rafmacalaba/datause-encoder-data huggingface dataset:
_The Handbook of Organizational Economics_ , chapter 27, pages 1109–1147. Princeton University Press, Princeton, 2012.

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- Daniel Kaufmann and Shang-Jin Wei. Does "grease money" speed up the wheels of commerce? Working Paper 7093, National Bureau of Economic Research, 1999.

- Caroline Freund, Mary Hallward-Driemeier, and Bob Rijkers. Deals and delays: Firm-level evidence on corruption and policy implementation times. _The World Bank Economic Review_ , 30(2):354–382, 2016.

- Cyril Chalendard, Gaël Raballand, and Antsa Rakotoarisoa. The use of detailed statistical data in customs reforms: The case of madagascar. _Development Policy Review_ , 37(4):546–563, 2019.

- Cyril Romain Chalendard, Alice Duhaut, Ana M Fernandes, Aaditya Mattoo, Gael JRF Raballand, and Bob Rijkers. Does better information curb customs fraud? World Bank Policy Research Working Paper 9254, 2020.

- Cristina P Sison and Joseph Glaz. Simultaneous confidence intervals and sample size determination for multinomial proportions. _Journal of the American Statistical Association_ , 90(429):366–369, 1995.

- Itzhak Krinsky and A Leslie Robb. On approximating the statistical properties of elasticities. _The Review of Economics and Statistics_ , pages 715–719, 1986.

- Christian Broda and David E Weinstein. Globalization and the gains from variety. _The Quarterly journal of economics_ , 121(2):541–585, 2006.

- Lionel Fontagné, Houssein Guimbard, and Gianluca Orefice. Product-level trade elasticitiess. _Journal of International Economics_ , 137, 2022.

- Julien Martin, Isabelle Mejean, and Mathieu Parenti. Relationship stickiness and economic uncertainty. CEPR Discussion Paper DP15609, 2020.

- James E Rauch. Networks versus markets in international trade. _Journal of International Economics_ , 48(1):7–35, 1999.

36
||(1)<br>|(2)<br>After on<br>|(3)<br>e month<br>Other<br>|(4)<br>|(5)<br>|(6)<br>Over whole se<br>|(7)<br>ason|
|---|---|---|---|---|---|---|---|
||Total<br>spending|Spending at<br>input fair|spending<br>on inputs|Fertilizer<br>per acre|Fertilizer<br>per acre|Land<br>cultivated|Yield|
|Insurance|0.0003|0.0003|0.0004|0.0002|0.0002|-0.0001|0.0001|
|amount (FCFA)|[0.0001]**|[0.0002]|[0.0003]|[0.0001]*|[0.0001]*|[0.0002]|[0.0000]*|
|Agricultural|0.1230|0.4313|0.4877|0.2881|0.0444|0.3922|0.1209|
|savings|[0.3051]|[0.2618]*|[0.4343]|[0.1721]*|[0.1861]|[0.8965]|[0.0634]*|
|Low|0.0656|-0.0941|-0.0024|-0.6254|-0.2068|-0.8998|-0.0206|
|commitment||||||||
|savings|[0.4559]|[0.2505]|[0.4225]|[0.2664]**|[0.2445]|[0.7210]|[0.0617]|
||0.5940|0.9633|-0.0668|0.1705|0.7489|-0.2759|-0.0109|
|Low commit*|||||[0.2601]**|||
|Burkina Faso|[0.5417]|[0.5455]*|[0.5311]|[0.2995]|*|[0.9020]|[0.0749]|
|Burkina Faso|-1.1464|3.4903<br>[0.8192]**|-1.7189<br>[0.6392]**|0.9038|0.4904|-0.9853|-0.0291|
||[0.4934]**|*|*|[0.3758]**|[0.3804]|[0.9023]|[0.0670]|
|Male|1.0251|-0.2028|1.6748<br>[0.6044]**|0.8204|0.4798|1.7119|0.0990|
||[0.4104]**|[0.8331]|*|[0.3271]**|[0.3525]|[0.8374]**|[0.0765]|
|Constant|9.6026|0.6325|7.6769|1.4609|3.2106|6.6218|-0.1671|
||[0.4796]**||[0.5316]**|[0.3128]**|[0.3353]**|[0.7969]**||
||*|[0.5999]|*|*|*|*|[0.0650]**|
|Observations|804|804|804|780|781|787|804|
|R-squared|0.0529|0.2187|0.0796|0.0752|0.0448|0.0234|0.0088|

