rafmacalaba/data-use-mentions-extended huggingface dataset:
for Indonesia. More recently, Beverelli et al. (2017) found a cross-section of countries at different stages of development, also unveiling positive productivity effects downstream associated with increased presence of FDI upstream.

The evidence on productivity and its links to integration in the global marketplace for Pakistan is limited, and particularly that relying on firm-level data. Indeed, data availability has been a major constraint and existing research tends to rely on small or sector-specific samples, or cross-sectional data. Wadho et al. (2019), for example, using a survey of about 600 firms in the textiles sector find that export intensive firms grow more slowly in terms of employment and this could be explained by exporting firms being on average larger and older than other firms. Choudhary et al. (2018) collect information on management quality, as a measure of performance, for about 4,500 manufacturing firms. They find that Pakistani firms are well behind the global frontier yet have been displaying some improvement over the past decade. They also find that firms that are more export oriented have more structured management practices, themselves a proxy for productivity. Finally, Kinda (2012) uses a cross-section of firms in Pakistan, and in four other developing countries, and finds that firms that sell more of their production to multinationals tend to be more efficient. Few studies provide a macrolevel overview of productivity in Pakistan. Siddique (2020), for example, investigates the main drivers of aggregate TFP growth, while Chaudhry and Haseeb (2014) explore misallocations in productivity across plants in the manufacturing sector.

This paper examines productivity dynamics of publicly listed firms in Pakistan, and the extent to which integration in the global marketplace explains their productivity growth. We exploit substantial variation in import duties along the period of analysis, as well as in FDI penetration to measure the impact of integration upstream in terms of both merchandise and services inputs on productivity downstream, and examine how policy distortions in upstream sectors have affected productivity growth in firms operating downstream. Hence, to the best
capital, to affect growth. A World Bank ( 1991) study found that economic growth **rates** in a sample of 60 developing countries during 1965-87 (Updated to 1994 later) were especially high where there was a combination of a high level of education and macroeconomic stability and openness. The impact of trade openness on long-term growth thus depends on how well people are able to absorb and use the information and technology made available through trade and foreign investment.

Thomas and Wang (1997) looked at the interaction of openness and education and the impact on the performance of the World Bank's lending projects.<sup>3</sup> On an average, countries with a more educated labor force and a more open economy had a rate of return on projects 3 percentage points higher than those that had only one or the other (figure 1).

**Figure 1. Education and openness interact and increase investment returns**

<!-- Start of picture text --> Education, Openness and Economic Rates of Return<br>of 1,265 World Bank Projects<br>18.0<br>18.0 15.2<br>17.0<br>0 16.0 14.8__<br>E 14.0 ,,, Nbre open<br>0 ~~13.0<br>12 0 Less open<br>Low<br>education High<br>education<br><!-- End of picture text --> Note: Economic rates of return are from the evaluation database of the Operations Evaluation Department. Education is measured by the average level of schooling of the labor force. and openness by the logarithm of the foreign exchange parallel market premium. Source: Author's calculations.

We have examined case studies of the importance to economic growth of the distribution of education and of the interaction of policy reform and education. We turn next to theoretical models and empirical investigations of
# **4.4 Robustness Checks**

To assess whether the treatment effect heterogeneity of T2 holds for smaller intervals, I estimate the model using deciles. The results are reported in the Online Appendix. The same treatment effect pattern persists: respondents holding correct beliefs are clearly the subgroup who reacts most to treatment.

As a more general robustness check of my findings, I re-estimate the above models without imputing missing baseline covariate values. I also drop the respondents who did not find the provided information credible (1.5 percent) or not very credible (9.1 percent). I next drop respondents who take unusually little time to respond to the survey (less than 20 minutes, 5.5 percent). My main findings also hold for these smaller subsamples. Moreover, I construct an index of respondents’ score on the Crowne and Marlowe (1960) propensity to give socially desirable answers scale and re-estimate my main models on the subsample of those who score in the bottom 6 deciles of the distribution. The positive treatment effect of T1 on the ideal bequest is smaller and no longer statistically significant but still positive. This suggests that some of the treatment effect may be driven by social desirability bias. I estimate all models without imputing missing baseline covariate values. All these results are reported in the Online Appendix.

