rafmacalaba/data-use-mentions-extended huggingface dataset:
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# **1. Introduction**

The global financial crisis has highlighted the crucial role that incentives play in finance. Misalignments between public interest and private incentives can become a problem in any economic sector. But they are particularly important in the financial sector, not only because of that sector’s critical role in development, but also because any incentive misalignments are compounded by other issues specific to finance, in particular asymmetric information. If economic agents in the financial system face a bad structure of incentives, this tends to lead to a build-up of systemic risk. These incentives include bank managers’ incentives to boost short-term profits and create banks that are “too big to fail”, credit rating agencies’ incentives to keep issuing high ratings for subprime assets, regulators’ incentives to forebear and withhold information from other regulators in stressful times, and policymakers’ incentives to keep bailing out weak financial institutions rather than allowing timely exit from the financial sector. These points are well documented in the existing literature. For example, Caprio, Demirgüç-Kunt, and Kane (2008) examine incentive conflicts as an explanation of how securitization went wrong and why credit ratings proved so inaccurate, and Levine (2009) discusses the role of incentive failures in regulatory authorities.

The crisis has also re-opened important policy debates on financial regulation. Among other things, at the global level, the Basel Committee has started working on new capital and liquidity requirements (e.g., Basel Committee 2010), and the Financial Stability Board (FSB) has developed an impressive agenda of reform (e.g., FSB 2010). At the country level, various national authorities have started reviewing their bank resolution regimes, macroprudential policy, consumer protection, and other aspects of their regulatory framework. (For a comprehensive review, see the World Bank’s Bank Regulation and Supervision Survey, presented in Čihák, Demirgüç-Kunt, Martínez Pería, and MohseniCheraghlou 2012.) How far have these reforms gone in addressing the underlying incentive failures in the financial sector
<!-- Start of picture text --> Ukrainian services sector in 1991 - 2009<br>fo)wo 49.6<br>=<br>as 5<br>oO<br>@ :<br>i=)<br>aig OD<br>®:<br>2<br>o<br>a= “ 18.8<br>‘oO ao<br>io<br>2<br>19g0 1995 2000 2005 2010<br>Year<br>—*— Services sector —*— Financial and business services<br>Saurce: Authors’ calculation. Data from the National Accounts Main Aggregates Database provided by the UM.<br><!-- End of picture text -->
consider only those variables capturing financial depth, such as the credit to GDP ratio or stock market capitalization, to assess the degree of financial development. Financial indicators assessing the degree of financial access, financial stability, or the efficiency of the financial industry have largely been ignored in cross-country studies due to data limitations. The newly developed Global Financial Development Database (GFDD) represents a significant improvement in this respect and provides a comprehensive set of financial indicators that reflect various functions and characteristics of the financial sector. In addition to financial depth, the GFDD provides measures of the efficiency and stability of and access to financial markets. Although data availability remains somewhat limited, we extend the existing literature by including these additional dimensions of the financial sector in our regression analysis to more completely evaluate the effect of finance on growth. Specifically, the indicators we use represent the depth, stability, and efficiency of the banking sector and stock markets as defined by Cihak et al. (2013).<sup>1</sup> In addition to the GFDD, we employ the widely used data set on the determinants of long-term growth developed by Fernandez et al. (2001), which encompasses over 40 explanatory variables capturing various economic, political, geographical, and institutional indicators.

While it is commonly assumed that causality goes from financial development to economic growth, some scholars argue that a growing financial sector merely follows the increasing needs of the real economy or may be determined simultaneously with growth due to other factors. The quantitative survey of the finance and growth literature by Valickova et al. (2015), for example,

> 1. We did not include financial access indicators because of data unavailability. In our sample, the data on the proxy variable recommended for financial access by Cihak et al. (2013), bank accounts per 1,000 adults, are missing for 36 out of 60 countries. Including financial access in the analysis would therefore severely limit our cross-section of countries resulting in the non-negligible loss in the degrees of freedom
are not strongly associated across categories, and that associations remain constant over time. To do this, we calculate the Kendall rank correlation coefficients of the deprivation scores across categories for the three years, as illustrated in table 5. We choose Kendall correlation coefficients since we are concerned about changes in the ordinal association between the deprivation scores and the reference variable, which turn into changes in the association between deprivation score across categories. The results are encouraging: the correlation coefficients across categories are small and remain stable over time.

The third issue—the inability to use discrete random variables as reference variables with repeated cross-sections—requires significant attention. We need to address whether the continuous variable, income, is a proper reference variable for the methodology; if rank preservation is reasonable at the sample level; and, if not, we need to stratify the sample in order to make rank preservation more plausible. The observational data is uninformative in these matters, as it is not longitudinal data. A simulation exercise is carried out to address this issue, instead.