Notes: Robust standard errors in brackets *** p<0.01, ** p<0.05, * p<0.1.

The ITT and LATE estimates for the savings treatments are presented in tables 8, 9 and 10. The regressions in these tables include all that were in the savings treatments, but not those in the insurance treatment. As such they compare the effectiveness of different types of savings treatments to each other. The results indicate that the type of savings products, more than the amount of savings, affected the amount invested in agricultural inputs. Table 8 indicates that those that were in the emergency savings treatment had no different input use than those in the agricultural savings treatments. In Burkina Faso the envelope resulted in considerably higher spending on inputs during the fair and as a result higher input use. We are not quite clear why this treatment resulted in higher levels of spending during the fair. Less was saved in this treatment in Burkina Faso. As discussed above, there seemed to be a perception among participants in this treatment that any money in the envelope was not truly theirs, and behavior was consistent with this belief. If this was the case, then it could be that on the day of the fair when they realized the money in the envelope was indeed theirs it encouraged higher spending in the fair. There was no final impact on yields for those in this treatment.

**Table 8: Impact of Savings (ITT) on Agricultural Investment**

|(1)<br>(2)<br>(3)|(4)|(5)|(6)<br>(7)|
|---|---|---|---|
|After one month|||Over whole season|

21
term that is unfortunately hard to define in an operationally useful way for policy decisions.

The recognition that transport is important for peoples’ lives, especially among the poor, is not sufficient to justify subsidies specific to the transport sector. The main reason is that the same argument could be made for most other goods and services on which peoples’ lives depend upon, including water, electricity, telecommunications, food, etc. How are we to judge whether scarce funds should be used to improve individuals’ social inclusion through transport subsidies rather than by subsiding other services and merit goods? Or, why not let people decide for themselves by directly increasing their income instead of subsidizing services?

The poor are not just ‘transport poor’ or ‘fuel poor’ (to use the UK terminology)<sup>8</sup> , they are poor in an encompassing way. All basic needs, such as education, water, food, health and transport have a merit good component which justifies a social preoccupation with their affordability by the poor. Due to this multidimensional characteristic of poverty, a monetary transfer may be the best way to help the poor, rather than having a plethora of sectoral subsidies which are very hard to target correctly. Chances are that policies that operate through the welfare system —whose main objective is to identify and help the poor in a country— will have better distributive properties than sectoral subsidies. In principle then, equity and distributional concerns should perhaps be addressed by the general welfare system of a country and through the use of direct monetary transfers. This way the household decides how to spend this income among the different goods and services, based on their needs and constraints.

As a concrete example, in Chile, rising transport costs due to the increase in the international price of oil during the years 2004 through 2006 were neutralized through direct compensatory payments benefiting 40% of the population rather than through subsidizing transport fares or inputs. This example is discussed in more detail in Box 1.

That a monetary transfer may be better for households compared to a price subsidy can be shown by a very simple microeconomic argument reproduced in Appendix 1. The intuition is that with a transfer a household always has the option to use the additional resources to pay for their travel costs but it can also use these funds for other expenditures. Thus, it has an added degree of freedom which helps to raise welfare. An illustration of this point is given by the fact that in Brazil, many workers who receive transport vouchers from employers (‘vale transporte’) sell them in the secondary market instead of using them for travel.<sup>9</sup> These workers would be better off by an equivalent direct monetary transfer from employees rather than having to sell the vouchers with a discount in the secondary market.

