.

> 9Details of the RNE can be found here (in French). See for example Rijkers et al. (2017), Rijkers et al. (2014), and Baghdadi et al. (2019) for other studies using the RNE.

> 10For data privacy reasons, not all information from these sources could be provided for this study. Notably, we do not have access to information on paid taxes nor on details about exports, such as product-level amounts.

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# Figure 3. Monetary and Exchange Rate Seasonal Factors

<!-- Start of picture text --> 106. 1043<br>104 104<br>106. l .. .. . ... ... . .. ..... 101.........0 ~~~\ . ..<br>102~ ~ ~ ~ ~ ~~~~~~~~~~~~~~12... ..... ..... ...<br>99<br>9e8 . . -- 98<br>93 iP3'06i' 9107 ss!oi9ss07 96101 96107 s7'di 967' 93:1693.d:9i 9i409:7i 5:190di 7 s9 6:0 1 797:1 '<br>- Ml Seasonal Component _ M2 Seasonal Component<br><!-- End of picture text --> <!-- Start of picture text --> 104.<br>102.100<br>100.<br>98<br>93:0 930 40 40 50 50 60 60 70 707<br>-M3 Seasonal Component<br><!-- End of picture text --> <!-- Start of picture text --> 1041<br>98.. . .. . . .. . . . . .<br>~~~~~~~~~~~~~~~~~94<br>93:0 930 40 40 50 50 601 609'i9"<br>-USD Seasonal Component<br><!-- End of picture text -->

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<!-- Start of picture text --> Red bars:<br>80% Distribution of "distressed"<br>restructurings with China's state-<br>70% owned banks and enterprises<br>Grey bars:<br>a Distribution of "symbolic"<br>5 60% restructurings of Zero-Interest<br>3 Rate Loans of China's Ministry<br>en of Commerce<br>2 50%<br>eI<br>3<br>E<br>EB 40% we<br>3ae<br>oH<br>5os3 30%<br>Ss<br>n<br>20%<br>10%<br>0%<br>Low Moderate High<br>World Bank - IMF Risk Rating<br><!-- End of picture text --> <!-- Start of picture text --> 40%<br>Grey bars:<br>35% Distribution of "symbolic"<br>restructurings of Zero-Interest Red bars:<br>30% Rate Loans with China's Distribution of "distressed"<br>Ministry of Commerce restructurings with China's state-<br>g owned banks and enterprises<br>S<br>o 25%<br>op<br>E<br>8 20%<br>2<br>Ga<br>° 15%<br>oq<br>=|<br>n<br>10%<br>5%<br>0%<br>Y. x & &y Sex Fy &x xy SFx SYCw oC& © 3YN 9<br>Credit Risk Rating<br><!-- End of picture text -->
Consider the issue of inequality. “Technology (skill-based technological change),” as Joseph E. Stiglitz writes, “may be central to certain aspects of our current inequality problem, especially to the polarization of the labor market.”<sup>8</sup> Even more striking, the gender gap in many countries still persists under the so-called ‘Digital Divide.’<sup>9</sup> In this divide, “the difference is not necessarily determined by the access to the Internet, but by access to ICT (Information and Communications Technologies) and to Media that the different segments of society can use.”<sup>10</sup> The digital divide moreover extends to the poor, the elderly, and the disabled, including those who are active in creative work. Ultimately, the concern here may not be the Internet functionality per se—although that also deserves scrutiny. The worry is that as more and more things get done online, those who are less connected (or not connected at all) are left behind.

This has development implications. In our context, if women and girls in the developing world have less Internet access, this is likely to diminish their creative agency in the digital space. See Table 1 on selected countries across regions on women’s connectivity in <u>Women and the Web — this report is the first of its kind to compile data on “how women</u> in developing countries access and use the Internet.”<sup>11</sup>

> 8 Joseph E. Stiglitz, _The Price of Inequality: How Today's Divided Society Endangers Our Future_ (New York: W.W. Norton & Company, Inc., 2013), 100. See also Patrick Kabanda, “Music for Development in the Digital age,” March 2016, accessed May 9, 2016. <u>http://pubdocs.worldbank.org/pubdocs/publicdoc/2016/3/923101459255847647/WDR16-BP-Music-forDevelopment-Kabanda.pdf.</u>

> 9 “The Digital Divide, or the digital split, is a social issue referring to the differing amount of information between those who have access to the Internet ([especially] broadband access) and those who do not have access. The term became popular among concerned parties, such as scholars, policy makers, and advocacy groups, in the late 1990s.” — “The Digital Divide, ICT and the 50x15 Initiative,” Internet World Stats, accessed March 19, 2015, <u>http://www.internetworldstats.com/links10.htm.</u> 10 Ibid.

> 11  Shelly Esque, Foreword in “Women and the Web: Bridging the Internet Gap and Creating New Global Opportunities in Low and Middle-Income Countries,” Intel Corporation and Dalberg Global Development Advisors, accessed March 19, 2015, http://www.intel.com/content/www/us/en/technology-in- <u>education/women-in-the-web.html.</u>

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<!-- Start of picture text --> 100<br>— 80<br>&<br>o<br>ow 60<br>2]<br>o<br>o<br>o<br>~*~<br>© 40<br>c<br>o<br>2<br>o<br>o 20<br>0<br>15 20 25 30 35 40<br>Percentage of road lengths under extremely high<br>susceptibility (%)<br><!-- End of picture text -->
efficiency of firms with larger market shares and find that non-competitive price behavior could explain the relationship. Other studies have focused on the effects of consolidation in the banking systems (for a review of some of the earlier studies on consolidation and its effect on bank lending terms, see Gilbert 1984; for a review of more recent studies on the effects of consolidation, including studies on the effects of consolidation on access to financing, see Berger, Demsetz, and Strahan 1999). While many of these papers are not formal structureperformance-conduct tests, their results have been interpreted as indicative of the degree of competition and/or its causes and consequences in the financial sector (Berger 1995).

A number of recent papers have investigated the effects of regulations and specific structural or other factors presumed to relate to the competitive environment on banking performance. In a broad survey of rules governing banking systems, Barth, Caprio and Levine (2001) document for 107 countries various regulatory restrictions in place in 1999 on commercial banks, including various entry and exit restrictions and practices. Using this data, Barth, Caprio and Levine (2003) document, among others, that tighter entry requirements are negatively linked with bank efficiency, leading to higher interest rate margins and overhead expenditures, while restricting foreign bank participation tends to increase bank fragility. These results are consistent with the view that tighter entry restrictions tend to limit competition and emphasize that it is not the actual level of foreign presence or bank concentration, but the contestability of a market that determines bank efficiency and stability.

In a cross-country study on banking structure, Claessens, DemirgUg9-Kunt and Huizinga (2001) investigate the role of foreign banks and show that entry by foreign banks makes domestic banking systems more efficient by reducing margins. Using bank level data for 77 countries, Demirgiiu-Kunt, Laeven, and Levine (2003) investigate the impact of bank 5