rafmacalaba/data-use-mentions-extended huggingface dataset:
to commodity cycle price booms that were highly prevalent in Latin America during our sample period. The impact of SPS provisions is measured on firm exports of agricultural and agro-food HS 6-digit products (i.e., those belonging to HS Chapters 01-24) while the impact of TBT provisions is measured on firm exports of any HS 6-digit products (except HS 25-27). Our key outcome variable is firm export value at the destination-HS 6-digit product-year level. For the PPML estimation of our firm gravity equation described in Section 3, we need to expand our initial exporting country-firm-destination-HS 6-digit-year database to account for zeros. Specifically, we fill in the initial database so that each firm-destination-HS 6-digit has an observation (a row) in all of that exporting country’s sample years, with a 0 export value in a year when exports by the firm-destination-HS 6-digit do not take place.<sup>19</sup> This expanded database combined with the type of fixed effects included in our firm gravity equation allows us to exploit changes in firms’ exports over time as PTAs enter into force over the sample period to identify our coefficients of interest.<sup>20</sup> Table 2 shows summary statistics for each exporting country on the number of exporting firms, their export values, destination and product diversification patterns. The statistics are averages across the sample period and are provided for all exporting firms (Panel A) and for firms exporting agricultural and agro-food products (Panel B). The numbers of exporting firms are quite similar across the three countries. The median number of destination markets and products per firm is low, 1 or 2 at most. Firms selling agricultural and agro-food products have higher average and median export values and are more diversified than the average for all exporting firms. These patterns are not surprising given our countries’ comparative advantage in agricultural and agro-food products.

> 19A similar fill-in procedure is used in the study of SPS standards and export decisions by Fernandes et al. (2016).

> 20Appendix Table C.
<!-- Start of picture text --> 55<br>54<br>53<br>52<br>51<br>50<br>49<br>2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> _Figure 7.1. Proportion of Workers Employed in Labor-intensive Industries in Total Industrial Employment (%). Note: The industries whose employment coefficients are 6.5 or above in 2004 are classified as laborintensive industries (see Appendix Table A.4 for the employment coefficients). Source: the Enterprise Survey data (GSO)._ <!-- Start of picture text --> 55<br>54<br>53<br>52<br>51<br>50<br>49<br>2000 2001 2002 2003 2004 2005 2006 2007<br><!-- End of picture text --> _Figure 7.2. Proportion of Workers Employed in Unskilled-labor-intensive Industries in Total Industrial Employment (%). Note: The industries whose proportion of production workers is 85.7 or above in 1998 in the Industrial Survey (GSO, 2000) are referred to unskilled-labor-intensive industries (see Appendix Table A.4 for the proportion of production workers). Source: GSO, 2000._ 42
Fernández, R. 2007. _Culture as Learning: The Evolution of Female Labor Force Participation over a Century._ NYU Working Paper.

Fernández, R., A. Fogli, and C. Olivetti. 2004. “Mothers and Sons: Preference Formation and Female Labor Force Dynamics.” _Quarterly Journal of Economics_ 119 (4): 1249–99.

Fogli, A., and L. Veldkamp. 2010. _Nature or Nurture? Learning and the Geography of Female Labor Force Participation._ CEPR Working paper.

Fox, G.L., and R.F. Kelly. 1995. “Determinants of Child Custody Arrangements at ” Divorce _. Journal of Marriage and Family_ 57 (3): 693-708.

Garenne, M. 2003. “Sex differences in health indicators among children in African DHS surveys.” _Journal of Biosocial Sciences_ 35 (4): 601–14.

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Green, L.W., H.C. Gustafson, W. Griffitshs, and D. Yaukey. 1972. _The Dacca Family Planning Experiment._ Special Issue No. 3, Pacific Health Education Report.

