rafmacalaba/datause-encoder-data huggingface dataset:
presented for three samples ─ namely the total sample, the sample minus three nontreatment communes with PS lower than any of those of the treatment communes (treatment support) and the sample further trimmed of six treatment communes to ensure common support.

Table 3 also gives the mean per commune kilometers allocated by RTP1, as recorded in the project data base. For our sample, RTP1 rehabilitated an average of just over four and a half kilometers per commune. Finally the last rows of Table 3 provide the p-values for two statistical tests of no fungibility. The first tests whether the rehabilitated kilometers are equal to that allocated by RTP1. The second tests whether there was fungibility within the roads sector ─ namely, whether the total kilometers rehabilitated and built over the project period equal the kilometers allocated by RTP1.

We first use the kernel matching method to calculate the kilometers that project communes would have rehabilitated without RTP1. The mean counterfactual of roads rehabilitated is 11.79 compared to the project allocation of 4.58 kilometers. The predicted amount in the absence of the project was greater than the project allocation for all but three communes verifying that full fungibility was feasible.

The simple DD comparison of the mean incremental kilometers rehabilitated by all communes indicates a statistically insignificant difference of 2.5 kilometers by the end of the project period (Table 3). In general the estimates are qualitatively similar within each method when we trim the comparison and/or treatment samples outside the region of common support (whether or not we trim the treatment group). They range from 1.68 to 3.88 kilometers rehabilitated, based on the simple DD and the PS-weighted DD, respectively. But, with the exception of the PS-weighted DD estimates (significant at the 10% level for the total and treatment support samples) none of the DD estimates of impacts on rehabilitation are significantly different from zero. The PS-weighted estimates indicate an average of 15 percent displacement relative to the per commune average of 4.58 kilometers rehabilitated by RTP1.

As an aside, we also tried a more conventional estimation method, namely using a parametric regression for the change in roads rehabilitated or built that includes initial conditions interacted with time in the date-specific outcome levels. As noted in Section 5, this method makes a number of rather different assumptions, including on functional form 16
more generally) require some assurance that in distinguishing between two subsets of finns in an industry on the basis of whether or not there is majority foreign ownership (or more generally foreign linkages of some kind) one is not ignoring other important determinants of the performance of firms.

**Table** 5: **Testing for Spillover Effects** (Firms without FDI)

|Dependent variable: Growth in Sales|OLS|Random-Effects|
|---|---|---|
|Amemiya Selection Bias Correction Variable|Yes|Yes|
|Sector-Specific Returns to Scale and Mark-ups|Yes|Yes|
|Spillovers (Share of assets of foreign affiliates in|-0.077|-0.074|
|total assets of the sector)|(1.425)|(1.218)|
|Dummy for 1994|0.003<br>(0.897)|0.002<br>(0.178)|
|Dummy for 1995|-0.032**<br>(-2.985)|-0.031**<br>(-2.257)|
|Dummy for 1996|-0.027*|-0.025|
||(-1.847)|(-1.514)|
|Observations|431|431|
|F-test|0.91||
|;Hlausman<br>test (random vs. fixed effects) a||4.13(14.45)|
|Adjusted R2|0.894|0.857|

Notes: Heteroskedasticity consistent (White correction); t-statistics in parentheses; a constant term is included in both regressions; a: Cut-off point in parentheses; * Significant at the 90% level; * * significant at 95% level.

One such determinant likely to be important is the technological effort of firms. The survey questionnaire revealed that joint venture firms invested significantly more in training and new technologies than pure "domestic" firms. It may be that the technological ability and effort expended by many of the firms without foreign partners is too low to be able to absorb spillovers when they occur, or that the firms with foreign linkages have absorbed a significant share of the available stock of labor with requisite skills. Also, given that FDI and joint ventures together account for significant shares of total assets, sales and employment in the Czech Republic, the potential for positive spillovers among firms with foreign partnerships, e.g. from FDI firms to 19
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minimizes reverse causality. It is still possible that high-corruption areas experience low-growth rates and have inherently poor business environments. In our identification strategy we include a set of location dummy variables to control for local effects.