We build a panel dataset on the basis of the 2010 observations, adding an additional simulated period of data. For this added period, we simulate changes in the deprivations of individuals, in order to replicate the actual changes that took place in the uncensored headcount ratios by dimension between 2010 and 2012. We also change household income to reflect the income growth over the period. To do this, we calculate the variation in mean income within income deciles between the two years from the observational data, and add a normal random variable centered in this mean change to each individual within the income decile. The variance of this increase is chosen to match the change in within-decile variances that occurred between both years. We change deprivations in all the categories, initially one category at a time, and then all at once. We then assess different choices in the ASN methodology. We address the performance of these choices by comparing the results of the decomposition to those that would have been obtained
each intervention to poverty and inequality? (iii) Who bears the burden of taxes and receives the benefits? (iv) What is the likely distributional impact of new policies, specifically the changes to personal income tax and the introduction of the GST? The paper assesses the individual and combined effects of tax and spending policies. The results are also benchmarked against other countries where similar analysis has been conducted.

The analysis relies on the 2017 Bhutan Living Standard Survey collected by the National Statistics Bureau of Bhutan, in conjunction with macroeconomic and fiscal data from national accounts. An input-output matrix is used to capture cascading effects of indirect taxes (such as sales taxes and excise taxes) on intermediate goods, which can be passed through to the prices of final consumptions. Given the available information and data, the analysis incorporates the fiscal components that capture around 45% percent of fiscal revenues, including direct taxes and indirect taxes. On the spending side, the analysis primarily accounts for spending on health and education which accounts for 29% percent of total government spending.

Our findings show that the combined impact of government taxes and social spending in Bhutan is to reduce inequality and slightly increase poverty as of 2017 (before recent major fiscal reforms). The increase in poverty is mainly due to the burden of indirect taxes and social contributions that are not offset by other direct transfers. Disaggregating the results by fiscal intervention shows that households in the bottom 80 percent are net receivers of fiscal interventions, with fiscal benefits primarily occurring through education and health benefits which are both progressive. In fact, most households do not pay much into the system as of 2017, as personal income taxes have a high exemption threshold and sales taxes only apply to a selected number of goods that are more likely to be consumed by richer households. Due to the lack of direct transfers, the net cash position, which is the sum of all tax payments and cash benefits, is negative for poor households, though the magnitude is very small. Simulations suggest that recent
growth and light-predicted growth, as the dependent variables in columns 3 and 4, respectively. If our logic about the political cycle effects is correct, the previous results should be mostly driven by the reported growth. The light-predicted growth rates, being immune to manipulation, however, should be much less affected. The latter specification can thus be viewed as a placebo test. This is indeed corroborated by the estimation results. We find a significant inverse U-shaped pattern for the reported growth, whereas the coefficients on the linear and quadratic terms of _YearsInOffice_ are almost 0 for the light-predicted growth. Similarly, using lights growth as the dependent variable in column 5 also yields no political cycle effects. In addition, the political cycle effect is robust to controlling for the growth target. As expected, the higher the target is, the more a city overreports (column 8). Finally, in columns 9 and 10, we estimate a specification controlling for one-year lagged over-reporting by OLS and the system GMM, respectively.<sup>39</sup> The inverse U-shaped pattern continues to survive, the vertex of which stabilizes around 5.

# **5.2.4 Subsamples and alternative levels of clustering**

First, some city PSs stay in office for a short time. In column 1 of Table A.3, we thus drop city PSs whose total tenures are shorter than a full year. Column 2 drops observations for PSs with total tenures less than 2 years. In column 3, we drop deputy-provincial-level cities as these cities are regional centers, and evaluations on their secretaries might not be primarily based on economic performances.<sup>40</sup> Additionally, the period of our baseline results (2001—2013) overlaps with the tenure of three top leaders, among which Hu Jintao’s tenure is the longest (2003—2012). Column 4 thus restricts the sample to Hu’s tenure. In column 5, when matching the city leader data with annual data on night lights
people in selected case studies**

We assume there is no exposure bias for other hazards, due to the large scale of these events. This is a simplification considering for instance that local soil characteristics have an impact on earthquake vulnerability.

# 3.4. Macroeconomic characteristics

First, we translate the population exposure (separated into poor and non‐poor people) into asset exposures for the two categories. To do so, we assume that the capital used by an individual to generate his or her labor income is the ratio of this labor income by the average capital productivity. Since we have the labor income of the poor and non‐poor, we can translate those into the exposed non‐diversified capital of the poor and non‐poor. We calculate the average capital productivity as output‐side GDP divided by total reproducible capital within a country, both variables from Penn World Tables.