Finally, to address potential selection bias created by survey non-response, I follow Valliant and Dever (2018) by estimating the pseudo-inclusion probabilities into my survey sample: I treat the Tunisian Labor Market Panel Survey (TLMPS), the most recent face-to-face panel survey (ERF, 2014), as reference probability sample, which I code as “0”. I then append it to my survey data, which I code as “1”. Using a logit model, I estimate _Pr_ ( _y_ = 1 _|X, β_ ), where _X_ is a set of covariates with common support in both samples.<sup>16</sup> This allows me to estimate pseudo-weights
years in office increases. When the PS stays in the position for more than 5 years, the chances of promotion drop sharply. The fifth year can be viewed as the last fast-track train to promotions. Therefore, we assume that the promotion incentive should be the strongest as the number of years in office approaches five when the full term expires and the typical wholesale changes of government cadres happen.

# **3.3 Other data**

We also collect data of city-level socioeconomic conditions from 2000 to 2013. The variables include the level and growth rate of GDP, electricity consumption, population, and the shares of industrial and tertiary value added in GDP, all from _the China City Statistical Yearbooks_ , supplemented by _the Statistical Yearbooks on Regional Economy_ and the statistical yearbooks of provinces. Data on provincial and prefectural GDP growth targets are from _the Report on the Work of the Government_ of the corresponding administrative units. In our analysis, we focus on all the prefectural and subprovincial level cities, but exclude prefectures, autonomous prefectures and leagues.<sup>27</sup> The final panel contains observations for 281 localities for 13 years. Descriptive statistics of these variables are in

> 25 We fail to find information on destination positions for 22 city-leader pairs. 26 The promotion and demotion rates would differ for the provincial leaders and prefecture leaders as well. 27 This is because these prefecture-level administrative units lack data on some key socioeconomic variables. 12
- Jones, D., D. Molitor, and J. Reif (2018): “What Do Workplace Wellness Programs Do? Evidence from the Illinois Workplace Wellness Study,” Working Paper 24229, NBER.

- Karlan, D. and M. Valdivia (2011): “Teaching entrepreneurship: Impact of business training on microfinance clients and institutions,” _Review of Economics and Statistics_ , 93, 510–527.

- McKenzie, D. (2020): “Small Business Training to Improve Management Practices in Developing Countries: Re-assessing the evidence for “training doesn’t work”,” Mimeo. World Bank.

- McKenzie, D. and C. Woodruff (2014): “What are we learning from business training and entrepreneurship evaluations around the developing world?” _World Bank Research Observer_ , 29, 48–82.

- MICAF (2017): “Jamaica: Micro, Small, and Medium Enterprises (MSME) and Entrepreneurship Policy,” Ministry of Industry, Commerce, Agriculture and Fisheries (MICAF): Kingston.

- Nelson, P. (1970): “Information and Consumer Behavior,” _Journal of Political Economy_ , 78, 311–329.

- The Economist (2018): “In Jamaica’s tourist hub, a state of emergency,” Available at: https://www.economist.com/the-americas/2018/01/25/in-jamaicas-tourist-hub-astate-of-emergency.

- Ubfal, D., I. Arraiz, D. Beuermann, M. Frese, A. Maffioli, and D. Verch (2019): “The impact of soft-skills training for entrepreneurs in Jamaica,” Discussion Paper Series 12325, IZA Institute of Labor Economics.

- Van Lieshout, S. and P. Mehtha (2017): “The Next 15 Million: Start and Improve Your Business Global Tracer Study 2011-2015,” International Labour Organization: Geneva.

