rafmacalaba/data-use-mentions-extended huggingface dataset:
<!-- Start of picture text --> 120 Climate change<br>impacts<br>100 Low<br>High<br>80<br>60<br>40 39.7 35.7<br>20<br>14.2 13.9<br>7.5 5.8<br>3.3 2.4<br>0<br>SSA SAS EAP LAC<br> 2030)<br>(million,<br> people living in extreme poverty<br>due to climate change<br>Additional<br><!-- End of picture text --> Figure 3: Regional breakdown of people pushed to extreme poverty in 2030 poverty is by and large eradicated by 2030 in most scenarios, so even a relatively small absolute increase in poverty due to climate change has a major relative effect. On the other hand, Sub-Saharan Africa experiences an increase in average poverty headcount of “only” around 17.5%. This is simply due to the larger denominator (i.e., number of people in extreme poverty in the baseline scenarios) in the poorest regions of the world.

Table 2: Summary of poverty headcount based on different scenarios. Numbers in climate change scenarios <u>are additional headcount from the baseline scenario</u>

||Bli|High climate|Low climate|High climate|Low climate|
|---|---|---|---|---|---|
||asene<br>(illil)|change (million|change (million|change (increase|change (increase|
||mon peope|people)|people)|from baseline)|from baseline)|
|EAP|11.8|7.5|3.3|63.6%|28%|
|LAC|1.9|5.8|2
events, which are associated with flooding and waterlogging (Trancoso et al. 2024; Letsch, Dasgupta, and Robinson 2023; Otto et al. 2023; Nanditha and Mishra 2024).

Our analysis draws on newly available, spatially granular data on relative wealth - the Relative Weath Index (RWI) (Chi et al. 2022) - and variation in extreme heat and flood exposure across South Asia. While most of South Asia is very hot, sizable areas of the region are at a high elevation and have low average temperatures. Even in non-mountainous areas, average maximum temperatures range from 28 to 34 degrees (figure 1). Similarly, while flooding affects almost a third of South Asia, exposure to it varies within provinces and districts (figure 1). Given this variation, we estimate regressions on high-resolution spatial data to examine the association of the RWI with heat and floods.

Our preferred specification regresses the RWI on indicators for ranges of average annual maximum temperature, allowing for the possibility of non-linearities in the relationship between heat and wealth or firm size. We use an indicator for any flooding as well as the number of floods as our measure of exposure to flooding. Given the observation difference in the distribution of the RWI in urban and rural places, we estimate separate regressions for urban and rural locations.

We find that places with lower wealth are more exposed to heat, in both urban and rural areas of South Asia. Compared to places at an average temperature of 30 degrees Celsius, places with an average temperature of 34 degrees Celsius have 0.5 standard deviations (SDs) lower RWI in urban areas and 0.3 SDs in rural areas. Additionally, in urban areas, places with lower wealth are _more_ exposed to flooding. An additional flood in urban areas is associated with a 0.004 SDs lower RWI. The reverse is the case in rural areas: in rural areas, places with lower wealth are _less_ exposed to flooding.

In low- and middle-income countries, a large share of
>40 .4<br>30 .3<br>.2<br>20<br>.1<br>10<br>Revenue Demeaned (Bio. Col) Revenue Demeaned (Bio. Col) 0<br>0<br>-5 -.1<br><!-- End of picture text --> Notes: This gure shows the impact of the withholding rate reform in August 2011 on aggregate sales tax revenue (upper panel), and on aggregate revenue from the quarterly simplied regime tax, as a counterfactual (bottom panel). The sales tax data is based on ocial statistics from the Ministry of Finance, and the simplied regime data is based on rm-level tax declarations. The dots represent semester-wise tax revenue in billions of Costa Rican colones demeaned by the pre-reform semster-level means. For the upper panel, semesters are dened to t exactly around the time of the reform. For each year, the rst semester includes February to July, and the second semester includes August to December, and January of the following year. The red line is a linear t, allowing for a discontinuity at the time of the reform. The text displays the pre-reform semester-level mean revenue from the tax (in blue), and the coecient and standard error on the post-reform dummy (in red). The results are robust to running the analysis on monthly or quarterly data, using shorter or longer time series, and adding controls for the months of December and January (in the monthly data).

