rafmacalaba/datause-encoder-data huggingface dataset:
_Table 2: VHI coverage by consumption quintile_

|Quintile|Mean|Std.Dev.|# cases|
|---|---|---|---|
|1|0.0911|0.2878|3656|
|2|0.1225|0.3279|3754|
|3|0.1690|0.3748|3586|
|4|0.2041|0.4031|3473|
|5|0.2709|0.4445|3355|
|Total|0.1693|0.3750|17824|

Source: VLSS data.

_Figure 2: Enrollment in 1997/98 by age in 1992/93_ <!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0 10 20 30 40 50 60 70 80 90<br>Age in 1992/93<br>% enrolled in 1997/98<br><!-- End of picture text --> Source: VLSS data.

On the face of it, VHI enrollment ought to be almost perfectly predictable on the basis of employer type, pensioner status and schooling status. In the event, our probit enrollment equation (Table 3) gives a pseudo R-squared of only 0.19, pointing to considerable variation across similar individuals in the probability of being enrolled. As expected, being in school (around 25% of our sample is currently in school) has a significant positive effect on VHI enrollment. Age has no significant effect on enrollment, once being in school and pensioner status are controlled for. Unsurprisingly, too, a person’s job has a marked impact on insurance status. The equation includes the employer of individuals with one or more formal sector jobs in the week prior to the VLSS interview. The omitted category from the employer dummies is “no employer”. Very few individuals in the sample have formal sector jobs and even fewer have jobs in organizations providing VHI coverage under the compulsory scheme. As expected, the enrollment probability is higher for individuals having a job in the communist party, government or army, as well as for those having a main job in a state enterprise, social organization or a cooperative. Those with a main or second job in the private sector are significantly less likely to be enrolled in VHI. Being a pensioner increases the probability of enrollment.

8
# **Table 16: New exports in 2007**

|sitc4|Short description|Trade<br>Value in<br>mill. RM<br>(2007)|Lall Class|PRODY<br>(2006)|
|---|---|---|---|---|
|6899|Base metals, nes and cermets, unwrought (including waste and scrap)|9.63|RB2|11,492.50|
|2786|Slag, scalings, dross and similar waste, nes|5.11|PP|10,572.04|
|7923|Aircraft of an unladen weight from 2000 kg to 15000 kg|4.27|HT2|9,061.12|
|240|Cheese and curd|2.19|RB1|13,470.20|
|6975|Base metal indoors sanitary ware, and parts thereof, nes|1.56|LT2|13,829.75|
|2922|Natural gums, resins, lacs and balsams|0.45|PP|764.04|
|7213|Dairy machinery, nes (including milking machines), and parts nes|0.40|MT3|18,444.62|
|7938|Tugs, special purpose vessels and floating structures|0.36|MT3|5,878.79|
|118|Other fresh, chilled or frozen meat or edible meat offal|0.19|PP|12,045.39|
|2112|Calf skins, raw, whether or not split|0.18|PP|2,644.78|
|3354|Petroleum bitumen, petroleum coke and bituminous mixtures, nes|0.18|RB2|7,794.08|
|7131|Internal combustion piston engines, for aircraft, and parts, nes|0.14|MT3|4,749.05|
|7842|Bodies, for vehicles of headings 722, 781-783|0.11|MT1|11,688.27|
|4113|Animals oils, fats and greases, nes|0.11|RB2|11,921.18|
|611|Sugars, beet and cane, raw, solid|0.10|RB1|2,462.11|
|2613|Raw silk (not thrown)|0.05|PP|4,263.63|
|430|Barley, unmilled|0.05|PP|7,652.19|
|2111|Bovine and equine hides, raw, whether or not split|0.04|PP|7,299.51|
|2481|Railway or tramway sleepers (ties) of wood|0.04|RB1|3,027.00|
|616|Natural honey|0.04|RB1|6,207.73|
|421|Rice in the husk or husked, but not farther prepared|0.03|PP|2,375.16|
|6594|Carpets, rugs, mats, of wool or fine animal hair|0.02|LT1|7,759.34|
|7126|Steam power units (mobile engines but not steam tractors, etc)|0.02|HT2|16,290.06|
|2682|Wool degreased, uncombed of sheep or lambs|0.02|PP|11,988.34|
|7911|Rail locomotives, electric|0.02|MT2|8,547.08|
|571|Oranges, mandarins, etc, fresh or dried|0.01|PP|7,843.32|
|3413|Petroleumgases and othergaseous hydrocarbons,nes,liquefied|0.00|PP|8,748.64|

_Note:_ PP: Primary Products, RB1: Agro-Based, RB2: Other Resource-Based, LT1: Textile, garment & footwear, LT2: Other, Low-Technology, MT1: Automotive, MT2: Process, MT3: Engineering, HT1: Electronic & Electrical, and HT2: Other, High-Technology. Source: Authors’ calculation 39
That exports exert a progressive impact on incomes at the local level contrasts with the findings of plant-level studies documenting the unequalizing impacts on wages in Mexico. For example, Frías, Kaplan, and Verhoogen (2012) find that exports raise within-plant wage inequality, and Verhoogen (2008) finds that they increase within-industry wage inequality. However, the results of the analysis are broadly in line with the findings of the literature exploiting regional variations in exports and labor market outcomes (Airola 2008; Chiquiar 2008; Hanson 2007).