- Westfall, P. H. and S. S. Young (1993): “Resampling-based multiple testing: Examples
of the ELL methodology (Haslett et al., 2010 and MR, 2010). The final important change was to consider a different bootstrap approach for obtaining the EB estimators and their corresponding error measures.<sup>1</sup> To make the application of the World Bank’s method friendlier to more advanced practitioners and to those who seek more flexibility, a World Bank team produced a Stata version of PovMap (Nguyen et al., 2018) which is available under the name “sae”. The development of the tool has made running multiple simulations and tests of the software much more straightforward. Since the creation of the “sae” Stata suite of commands, the World Bank has shifted towards training statistical agencies using this approach, which can facilitate replicability. Additionally, the code in the Stata package is open, so that curious practitioners can see in detail how methods have been implemented.

Small area estimation is a broad branch of statistics that focuses on improving estimates’ precision when household surveys are not large enough to achieve a desired level of precision. Within small area estimation methods, model-based techniques “borrow strength” from a larger data set or auxiliary data across areas through models linking the areas (regression-type techniques), which yield indirect estimators (MR, 2010). Most of the model-based techniques fall into two groups, methods based on unit-level models and those based on area-level ones. The former are commonly used when data on units (e.g. households) are available; when only area-level data are available (e.g. area means), the latter are used, see Fay and Herriot (1979).

In the context of poverty, methods based on unit-level models typically rely on estimating the distribution of the household’s welfare given a set of auxiliary variables or correlates. The model parameters are then used to simulate multiple welfare vectors from the fitted distribution for every single household in the census which commonly lacks a welfare measure for poverty measurement (Elbers et al.
**Figure 6:** Effects on Public Goods Provision & Village Actions (Survey Data)

<!-- Start of picture text --> (a) Access to services (b) Service quality<br>-.2 -.1 0 .1 .2 -.2 -.1 0 .1 .2<br>Margin of victory of best-ranked challenger Margin of victory of best-ranked challenger<br>0.09 0.2<br>0.03 0.1<br>-0.03 -0.1<br>Service quality (index)<br>Access to services (index)<br>-0.09 -0.2<br>-0.15 -0.4<br><!-- End of picture text -->

**(c)** Village actions to improve services

<!-- Start of picture text -->
-.2 -.1 0 .1 .2<br>Margin of victory of best-ranked challenger<br>0.20<br>0.08<br>-0.04<br>Village Govt Takes Action Index -0.15<br>-0.27<br><!-- End of picture text -->

_Notes_ : In panel (a), the dependent variable is an index of access to local services constructed using our survey data. In panel (b), the dependent variable is a standardized index of service quality. In panel (c), the dependent variable is an index indicating whether the village government took action or made investments on a public service. All indices have the following components: water access, garbage collection, paved roads, electricity, kindergartens, primary schools, and community healthcare. The dots are conditional means of each outcome across binned intervals of the margin of victory of the best-ranked challenger on each side of the RD threshold, with 95
<br>xpenditures. The th<br>lumns 4–6 restrict t<br>ditionally restrict t<br>5, respectively. M<br>nificance at 1%, 5%<br>mary school in peri<br>ol classrooms a r|emales in periodton<br>mates in Columns 2–<br>ird column of results<br>o municipalities with<br>o municipalities that<br>unicipality-clustered<br>and 10%.<br>odt−5in each<br>ox for re-school|

‡ _Pre-school room_ reflects the contemporaneous number of municipal pre-school classrooms, a proxy for pre-school availability.

Source: Authors’ analysis based on school data from the 2002 wave of the Brazilian School Census; official population estimates from the Brazilian Census Bureau; births by age from Brazilian Vital Statistics.

33
|Youth literacy rate (% of people ages 15-24)|69.1|n.d.|74.6|
|---|---|---|---|
|Life expectancy at birth (years)|42.3|45.3|47.8|
|Under-5 mortality rate (per 1,000 live births)|149.6|133.2|111.0|
|Maternal mortality rate (per 100,000 live births)|600.0|560.0|470.0|
|Prevalence of HIV (% of population ages 15-49)|14.2|13.8|13.5|
|Headcount Poverty ratio (% of population)|n.d.|59.3|n.d.|