45
12. However, the literature on the effectiveness of HEs in poverty reduction is limited as the focus of most existing studies on small enterprise development has been on MSMEs and the informal sector in general. Yet the number of households with an HE is remarkably large. This signifies the importance of HEs to poor households as a livelihood strategy and as a means to supplement their income, and therefore a practical option for increasing their welfare.

# **2.2 Trends in the Growth of Employment and HEs**

13. By far, agriculture remained the main economic activity of the labor force in Tanzania in 2006, employing 77 percent of women and 72 percent of men. However, within the nonagriculture sector, when family helpers are included, the HE share in the labor force increased to over 50 percent for male and 75 percent for female (Figure 2.1). In urban areas in 2006, HEs employed a larger share of the labor force than wage employment, i.e., 40 percent, the largest category (Figure 2.2).

14. Non-farm sector employment has been growing very rapidly in Tanzania as incomes in agriculture stagnate. However, despite a very rapid growth in non-farm wage employment, especially in urban areas, the supply of labor seeking non-farm employment outpaced the demand in the wage sector, leaving many labor force participants with no choice but to create their own employment. A comparison of data from the two rounds of the ILFS survey, 2000/01 and 2006, shows that employment in the HE sector grew by 13 percent, higher than the overall change in the labor force and faster than the growth of wage employment in both non-agriculture and agriculture sectors (Figure 2.3).

15. Since the labor force perspective considers only one activity per individual, it understates the economic role of HEs. Even more remarkable than the importance and growth of HEs as a primary employment source is their role as secondary employment as Tanzania transitions from an agrarian economy. For individuals
heterogeneity in education systems or pupil populations. They are only meant to illustrate the potential use of indicators of inequality of opportunity for future studies of the distributive impacts of education policies. Future extensions – notably involving the use of panel data - might allow for causal analysis of these relationships.

# **6. Conclusions**

Internationally comparable information on learning outcomes, such as the standardized test scores collected by PISA surveys, represents a revolution in the quality of data available for research on education. It allows for potentially much greater insight into the determinants of educational achievement, and might therefore contribute to the design of policies that raise average learning levels, or that reduce educational disparities.

The measurement of educational disparities using this kind of data is not, however, a trivial extension of inequality measurement in years of schooling, or in other variables like income. This paper has highlighted two issues that require special attention in the measurement of inequality in educational achievement, and which appear to have been overlooked so far. The first is the standardization of test scores, to which all meaningful measures of inequality are cardinally sensitive. More importantly, many common measures of inequality, including the Gini coefficient and the Theil indices, are not event ordinally invariant to standardization, invalidating country rankings that are based on them.

We show that the simple variance (or the standard deviation) of test scores is ordinally invariant to standardization, and present estimates for all 57 countries that took part in the 2006 round of PISA surveys, in all three subjects for which tests are carried out: reading, mathematics and science. There is considerable international variation in educational inequality thus measured. The standard deviation in Math scores ranges from around 80 in Indonesia, Estonia and Finland, to nearly 110 in Belgium and Israel.

The second measurement issue that may compromise international inequality comparisons based on PISA test scores is the possibility of sample selection. The surveys are designed to be representative of the population of 15 year-olds enrolled in school, and attending grades 7 or above.
Here the dependent variable, , is the total factor productivity of firm in city , industry and country , is a row vector of firm ’s characteristics that may affect productivity (such as firm age and firm size), and is a row vector that captures the local business environment in city and country . We allow for country-specific effects, , which would capture the productivity effects of country-specific factors, including culture and the political system. We also allow for industryspecific effects, . We allow the effects of firm characteristics to differ by country.

