rafmacalaba/datause-encoder-data huggingface dataset:
Another approach using the dynamic GMM model in equations (2) and (3) is to see how project effects vary by household quantile/initial position  ( _y_ 0) in the outcome distribution (e.g., whether the outcome is expenditure or assets): _Q_  ( _y_ 0 ) (  _yit_    _yit_  1   1  _xit_   2  _xit_  1  1  _pit_  ( _vi_ 0 * _t_ )   _uit_ ),   ()1,0 (5) In the analysis, we examine distributional impacts through both equations (4) and (5).

# **5. Data and context**

The panel data used in this study, collected by BIDS, are based on household and community surveys conducted prior to and following implementation of RRMIMP. Households in control and treatment villages were followed over the period. The project was funded by the World Bank as part of its efforts to promote rural infrastructure development and, in turn, rural income growth and poverty reduction.

RRMIMP included improvement of 574 km of feeder roads to bitumen-surfaced standard, construction of 1,900 meters (m) of culverts, 1,750 m of bridges, and 2,200 m of small drainage structures on rural roads. In total, 10 roads across various districts were selected for the project. Also, two control roads were selected from two separate districts in the same region.

Data collection was financed as part of the road-paving projects and conducted by BIDS as part of the Bangladesh government’s efforts to assess household- and village-level impacts of rural road improvements over time. The data in our study cover 1,284 households across 28 villages over the three rounds.<sup>5</sup> The first phase of the RRMIMP survey collected pre-project benchmark information on project and control households during May–September 1997. The second phase

> 5 This represents an increase in the sample of 415 project and control households from Khandker, Bakht, and Koolwal (2009).

12
- Handley, Kyle and Nuno Limão. forthcoming. “Trade and Investment under Policy Uncertainty: Theory and Firm Evidence,” American Economic Journal: Economic Policy.

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- Hoekman, Bernard M. and Caglar Ozden. 2007. “Introduction.” In Trade Preferences and Di¤erential Treatment of Developing Countries, edited by Bernard M. Hoekman and Caglar Ozden, xi-xlii. Cheltenham, UK: Edward Elgar.

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110
27 Figure 1. Elasticity of demand and efficiency of taxation <!-- Start of picture text --> Price<br>A -B C (l+t)<br>TARIFF<br>REVEN\<br>G .<br>I I I\<br>I I I \<br>I I I \.<br>I I I D<br>I l II I ~DL<br>1H VML yH Imports<br>I 1<br><!-- End of picture text -->

# Notes

ACEG, CDE a revenue and welfare loss (low elasticity). ABFG, BEF a revenue and welfare loss (high elasticity).
**Updating the Poverty Estimates in Serbia in the Absence of Micro Data: A Microsimulation Approach**<sup>∗</sup>

Alexandru Cojocaru Sergio Olivieri<sup>†</sup> _Keywords: Micro-simulation, poverty, crisis, Serbia_ _JEL codes: E27, I32_

> ∗ The paper benefited from discussions with Caterina Ruggeri Laderchi, Carolina Sánchez Páramo, and Talip Kilic, and seminar participants at the World Bank. The authors are responsible for any remaining errors. † Alexandru Cojocaru: The World Bank Group, 1818 H St, NW Washington, DC 20433; acojocaru@worldbank.org. Sergio Olivieri: The World Bank Group, 1818 H St, NW Washington, DC 20433; solivieri@worldbank.org.
the program. However, this approach may be confounded by selection bias (e.g., firms with better environmental performance may be more likely to be rated).

It is rare to have pollution data for both rated and unrated firms before and after a PRD program. Garcia et al. (2007 and 2009) are the researchers to address the effect of a PRD – Indonesia’s PROPER -- on the measured pollution of rated and unrated firms, both ex ante and ex post. Their 2007 study suggests that PROPER has reduced emissions intensity, with a particularly rapid and strong impact on firms that have a poor initial compliance record. The 2009 study finds a strong reactive response during the first six months of disclosure, followed by a more moderate, but still significant, longer-run response as management adjusts to the new regime.

