rafmacalaba/datause-encoder-data huggingface dataset:
(Atuhumuza, et al., 2019). A similar multi-country mystery shopper study in Ghana, Mexico, and Peru found that actors posed as potential customers rarely received the cheapest product offer or the correct costs. The reason may be that financial provider staff are often paid more when customers accept more expensive products; that creates an incentive for staff to withhold information or even to actively mislead (Giné and Mazer, 2022).

Developing and enforcing clearer regulatory guidelines about disclosure and pricing transparency could help build trust in the financial system—another factor, as discussed, that makes unbanked adults wary of opening an account. When presented in a clear and transparent way, account holders can understand product information and use it to make product decisions that fit their needs, as shown in lab experiments in Mexico and Peru (Giné, et al., 2017).

Financial literacy training is another way to improve financial knowledge and encourage more responsible financial behaviors (Kaiser, et al., 2022). A meta-analysis of 76 randomized experiments finds that traditional classroom-based financial education, has, on average, improved financial knowledge and financial behavior (Kaiser, et al., 2022). In addition, financial training structured as learning-by-doing during financial account onboarding can lead to regular use of accounts and help users become savvier financial customers (Breza, et al., 2020; Lee et al., 2021). The evidence also points to the importance of real-time information to increase trust and usage—for example, through functionality that allows users to check balances using a card, phone, or the internet (Bachas, et al., 2021).

Beyond the direct steps that financial institutions can take to improve transparency and embed clarity in product design, regulators also have an opportunity to expand and innovate, to identify bad actors and enforce the rules. As noted throughout this paper, digital financial services have already transformed how people move and manage their money in every region of the world. Although millions of people have been able to take advantage of these offerings, they also bring the potential for increased harm from aggressive marketing, poor dispute resolution, data or identity theft, fraud, and over-indebtedness. A recent study in Malawi, for example, found that consumers who took out digital loans offered via text message marketing campaigns were less likely to repay them (Brailovskaya, et al., 2021). In Mexico, faster origination of internet-based loans is associated with lower loan repayment (Burlando, et al., 2021).

20
Hence the financial crisis that has affected the comercial banking system since the end of 1982 is not surprising.la' But, this crisis is not the result of the increased competitiveness of the financial system. Rather, it is the incompleteness of the deregulation--particularly the restrictions on lenders asset powers--that has created the problems.

12/ See World Bank (1987) and (1983) for discussions of the financial stress in Colombia. In 1982 this stress lead to a financial crisis and the creation of Fund to Guarantee Financial Institutions. The former study suggests that since 1985, after our estimation period, the structural weaknesses of the financial system became pronounced, p. 72.
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Table 6 – Logistic Regression for the Propensity-Score