For the business environment factors, we include two components. The first is the direct effects of local business environment, captured by the second term on the right hand side of (2). In the specification, we allow the direct effects to differ by country. The inclusion of this term follows the diagnostic approach to growth (Hausman, Rodrik, and Velasco 2005), which emphasizes countryspecific impact of policies. The second component, captured by the third term in (2), includes interactions among the business environment variables. This is again paying heed to the diagnostic approach to growth, and more specifically, to the notion of country-specific bottlenecks (Kremer 1993). Policies often have important indirect effects on other policies, and some bottlenecks may have especially pronounced indirect effects. Those with important positive externality for other policies are likely binding constraints and should receive higher priority in the reform agenda. This literature also notes that it is often difficult to find the interaction effects, perhaps because most existing studies have relied on country-level data, which may not offer sufficient variations. Our investigation of the indirect effects of policies is, therefore, new to the diagnostic approach. We also allow for country-specific coefficients for the interaction terms so that the indirect effects of a policy reform are allowed to differ because of the difference in policy complementarity.

Since the business environment measures, , are observed only once at the end of the sample period, to avoid exaggerating estimation precision, we only use observations
% 30.9% 26.1%<br>12.7% 12.9%<br>6.9% 12.5% 8.1% 8.7% 8.7% 6.7% 12.3% 7.2% 5.8% 7.2%<br>0% 0%<br>2006 2007 2008 2009 06-09 2006 2007 2008 2009 06-09<br>1 dest. 2-5 dests. 6 or more dests. 1 prod. 2-29 prods. 30 or more prods.<br><!-- End of picture text --> _Source:_ Authors‟ calculation based on Lao PDR customs data The degree of sophistication of the national export basket as well as its potential for expanding into a wider range of products (diversification is a major concern for a developing economy like Laos (Record and Nghardsaysone 2009)), is another area where the customs data can shed light. Figure 4 shows how yearly export volumes are divided among products with low, medium and high sophistication (as measured by their PRODY value) and similarly for the products‟ potential for diversification (PATH values). On the left side, we see the picture of an economy dominated by products with a low income potential (produced mostly by low-income countries), and a rather stable composition in time. In 2006 there seems to be a reversal between low and midlevel PRODY, but this is largely an artifact caused by a type of copper product with a very high PRODY value and which was produced in large quantities that year.

_Exporting from a small landlocked economy_ _9_
percent of GDP for OECD countries. At 29.1 percent of GDP, China’s overall domestic revenue mobilization in 2018 was above the UMIC average (Figure 10).

However, China collects a greater share of revenues in indirect taxes compared to richer countries. China’s heavy reliance on indirect taxes such as the VAT and its relative underutilization of direct tax instruments such as the PIT make its revenue mix somewhat inconsistent with its level of development. As countries develop, their reliance on broad consumption-based indirect taxes declines. For example, VAT and excise taxes account for 34 percent of total revenue for LMICs, 33 percent for UMICs, and 23 percent in OECD countries. In China, indirect taxes accounted for 35 percent of total revenue in 2018. Richer countries also collect more in PIT. PIT accounts for 2.5 percent of GDP in LMICs, 2.8 percent in UMICs, and 8.5 percent of GDP in the OECD. As share of total revenue, PIT is 21 percent in the OECD. By comparison, only 5 percent of total revenues come from PIT in China.

**Figure 10. Richer countries rely more on direct taxes such as PIT; reliance on indirect taxes such as VAT is higher among poorer countries** _(Revenue as % of GDP)_

<!-- Start of picture text --> 40<br>30<br>20<br>10<br>0<br>China 2018 OECD Non-OECD HIC UMIC LMIC LIC<br>VAT Trade Excises PIT Payroll + SSC CIT Other Non-tax rev. Grants<br><!-- End of picture text --> _Source:_ International Centre for Tax and Development. Numbers for China are based on Finance Yearbook of China 2019. _Note:_ The figure shows the composition of government revenue as a percentage of GDP, aggregated by income group. OECD countries form a separate group. Data by revenue type are from 2020 when available or the most recent available
international comparison given that there is considerable heterogeneity in revealed preferences among job quality components such as pay, stability, or flexibility in the population and across countries (Assy et al. 2019; Kumar et al. 2019; Wiswall and Zafar 2018). Nonetheless, analysis on a single country and future research might consider this alternative.

