rafmacalaba/datause-encoder-data huggingface dataset:
Hence, our regressions include a set of fixed effects to control for these randomization strata.

At the start of each session, loan officers were assigned to an incentive treatment, received a one-on-one introduction to the incentive scheme and completed a short questionnaire to verify comprehension. We report summary statistics for the population of participating loan officers in Table II, Columns (1) to (4). The median loan officer in our sample is a Level II public sector bank employee who is 35 years old, and has 10 years of work experience. In Table II, Columns (5) to (8) we report comparable characteristics from a sample of all loan officers from a major bank in the state in which our experiment takes place. Our sample is quite representative of this reference population in terms of age, rank and experience.

A customized software interface reproduced each section of the loan application on a separate tab: a description of the applicant’s business, balance sheet, trade reference, site visit report, document verification and credit bureau report when available. While reviewing this information, participants were asked to assess the applicant’s credit risk using a form adapted from the standard format of a leading Indian commercial bank, with categories for personal risk, business risk, management risk and financial risk. The risk ratings serve three purposes: first, they add realism to the lab session, as completing an internal risk rating is a routine part of evaluating applications; second, they allow us to elicit a measure of perceived credit risk that is not tied to loan officer compensation. Finally, internal ratings serve to assist the loan officer in aggregating information about the application in a systematic way. Within each experimental session, the sequence of loan files was randomly assigned, but the ratio of performing, non-performing and declined loans was held constant at four performing loans, one non-performing loan and one loan declined by the Lender.<sup>16</sup> Loan officers were asked to evaluate these loans based on their best judgment, but were not given information about the ratio of good and bad loans or the outcome of any particular loan under evaluation.

> 16We chose this ratio to match responses to a pilot survey, in which we elicited the expected distribution of performing, non-performing and ex-ante declined loans for loans of the type used in the experiment.

15
<!-- Start of picture text --> Figure 7. Progressivity and Redistributive Effect of Taxes<br>Figure A. Direct Taxes<br>0.70 0.035<br>0.60 0.030<br>0.50 0.025<br>0.40 0.020<br>0.30 0.015<br>0.20 0.010<br>0.10 0.005<br>0.00 0.000<br>Kakwani Marginal contribution<br>Figure B. Indirect Taxes<br>0.10 0.010<br>0.005<br>0.05<br>0.000<br>0.00<br>-0.005<br>-0.05 -0.010<br>-0.10 -0.015<br>-0.020<br>-0.15<br>-0.025<br>-0.20<br>-0.030<br>-0.25 -0.035<br>Kakwani Marginal contribution<br>(change in Gini points)<br>Kakwani Index Marginal contribution to equity<br>Regressive <------------------> Progressive<br>Kakwani Index (change in Gini points)<br>Marginal contribution to equity<br>Regressive <------------------> Progressive<br><!-- End of picture text --> Source: Source: Armenia: Younger et al (2016); Bolivia: Paz Arauco et al (2014); Brazil: Higgins & Pereira (2014); Chile: Ruiz-Tagle & Contreras (2014); Colombia: Melendez (2014); El Salvador: Beneke et al., (2014); Georgia: Cancho & Bondarenko (2016); Mexico: Scott (2014); Peru: Jaramillo (2013); Poland: Goraus & Inchauste (2016); Russia: Lopez-Calvo et al (2016); Sri Lanka: Arunatilake et al (2016); South Africa: Inchauste et al (2016); Uruguay: Bucheli et al (2014); Croatia: own estimates based on the 2014 PIS and HBS. Note: Old-age pensions are treated as deferred income for all countries shown. Marginal contributions are the <u>difference between the consumable income Gini coefficient with and without indirect taxes.</u> 21
# **_Robustness_**

It is possible that changes in education and other socio-economic variables could affect our results. For example, an expansion of educational opportunities (whether or not targeted specifically at girls) could also cause a delay in the age of marriage by increasing time spent in school. Similarly, differential economic growth across areas could result in marriage delays. While we do not have per capita income at the sub-national level in Ethiopia, we have data on households’ asset ownership. Wealth data are actually relatively less likely to undergo high fluctuation over a five-year period. In addition, we explore whether the effects are robust to different geographic areas, including differentiating between urban and rural areas in regions.

To examine whether our results are robust to the above changes, we estimate equation (2), which augments equation (1) by including average female education ( _EDUC_ it) for both years, as well as average wealth in the region or chartered cities<sup>16</sup> .