|Dependent variable:||||T|echnical Edu|cation Dumm|y||||
|---|---|---|---|---|---|---|---|---|---|---|
||TE|C-geral|<sup>TEC – a</sup><br>ed|<sup>fter general</sup><br>ucation|TEC – concu<br>general e|rrently with<br>ducation|Te<br>educa<br>terti|chnical<br>tion at the<br>arylevel1||FIC|
|||(1)||(2)|(|3)||(4)||(5)|
||coef|se|coef|se|coef|se|coef|se|coef|**se**|
|Experience|0.019|(0.004)***|0.011|(0.005)**|0.023|(0.004)***|0.058|(0.020)***|0.018|(0.002)***|
|Potential experience|0.078|(0.016)***|0.139|(0.025)***|0.033|(0.020)|0.580|(0.096)***|-0.188|(0.003)***|
|Self-employed / employers|-0.063|(0.038)*|-0.143|(0.055)***|0.036|(0.049)|-0.366|(0.213)*|0.053|(0.018)***|
|Agriculture job|1.099|(0.240)***|1.494|(0.333)***|0.857|(0.309)***|0.527|(1.014)|0.570|(0.104)***|
|Activity - extractive industries|1.277|(0.198)***|1.598|(0.259)***|0.951|(0.265)***|0.247|(1.087)|0.939|(0.104)***|
|Activity - manufacturing|0.731|(0.121)***|0.842|(0.173)***|0.623|(0.159)***|0.426|(0.378)|0.967|(0.040)***|
|Activity - construction|0.608|(0.143)***|0.653|(0.207)***|0.579|(0.185)***|0.644|(0.498)|0.519|(0.050)***|
|Activity - commerce and repair|0.100|(0.122)|0.122|(0.175)|0.043|(0.161)|-0.059|(0.403)|0.684|(0.040)***|
|Activity - lodging and food|0.289|(0.137)**|0.447|(0.195)**|0.118|(0.182)|-0.197|(0.685)|0.659|(0.045)***|
|Activity - transportation, warehousing and<br>communications|0.331|(0.131)**|0.551|(0.185)***|0.112|(0.174)|-0.360|(0.531)|0.761|(0.047)***|
|Activity - financial services|0.316|(0.141)**|0.101|(0.212)|0.368|(0.179)**|-0.495|(0.476)|0.847|(0.063)***|
|Activity - real estate activities and service<br>activities|0.625|(0.120)***|0.835|(0.172)***|0.416|(0.158)***|0.063|(0.379)|0.997|(0.040)***|
|Activity - public administration|0.380|(0.121)***|0.342|(0.176)*|0.360|(0.159)**|-0.790|(0.418)*|0.689|(0.044)***|
|Activity - education|0.292|(0.120)**|-0.136|(0.178)|0.441|(0.157)***|-0.835|(0.386)*|0.744|(0.043)***|
|Activity - health and social services|1.206|(0.119)***|1.770|(0.169)***|0.508|(0.162)***|-0.595|(0.441)|0.966|(0.046)***|
|Activity - others collective, social and personal<br>services|0.309|(0.130)**|0.389|(0.187)**|0.200|(0.171)|||1.351|(0.041)***|
|Activity - household activities|||||||||||
|Occupation - sciences and arts professional|-0.063|(0.059)|-0.256|(0.092)***|0.040|(0.072)|-0.100|(0.234)|-0.086|<br>(0.039)**|
|Occupation - technician with secondary level of<br>education|0.734|(0.053)***|0.939|(0.075)***|0.549|(0.067)***|0.245|(0.256)|0.360|(0.035)***|
|Occupation - administrative services|-0.203|(0.058)***|-0.316|(0.085)***|-0.111|(0.073)|-0.013|(0.291)|0.134|(0.035)***|
|Occupation - general services|-0.440|(0.069)***|-0.514|(0.098)***|-0.369|(0.090)***|0.388|(0.472)|0.155|(0.035)***|
overall fall in poverty across the 1958-1992 period.

Effective institutions for enforcement of contracts are another source of 1<sup>st</sup> advantage. Djankov and others (2002) examine court procedures to evict a tenant or to collect a bounced check in 109 countries. They find that common law countries exhibit greater procedural formalism which is associated with slower judicial proceedings, more corruption, less consistency, less honesty, less fairness in judicial decisions, and inferior access to justice. Johnson, McMillan and Woodruff (2002a) surveyed small manufacturing firms in Russia, Ukraine, Poland and Romania, and found that perceived effectiveness of courts in resolving commercial disputes affected the propensity of entrepreneurs to reinvest profits. Well functioning courts also encouraged entrepreneurs to form new trading relationships with customers and suppliers (Johnson, McMillan and Woodruff 2002b). Besley and Burgess (2003a) find that long backlogs in state high courts in India are associated with lower output in informal sectors where contract enforcement problems may be acute.

Where laws of contract are inadequate informal relationships such as business networks may substitute for courts in allowing deals to be made. Development of these networks is typically fostered by proximity and by frequency of interaction. This is a 2<sup>nd</sup> advantage. A locality with a tightly knit business sector develops this advantage, helping to explain why new economic activities tend to be clustered. For example, McMillan and Woodruff (1999) use the amount of trade credit extended by small manufacturing firms to suppliers and customers in Vietnam as a measure of business trust. They find that relationships of longer duration, visits to premises and being part of the same business network all enhanced the propensity to extend trade credit. They argue that relational contracting within business networks has enabled manufacturing to flourish in Vietnam even in the absence of formal institutions. Formal institutions to support finance and contracting, however, will be required as firms grow (McMillan and Woodruff 2002).