_Source: World Bank World Development Indicators._ Aside from simple growth, fiscal policy is one of the main tools to promote economic equity and reduce poverty. Goñi, Lopez, and Serven (2008) demonstrate the redistributive potential of fiscal policy. They show that the difference in income inequality between Latin America (the most unequal region in the world) and Western Europe (the most equal region) is striking only after taxes and transfers are taken into account.<sup>2</sup> In Sub-Saharan Africa, Sahn and Younger (2000) conclude that even though social spending is progressive, it is not pro-poor because social services and other public expenditures disproportionately benefit the wealthy.<sup>3</sup> Similarly, analyses of Zambian public expenditures do not support the notion of a deliberate equalization policy. Giugale, Narayan and Saavedra (2011) show that greater equality in children‘s access to key services, such as education and immunization, are mostly explained by a scale effect, that is, an expansion of service coverage rather than the more equitable distribution of the service.

At the sector level, recent evidence (see Cuesta and Martinez-Vazquez [
|high income|L69|(67|7251|119.35|1.13|2.71|
|middle upper income|L760|O<.72)|«667.506|678.76|4.00|13.95|
|middle lower income|235|(0.76|93.99|246.423|1.12|23.33|
|lowincome|3.96|(0.82|163.295|178.02|2.58|5.45|

Source: Authors’ calculations based upon data sources reported in Annex Table Al. Note The classification of the countries is according to the World Bank.
<!-- Start of picture text --> Figure 4: Earnings by age group and gender: 2000-2015. Restricted to workers with<br>college.<br><!-- End of picture text --> <!-- Start of picture text --> A. Males B. Females<br>Year - Quarter Year - Quarter<br>23-30 31-40 23-30 31-40<br>41-50 51+ 41-50 51+<br>20000 20000<br>16000 16000<br>Monthly Labor Earnings 12000 Monthly Labor Earnings 12000<br>8000 8000<br>2000q2 2001q2 2002q2 2003q2 2004q2 2005q2 2006q2 2007q2 2008q2 2009q2 2010q2 2011q2 2012q2 2013q2 2014q2 2015q2 2000q2 2001q2 2002q2 2003q2 2004q2 2005q2 2006q2 2007q2 2008q2 2009q2 2010q2 2011q2 2012q2 2013q2 2014q2 2015q2<br><!-- End of picture text --> Notes: Authors’ calculations using quarterly Labor Force Surveys from 2000 to 2015. Earnings are in constant Mexican Pesos 2014:1. Earnings refer to ful-ltime workers (at least 30 weekly hours of work) with a valid wage. Includes workers with college education: salaried, self-employment and owners. Earnings below and above the 1 and 99 percentile (within gender and education group) are dropped. Lines are smoothed by a moving average with a 0.4 weight to the current observation and 0.3 to the previous and post observation.

As discussed, if a degraded tertiary effect is taking place, we would expect that inequality among younger workers would increase more (or decrease less) than for workers of other age groups. However, as shown in Figure 5, wage inequality among the younger age group declined. There is no clear difference in the pattern of inequality change among workers of different ages (whether males or females). Figure 5 shows a decline in inequality for all workers, including college-educated older workers. Skill-obsolescence (or lower relative demand for older workers in recent cohorts) might imply that high-wage college-educated older workers are earning a lower wage and
�� in equation (1) is the set of other controls in our estimating equation, such as the initial value of the dependent variable, the share of female employment in total employment in the given industry _i_ or the change in this share over the considered period. �� is the i.i.d. error term.

� is the coefficient of interest to us. A change in reform is defined in such a way that it represents an increase in competition. For example, for trade liberalization reforms, we define our explanatory variable as a decline in tariffs or ERP, while in the case of de-reservation reforms, we measure the increase in the share of de-reserved products. Theory predicts � to be negative with respect to segmentation because an increase in competitive forces is likely to reduce the extent of segmentation. We next move to describing female activity and segmentation patterns in India and examining whether enhanced competition by way of domestic and international reforms has played a role in changing these trends.