The effect of bribery on sales growth from 2007 to 2009 is examined for firms in the LAC region. We find evidence that when established (age `≥` 10 years) or large firms are bribed, their real annual sales growth is significantly less negatively affected than for younger or smaller firms.

First, we find significant differences in the impact of bribery by the size of firms based on their sales levels. Firms who are bribed grow 23.6 percent slower than firms who are not bribed. We also find that distortion from bribery decreases as annual sales increase. Compared to bribed firms with sales level in the 75<sup>th</sup> percentile, bribe-payers with sales in the 25<sup>th</sup> percentile have sales growth that is 43 percentage points lower (more than one standard deviation). These results are robust to a series of robustness tests including variations in country groupings, constructions of the bribery measure, and sample restrictions. We also obtain similar results when using a panel data set of firms surveyed in both 2006 and 2010.

Secondly, among small and medium-size enterprises (SMEs) with fewer than 100 full-time employees we find that young firms (age < 10 years) are also more adversely affected by bribery solicitations than old firms. Age has an even larger compensating effect for bribery than being a firm with higher sales. While age and sales are strongly correlated, results including both interactions are significant for the sample of SMEs. Older firms have significantly smaller bribery distortions in non-Caribbean economies.

The remainder of the paper is organized as follows. Section 2 discusses the empirical strategy. Section 3 discusses the data and how the measures of bribery are constructed. Lastly, empirical results and sensitivity analyses are discussed in Section 4. Section 5 concludes.

# **2. Empirical Strategy**

This section discusses the empirical strategy to identify the effects of bribery on firm growth. Since the data used in the analysis is a cross-section, endogeneity of bribery may lead to 5
Figure 3.16 Percentage of vocational upper secondary school enrollment by province, 2013 versus 2016 <!-- Start of picture text --> 60.0%<br>50.0%<br>40.0%<br>30.0%<br>20.0%<br>10.0%<br>0.0%<br>% voc 2013 % voc 2016<br>普普 Guangxi 普普 Shanghai 普普 Beijing 普普 Fujian 普普 Sichuan 普普 Hainan 普普 Anhui 苏普 Jiangsu 普普 Qinghai 普普 Zhejiang 普普 Hebei 普普 Yunnan 东普 Guangdong 普普 Total 普普 Henan 普普 Hunan 东普 Shandong 普普 Ningxia 普普 Tianjin 贵普 Guizhou 普普 Jiangxi 庆普 Chongqing 普普 Shanxi 辽普 Liaoning 普普 Xinjiang 陕普 Shaanxi Inner Mongolia 龙普 Heilongjiang 肃普 Gansu 普普 Jilin 普普 Hubei 普普 Tibet<br>普<br>普普普<br><!-- End of picture text --> _Sources_ : <u>http://www.moe.edu.cn/s78/A03/moe_560/jytjsj_2016/2016_gd/201708/t20170828_312534.html http://www.moe.edu.cn/s78/A03/moe_560/jytjsj_2016/2016_gd/201708/t20170828_312546.html http://www.moe.edu.cn/s78/A03/moe_560/s8492/s8494/201412/t20141216_181697.html http://www.moe.edu.cn/s78/A03/moe_560/s8492/s8494/201412/t20141217_181848.html</u> _Note_ : 2013 is the earliest year with provincial-level data.