> 8 See DETR (2000) for the use of the first term and Waddams-Price, Bennett and Cooke (2002) for the second.

> 9 This subsidy scheme is described in more detail in Box 3.

6
# 1. Introduction

Labor force surveys are a useful data source to understand employment dynamics in both developing and developed countries. These surveys provide vast information on the labor market status at higher frequency levels than living conditions surveys. In some cases, they are the only source of information to describe and examine the structure of the labor markets. In the Latin American and the Caribbean region, countries like Bolivia, Costa Rica, Ecuador, Jamaica, Mexico, Peru, and Uruguay collect their labor force surveys quarterly as opposed to a yearly basis, which is the case of most household and living standard surveys.

One of the key features of these labor surveys is that they provide information on the wages and salaries of workers. This allows to estimate job market trends and obtain inequality measures of labor income among workers. However, the full income distribution in many countries cannot be retrieved because labor income is reported in brackets. Because of this, the estimation of inequality or poverty measures, as well as regression type analysis, is difficult. This is the case of the labor force survey for all countries in the Organization of Eastern Caribbean States (OECS).

This is not unique to the Caribbean region. Countries like Colombia, Germany, Australia, New Zealand, Bosnia and Herzegovina, North Macedonia and Serbia, among others, have similar data collection protocols for their micro census (Walter and Weimer 2018). In the U.S, the current population survey (CPS) collects detailed family income only once a year, in the March supplement, but collects family income in brackets on a monthly basis.

One argument in favor of using interval-censored questions to collect information on income is the higher response rate compared to questions asking to report exact amounts (Wang et al., 2013). This happens because income information is considered “sensitive”, and people are reluctant to report actual earnings, and may choose not to respond to those questions at all (Moore et al., 2000; Hagenaars and Vos, 1988). Field tests conducted in the past have shown that asking follow-up income questions in a series of unfolding brackets achieves superior results in terms of response rates for income amounts, as was the case of the National Health Interview Survey (NHIS) and the Behavioral Risk Factor Surveillance System Survey (BRFSS), both administered by the Center for Disease Control and Prevention of the United States (Angelov and Ekstrom 2018, Yan et al. 2018).
# **Putting Your Money Where Your Mouth Is:**

# **Geographic Targeting of World Bank Projects to the Bottom 40 Percent**

Hannes Öhler,<sup>1</sup> Mario Negre,<sup>1,2</sup> Lodewijk Smets,<sup>3,4</sup> Renzo Massari,<sup>2</sup> and Željko Bogetić<sup>3</sup> 1 German Development Institute 2 World Bank 3 Independent Evaluation Group, World Bank 4 LICOS Centre for Institutions and Economic Performance, KU Leuven

**Keywords:** Inequality, Poverty, Distribution, Aid Allocation, Geographic Targeting, Shared Prosperity, World Bank

**JEL codes:** F35, F53, O10, O19, O20, O29
# _3.2 Quality adjustment of higher education attainment_

Quality adjustment of higher education should be done primarily by measuring the quality of outputs, such as the skill proficiency of university graduates (just as harmonized test score results are used to measure the quality of learning among primary and high school students). However, measures of adult skill proficiency (from the <mark>Programme for the International Assessment of Adult Competencies [P</mark> IAAC] or <mark>Skills Towards Employability and Productivity [STEP]</mark> surveys, for example) are available only for a limited set of countries.<sup>1</sup> The ECA-HCI therefore measures the quality of inputs—such as the quality of universities—which are more widely available. However, measures of the quality of universities and adult skill proficiency correlate very well for countries for which both measures are available (see appendix A for a comparison of the input-based quality adjustment presented here and an alternative skill-based quality adjustment).