# **V. Results**

# **_Descriptive Tabulations_**

Table 1a broadly describes the manufacturing and services sector data. Panel A presents the raw count of observations available for the years 2001, 2006 and 2010 in both the manufacturing and the services sectors. The services sector is officially unorganized, including its largest 19
of social programs may lead to bias due to measurement error, as documented in the literature.** Survey data capturing social benefits, such as the SILC data, often undergo harmonization processes. Still, it may exhibit bias because lower-income households tend to underreport the benefits they receive. This underreporting is typically attributed to difficulties in recalling specific benefit details or a reluctance to disclose such information due to social stigma or other factors, such as survey respondents confusing social benefits with earnings (Bargain et al., 2012; Figari et al., 2012; Lynn et al., 2004; Bollinger & Tasseva, 2023, Meyer, 2019). Survey underreporting of transfer programs carries significant implications for our analyses of low-income populations, as this bias may result in an underestimation of the actual level of support offered by Minimum Income (MI) programs. Relying solely on survey data markedly understates the income of poor households, which could lead to a skewed and inaccurate representation of their welfare. Moreover, it can distort our comprehension of how effectively these programs are targeting those in need, and, perhaps most importantly, it can significantly downplay the actual impact of anti-poverty initiatives. In essence, survey underreporting undermines our ability to

> 15 In the SILC, mean-tested noncontributory programs are captured in the variable HY063 (means-tested, noncontributory social exclusion not elsewhere classified). These include means-tested social assistance (GMI),  heating allowances, and other benefits aiming to support low-income families with children, including the monthly child allowance, the monthly allowance for raising a child under the age of one, targeted allowance for schoolchildren enrolled in 1st grade at a state or municipal school, and other non-contributory and means-tested benefits including a lump-sum pregnancy grant for uninsured mothers and financial support to prevent the abandonment of a child or bringing up of a child by relatives or foster family.  The GMI and the heating allowances cannot be directly identified from the household survey. Therefore, these benefits need to be simulated in the survey using eligibility conditions and administrative data on beneficiaries
permanent access to the raw customs data at the exporter-product-destination-year level. A few of the data providers authorize the access by researchers outside the World Bank to the raw customs data at the exporter-product-destinationyear level. The list of countries for which such data access is authorized is available (and subject to updates) at http://econ.worldbank.org/exporter-dynamics-database.

> 9 This number of observations covers the countries for which we have access to the raw exporter-level customs data (i.e., it does not account for the observations for Brazil, Egypt, Estonia, Laos, New Zealand, Norway, Portugal, Spain, Sweden, and Turkey).

6
This paper provides a first step in understanding the linkages between firms’ discriminatory behavior in the presence of gender-based legal discriminations and labor market outcomes for women in a developing country setting. The paper focuses on the DRC and takes advantage of, to the best of our knowledge, previously unused data on firms’ behavior, which were collected through the 2013 World Bank Enterprise Surveys (ES) in the DRC. As documented by the World Bank’s Women, Business and the Law (WBL), at the time of the 2013 ES, the DRC was characterized by the presence of several gender-based discriminatory provisions in areas such as family and inheritance laws, labor laws and land laws that could potentially limit women’s participation in economic activities. Several gender disparities were also present in the country. Although women accounted for almost 48 percent of the labor force, 80 percent of women were employed in agriculture, only 4 percent in industry, and 16 percent in services. Moreover, human capital accumulation was still lower for women than for men. About 28 percent of women had never gone to school in contrast to 14 percent for men (World Bank, 2013) and secondary and tertiary school enrollment was lower for girls than for boys. Finally, about 75 percent of women in the DRC indicate they have no say in household decisions (UN Women, 2011).

Development practitioners have encouraged and worked with governments to reform laws that hold women back from working and doing business. However, there is some concern that some of these laws suffer from lack of enforcement, especially in developing economies with weak institutions. The divergence between _de iure_ and _de facto_ situations may imply that even though there may be gender-based discrimination in the law, in practice women benefit from the same rights as men or, vice versa, that women may be still discriminated despite the presence of laws that protect them from gender-based discrimination. Such differences have been documented for the business environment regulations in general using firm-level data (Hallward-Driemeier and Pritchett, 2015)