Labor market returns do not lend support to the efficiency argument for the tracking policy. There is an argument that the tracking arrangement and selective promotion to tertiary education increases the efficiency of investment in education by matching the different levels and types of schooling with labor market need. There is however a lack of empirical evidence on this. The moderate labor market returns to higher education in recent years in China (World Bank, forthcoming) not only signal the lower value added of higher education but also raise the question of whether the cost of early tracking overweighs the benefit. According to the study, with the average returns to college of 59 percent, the private internal rate of return (IRR) to investing in college is only 4 percent a year, and when accounting for public spending, the social IRR is only at 1.8 percent a year. Using the distribution of estimates obtained in the quantile treatment effects, the study further estimates that about 10 percent of the quantiles yield a negative IRR using only private costs. With the inclusion of public expenditures, the IRRs are negative for 36 percent of the quantiles. This suggests that a significant portion of the college graduates are actually receiving a negative rate of return to their financial investment in tertiary education. Two important factors may contribute to this: (a) the students selected through the examination results especially at Zhongkao may not actually be of higher ability in the labor market and (b) the content of the education from academic senior secondary curriculum to higher education may add limited value in terms of relevant knowledge and skills gained.

35
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- Consoli, S., L. T. Pezzoli, and E. Tosetti (2021). Emotions in macroeconomic news and their impact on the European bond market. _Journal of International Money and Finance 118_ , 102472.

- Couttenier, M., S. Hatte, M. Thoenig, and S. Vlachos (2023). Anti-muslim voting and media coverage of immigrant crimes. _Review of Economics and Statistics forthcoming_ .

20
**Table 2: One-year labor market impacts**

||JOB|LOSS|JOB|LESS|MONTHS WI|THOUT WORK|
|---|---|---|---|---|---|---|
||up to Feb|/Mar 2021|in Feb/|Mar 2021|up to Feb|/Mar 2021|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Respondent is female|0.0534|0.0484|0.0250|-0.00777|0.0212|-0.0932|
||(0.0517)|(0.0521)|(0.0428)|(0.0453)|(0.227)|(0.247)|
|Age|-0.000768|-0.000387|0.000686|-0.000119|0.00578|0.00285|
||(0.000806)|(0.000931)|(0.000774)|(0.000852)|(0.00395)|(0.00444)|
|Primary education|-0.0209|-0.0192|-0.0494**|-0.0606**|-0.148|-0.209|
||(0.0261)|(0.0303)|(0.0230)|(0.0260)|(0.122)|(0.143)|
|Secondary education|0.0381|0.0326|-0.0442*|-0.0476*|-0.0730|-0.137|
||(0.0296)|(0.0360)|(0.0253)|(0.0273)|(0.143)|(0.156)|
|Tertiary education|0.0453|0.0447|-0.0725**|-0.0647**|0.0103|-0.213|
||(0.0362)|(0.0420)|(0.0287)|(0.0323)|(0.177)|(0.183)|
|Urban|0.0245|0.0126|-0.00390|0.00210|0.183**|0.0959|
||(0.0225)|(0.0248)|(0.0162)|(0.0184)|(0.0851)|(0.104)|
|Children 0-4 present|0.000256|0.0117|-0.0328*|-0.0338|-0.0691|-0.0457|
||(0.0193)|(0.0232)|(0.0198)|(0.0210)|(0.106)|(0.121)|
|Female*Children 0-4|-0.000820|-0.0149|0.0529|0.0434|0.231|0.170|
||(0.0400)|(0.0499)|(0.0358)|(0.0412)|(0.184)|(0.224)|
|Children 5-18 present|0.00165|0.00217|-0.00548|-0.00600|-0.256**|-0.222*|
||(0.0274)|(0.0287)|(0.0199)|(0.0218)|(0.123)|(0.125)|
|Female*Children 5-18|-0.00551|0.0322|-0.0475|-0.00729|0.138|0.294|
||(0.0624)|(0.0577)|(0.0405)|(0.0442)|(0.250)|(0.270)|
|Income diversification|-0.0499**|-0.0499**|-0.0198|-0.00910|-0.250***|-0.224**|
||(0.0224)|(0.0232)|(0.0176)|(0.0185)|(0.0949)|(0.0944)|
|Manufacturing & Utilities||0.0922**||0.00272||-0.0593|
|||(0.0406)||(0.0350)||(0.174)|
|Construction, Transport &||0.0833**||-0.00806||0.342**|
|Professional services||(0.0357)||(0.0289)||(0.165)|
|Commerce||0.0388||-0.0306||0.0112|
|||(0.0303)||(0.0274)||(0.124)|
|Public Administration||0.0809||-0.0476||0.0339|
|||(0.0594)||(0.0511)||(0.198)|
|Other services||0.0565*||-0.0557**||0.253*|
|||(0.0310)||(0.0244)||(0.146)|
|Constant|0.751***|0.673***|0.209***|0.240***|1.942***|1.931***|
||(0.0498)|(0.0573)|(0.0495)|(0.0534)|(0.242)|(0.271)|
|Observations|2,459|1,993|2,446|1,984|2,459|1,993|
|R-squared|0.161|0.167|0.013|0.015|0.018|0.014|