The quality of higher education is calculated under the assumption that a high-quality degree is a degree that makes its holders more productive in the labor market—the working assumption of the broad literature on the effects of college quality on earnings in the United States. Standard ordinary least squares (OLS) estimates of the impact of college quality (usually measured by the average SAT score of admitted students) on earnings show that there is a positive and significant association between them. Given the existence of a selection process into college—high school students decide which colleges to apply to—these estimates may suffer from a substantial selection bias.

To address this issue, the literature has followed two approaches. The first is a “selection-on-observables” approach, in which the decision to apply to a given type of college is modeled based on observable variables such as net college costs or high school grade point average (Brewer, Eide, and Ehrenberg 1999; Andrews, Li, and Lovenheim 2016). This approach has confirmed the existence of a positive and significant return of the quality of college education on earnings.

The second is a “selection-on-unobservables” approach, in which, rather than modeling college choice, the researcher compares the outcomes of students who were admitted to the same set of colleges but chose to go to different ones (Dale and Krueger 2002, 2014). This approach is a “self-revelation” method, because it assumes that the set of students admitted to a given college share the same “unobservable” characteristics. This method shows that, for the average student, there is no significant effect of college quality on earnings. The effect is significant for minority students and those from poor backgrounds, however.

> 1 For a comparison of output quality in tertiary education, see Loyalka and others (2019), who compare the computer science skills of computer science undergraduates in their last year in China, India, the Russian Federation, and the United States.

6
POLICY RESEARCH WORKING PAPER **2399**

# **Summary findings**

> Kaminsky, Lyons, and Schmukler address the trading

> strategies of mutual funds in emerging markets. The data set they develop permits analyses of these strategies at

> the level of individual portfolios.

**A** methodologically novel feature of their analysis: they disentangle the behavior of fund managers from that of investors.

For both managers and investors, they strongly reject the null hypothesis of no momentum trading. Funds' momentum trading is positive: they systematically buy winners and sell losers.

Contemporaneous momentum trading (buying current winners and selling current losers) is stronger during crises, and stronger for fund investors than for fund managers.

> Lagged momentum trading (buying past winners and

> selling past losers) is stronger during noncrises, and stronger for fund managers.

Investors also engage in contagion trading-selling assets from one country when asset prices fall in another. These findings are based on data about mutual funds that represent only **10** percent of the market capitalization in the countries considered. Were it a larger share of the market, finding counterparties for their trades (the investors who buy when they sell and sell when they buy) would be difficult-and the premise that funds respond to contemporaneous returns rather than causing them would become tenuous.

> This paper-a product of Macroeconomics and Growth, Development Research Group-is part of a larger effort in the

> group to understand capital flows to developing countries. The study was funded **by** the Bank's Research Support Budget under the research project "Mutual Fund Investment in Developing Countries." Copies of the paper are available free from the World Bank, **1818** H Street NW, Washington, **DC** 20433. Please contact Emily Khine, room **MC3-347,** telephone 202- **473-7471,** fax **202-522-3518,** email address kkhine@worldbank.org. Policy Research Working Papers are also posted on the Web at www.worldbank.org/research/workingpapers. The authors may be contacted at graciela@gwu.edu,

> lyons@haas.berkeley.edu, or sschmukler@worldbank.org. July 2000. **(36** pages)

> _devlomet ssus.Anobectveofth seie i t ge teininsequl, hen ithe_ buy)ntawould _bre difficanul-anlsd Therms_

_paprscaryth o te nme utorsan soud e itd ccrdngy.Th nduing ithreaios_ woud _comtncuos.esd nt_ crises _and_ s _tirey fo of inesthors. Than for fndncsaiyrpeetteve fteWol ak t xctv ietr,oh_ gru _o undrestand_ cpietalfost.eeoigcutis h td asfne yteBn' eerhSpotBde Poherhe _Research Policy_ D inaion Cenvters wh b w _e s an_