13
20 Nolte and McKee’s (2008) list of amenable causes. These are: malignant neoplasms of the colon and rectum (ICD10 C18-21); malignant neoplasm of the breast (females only) (C50); malignant neoplasm of the cervix uteri (females only) (C53); diabetes (E10-4); ischemic heart disease (ICD10-I20-5); cerebrovascular disease (I60-9); influenza and pneumonia (J10-28); maternal death (O00-99); and perinatal deaths other than stillbirths (P00-96).<sup>12</sup> Employment includes formal-sector workers (employees paid a wage or salary) as well as the self-employed. The variable is defined as employment as a percentage of the population aged 15-65. The formal-sector employment variable is defined as the number of people with a salaried or waged job, expressed as a fraction of the number of people in employment.

Descriptive statistics are shown in Table 2. Health spending per capita is higher in SHI country-year combinations than in tax-financed country-year combinations. SHI country-year combinations have higher PYLL for six of the nine amenable causes of death, and have lower rates of formal sector employment and employment. These are, of course, simple bivariate comparisons, and tell us nothing about the causal effect of SHI on these outcomes.

# _4.3 Covariates in the estimating equation_

As in WMS (2008b), the only covariate included is GDP per capita. There is some evidence that in central and eastern Europe and central Asia SHI schemes emerged first in countries with higher initial (i.e. pre-transition) per capita income levels, whilst tax-based funding prevailed in countries with lower initial per capita income (Preker _et al._ 2002).

> 12 Nolte and McKee (2008) argue that only half of ischemic heart disease deaths below the age of 74 are avoidable, and use an upper limit of 49 for diabetes. The OECD Health Data PYLL use an age limit of 70 for all ICD codes, so there is nothing that can be done about the latter. In both cases, we might expect that the choice of health system will matter less for these two causes of death than for other causes.
# **1. Introduction**

The efficient mobility of goods and labor is one of the most important features of a functioning economy. Higher transport costs reduce real income in trading regions by reducing the extent to which gains from trade can materialize. Donaldson (2010) estimates that increased railroad access raised real income by 16 percent in the average Indian district at the beginning of last century. The cost and time of commuting to work can also affect both labor market participation as well as wages (Gibbons and Machin, 2006). Therefore it is not surprising that both national governments and international organizations spend a large share of resources on transport infrastructure projects.<sup>1</sup> This paper provides novel evidence on the labor market effects of an intricate system of mobility restrictions: the system of checkpoints, roadblocks and other barriers installed by Israel inside the West Bank. Such a system is part of the broader ‘closure’ regime which was initially put in place by Israel after the first Palestinian uprising in 1987 (‘first Intifada’) as a security measure to control the movement of goods and persons across the borders and within the West Bank and Gaza. Eventually the system was dramatically expanded during the Palestinian uprising known as the ‘second Intifada’ in the first half of 2000s to become one of the most pervasive systems of restrictions to mobility imposed in modern conflicts.

Our analysis relies on a number of unique features, which ensure a relatively clean identification of the effects of restrictions on the labor market. First, check-points and other barriers to mobility inside the West Bank have been varying in number and location throughout the years; second, as we argue below, their placement has been related to Israeli security concerns and thus has been largely exogenous to local economic conditions and labor market conditions in particular. The inclusion of a measure of conflict intensity as a control in the analysis further strengthens the case for the exogeneity of the barriers. Third, systematic labor market data have been consistently collected throughout the last decade of the conflict in the West Bank (and Gaza) even during the most intense spurts of violence.<sup>2</sup> In addition, yearly data on the location of the physical barriers to movement within the West Bank throughout the 2000s have been collected by the Applied Research Institute of Jerusalem (ARIJ) and the UN Office for

> 1 For example well over 15 percent of the World’s Bank lending commitments in the 1995-2005 period was directed to transport infrastructure (World Bank, 2007c).

> 2 The Palestinian Bureau of Statistics started to collect the data immediately after its foundation at the end of 1995.

2
The findings of the current paper also speak to ongoing policy debates in education circles in both industrialized and less developed countries regarding the desirability of academic merit awards. Many scholarships in the United States were merit-based historically, but over time there was a move toward need-based awards. Recently, however, there has been a resurgence in merit scholarships: while more than three-quarters of all state-funded college scholarships in the United States are still based on financial need, merit funds have grown almost 50% in the past five years (College Board 2002).