Using data from China’s Green Watch program, this study extends previous research on PRDs by considering firms’ perceptions of public and market responses to their ratings. The remainder of the paper is organized as follows. Section 2 reviews the relevant literature, focusing on the role of PRD programs in developing countries. Section 3 describes China’s Green Watch program, while Section 4 describes our survey instrument and provides descriptive statistics for major variables. Section 5 presents our estimation model and results. Section 6 summarizes and concludes the paper.

# **2.   Previous Research**

The literature on pollution control includes extensive work on command-and-control, market-based and information-based instruments (Tietenberg 1998; Dasgupta et al. 2006; Garcia et al. 2007). Command-and-control instruments are often inefficient and ineffective in developing countries, because firms fail to report adequately, regulators lack the technical and administrative capacity for effective monitoring and enforcement, and judicial systems are weak 3
# FIGURE 9: SHARE OF CONSUMER SERVICES (EXCLUDING HOUSING FOR USSR) IN VALUE ADDED:

# USSR AND OECD

<!-- Start of picture text --> 15%-<br>GRC<br>nu,<br>R C GRRC<br>10%-_<br>USRL<br>Ni.D ~GBR30[D C-A<br>DNK<br>USSR NORN0<br>US6R AUT AsT<br>0%i 1 -- I I_I<br>7 L 9 10<br>LOG OF PER CAPITA INCOME<br><!-- End of picture text -->
**Figure 1. Acceptance Rate at Each Price**

<!-- Start of picture text --> Acceptance rate at each price<br>100<br>80<br>60<br>40<br>20<br>0<br>0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 6 7 8 9 10<br>proposed prices<br>Easily Acceptable Easily and Probably Acceptable<br>Barely Acceptable and beyond No Strong Rejection<br>population<br>percentage in<br><!-- End of picture text -->

**Figure 2. Acceptance Rate at the Proposed Prices**

<!-- Start of picture text --> Acceptance at the Proposed Prices<br>2.5 Yuan/Ton 3 Yuan/Ton 3.5 Yuan/Ton<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Easily Acceptable Barely Rejection Strong<br>Acceptable Acceptable Rejection<br>acceptance<br>population<br>percentage ini<br><!-- End of picture text -->
and a range of bilateral variables including: common language, common border, colonial links, whether a country is landlocked or an island as well as common currency dummy.

The figure shows that in line with our previous finding that timeliness is more important for trade in intermediate than in final goods, the coefficient of distance for trade in intermediate goods is larger than that for the total trade over the whole period. In particular, we find that the coefficient of distance in trade in intermediate goods increases faster than that of total trade. This suggests that the composition of trade between trade in intermediate and final goods may partially explain the increase in the distance coefficient. Yet, rather than solving the puzzle, this result simply raises the question of why trade in intermediates is increasingly more time sensitive.

This result supports the pessimistic interpretation of the recent finding by Carrère, de Melo and Wilson (2010: 32) that if “trade costs can be viewed as a growing impediment in the supply-chain production. Then, if low-income countries’ trade costs (in particular distance-dependant costs such as high mark-ups in international shipping) remain high compared to other developing countries’ trade costs, the observed regionalisation of trade could be interpreted as a marginalisation of these countries."

**Figure 1: The distance coefficient in total and intermediate goods trade,  gravity estimations**

<!-- Start of picture text --> 1980 1990 2000 2010<br>Years<br>Total Trade Trade in Intermediates<br>1.6<br>1.5<br>1.4<br>Estimated Coefficients<br>1.3<br><!-- End of picture text -->

**VI.  Conclusions**

21
Second, we produce imputation-based poverty estimates for 2011/12 using the same (model) specifications as with the first step, but with the estimated parameters from the 2009/10 round on the data from the 2011/12 round. Put differently, the first step would offer further validation that this imputation method works in the context of India, as well as provide the appropriate specification to use for the imputation; these two steps would satisfy the two assumptions discussed earlier.