> T ~ ~ ~~~Prdcdb WoknDaerSr he Policy Res **e** arch isseminatestefnigifwr npogrs toenourag h xhneo dabu
**Table 4B: Matching results on Access to Credit** <u>[effect of participation in productive association on access to loans]</u>

||_N treated on_<br>_support_|_N control on_<br>_support_|_ATET_|_Boostrap_<br>_s.e._|_z–_<br>_Statistic_|_95% Conf._|_Interval_|
|---|---|---|---|---|---|---|---|
|**Probability of receiving credit**<br>||||||||
|Kernel Matching (epanechnikov)|499|6,132|8.0%|0.023|3.39|3.4%|12.5%|
|Kernel Matching (Gaussian)|499|6,132|6.8%|0.035|1.89|-0.2%|13.9%|
|Radius Matching|500|6,132|9.5%|0.017|5.39|6%|13%|
|Radius Matching, caliper(0.01)|498|6,130|7.3%|0.024|2.98|2.5%|12%|
|Radius Matching, caliper(0.05)<br>|499|6,132|8.0%|0.020|3.96|4%|12%|
|Calibrated cofounder<sup>++</sup>|||||-|-|-|
|Pr U=1 if T=1 is 0.30; if T=0 is 0.10|500|6132|7.6%|0.003||||
|**Less interest rate paid (monthly) in points**||||||||
|Kernel Matching (epanechnikov)|40|175|-1.48|0.698|-2.12|-2.9|-0.1|
|Kernel Matching (Gaussian)|40|175|-0.64|1.580|-0.41|-3.8|2.5|
|Radius Matching|40|175|-1.34|0.526|-2.55|-2.4|-0.3|
|Radius Matching, caliper(0.01)|40|175|-0.66|1.000|-0.66|-2.6|1.3|
|Radius Matching, caliper(0.5)|40|175|-1.55|0.833|-1.86|-3.2|0.1|
|**Probability of wanting more credit in %**||||||||
|Kernel Matching (epanechnikov)|123|854|-7.2%|0.039|-1.83|-15%|0.5%|
|Kernel Matching (Gaussian)|123|854|-3.2%|0.069|-0.46|-17%|10.5%|
|Radius Matching|123|854|-9%|0.039|-2.29|-16.6%|-1.3%|
|Radius Matching, caliper(0.01)|120|853|-4.8%|0.050|-0.96|-14.6%|5%|
|Radius Matching,caliper(0.5)|120|853|-7.2%|0.048|-1.50|-16.6%|2.2%|

Source: Authors using the 2005 Nicaragua EMNV.<sup>++</sup> The program, previous to perform the sensibility analysis, calculates a baseline estimate. In this case using kernel matching and only observation on the common support the baseline estimate is 8.5 %.
# **Table 4. Drivers of Digital Payment Usage during COVID-19**