A number of studies suggest university scholarships increase enrollment (for instance, Dynarski 2003), but few examine the incentive effects of merit scholarships, and those that do find mixed impacts. Binder et al. (2002) find that while scholarship eligibility in New Mexico increased student grades, the number of credit-hours students completed decreased – suggesting that students took fewer courses to keep their grades high. Similarly, the average SAT score for Georgia’s high school seniors rose almost 40 points after the HOPE college scholarship program was introduced (Cornwell et al. 2002), but it resulted in a 12% decrease in full course-load completion, 2% average reduction in completed college credits, and 22% increase in summer school enrollment (Cornwell et al 2003), presumably again to boost grades, thus undermining the key program objective of increased learning.

In the work most closely related to the current study, Angrist and Lavy (2003) find that cash awards raised test performance among 500 high school students in Israel. They examine a pilot scholarship program that randomly selected 20 program and 20 comparison schools. Program school students were able to earn cash for good performance on matriculation exams. Students offered the merit award had approximately 8.5 percentage points higher exam completion rates than comparison students, with the largest effects among the top quartile of students.

The program evaluated in Israel differs from the one evaluated in this paper in several important ways. First, due to political and logistical concerns, the program in Israel and its evaluation, which was meant to run for three years, were discontinued after the first year – making it impossible to estimate longer-term impacts, and impacts once the incentive was removed. Second, the sample in the current study is much larger, containing more than three times as many schools as Angrist and Lavy – 127 versus 3
I Perceive Therefore I Demand: The Formation of Inequality Perceptions and Demand for Redistribution Maurizio Bussolo Ada Ferrer-i-Carbonell The World Bank IAE‐CSIC, Barcelona GSE and IZA Anna Giolbas Ivan Torre The World Bank The World Bank JEL Classification: D31, D63, D72, D83, H23 Keywords: Perceptions, Inequality, Fairness, Redistribution.
# **Table 7:  De Facto Measures of Regulation**

||**Performan**<br>|**ce Difference**<br>**Quantile**<br>|**s Conditional**<br>**Regressions**<br>|**on Sector Du**<br>|**mmies**<br>|<br>|**OLS**<br>|
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|_Quantile_|**10**<br>coef/se|**50**<br>coef/se|**90**<br>coef/se|**95**<br>coef/se|**99**<br>coef/se|**Simple**<br>coef/se|**Weighted**<br>coef/se|
||**Depe**<br>|**ndent Varia**<br>|**ble: Market S**<br>|**hare(Output)**<br>||||
|Ben Ali firm (BA)|0.000**|0.006|0.152***|0.198***|0.633***|0.034***|0.047*|
||(0.000)|(0.005)|(0.008)|(0.010)|(0.068)|(0.013)|(0.024)|
|BA*Highly Regulated|0.005***|0.039***|0.756***|0.728***|0.244***|0.177***|0.376*|
|(de Facto)|(0.000)|(0.005)|(0.008)|(0.011)|(0.050)|(0.051)|(0.218)|
|Firm Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Weights|None|None|None|None|None|None|Output|
|Number of observations|49,446|49,446|49,446|49,446|49,446|49,446|49,446|
|R2|0.099|0.107|0.108|0.107|0.062|0.099|0.107|
||**D**|**ependent V**|**ariable: Profi**|**ts Z-Score**||||
|Ben Ali firm (BA)|-0.679***|-0.008|0.785***|2.088***|2.936***|-0.096|-4.822|
||(0.098)|(0.012)|(0.094)|(0.164)|(0.319)|(0.255)|(6.124)|
|BA*Highly Regulated|-0.123|0.654***|82.702***|150.241***|162.247***|6.535|38.016|
|(de Facto)|(0.100)|(0.015)|(0.106)|(0.222)|(0.613)|(5.684)|(25.970)|
|Firm Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Weights|None|None|None|None|None|None||Net Profits||
|Number of observations|43,128|43,128|43,128|43,128|43,128|43,128|41,824|
|R2|0.012|0.089|0.101|0.101|0.101|0.064|0.836|

Note: *,**,*** denote significance at the 10%, 5% and 1% significance level respectively. Standard errors are clustered by sector. The sample is confined to 2006-2010 and to firms which report using hired labor, producing more than 1600 USD worth of output and having labor productivity in excess of 400 USD, operating in sectors governed by the investment code in which at least one Ben Ali firm is contemporaneously active. Standard errors are clustered at the 5-digit sector level.