Figure 5 shows the average productivity of capital against income level. In spite of a large variance, high income countries tend to have more capital and as a result their capital is on average less productive. As shown in Hallegatte and Vogt‐Schilb (2016), countries with a high average productivity of capital have in general less to spend (in proportion of their income) in reconstruction, and this increases their resilience. This effect mitigates the effect of all other drivers, which tend to make richer countries more resilient than poorer ones.

> 8 <u>http://www.dhsprogram.com/.</u>

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To better understand whether, when, and how the World Bank effectuates development policy change, we leverage new data on the organization’s lending and non-lending operations as well as a unique survey of 1,328 public officials that measures the policy influence of the World Bank in 24 issue areas and 113 developing countries. We hypothesize that the World Bank’s non-lending instruments are more effective than its lending instruments at influencing the policy priorities of client countries. Our empirical results not only provide support for this hypothesis, but also demonstrate that World Bank AAA products influence the design and implementation of government policy.

# **Theory and Hypotheses**

## _Role of Incentives_

The World Bank is a lending institution that depends on governments who are willing to borrow funds and repay their debts. These borrowers have access to two different types of loans: investment loans for stand-alone projects and development policy loans (DPLs) that do not support stand-alone projects but instead provide budget support in exchange for policy reform.<sup>3</sup> The conventional wisdom is that if the World Bank has any influence on the nature, speed, sequencing, and extent of policy reform in developing countries, it is primarily through its DPL program (Smets and Knack 2016, 2018; Giordano and Pagano 2017). A key feature of this program is the fact that financial disbursements are linked to the adoption of specific policy conditions. The World Bank refers to these conditions as “prior actions” (PAs), and its ability to withhold disbursements—or threaten to withhold disbursements—from borrower governments is a major source of bargaining power.

> rectives.” Similarly, in a review of health care and pension reforms undertaken in Latin America during the 1980s and 1990s, Weyland (2006: 10) finds that “the IFIs most powerful instrument of coercion, loan conditionality, has proven to be a blunt weapon in the enactment of complex institutional reforms. Reforms of social security and health systems involve a wide range of actors. This
Although equal rights and opportunities for men and women are now guaranteed by the national constitution and laws, women are underrepresented or have secondary roles in many fields including science, technology, engineering, and mathematics (STEM). The female labor force participation rate in Uzbekistan has remained consistently much lower than the rate among men, by as much as 28 percentage points according to the Listening to Citizens of Uzbekistan survey in 2018. These trends are partly the result of gender related stereotypes, social norms, and tradition-bound notions of family and motherhood that are widespread in Uzbekistan. If deciding to have a career, women are commonly expected to balance family life and work in ways that are not expected of men. The 2016 round of the Life in Transition survey found that 80 percent of the population supports a traditional family model of marriage in which “the man works outside the home and earns money while the woman takes care of children and the house” (EBRD, 2016; FAO, 2019). According to the 2018 Asian Development Bank Country Gender assessment (ADB, 2018), traditional gender norms, though usually not expressed in formal laws or policies, constrain the roles of women and men in the family and society, define implicit and explicit rules, and assign different jobs, values, responsibilities, and obligations to men and women (ADB, 2018). These largely qualitative assessments suggest that professional choices for women and men are significantly affected by gender stereotypes related to these social roles and expected feminine or masculine behavior, which in turn influence young people’s opportunities in education and in the selection of fields for future career development.

Providing equal opportunities for both men and women to be employed in nontraditional male or female occupations can lead to better job matching, a more productive labor force, and higher incomes. A growing literature attests to occupational integration on average helping both sexes contribute their human capital to enhancing the productivity of firms. Since 1960, occupational integration in the United States was responsible for driving 15 percent to
Table A.1: Under-5 mortality rates by time-period; pooling firstborn-girl and firstborn-boy families

||Male|Female|EFM|
|---|---|---|---|
||(1)|(2)|(3)=(2)-(1)|
|Pre-ultrasound: 1973-1984|11.72|13.09|1.37|
|N|35,021|32,428||
|Post-ultrasound: 1985-1994|9.53|10.44|0.91|
|N|84,207|78,018||
|Post-ultrasound: 1995-2005|7.41|8.01|0.60|
|N|51,594|46,804||

NOTES: This table reports the percentage of second- and higher-order children, by child’s gender, who suffered from mortality within 5 years of birth, over the three time-periods in our sample. Column (3) reports the difference between the mortality numbers in columns (1) and (2). Data: NFHS.