|DEPENDENT VARIABLE: Share adults making or receiving payments, 2021|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|EXPLANATORY VARIABLES:|||||||||
|Overall Restrictions on Banking Activities (2016)|0.154|0.075|0.101|0.039|0.201|0.344|-0.702|-0.524|
||(0.442)|(0.455)|(0.422)|(0.439)|(0.441)|(0.482)|(0.800)|(0.895)|
|Made or received a digital payment (% age 15+) (change 2017-2014)|9.681|9.941|17.741|16.181|20.681*|10.278|26.410|27.282|
||(11.374)|(11.813)|(10.904)|(10.602)|(11.139)|(10.829)|(19.946)|(18.926)|
|Made or received a digital payment (% age 15+) (2017)|0.940***|0.956***|0.883***|0.947***|0.880***|0.975***|0.985***|0.962***|
||(0.038)|(0.037)|(0.045)|(0.039)|(0.048)|(0.039)|(0.071)|(0.078)|
|Banks' assets (% GDP) (2019)|0.020|0.013|0.011|-0.002|-0.004|-0.021|0.051|0.057|
||(0.018)|(0.017)|(0.017)|(0.018)|(0.019)|(0.021)|(0.035)|(0.035)|
|GDP per capita growth (mean over 2015-2019)||0.451|0.335|0.174|0.362|0.350|-0.969|-1.041|
|||(0.505)|(0.466)|(0.494)|(0.452)|(0.478)|(0.862)|(0.812)|
|log(population) (2019)||0.967*|1.289**|1.114**|1.414***|1.276**|2.138**|2.000*|
|||(0.498)|(0.524)|(0.515)|(0.531)|(0.511)|(0.924)|(0.989)|
|Inflation, consumer prices (annual %) (2019)||-0.008|-0.012|-0.000|0.004|-0.005|-0.377|-0.346|
|||(0.013)|(0.011)|(0.013)|(0.012)|(0.012)|(0.454)|(0.467)|
|Mean years of schooling (2019)|||<br>1.059**<br>(0.421)||||||
|Access to electricity (% of population) (2019)||||0.111*<br>(0.058)|||||
|Individuals using the Internet (% of population) (2019)|||||0.160***<br>(0.060)||||
|Mobile cellular subscriptions (per 100 people) (2019)||||||0.089***<br>(0.034)|||
|Enablers data economy (2020)|||||||-0.155<br>(0.506)||
|Safeguards data economy (2020)||||||||0.165<br>(0.468)|
|Constant|7.714|-9.449|-21.186*|-19.998*|-22.977**|-25.586**|-23.862|<br>-25.178|
||(5.385)|(9.365)|(11.197)|(11.777)|(11.325)|(12.397)|(15.511)|(16.854)|
|Observations|83|83|83|83|83|83|44|44|
|AdjustedR-squared|0.922|0.924|0.929|0.927|0.93|0.931|0.889|0.889|

Notes: Parameter estimates are unweighted ordinary least squares treating each country or economy as the unit of observation. Standard error of the estimates, in parentheses, are robust to heteroskedasticity of general form. *, **, *** denote statistical significance at the standard confidence levels of 10%, 5%, and 1%.

36
_- 5-_

# Average charges

The charges in the Chilean pension system are the sum of the commission incomes of AFPs during active life, and of life insurance companies, assuming the affiliate chooses annuities upon retirement. Each one has special features that must be taken into account.

In the case of AFPs, it is necessary to separate AFP income devoted to pay insurance benefits for invalidity and survivorship. Most of these funds are returned to workers in the form of invalidity and survivorship insurance payments, just as old-age pension payments, and are not administrative charges. Still, AFPs and their reinsurers charge some commissions in this business, which must be taken into account.

From the financial statements of AFPs in 1991, we get a total commission income of 92.208 billion pesos. Of this, 28.800 bilion pesos was spent in buying group invalidity and survivorship insurance for the affiliates. Therefore, the net cost for affiliates was 63.408 billion pesos, or 169.3 million dollars. Dividing by 3.282 million effective contributors and using the market exchange rate as of March 1992, we obtain an average annual charge of 51.6 dollars per effective contributor. We will see below that most of this is used up in collection costs. The costs of paying benefits are not significant because of the low average age in the AFP system: there were just 91,328 registered persons that were pensioned as of March 1993.

The Chilean AFP industry had 4,566 employees, 3,439 salespeople and close to 450 branch offices in December 1990. The average compensation per AFP worker, obtained from income and loss statements for 1991, was 10,878 dollars per administrative employee and 7,273 dollars per salesperson<sup>6</sup> . These employees are near the top of the labor market, as seen when comparing with the average covered wage, which was 3,900 dollars per year in 1991.

As explained there is an additional charge upon disability and survivorship insurance. This charge is divided up between reinsurance companies and AFPs. Valdes-Pricto and Navarro (1992) estimated econometrically that the back-payments to workers amounted to 0.84% of

> 6 Source: 1990 wage expenses from the income and loss smtements of AFPs and the number of workers, both reported by Habitat (1991), and then corrected by the variation of the general wage index from December 1990 to March 1992. The variation of the genel wage index and the average covered salary for March 1992 were taken from Bulletin No. 110, Superintendency of AFP.