Greenwood, J., A. Seshadri, and M. Yorukoglu. 2004. “Engines of Liberation
0|109|341|
|Total|2,442|1,339|34|318|121|1,813|4,255|

_Source:_ Derived from Foster and Briceño-Garmendia (2009). _Note:_ O&M = operations and maintenance; ODA = official development assistance; PPI = private participation in infrastructure; CAPEX = capital expenditure; OECD = Organisation for Economic Co-operation and Development; WSS = water supply and sanitation; ICT = information and communication technology. The public sector figures are averages of actual spending for 2007–09 in the case of government data. In the case of state-owned enterprises (SOEs), the average spans over 2004–08 and in some cases 2004–09. Funding from external financiers is averaged over the 2002–07 period.

Angola’s existing spending amounts to almost 14 percent of its 2005 GDP (figure 22). This represents quite a high level of effort, well above the averages for the country’s regional peer groups. While total spending is high, the balance between investment and operating expenditure is unusual. While Angola’s capital spending of 6 percent of GDP is almost on par with its resource-rich peers, its operating expenditure at 8 percent of GDP is several times the level found in other resource-rich countries.

Angola’s pattern of capital investment in infrastructure differs from that of comparator countries. It is heavily skewed toward transport (taking 70 percent), leaving much smaller shares for power (14 percent), ICT (8 percent), and water supply and sanitation (8 percent). Relative to its peer group, Angola is much more reliant on public funding for capital investments for the transport, power, and ICT sectors. The level of recent public investment in the transport sector is particularly high. Non-OECD financing is important across the board in Angola. Private sector investment has been limited to the ICT sector, while other resource-rich countries have received private flows also to other infrastructure sectors. Angola’s investment
Figure 10: Accounting for TFP Gaps in the Data <!-- Start of picture text --> Belgium<br>France<br>Portugal<br>SpainChile<br>FinlandRomania BulgariaItaly Peru<br>Malaysia<br>Hungary<br>ColombiaKenya<br>Latvia<br>IndiaSalvador<br>Ghana<br>Ethiopia<br>2 4 6 8 10 12<br>TFP−USA/TFP_i<br>1.0<br>0.8<br>0.6<br>0.4<br>Fraction of TFP gap closed<br>0.2<br><!-- End of picture text --> Note: The figure shows the TFP gains from dismantling _τi_<sup>_E_</sup> and _γi_ in the model relative to the productivity gap between the US and each country’s TFP in the data. Formally, we plot TFP gapclosed = _Model T F P_ _<u>{</u>_ 0 _<u>,</u>_ 0 _<u>}</u>_ <u>�</u> _T F PT F P {τi_<sup>_USAE,γi}_</sup> <u>�</u> _Data_ against the observed gaps; � _<u>T F PT F P</u>_<sup>_USA_</sup> _i_ � _Data_ . TFP in the data is read from _ctfp_ in <u>�</u> _T F Pi_ <u>�</u> Penn World Table version 9.0 for the year 2014, with the exception of Ethiopia, Ghana, and El Salvador for which _ctfp_ is missing. In these cases, TFP was computed as _K_<sup>1</sup><sup>_/_3</sup> _<u>YL</u>_
>15</sup> Gridded precipitations are retrieved from the CHIRPS-2.0 product of the Climate Hazard Group. Monthly precipitations at a resolution of 0.05<sup>_◦_</sup> are available for the period 1981 to present. We produce monthly grids of NDVI and EVI at a 500m resolution from NASA’s MODIS Terra satellite products that cover the period 2000 to present. Similarly, we create monthly 1km grids of DMP using data from the Global Land Service of Copernicus (PROBA-V satellite mission) for the period 1999 to 2019. We aggregate monthly gridded – – values at administrative levels from regions (level 1) to communes (level 3) and we calculate RSQ indices at various temporal scales.

We provide a correlogram of department-level RSQ indices in Figure 4 to appreciate differences between index types<sup>16</sup> . While we observe high pairwise correlations between

> 15The maximum time scale of five months corresponds to the typical duration of the rainy season in Mauritania.