_W_ ijt = _CONSTANT +_ **_φ_** _*GEO AREA*YEAR DUMMIESit +_ **_θ_** _*OCCUPATION DUMMIESj_ _+_ **_β_** _*(REFORMit*Xj) +_ **_θ_** _*OCC_SHAREijt + εijt_ + **γ** * _EDUC_ it + **ξ** * _WEALTHit_ + _ε_ ijt (2) What we find, however, is that the inclusion of the wealth and education variables does not significantly affect the results, nor are they significant in themselves. As such they are not reported, but are included in the other robustness checks performed here.

Table 6 restricts the sample solely to urban women and excluding agricultural occupations. This facilitates extrapolating from other changes that may have happened in rural land rights and tests for how widespread the effects were in the economy. For non-agricultural urban women in particular, the law has a significant interactive effect with three of the four

> 16 We use principal component analysis to construct a wealth index from households’ ownership of assets. The assets used are: radio, TV, bicycle, motorbike, car, telephone and floor materials (earth, vinyl, ceramic, cement and carpet). These are all dummy variables. The first principal component, explaining the largest variance, was used.

28
proportion of domestic services or inputs in total outputs tends to rise (McCaig 2013 [6]; Ghosh 2021). At first glance, this might suggest that a larger proportion of employment remains unaffected by trade, given the larger non-tradable component of the services sector. However, more trade can even indirectly influence non-tradable industries that serve as inputs to tradeable sectors.

This paper focuses on estimating the total effect on labor market outcomes in response to an export shock at the provincial level in Viet Nam from 2009 to 2019. In order to fully capture the overall impact of trade, we need to consider crucial supply chain linkages previous research has overlooked. Our analysis goes beyond solely focusing on directly exporting industries and accounts for industries indirectly affected by the rising demand for exports.

To accomplish this, we adopt a multi-stage approach. Initially, we employ an instrumental variable (IV) methodology to isolate an exogenous component of trade driven solely by foreign demand. The chosen IV is the proportion of a given trading partner country’s share in a specific commodity relative to Viet Nam’s total export value in that commodity, adjusted by the observed GDP growth of the partner country. This IV demonstrates a strong predictive capacity for Viet Nam’s export values while maintaining plausible independence from any supply-side determinants of exports.

Subsequently, leveraging the predicted export exposure obtained in the first stage, we construct a matrix delineating the direct and indirect exposure of each industry in each province. This is accomplished by utilizing input-output tables that document the flow of intermediate goods among different sectors in the economy. The predicted export exposure calculated earlier is then distributed proportionally to the labor share of each industry within each province.

Finally, we estimate the direct and indirect effects of exports by conducting a regression analysis of these computed exposures against various labor market outcomes of interest at the provincial level: wage levels, income disparities, the premium on college education wages, gender wage differentials, employment rates, rates of inactivity, informality in employment, and female employment rates. Additionally, we conduct a detailed examination of how these labor effects differ 2
contracted crop (paprika), which we interpret as a reduction in moral hazard. When we compare these benefits to fairly conservative costs of implementation, we find that adoption of fingerprinting is cost-effective (the benefit-cost ratio is 2.27).

The key contribution of the paper is that, to our knowledge, it is the first randomized field experiment examining the impact of a technology that improves the effectiveness of dynamic incentives in a credit market. Our analysis is further distinguished by the fact that, in addition to measuring impacts on borrowing decisions and repayment (using the lender’s administrative data), we also estimate impacts on specific behaviors related to moral hazard using a detailed follow-up survey of borrowers.

Substantively, our intervention most closely resembles the promise of a future lower interest rate conditional on current loan repayment in Karlan and Zinman (2009), henceforth “KZ”, who find evidence of moral hazard and weaker evidence of adverse selection in an experiment with a South African provider of consumer loans. Our experiment differs from KZ’s in several important respects. First, our experiment is concerned with lending for productive investment in rural areas, while KZ’s involves consumer loans for urban customers. Second, our experiment estimates the impact of manipulating the ability to impose dynamic incentives via a technological innovation. KZ, on the other hand, measures the impact of informing borrowers of the existence of a dynamic incentive. Third, we implement a follow-up survey of borrowers to provide additional insight into the specific behaviors that are changed by the intervention and that result in higher repayment. KZ, by contrast, relies exclusively on the lender’s administrative data for analysis and so cannot shed light on what borrower behaviors may have changed.