# **c. Country coverage and ex post harmonization of data on job quality**

The ideal measurement approach to job quality provides detailed information on all working individuals in each of the four dimensions for all jobs, including potential secondary and tertiary employment. Compared with multidimensional poverty measures, data on job quality attributes are not systematically collected in all surveys. The quality and availability of microdata on dimensions of job quality vary across countries, within countries across different employment types, such as wage workers or the selfemployed, and across primary, secondary, or tertiary jobs on the individual observation level. The three main data constraints on the proposed analysis are therefore an insufficient overlap in relevant survey questions, an insufficient overlap in categorial definitions of employment status, and high unit nonresponse on crucial survey questions to build this metric.

The production of the proposed JQM based on the Alkire-Foster framework thus requires a substantive effort at ex post harmonization of data across developing countries. To compile the harmonized dataset of job characteristics and demographic attributes, the analysis identified 40 countries with household or labor force surveys that contain questions that are both comparable across countries and pertinent to job quality. The resulting sample spans all regions and income groups across the globe. The 40 countries include 12 in the Sub-Saharan African region, 6 in the East Asia and Pacific region, 12 in the Latin America and Caribbean region, two in the Europe and Central Asia region, 6 in the South Asia region, and two in the Middle East and North Africa region.<sup>18</sup> Survey selection for each country depends on which survey contains more relevant employment information. For many countries, the choice of a survey is
sample attend schools with a vocational focus while the remaining 20% are enrolled in general schools that traditionally prepare them for tertiary education. We randomly assign half of the graduating students in each school to be eligible for the wage incentive and test its effect on employment outcomes over two years using survey and high-frequency social security data.

We offer four main results. First, the employment incentive does not lead to (short-run) changes in employment for graduates of general schools, nor for students who plan to continue their education at baseline. This is an important finding for policymakers who are concerned that programs offering (short-term) benefits to encourage formal work may have hospitality and 41% in warehousing (Moon et al., 2021). High turnover can adversely affect firm performance. Moon et al. (2022) find that an additional percentage point in turnover among Chinese consumer mobile devices workers increases quality failure rates by 0.74%-0.79%.

2
# **3. METHODOLOGICAL FRAMEWORK**

3.1 The development of a social policy framework capable of meeting the objectives defined above, requires an empirical evaluation of the efficacy of current social policies as well as the potential for improvement from the adoption of alternative instruments. This analysis will be based on the household survey undertaken specifically for the current study, and will make use of analytical techniques developed in the income distribution literature.

## **HOUSEHOLD SURVEY**

3.2 The empirical analysis will be based on the household survey conducted by OPSM in May 2002. The survey covered 2,505 households distributed across 39 localities nationwide with more than 5,000 inhabitants. The survey is representative of urban areas at the national level, and also at the level of the Metropolitan Area and the five major regions of the interior. The sampling errors is plus or minus 1.96% at the national level, and plus or minus 5.0% at the regional level.

3.3 The most innovative aspect of the survey is the combination of expenditure data and consumption patterns for infrastructure services with data on household income and socioeconomic characteristics. In this sense, the survey can be contrasted both with utility customer databasesthat offer extensive information on consumption patterns without any accompanying information on socioeconomic characteristics of the householdand national household surveysthat offer extensive information on socioeconomic characteristics without reporting expenditure patterns on utility services<sup>5</sup> .

3.4 In November 2002, OPSM conducted a second household survey with wider poverty measurement objectives, which collected detailed information on the structure of household expenditure, including expenditure on infrastructure services.