> 16We also provide a similar correlogram in Figure B.1 considering a shorter time scale for our four indices (1 month) in appendix B and make comparable observations.

15
Policy Research Working Paper 5933

# **Abstract**

This paper contributes new evidence from two large household surveys on the compliance of firms with severance pay regulations in Indonesia, and the extent to which changes in severance pay regulations could affect employment rigidity. Compliance appears to be low, as only one-third of workers entitled to severance pay report receiving it, and on average workers only collect 40 percent of the payment due to them. Eligible female and low-wage workers are least likely to report receiving payments. Widespread non-compliance is consistent with trends in employment rigidity, which remained essentially unchanged following the large increases in severance mandated by the 2003 law. These results suggest that workers may benefit from a compromise that relaxes severance pay regulations while improving enforcement of severance pay statutes, and possibly establishing a system of unemployment benefits.

This paper is a product of the Social Protection Unit, Human Development Network. 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 author may be contacted at dnewhouse@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 and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
on measures of governance. Hollyer et al, (2011) create a transparency index based on the availability of country-level data – 172 variables in total categorized under Economic Policy and Debt in the World Bank’s World Development Indicators – to show that democratic countries are more transparent. Similarly, Williams (2009) constructs a transparency index based on the quantity of data released by governments through the availability of socio-economic data contained in the World Development Indicators and the International Finance Statistics databases. Choi and Hashimoto (2018) proxy for data transparency policy reforms using subscriptions to the IMF’s Data Standards Initiatives and find that such reforms lead to falls in sovereign bond spreads. The study finds that the larger quantity of data produced, as measured by the index, is positively correlated to the quality of bureaucracy, investment and financial sector development.

Finally, there is the question of whether data transparency can contribute to long term growth. The role of data transparency in facilitating economic growth is closely tied to institutions in the literature. The quality of institutions is largely dependent on their degree of accountability and corruption. Institutions operating in secrecy – lack of transparency - tend to be poor in quality as public officials can seek personal gain with little accountability. A large body of literature documented the detrimental effects of corruption on economic growth (Mauro 1995; Ehrlich and Lui, 1999). The secretive nature of corruption makes it distortionary, thereby discouraging investment and slowing growth (Shleifer and Vishny, 1993). Corruption can discourage foreign direct investment, entry of new firms, and misallocate public expenditures (De Soto, 1989; Mauro, 1998; Wei, 2000; Brada et al., 2019). Furthermore, institutions or even corruption that is “unpredictable” discourages investment and lowers growth (Campos et al., 1999). Corruption may also reduce parental investments in human capital (Varvarigos and Arsenis, 2015; Brunetti et al., 1998). Islam and Lederman (2020) find a statistically significant impact on transitional growth to
leads to lower estimates of equivalence scales than using income satisfaction data (Charlier, 2002; Falter, 2006).

Yet, these findings may not necessarily apply to Russia, given the latter’s different demographic structures. We show four such indicators in Figure 1: the average household size (Panel A), single-person households as a percentage of the total population (Panel B), three-ormore-adults households as a percentage of all households (Panel C), and three-or-more-adults households with children as a percentage of all households (Panel D). Russia has the largest household size, which averaged at least 2.6 persons per household for the last 10 years, which is followed by the United Kingdom (2.3 persons), Switzerland (2.2 persons), and Germany (2 persons) (Panel A). Single-person households are also least common in Russia, accounting for less than 10 percent of the total population on average, while the corresponding figure for Germany is roughly twice higher at 20 percent (Panel B). The corresponding figures for the United Kingdom and Switzerland fall somewhere in between, with Switzerland catching up quickly with Germany. Figure 1, Panels C and D also display a clear cross-country difference in the proportion of extended households (i.e., households where multiple adults are present). While less than 10 percent of households in the other three European countries consist of three or more adults (with or without children) on average, the corresponding figure is at least three times higher for Russia.