# **III.2. Estimation Results**

Since changes in household (heads’) characteristics may indicate the corresponding changes in household consumption, it can be useful to examine as a preliminary check the distributions of household characteristics across the two survey rounds in 2009/10 and 2011/12. The summary statistics provided in Appendix 1, Table 1.2 show that these changes appear rather negligible with most of the differences being not statistically significant. Some characteristics that are associated with higher levels of household welfare (e.g. heads with completed post-graduate education, household members with regular salary incomes, or urban residents receiving regular wages) show a statistically significant improvement over time; but others that have opposite effects (e.g., backward classes and radio ownership) also have statistically significant changes.<sup>14</sup> The picture provided from considering the pairwise changes in the distributions of these variables over time thus seems mixed at best.

We then proceed to impute poverty for the target survey in 2009/10, using the estimated parameters from the base survey in 2004/05. Assumption 1 on survey comparability is satisfied since the questionnaires (and sample design) for these two survey rounds remain the same. To satisfy Assumption 2, we can then consider five different household consumption model

> 14 We can infer the direction of the correlation between these characteristics and household consumption from the regression results in Appendix 1, Tables 1.3 and 1.4.

12
**Table 6. Impacts on Labor Income and Labor Supply**

||_Working_<br>_Hours Worked_|_Hourly Wage_|
|---|---|---|
|**Sub-groups **|(1)<br>(2)|(3)|
||**Panel A: Total**||
|Total|0.000<br>0.876<br>[0.007]<br>[0.702]|-0.505**<br>[0.249]|
|Men|-0.002<br>0.874<br>[0.005]<br>[0.694]|-0.707**<br>[0.305]|
|Women|0.010<br>1.049|-0.028|
||[0.013]<br>[1.014]|[0.314]|
||**Panel B: Urban**||
|Total|0.003<br>1.990**|-0.886**|
||[0.013]<br>[0.864]|[0.352]|
|Men|0.002<br>2.514***|-1.031**|
||[0.013]<br>[0.945]|[0.493]|
|Women|0.005<br>1.471|-0.561|
||[0.017]<br>[1.281]|[0.554]|
||**Panel C: Rural**||
|Total|-0.007<br>0.353|-0.112|
||[0.008]<br>[0.959]|[0.307]|
|Men|-0.004<br>0.394|-0.294|
||[0.005]<br>[0.936]|[0.352]|
|Women|-0.009<br>0.375|0.475|
||[0.019]<br>[1.563]|[0.426]|

Observations: 55,194 Total; 23,323 Urban; 31,871 Rural. 58% are men. 23% do not report wage. _Notes_ : Results from diff-diff regression on the sample of all adults 17 to 65 years old controlling for age, gender, years of education, marital status and race of the household head as well as location (urban or rural). Robust standard errors in brackets clustered at the municipality level. Working represents the binary variable that identifies economically active individuals that were employed or are actively looking for a job during the four weeks preceding the survey. Hours worked per week. Hourly wage per week in Quetzales of 2006. *** p<0.01, ** p<0.05, * p<0.1. _Source:_ calculations by the authors based on data from Encovi (INE) and Insivumeh.

29
depositories of some types of collateral: the ―Registre de Credit et Commerce Mobilier‖ (RCCM) in the civil law countries of the Maghreb or the Company Registry in some of the countries with a common and/or Islamic law system (Saudi Arabia, UAE, Yemen). These registries are very limited if we compare them with the notice based registry described and are usually inefficient in serving the purpose of a modern collateral registry. Where movable collateral registries exist, they tend to exist to ―validate‖ or ―stamp‖ loan agreements, but they do not otherwise promote effective notice to other lenders, or assist in creating execution rights and priorities over collateral. If there is a dispute over the terms of the contract, a certified copy can be retrieved. Thus it assists litigation, rather than lenders trying to assess their risk or assert their priority in an asset.