Note: OLS estimation, standard errors clustered at EA level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. All estimations include country fixed effects. Job loss is captured for the period April 2020 to February/March 2021, based on HFPS R1-10 for Ethiopia and Nigeria, R1-8 for Malawi, R1-11 for Nigeria and R1-5 for Uganda. Columns 3 and 4 use the latest of these rounds per country. Columns 5 and 6 use the same rounds as columns 1-2. Samples include only respondents who were working preCOVID-19.

# **5. Inequality in impacts on household income**

In addition to job loss, households’ income could have been affected by reduced business activity, a decrease in working hours, or factors not directly related to employment of the respondent, such as declining remittances or job loss by a household member. In this section we analyze how household demographics and reliance on different income sources are related to the probability of households’ loss of income.

16
# **Appendix A: Sources for the 1998 Corruption Perceptions Index**

|No|Source|Year|Who was Surveyed?|Subject Asked|Number<br>of Replies|Number of Countries|
|---|---|---|---|---|---|---|
|1|Political & Economic Risk<br>Consultancy|1997<br>1998|Expatriate Business<br>Executives|Extent of Corruption in a way that<br>detracts from the business environment<br>for foreign companies|280|12 Asian countries|
|2|Gallup International|1997|General Public|Cases of corruption for the following<br>group of people: politicians, public<br>officials, policeman, and judges|over<br>34000|44 mostly developed<br>countries|
|3|Institute for Management<br>Development<br>(World Competitiveness<br>Yearbook)|1996<br>1997<br>1998|Business Executives<br>in Top and Middle<br>Management|Improper practices (such as bribing or<br>corruption) in the public sphere|3102<br>2515<br>4314|46 mostly developed<br>countries|
|||1996||Irregular, additional payments<br>connected with imort and exort|1537|40 developed and<br>developing countries|
||World Economic Forum (Global|||p p<br>||56 developed and|
|4|<br>Competitiveness Survey)|1997|Business Executives|permits, business licenses, exchange<br>controls tax assessments police|2778|<br>developing countries|
|||1998||, ,<br>protection, or loan application|3500|68 developed and<br>developing countries|
|5|Political Risk Services<br>(International Country Risk<br>Guide)|1998|Assessment of Staff|Assessment of corruption in<br>government|-|135 developed and<br>developing countries|
|6|World Bank<br>(World Development Report-<br>Private Sector Survey)|1997|Business Executives|Irregular, additional payments are<br>common and represent an obstacle to<br>doing business|over 3500|73 developed and<br>developing countries|
|7|Economic Intelligence Unit<br>(Country Risk Service and<br>Country Forecast)|1998|Assessment by Staff|Assessment of pervasiveness of<br>corruption among politicians and civil<br>servants|-|115 developed and<br>developing countries|

29
# **Improving the Well-Being of Adolescent Girls in Developing Countries**

Katy Bergstrom<sup>1</sup> and Berk Özler<sup>2</sup> JEL codes: I15, I25, J12, J13, J16, J24 Keywords: marriage, fertility, education, adolescent girls

> 1 Development Research Group, World Bank. Email: kbergstrom@worldbank.org.