# **3. Empirical Strategy**

# **3.1. Measuring Scale Elasticity**
**Growth (But Not Only) Is Good for the Poor: Some Cross-Country Evidence to Promote Growth and Shared Prosperity in Haiti**<sup>*****</sup>

**_Kassia Antoine_** _, The World Bank Group_ <u>kantoine@worldbank.org</u>

**_Raju Jan Singh_** _, The World Bank Group_ <u>rsingh9@worldbank.org</u>

**_Konstantin M. Wacker_** _, University of Mainz and World Bank Group_ <u>kwacker@uni-mainz.de</u>

**<u>Keywords</u>** : Haiti, growth, inequality, education, infrastructure, macroeconomic stability, panel data **<u>JEL Classifications</u>** <u>: O11, D31, O54</u>

> * Corresponding author: Raju Jan Singh. This paper has been prepared in the context of the 2014 Haiti Systematic Country Diagnostics. The authors wish to thank Roland Kpodar, David Cal McWilliams, Ambar Narayan, Vinaya Swaroop and Cédric Tille for their helpful comments on earlier drafts and Aart Kray for sharing his data, as well as the participants at the Conference on “Dynamics of Inequalities and their Perception” organized by Aix-Marseille University, May 26-27, 2016, in Marseille, and at the 2016 Annual Congress of the Swiss Association of Economists and Statisticians on “Inequalities – Causes and Implications for Economic Policies”, June 9-10, 2016 in Lugano.
.

Table D.8: Treatment Effects on Self-Beliefs through Time

||(1)<br>Perceived numer|(2)<br>acy tercile correct|(3)<br>Above-medi|(4)<br>an self-esteem|
|---|---|---|---|---|
|Public|0.233|0.315|0.001|-0.001|
||(0.013)|(0.015)|(0.013)|(0.015)|
|Private|0.200|0.333|-0.002|0.016|
||(0.015)|(0.016)|(0.014)|(0.015)|
|p: public = private|0.010|0.240|0.806|0.239|
|Mean outcome|0.396|0.399|0.553|0.479|
|# observations|6601|5297|6609|5027|
|# clusters|84|84|84|84|

Coefficients are from regressing each outcome on a vector of treatment assignments, randomization block fixed effects, and prespecified baseline covariates (measured skills, self-reported skills, education, age, gender, employment, discount rate, risk aversion). Heteroskedasticity-robust standard errors shown in parentheses, clustering by treatment date. Mean outcome is for the control group. Above-median self-esteem is an indicator equal to one if the candidate’s response on a shortened version of the Rosenberg (1965) self-esteem scale is above the sample median. Columns (1) and (3) report results from the main phone follow-up survey. Columns (2) and (4) report results from the text message survey conducted 2-3 days after treatment. The sample sizes differ across columns due to item non-response, mostly from respondents reporting that they don’t
explores which aspects of a teacher – who teachers are, what teachers know, what teachers do, or what teachers believe – are most predictive of student learning gains.

Machine learning (ML) is a well-suited (albeit novel) approach for identifying key predictors of teacher effectiveness from a large set of teacher, student, and school covariates since most studies in the TVA literature use linear modeling techniques (Koedel et al. 2015). ML applications, which are increasingly common in econometrics (Athey and Imbens 2016, Mullainathan and Spiess 2017), often involve predictions about some variables given others. They manage to uncover generalizable patterns and discover complex structures that were not specified in advance (Mullainathan and Spiess 2017). ML algorithms attempt to select flexible models that fit the data well, but not so well that out-of-sample prediction is compromised (Athey and Imbens 2016). They can be particularly successful on high-dimensional data where we observe many pieces of information on each unit (Athey and Imbens 2016).

In our case where data are relatively high dimensional (with 52 explanatory variables), using ML algorithms helps us avoid ad-hoc model selection. Instead, the ML algorithm helps show which teacher covariates matter more for predicting student learning gains, by allowing for highly flexible models that are evaluated on the basis of out-of-sample replicability. ML algorithms also help avoid the problem of multicollinearity

> 2 See for example: Hanushek and Rivkin 2010; Nye et al. 2004; Chetty et al. 2014a; 2014b; Buhl-Wiggers et al. 2017; Bau and Das, 2020.