When we compare the main principles that a modern registry should be governed by with the existent infrastructure in MENA, the lack of modern systems sticks out as a major gap (see Table 7). First of all, none of the registries in MENA are notice based registries. They tend to be depositories for documents and serve no useful purpose for publicizing security interests in movable property or determining the priority of creditors. The majority of these registries are decentralized (except for the ones in Kuwait, Lebanon, Oman and West Bank & Gaza), meaning that the data is registered in different locations depending where the asset or creditor is located within the country. If we add the decentralization aspect to the fact that most of the registries in MENA are paper based and not electronic systems (Kuwait seems to have the only fully electronic registry in the region), we can assume that most of these registries are not searchable by the public and they do not assist lenders or borrowers in establishing priority among security interests held by multiple lenders. Finally, as illustrated in the table below, most of these registries limit the registration to a few types of movables (equipment, enterprise pledges, etc) and do not present a comprehensive picture of the different types of security rights that exist. A number of the MENA countries have a diversity of registries depending on the type of movable that is secured (vehicles, floating charges, trademarks, securities, enterprise charges, etc.).

20
raises the problem of the available set of alternatives with their capital costs, relative prices and quality. In many cases the telecommunications tariffs are such that – for small amounts of traffic - the marginal cost of the calls is negligible relative to the fixed and/or capital costs. If this is the case, the consumption threshold for an affordability assessment of the telecommunication services can be identified with these costs.

# **_Data Requirements_**

An affordability assessment exercise could be described as a multistage process with different data requirements at each stage (Foster 2000). First, it is necessary to identify the basic needs the affordability concept refers to. This can be the outcome of consultations with representatives of the stakeholders (e.g. governmental and donors agencies, consumer groups and service providers). At the second stage the providers’ datasets together with engineering considerations can be used to estimate the minimum expenditure necessary to meet the basic needs. Survey data are necessary at the third stage in order to analyse how many households can afford the utilities and which are the main causes of un-affordability and under-consumption. Here we focus on the third stage, namely on the information the survey data should provide. The ideal survey should provide valuable information on household conditions, their choice sets, their actual choice and their constraints. In more detail, the data should help to answer to the following questions:

1. <u>Is the service accessible? Expansion of the service network has been often</u> presented as a positive outcome of utility markets reforms. An affordability assessment without taking into consideration whether the households can or cannot access the services makes little sense. All households without access _up_ could be deemed as falling below the _q_ line in Figure 2, and therefore as having an affordability problem **.** Accessibility information is therefore necessary to distinguish households who _choose_ to spend less than the threshold and those who spend less than the minimum amount (probably, zero) because they do not have access. But a further distinction is necessary: some households may choose not to have access to the service, while some may be excluded because their area is not reached by the network. The survey should thus first of all provide information about the overall coverage (i.e., the potential basin reached by a

21
life are both small and insignificant. By contrast, the coefficient on per capita GDP in the first month is large and significant—for example, in the specification that includes a cubic time trend it is -53.93, with a standard error of 20.85. Once again, it appears that economic conditions around birth are most important for determining infant survival.

Our analysis of timing has obvious limitations because we only observe annual GDP data. For example, if instead of focusing on the month of birth we define a variable for the first _two_ months after birth, and run our basic regression including the cubic time trend and variables for per capita GDP in utero, and after the second month of life, the coefficient on per capita GDP in the two-month window after birth is -45.81, with a standard error of 17.94. The coefficients on the other two exposure variables are small and insignificant, as before. Similarly, when we define a variable for the last two months in utero, and run our basic regression including the cubic time trend and variables for per capita GDP in the first seven months in utero and the twelve months after birth, the coefficient on per capita GDP in the two-month window before birth is -39.23, with a standard error of 21.30. Once again, the coefficients on the other two exposure variables are small and insignificant. Clearly, with annual GDP data, it is not possible to tease out the relative importance of conditions in narrow windows of exposure. Rather the results suggest that conditions close to the time of birth appear to be more important in determining survival than those in the early in utero period, or in the later part of a child’s first year of life.<sup>17</sup>