> 2 Development Research Group, World Bank. Email: bozler@worldbank.org.
there is no strong evidence of convergence in agriculture and services. More importantly, their results show that the rate of convergence in manufacturing and mining has been strong enough to lead aggregate convergence across regions, albeit at a slower pace because of high employment shares of agriculture and services.<sup>8</sup> In the case of Brazil, Azzoni and Castro (2023) examined regional disparities from 2002 to 2019. Their findings indicate a general increase in _σ_ -convergence in per capita income across regions. Moreover, periods of economic crisis, such as the Great Recession of 2008 and the economic crisis of 2014, were linked to more pronounced convergence. The study also reveals evidence of conditional convergence in regional per capita income and wages (serving as a proxy for labor productivity) during these crisis periods.

Overall, this observed (or lack thereof) convergence and its speed call for further evidence that accounts for the drivers of factor accumulation, sectoral composition, technology diffusion, and production externalities within and across regions and countries.

# **3 Empirical approach**

## **3.1 Data**

Given data availability, we focus on the period from 2002 to 2018. The main dataset comes from the Relação Anual de Informações Sociais (RAIS), which is a matched employer-employee dataset put together by the Ministry of Labor and Employment (Ministério do Trabalho e Emprego). RAIS covers all registered firms and formal employees in which a firm is identified by its registration number (CNPJ) and a worker by its identification number (PIS). Most importantly, both the PIS and CNPJ do not change over time, even when a worker stops to work in the formal sector. These unique features allow us to track workers and firms over time. The dataset contains detailed information on firms’ geographic location, economic classification (CNAE 5-digit), employment size, and personnel records such as monthly wage, age, gender, and education, among others.<sup>9</sup> This dataset is used to compute the key variable of interest: labor productivity at the sectoral and industry levels.

Another data source is derived from the Pesquisa Nacional por Amostra de Domicílios (PNAD).

> 8 They also report lack of convergence in poverty rates across regions because of limited labor mobility toward converging sectors and high employment of the poor in sectors exhibiting weak convergence.

> 9 National Classification of Economic Activities (CANE) is Brazil’s system of economic activity classification in the production and dissemination of economic statistics.

8
_17_ the People's Bank of China (PBOC) for use by the commercial banks. The technological design anticipates using the same data communications network for the latter two systems. However, the base product functionality of the two electronic systems differs substantially according to the required response times, security and other features. These differences should be reflected in the pricing structure in line with the costs of providing these different features. In addition to the different basic features of each system it is also clear that the two systems have differing levels of service, for instance the high value system allows for direct electronic interface, voice interface and batched electronic or paper based interface via the branches of the PBOC. Each of these different features should be separately priced and perhaps added to the unit price of the basic service.

- **_Incentives:_** Various forms of incentive pricing schemes can be used to influence the behavior of the user.

_High volume discounts:_ The pricing structure could include high volume discounts to encourage the participating banks to use the system. The discounts might be activated once a previously agreed minimum transaction volume for each participant bank or bank branch has been achieved. For example, it is not untypical to base overall costs on some percent utilization of overall system capacity, say sixty percent. So, if the actual system utilization is in excess of sixty percent the service provider will be able to give discounts and still recover costs.

_Minimum daily usage charge:_ A minimum daily charge might be introduced to encourage subscribers to use the system. However the fixed charge should not be set at a level which is so high as to discourage the commercial banks from using the system. This minimum charge could be presented as a fixed annual subscription fee to ail participants.

_Subsidized price structures:_ Subsidized prices may be required during the initial implementation period as a basis for positively encouraging use of newly introduced systems. The subsidy can be applied using a variety of approache **s** uch as no charges at all, discounts based on volumes (principally based upon long term (high volume) per item costs), or full cost recovery with annual rebates to high volume users. The level of the subsidy should be considered carefully. In essence, it should be pitched at a level that encourages use, is affordable, yet does not institutionalize the concept of subsidization. Some form of gradually reducing subsidy might be appropriate to overcome a low transaction volume start-up situation.

Based on the above, it would seem appropriate that an initial price structure is developed around one or more of the following four core prices.