## **MEASURING CONSUMPTION**

3.5 The following procedure was adopted to estimate consumption of infrastructure services from the survey data. First, for the 25%-35% of households that were able to provide the interviewer with a copy of their utility bill, the level of consumption could be

> 5 The family expenditure survey of 1997
Given that we allow for a lagged effect of restrictiveness in services on manufacturing TFP, we have three overlapping periods on which the model is estimated. In the first period, the matching data are the restrictiveness index for 1997 with firms’ productivity averages for 1998, 1999 and 2000. In the second, the restrictiveness index for 2003 with firms’ productivity averages for 2004, 2005 and 2006. In the third, the restrictiveness for 2006 with firms’ productivity averages for 2007, 2008 and 2009.

The X vector includes controls for other factors related to integration with the global marketplace that may affect performance.<sup>14</sup>

- (1) Policy restrictiveness in upstream manufacturing sectors, as this may also affect a firm’s TFP in addition to that in service sectors. Here, we rely on the OECD restrictiveness indices for manufacturing, and weight them in the same way as in ‘rest’, using inputoutput coefficients.

- (2) The level of tariff protection in upstream manufacturing sectors (again weighted by the input-output coefficients).

- (3) The degree of competition faced by firms, proxied by (a) the level of tariff protection and (b) the share of output produced by foreign firms operating in the same sector (which in turn also captures potential horizontal spillover effects).

We consider five variations of our baseline specification above, to obtain answers to the following specific questions:

- (1) Is the effect of ‘rest’ on firm’s TFP different for domestic and for foreign firms?  For this, we estimate equation (2) on the subset of domestic firms only, following Arnold et al (2011).

- (2) Is ‘policy restrictiveness’ capturing the effect of restraining FDI flows from coming in? To answer this, we replace ‘rest’ with FDI flows in equation (2).

- (3) Are the effects of ‘rest’ on firms’ TFP the same for firms at the bottom, at
00381]***|
|Household Home & WASH index|0.00841|0.00861|0.00722|0.00747|0.0161|
||[0.00163]***|[0.00163]***|[0.00140]***|[0.00167]***|[0.00403]***|
|Urban|0.072|0.0715|0.0628|0.0748|-|
||[0.00541]***|[0.00554]***|[0.00587]***|[0.00509]***||
|Use internet in the last 3 months|0.114|0.114|0.115|0.114|0.112|
||[0.00411]***|[0.00410]***|[0.00501]***|[0.00810]***|[0.00478]***|
|Washing machine|0.025|0.0247|0.0444|0.0347|0.0286|
||[0.00436]***|[0.00440]***|[0.00453]***|[0.00724]***|[0.00353]***|
|_Social norms & attitudes towards women_||||||
|Men-to-all agreement with gender parity in teams||||0.0416||
|||||[0.0200]**||
|Men-to-all agreement with gender parity in STEM skills||||-0.0453<br>[00274]||
|_Social policy variables_||||.||
|<br>Public preprimary centers per 100 children 4-6 in the municipality (2017)
Table 2: Summary of Pre-MNREGA Economic, Agricultural and Fire data

|Phase 1<br>(1)|Phase 2<br>(2)|Phase 3<br>(3)|
|---|---|---|
|**Data from 2001 Census**<br>|||
|Total Population (000s)<br>1,712.12|1,772.15|1,693.61|
|Total Households (000s)<br>320.12|326.19|320.43|
|% Rural<br>0.84|0.83|0.73|
|% Urban<br>0.16|0.17|0.27|
|% Scheduled Castes<br>0.15|0.15|0.15|
|% Literate<br>0.48|0.52|0.57|
|% with domestic electricity<br>0.88|0.87|0.91|
|% with agricultural electricity<br>0.74|0.59|0.72|
|% with electricty<br>0.75|0.65|0.74|
|% Paved road<br>0.49|0.56|0.68|
|% Mud road<br>0.82|0.82|0.76|
|**Data from 2011 Census, 2012 SECC and VC**|**F Data**||
|Distance to nearest place with _>_100k population<br>50.78|41.87|40.74|
|Per capita consumption, rural<br>15.64|16.59|19.02|
|% HH with cultivation as main income<br>0.35|0.37|0.38|
|Avg. Forest Cover,