The fourth and final key difference is in the timing of the intervention relative to the borrowing decision. In KZ, the dynamic incentive is announced _after_ clients have agreed to borrow (and all loan terms have been finalized). As a result, differences in repayment can only be due to moral hazard. In our case, the lender’s ability to use dynamic incentives (due to fingerprinting) is revealed _before_ agents decide to borrow. Consequently, the composition of borrowers and the choice of loan terms may change as well. Because potential borrowers cannot 3
**Table A2.CU.A: Intergenerational Persistence in Schooling in Urban China -** **<u>(18 35 Years Age Cohorts)</u>**

|||**LINEAR CEF**||**Q**|**UADRATIC C**|**EF**|
|---|---|---|---|---|---|---|
||**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|**Sons (S)**|**Daughters**<br>**(D)**|**D-S**|
|Linear Coefft.|0.29|0.35|0.061|0.093|0.32|0.22|
||(0.016)***|(0.016)***|(0.023)***|(0.045)**|(0.045)***|(0.063)***|
||[0.027]***|[0.020]***|[0.025]**|[0.063]|[0.060]***|[0.074]***|
|Quadratic Coefft.||||0.015|0.0023|-0.012|
|||||(0.0029)***|(0.0029)|(0.0041)***|
|||||[0.0034]***|[0.0037]|[0.0049]**|
|Intercept|8.97|8.51|-0.46|9.33|8.57|-0.76|
||(0.15)***|(0.15)***|(0.21)**|(0.18)***|(0.18)***|(0.25)***|
||[0.20]***|[0.27]***|[0.274]*|[0.25]***|[0.30]***|[0.31]**|
|No. Observations|3192|3241|6433|3192|3241|6433|
|||**Estima**|**tes of Mobility**|**from Quadrati**|**c CEF**||
||**Absolute**|**Mobility**|||**Relative**|**Mobility**|
||**Sons**|**Daughters**|||**Sons**|**Daughters**|
|**_ES0 _**|9.33|8.51||**_IGME0 _**|0.093|0.35|
|**_ES6 _**|10.43|10.61||**_IGME6 _**|0.268|0.35|
|**_ES9_**|11.38|11.66||**_IGME9 _**|0.354|0.35|
|**_ES16_**|14.66|14.11||**_IGME16_**|0.558|0.35|

Notes: (1) The data used are CFPS 2016 with children aged 18-35. (2) IGMEK is Intergenerational Marginal effect when father has K years of schooling, (3) ESK is expected schooling when father has K years of schooling. K=0,6,9,16. (4) Robust standard errors in parentheses; standard errors clustered at province-level in square brackets; *** p<0.01, ** p<0.05, * p<0.1.
INTRODUCTION XXXix made, which were specified above. 'Me assumed trends in vital rates partly reflect the efforts that have been made throughout the dev,loping world to restrain fertility, as well as to reduce mortality, in effect actually taking into account some exceptional past experiences at reducing growth. Altering these trends and producing more rapid deceleration of population growth is possible, but would probably require at least as much, and probably much more, demographic intervention as in the recent past.

# REFERENCES

Arnold, Fred. 1989. Revised estimates and projections of international migration. Policy, Planning, and Research Working Papers, No. 275. Washington, D.C.: World Bank. Bos, Eduard, and Rodolfo _A._ Bulat2o. 1989. Projecting fertility for all countries. Policy, Planning. and Research Working Papers. Washington, D.C.: World Bank. Forthcoming. Blum, Alain, and Roland Pressat. 1987. Une nouvelle table de mortalitd pour l'URSS (19841985). _Population_ 42(6):8436M2.

- Bulatao, Rodolfo A., and Eduard Bos. 1989. Projecting mortality for ali countries. Policy, Planning, and Research Working Papers. Washington, D.C.: World Bank. Forthcoming.

- Bulatao, Rodolfo A., and Ann Elwan. 1985. Fertility and mortality transition: Patterns, projections, and interdependence. World Bank Staff WVorking Paper No. 681. Washington, D.C.

- Coale, Ansley J., and Paul Demeny (with Barbara Vaughn). 1983. _Regional Model Life Tables and Stable Populations._ 2nd ed. New York: Academic Press.

- Eurostat. 1987. _Demographic Statistics._ >.uxembourg: Statistical Office of the European Communities.

- Hill, Kenneth. n.d. ProjPC;II: Users manual. Washington, D.C.: World Bank.

- Jamison, Ellen, Peter D. Johnson, and Richard A. Engels. 1987. World Population Profile: 1987. W?shington, D.C.: Bureau of the Census, U.S. Department of Commerce.

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- United Nations. Department of International Ecenomic and Social Affairs. 1984. _Population and Vital Statistics Report: 1984 Special Supplement._ New York: United Nations.