> 3 Several studies show that teacher value-added measures, which control for a student’s prior-year test scores, provide unbiased forecasts of teachers’ causal impacts on student achievement (Bacher-Hicks et al 2017, Glazerman and Protik 2015, Chetty et al. 2014, Bacher-Hicks, Kane, and Staiger 2014, Rothstein 2014).

2
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- Ruhm, C.J. 2004. “Parental Employment and Child Cognitive Development.” _Journal of Human Resources_ 39(1):155-192.

- Schultz , T.P. 2004. “School Subsidies for the Poor: Evaluating the Mexican Progresa Poverty Program.” _Journal of Development Economics_ 74(1): 199-250.

- Schweinhart, L. J.2005. “The High/Scope Perry Preschool Study Through Age 40: Summary, Conclusions, and Frequently Asked Questions.” High/Scope Press.

- Shrimpton, R., C. Victora, M. De Onis, R. Costa Lima, et al. 2001. “Worldwide Timing of Growth Faltering: Implications for Nutritional Interventions.” _Pediatrics_ 107:75-81.

- Smith, J.R., J. Brooks-Gunn and P.K. Klebanov, 1997. “Consequences of Living in Poverty for Young Children’s Cognitive and Verbal Ability and Early School Achievement.” In : Duncan, J. Brooks-Gunn J. (eds.) _Consequences of Growing up Poor_ , Russell Sage Foundation, New York, 132-189.

- Stifel, D., and H. Alderman. 2003. “The "Glass of Milk" Subsidy Program and Malnutrition in Peru.” Policy Research Working Paper No. 3089. World Bank, Washington
UB period is 37.3 weeks.

- Among the six eligibility conditions for UB covered by the data, all of which are also ALMP, periodic reporting on active job search, participation in a training program, and registration with PES are most commonly used. Regarding the six services offered by PES, training programs, access to the vacancy database and job placement are most frequently used, followed by counseling and internship/apprenticeship programs. Support for geographic mobility is the least frequently used.

- The eligibility conditions most likely to be used together are periodic reporting on active job search and participating in training offered by PES, followed by periodic reporting and registration with PES. As for the services offered by PES, job placement and access to the vacancy database are most likely to be offered together, followed by job placement and counseling services.

- According to the World Bank's Social Protection and Jobs Policy Inventory that gathered data from March 2020 to January 2022, 223 economies implemented 3856 policies to safeguard workers, vulnerable groups, and economic activities from the Covid-19 crisis. Among these reforms, 159 were related to UB, 865 were related to ALMP and the rest was on social assistance and other labor market policies.

- High-income economies led 62 percent of UB reforms, with upper middle-income and lower middle-income countries contributing 26 and 11 percent, respectively.

> 22 Models using micro data or data from certain local geographic areas are not able to capture the secondary effects of UB and ALMP on other sectors and geographic locations, and yield results that are limited to that sector or location.

25
71

# Figure 15c: A Complete Decomposition

<!-- Start of picture text --> 0.5 __ _ ___ ___ __ __ _ _ _ _ _ _ _ __ _ _ _<br>0.0 -_ _ _ _ _ _ ._ _ _ _ _ _ _ _ _ _<br>0 0.01 , ._.-w .rf f _<br>-0.5 0- -<br>OU~ ~ i ~~~~~-rl" ... ... . . . ... .... . ...... .... .... ....<br>0<br>4)<br>1-4<br>I S5 - ---- --------- ------------------------------------------------------- --------------------------- -------------------<br>-2.0 -<br>Percentiles<br>ininalphas and betas-~alphas, betas, gammas -4mu(d), alphas, betas, gammas 1996-1976<br><!-- End of picture text --> Source: "Pesquisa Nacional por Amostra de Domicilios" (PNAD), 1976 and 1996.