> 17 We also experimented with breakdowns of the in utero period. For example, in a study of the effect of the Chernobyl nuclear disaster, Almond et al. (2007a) show that radiation exposure was particularly damaging during the period between 8 and 25 weeks after conception. The emphasis of our paper is on economic conditions, rather than radiation exposure, but it is conceivable that the period of 8 to 25 weeks post-conception is one in which health insults more generally are particularly damaging. However, in none of the specifications we ran was the coefficient on economic conditions in the period corresponding to 8-25 weeks after conception significant once we controlled for conditions in the last three months of pregnancy and after birth.

20
# **Conscientiousness and labor market returns: Evidence from a field experiment in West Africa**

Mathias ALLEMAND<sup>1</sup> Martina KIRCHBERGER<sup>2,</sup><sup>_∗_</sup> _University of Zurich Trinity College Dublin and CEPR_ Sveta MILUSHEVA<sup>3</sup> _The World Bank_ Carol NEWMAN<sup>2,</sup><sup>_∗_</sup> Brent ROBERTS<sup>4</sup> Vincent THORNE<sup>5</sup> _Trinity College Dublin University of Illinois at Urbana-Champaign Paris School of Economics_ Originally published in the Policy Research Working Paper Series on _March 2023._ This version is updated on _March 2024._ To obtain the originally published version, please email prwp@worldbank.org.

## **Keywords:** non-cognitive skills, labor markets, conscientiousness.

**JEL Classification:** J24, M53, O15.

We are extremely grateful for the tireless efforts of the field team, which included Mame Diarra Bousso Sarr, Marion Sagot and Paola Elice. Many thanks to Aram Gassama, Meyhar Mohammed, Marine Nelly Cecile Colon de Franciosi and Meriem Fouad for excellent research support and assistance. We are grateful to CETUD and APIX for their support in coordinating and implementing this work. We are indebted to Etienne Thibault, Moustapha Gueye, Birame Olivier Sarr, Ahouseny Sawadogo, Niang Diallo, Gisler Isidore Mandiamy and their teams at Eiffage Rail Senegal (Groupement Eiffage CSE Yapi Merkezi). For helpful comments we thank Achyuta Adhvaryu and Chris Woodruff as well as participants at the 2021 Jobs and Development Conference and Trinity College Dublin. We gratefully acknowledge financial support from RSB and from the ieConnect for Impact program, which is funded by the United Kingdom’s Foreign, Commonwealth & Development Office. Computational reproducibility verified by DIME Analytics. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All potential errors are our own. The list of authors is in alphabetical order. The trial was registered on the AEA RCT registry (AEARCTR-0004043 Version 2.0).

> 1 Department of Psychology, University of Zurich.

> 2 Department of Economics, Trinity College Dublin.

> 3 Development Impact Evaluation Department, The World Bank.

> 4 Department of Psychology, University of Illinois at Urbana-Champaign.

> 5 Department of Regulation, Environment and Market, Paris School of Economics.

_∗_ Corresponding authors: martina.kirchberger@tcd.ie, cnewman@tcd.ie.
-43 -

<!-- Start of picture text --> GRAPH 3a: ACTUAL COMMODITY PRICE CHANCES ALICPMM, FORECASTS AND<br>ERROR BARS<br>&LICPHN ___ FORECAST=<br>.6800<br>.490<br>2 399 - - -<br>* 299<br>-. 3W ~ ~ ~ ~ ~ ~~F<br>i979 1969 1981 1992 1993 1984 1985 i980 1997 1980 1989<br><!-- End of picture text --> Forecasted Sample Period: 1979-1988