- United Nations. Department of International Economic and Social Affairs. 1989. _World Population Prospects 1988._ New York. United Nations.

- United Nations. Department of International Economic and Social Affairs. Various years. _Demographic Yearbook._ New York: United Nations.

- United Nations. Department of International Economic and Social Affairs. Various years. _Population and Vtal Statistics Report._ New York: United Nations.

- U.S. Bureau of the Census. 1983. _World Population 1983--Recent Demographic Estimates for the Counries and Regions of the World._ Washington, D.C.

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- World Bank. 1989. _World Development Report 1989._ New York: Oxford University Press. Zachariah, K C, and My T. Vu. 1988. _World Population Projections: 1987-88 Edition._ Baltimore and London: The Johns Hopkins University Press.
CoMnsatory education. Studies show that 20<sup>percent of children with BLL above</sup> 25 tg/dl have lower cognitive development and<sup>require supplemental education for an average period</sup> of three years. This applies particularly to those<sup>children with BLL above 40 jug/dl. There is also</sup> evidence suggesting that, 'on average, BLL<sup>of 30 to 50 lAg/dl result in a 4 point decrement in IQ, and</sup> that lead levels of 50 to 70 jg/dl would reduce<sup>IQ by roughly</sup><sup>_5_points" (EPA 1985 u De la Burde</sup> and Choate 1975 and Rummo et al. 1979). Figures<sup>2, 3, and 4 show, respectively, the relationships</sup> between mean cell hemoglobin (MCH) and BLL<sup>after being adjusted for a number of significant</sup> variables,"<sup>3</sup> and the correlations between BLL,<sup>(full) IQ, and agility, the latter two having been made</sup> in Mexico City. All relationships apply only to children.

Figure 2. Relationship between MCH **and** BLL (after<sup>EPA</sup> 1985) Figure 3. Correlation of BLL and (Full) I.Q. in<sup>109 Childrea</sup> in Mexico City (after Secreuuia de Salud 1991) <!-- Start of picture text --> A 30~~~~~~~~3<br><!-- End of picture text --> <!-- Start of picture text --> 1e *o s 30 so<br>Ila" Laa4 Level (m141<br><!-- End of picture text --> For ease of display, each point zepresents<sup>an average</sup> of about 100 children with consecutive BLL.

We made the conservative assumption that a three<sup>year supplemental education would</sup> be required for those children with BLL above 40 _Agldl_<sup>(140,365 children in Mexico City fall in this</sup> group). Using the average annual cost of education<sup>per child in Mexico City of US$153 produces</sup> a total cost in compensatory education of US$21.5 million.'9 _18. The variables were age (under 2, 2-4, and 4-6),_<sup>_race, sex, degree of urbanization, farnily_</sup> _income, serum albumin, dietary calciwn, dietary calories,_<sup>_serum copper, dietary carbohydrates,_</sup> _dietary fat, serwn iron, blood lead, dietary phosphorous,_<sup>_dietary protein, transferrin saturation,_</sup> _dietary vitamin C, and serum zinc. Samiple size was_<sup>_1,967 children._</sup> _19. The total expenditure in education for children in_<sup>_Mexico was USS5.5 billion in 1990._</sup> _Assuming that expenditures were uniformly distributed_<sup>_throughout the country, the estimated_</sup> _expenditure in Mexico City was US$5.5 billion multiplied_<sup>_by the population of Mexico City in relation_</sup> _to the rest of the country, which is roughly 17 million_<sup>_over 80 million, or 21 percent. Thus the_</sup> _estimated ctmnual COSt of education per child in Me-xico_<sup>_City iS USS153. Multiplying by three years_</sup> _(discounting at S5percent p.a.) and multiplying by the_<sup>_140,365 cases gives the figure presented in the_</sup> _text - USS21.5 million._ _17_
**Table 8 Tax wedge and implicit tax rate on labor income as determinants of proportion of population expecting some punishment (fine or prison) for a person whose undeclared economic activity is detected. 20 EU** **<u>countries, 2007</u>**

|||Estimated co|efficients(r|obust stand|ard errors)||
|---|---|---|---|---|---|---|
||[1]|[2]|[3]|[4]|[5]|[6]|
|Tax Wedge on low wage earners, %|0.923***|1.236***|1.127***||||
|(lagged 1 year)<sup>a</sup>|(0.280)|(0.235)|(0.212)||||
|Implicit tax rate on labor income, %||||1.432***|1.657***|1.362***|
|(lagged 1 year)<sup>a</sup>||||(0.527)|(0.423)|(0.411)|
|Employment Protection||-10.410***|-7.934***||-8.117*|-5.760<sup>(</sup>*<sup>)</sup>|
|Legislation strictness, v. 2||(3.317)|(2.466)||(4.186)|(3.368)|
|Rule of Law|||11.160***|||8.782**|
|(lagged 1 year)<sup>a</sup>|||(3.457)|||(3.669)|
|R-squared|0.2652|0.4013|0.6338|0.3580|0.4503|0.5823|
|N|20|20|20|20|20|20|