Table A.2: Age distribution of missing girls in 2000

||No. miss|ing (in 000s)|% of all|missing women|% of und|er-5 missing girls|
|---|---|---|---|---|---|---|
|Age group|India|China|India|China|India|China|
||(1)|(2)|(3)|(4)|(5)|(6)|
|At birth|184|644|11%|37%|37%|83%|
|0-1|146|109|9%|6%|30%|14%|
|1-4|164|23|10%|1%|33%|3%|
|_<
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# Figure 4: Food-related government policies

## (A) Interventions in agricultural markets

- (B) Policy interventions during the 2007-08 food price spike

<!-- Start of picture text --> Percent Reference price Percent<br>1.05 Nominal rate of protection (RHS) 20 Percent of respondents<br>60<br>1.00<br>16<br>40<br>0.95<br>12<br>0.90 20<br>8<br>0.85 0<br>4<br>0.80<br>0.75 0<br>(C) Insulation and correction coefficients (D) Increase in world prices, 2010-11<br>Coefficient Insulation Correction Percent With active policy<br>0.0 50 Without active policy<br>-0.2 40<br>-0.4<br>30<br>-0.6<br>-0.8 20<br>-1.0<br>10<br>0<br>Rice Wheat Maize<br>2011 2012<br>Sugar<br>None<br>taxes Price stocks<br>subsidies<br>2005 2006 2007 2008 2009 2010 2013 2014 2015<br>Reduce foodgrain controls/consumer Increase foodgrain Export restrictions<br>Pork Rice Beef Poultry Wheat Maize Soybean<br><!-- End of picture text --> Sources: Ag-Incentives Database, Ivanic and Martin (2014b), World Bank. A. Nominal Rate of Protection (NRP) is computed as the price difference between the farm gate price received by producers and an undistorted reference price at the farm gate level. The reference price at the farm gate level is defined as the net price of the product when it leaves the farm, after marketing costs have been subtracted. The undistorted farm gate price is defined as the price prevailing in competitive world markets.
public firms are only a small portion of economically important firms in many developing countries. As a result, there is likely to be selection bias, particularly in the 1990s. To some extent, these problems with data have recently been mitigated by large scale multi-country firm surveys conducted by the World Bank. In particular, the World Bank‟s Enterprise Surveys program takes a stratified random sample of firm-level data from over 120,000 establishments in 125 countries. The surveys are quite exhaustive, providing information about firm financials, the obstacles they face, the level of competition and their interactions with the government and regulatory authorities. They have been used in many applications in finance, accounting and management research.

A second issue pertains to the measurement of the variables used in development finance. Often, the questions of interest hinge on how differences in institutions‟ across countries affect financing. Countries differ across many institutional dimensions, and it is often a priori unclear how to measure the relevant characteristics. Thus, for example, characteristics like “economic freedom,” “security from arbitrary government seizures of property,” and “the impartiality of the court system” are likely to have an important bearing of firm financing and often cannot be omitted from analysis. However, the conceptual and practical difficulties of measuring such characteristics often result in the use of indices created from survey data or drawn from appointed panels of experts. Some of these variables are likely to be subject to measurement errors. A somewhat less recognized problem is that these indices are then entered into regression equations in a linear manner, usually with little justification of why the dependent variable in question should vary linearly with an arbitrary index over its whole range. While this approach can be justified as reducing the risk of over-fitting in situation where theory does not offer much guidance on the functional form, it may also lead to the incorrect conclusion that no relation exists when in fact it does.<sup>12</sup>

# **4. Institutions and Access to Finance in
Policy Research Working Paper 5858

# **Abstract**

Do public sector employees earn less than their counterparts in the private sector? This paper addresses this question in the case of Peru, a country where civil service reform is being debated yet the only available empirical studies on wage differentials date back to the late 1980s. Using data from the 2009 national household survey, the authors perform a multiple step analysis. First, they estimate a single equation with a public sector dummy, which is found to be statistically significant and _positive_ when only monetary wages are taken into account. However, when in-kind payments and bonuses are included to measure compensation, the analysis finds a private sector premium. Second, they estimate for public and formal private employees two distinct wage functions, including the inverse Mills ratio. This takes into account the selection bias resulting from workers self-selecting into the public or private sector. Third, these results are used to decompose wage differentials using the standard Oaxaca-Blinder approach. The results show that the compensation differentials are not significant except for the sub-sample of employees that achieved a postgraduate degree.

This paper is a product of the Economic Policy Unit, Latin America and the Caribbean Region. 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 acoppola@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