_Highly Regulated (de Facto)_ is dummy variable taking the value 1 if the 5 digit sector in which the firm is operating is subject to an authorization which is de facto difficult to obtain according to one of Tunisia’s leading lawfirms, and 0 otherwise.

34
# **Table 2. Summary of Debt and Deficit Limits**

Percent of GDP

||**De**<br>|**ficit**||
|---|---|---|---|
|**Debt**|**Overall**<br>|**Current**|**Primary**|
|Albania<br>Decline to 45|2ifgt> 5|0||
|Bosnia and Herzegovina||||
|Federation of Bosnia and Herzegovina<br>60||0||
|Republika Srpska<br>55|3|||
|Kosovo<br>40|2|||
|North Macedonia||||
|Montenegro<br>60|3<br>|0|0|
|Serbia<br>45<br>|1.9 + 0.7dt−1−0.4gt<br>|||
|Sources: See text.<br>|<br>|||
|Note:gt is the rate of real economic growth anddt−1is last ye<br>deficit, but donor-financed investment is excepted, so it is a cu<br>investment. The formula for Serbia’s rule is explained below.|<br>ar’s overall deficit. Kosov<br>rrent-deficit rule to the ex|o’s rule appl<br>tent that don|ies to the overall<br>ors finance|

**In Albania, the main fiscal rule is designed to reduce debt to 45 percent of GDP** . In 2008, the Law on the Management of Budgets, whose amendment requires a three-fifths majority in parliament, limited public debt to 60 percent of GDP (article 58 of law 9936 of 2008). This rule was, however, repealed in 2012 (IMF 2017). In 2016, the law was amended again, to require each budget, supplementary budget, and medium-term plan to provide for debt to decline each year until it falls below 45 percent of GDP, save in exceptional circumstances (articles 4/1 and 4/4 of law 57 of 2016). Debt is defined as the direct and guaranteed debt of general government (article 4/2). The government has also set itself the interim target of reducing debt to 60 percent of GDP by 2021 (IMF 2017). To help ensure that compliance with the rule is not undermined by optimism, the forecasts of GDP used by the government cannot exceed those published by the IMF in its _World Economic Outlook_ —an example of the use of external checks to foster compliance with rules.

**Albania’s debt rule is supplemented by several others** . If the forecast annual rate of real economic growth is more than 5 percent, the cash deficit of general government must be less than 2 percent of GDP (article 4/1). There is also a golden rule, namely one that requires current spending to be paid for by current revenue while allowing investment spending to be financed by borrowing (article 4/1(4)). The government must also keep 0.7 percent of budgeted expenditure in reserve to deal with unexpected events (article 4/1). And there are rules that limit the speed at which deficits can build up during an election year: for example, the actual deficit for the first quarter of the year cannot exceed 30 percent of the planned deficit for the whole year (article 4/3).

11
channel in rural India (based on data for 1998–99), but Das and Desai (2003) find no support in a sample of rural and urban women in India in 1993–94.

In all, major determinants of women’s labor force participation over the course of economic development are income, wages, and access to jobs deemed appropriate for women. Education levels shape female labor force participation partly through these channels. But women’s education is likely to reflect other, unobserved determinants as well. We analyze the role of education and other determinants in detail in our econometric analysis in Section III. First, Section II gives a descriptive analysis of the most important trends in the data.

# **II. Female Labor Force Participation in Urban India**

The descriptions and empirical analysis in this paper are based on the so-called thick waves of the NSS Employment and Unemployment Survey, in 1987–88, 1993–94, 1999–2000, 2004–-05, 2009–10, and 2011–12 (henceforth 1987, 1993, 1999, 2004, 2009, and 2011). This cross-sectional survey is the official source of nationally representative employment and earnings data used by the Government of India.

Before discussing labor market developments in urban India in detail, Table A1.1 in appendix 1 summarizes the trends in the female population and labor force for urban and rural India, for different age groups. Female population growth in the age group 15–64, and especially 25–54, exceeds total female population growth. Due to this demographic change, the female labor force has almost doubled in urban India and stayed constant in rural India, despite stagnating female labor force participation rates in urban India, and declining rates in rural India. In that sense, India is already benefitting from its demographic dividend related to a favorable age structure (although to a much lower extent than it would if more women were working). As still more than two-thirds of the Indian population lives in rural areas, the rural trend of declining participation rates dwarfs 8