_Notes:_ See Annex 1 (Table A1) for details on explanatory variables. Constant terms are not reported. Countries covered: AT, BE, BG, DE, DK, EE, ES, FI, FR, HU, IE, IT, LV, NL, PL, PT, SE, SI, SK, UK. (*) , *, **, *** - estimates significant at 11%, 10%, 5%, 1% level, respectively.

_Sources:_ Dependent variable: Riedmann and Fischer (2008), based on _Special Eurobarometer_ 284. Explanatory variables - see Annex 1 (Table A1) _._ Model estimates - own calculations.

47
8

# types of flows, as well as the stpudard deviations and some other statistics, by region.

<!-- Start of picture text --> Bond F I ows to Lat iri Amer- I ca<br>Soo 800 ~~~~~Cuelectud coufltr-I6)<br>400<br>200<br>200-<br>B on(d F I o)ws to( As i a<br>600 -_.<br>0-<br>50 *;<br>19O6-01 1988-07 1989-01 1989-07 1990-01 1990-07 1991-01 1991-07 1992-01 1992-07<br>a Korge I- MneISSIa X Vhf I lOandI<br>F%we 2<br><!-- End of picture text -->
**Table 5** . Determinants of poverty

|**Column**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|---|---|---|---|---|---|
|||||**monetar**|**WB MP**|
|**VARIABLES**|**MP dummy**|**MP dummy**<br>|**MP dummy**|**y**<br>**dummy**|<br>**dummy**|
||logit|Linear<br>probability|logit|logit|logit|
|IDH|0.336***<br>(0.112)|0.016***<br>(0.005)||0.142*<br>(0.076)|0.223**<br>(0.103)|
|Pre03IDH|||0.005<br>(0.168)|||
|O3_05IDH|||0.657***<br>(0.216)|||
|Post05IDH|||0.675***<br>|||
||||(0.178)|||
|Head of household age|0.005**<br>(0.002)|0.001***<br>(0.001)|<br>0.005**<br>(0.002)|0.001<br>(0.001)|-0.010***<br>(0.002)|
|Dependency ratio|-0.001<br>(0.002)|0.001<br>(0.002)|-0.001<br>(0.002)|0.015***<br>(0.001)|0.023***<br>(0.002)|
|Head of household<br>education|-0.391***<br>(0.029)|-0.010***<br>(0.001)|-0.392***<br>(0.029)|-0.246***<br>(0.012)|-1.473***<br>(0.057)|
||-0.234*|-0.009|-0.231|0.362***|0.036|
|Head of household married|(0.142)|(0.009)|(0.142)|(0.107)|(0.134)<br>|
|Head of household male|0.124<br>(0.145)|0.004<br>(0.009)|0.131<br>(0.145)|0.388***<br>(0.106)|0.418***<br>(0.132)<br>|
|Household in rural area|1.414***<br>(0.068)|0.076***<br>(0.003)|1.409***<br>(0.068)|0.692***<br>(0.039)|1.037***<br>(0.057)<br>|
||-0.386|-0.023**|-0.365|-0.013|-0.594**<br>|
|Public distribution system|(0.281)|(0.011)|(0.281)|(0.199)|(0.254)|
|Per capita consumption|-0.001***<br>(0.001)|-0.001***<br>(0.001)|-0.001***<br>(0.001)|||
|Household share males|-0.756***<br>(0.275)|-0.041***<br>(0.012)|-0.755***<br>(0.275)|-2.607***<br>(0.198)|-1.660***<br>(0.275)|
|Household share females|1.011***<br>(0.304)|0.043***<br>(0.015)|1.023***<br>(0.304)|-1.642***<br>(0.290)|-0.628*<br>(0.356)|
|Constant|-1.512**<br>(0.616)|0.085***<br>(0.023)|-1.724***<br>(0.596)|-1.978***<br>(0.420)|-0.782<br>(0.579)|
|Marginal effect of IDH|0.017***<br>(0.006)|||0.016*<br>(0.010)|0.013**<br>(0.006)|
|Marginal effect of<br>preUS_IDH|||0.002|||

22