Table 6 illustrates a robustness check of the main results by controlling for the initial characteristics of municipalities. The impact of exports on the average income of the poorest decile is sensitive to the inclusion of this additional set of controls: the estimated coefficient becomes statistically insignificant if one controls for the sectoral composition of employment and exports. However, the impacts of exports on relative incomes continues to be statistically significant and progressive across all specifications, as shown by the effects on the Gini coefficient.

To understand the impacts of exports on the income distribution more precisely, the following subsections examine two mechanisms that may help explain why any potential gains in exports are not reflected in higher household incomes per capita and less poverty.

# **4.2. Labor Market Impacts**

Table 7 shows the impact of exports on the labor market. The estimates indicate that exports tend to increase labor force participation (panel a). In particular, a 10 percent increase in exports per worker raised the share of working-age individuals working or looking for a job by 0.15 percentage point (column 6). The economic significance of this impact is rather small, considering that the labor force participation rate in the average municipality in 2000 was 45.8 percentage points.

The OLS results suggest that exports per worker and employment rates are strongly associated, although the economic significance is small (panel b). In particular, a 1 percent rise in exports per worker is associated with an increase of 0.05 percentage point in the local employment rate, which represents only 0.11 percent of the employment rate of the average municipality in 2005. The instrumental variable estimates are less precise than the OLS estimates. The only statistically significant result is in the specification without any control variables, which shows 10
# 3. Methodology

## **3.1     Data**

We use cross-sectional data from the nationally representative 2012-13 Bangladesh Multiple Indicator Cluster Survey (MICS) to investigate the effect of arsenic contamination in drinking water on early childhood development outcomes in a sample of 7,502 children aged 3-5 years. The Bangladesh 201213 MICS is a nationally representative survey with health, nutrition, socio-economic, education, and housing characteristics indicators on women and children across urban and rural areas of the 7 divisions and 64 districts of Bangladesh. In answering our research question, we particularly examine indicators on arsenic contamination in household drinking water and early childhood development. The 2012-2013 MICS provides the latest nationally-representative data on household arsenic contamination and is the first to provide nationally-representative data on early childhood development skills domains.

To estimate arsenic exposure, we use the results from water quality testing conducted in the MICS. For the arsenic test, the MICS team randomly selected 5 households per cluster of 20 households and collected a water sample at the household’s reported source point for drinking water. Arsenic was measured by the MICS team using the Arsenic Econo-Quick Test Kit (Industrial Test Systems, USA) and lab tests (UNICEF, 2015). Our main independent variables of interest are arsenic exposure from household drinking water wherein arsenic levels are constructed in three different ways. The first arsenic indicator variable is constructed as a binary variable, where the variable takes a value of 1 if the level of arsenic exceeds 10 ppb as per the WHO Drinking Water Quality Guidelines and takes a value of 0 otherwise. The second arsenic indicator variable is also constructed as a binary variable, where the variable takes a value of 1 if the level of arsenic exceeds 50 ppb as per the Bangladesh standard (UNICEF, 2015) and takes a value of 0 otherwise. The last arsenic indicator is an ordinal categorical variable using the following groups: low-risk (<10 ppb), medium-risk (11-50 ppb), and high-risk (>50 ppb). The categorical variable was constructed in order to examine a wide range of arsenic exposure to evaluate a dose-response relationship. It also provides a convenient variable to compare the WHO and Bangladesh national arsenic standards.

Early childhood development is estimated using results from the Early Childhood Development Index (ECDI), a novel monitoring tool created by UNICEF in 2007 to determine whether children 7
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# CHAPTER VII

# LABOUR PRODUCTIVITY

One of the major purposes of our approach to measurement of real product and purchasing power is to provide information on comparative levels of labour productivity. The estimates of real value added for manufacturing branches in chapter III and IV make it possible to construct reasonable estimates of labour productivity levels.

Labour productivity is here expressed as output per person engaged in production, as the definition of the denominator is the same in the industrial censuses of all three countries. Figures on working hours are generally not available for Brazil, and there are only rough figures for Mexico. In 1975, average working hours in Mexico were 44.05 per week compared with 39.50 for production and non-supervisory workers in US manufacturing'. Reliable comparative information on time off for holidays and sickness is not available, so output per man hour cannot be calculated with any accuraicy, but it seems probable that aggregate hours per person engaged were longer in Brazil and Mexico than in the USA.

The labour productivity ratios presented here do not account for activities in head offices and auxiliaries in any of the three countries. We do not believe that the ratios would change very much by including head office and auxiliary employment. The head office share of total manufacturing employment was 4 per cent in both Brazil and Mexico and 6 per cent in the USA (see table 7.5).

Table 7.1 presents ratios of value added per person engaged in manufacturing branches for the Brazil/USA and the Mexico/USA comparison. These are derived from our estimates of value added levels (former national accounts concept) which are presented in tables 3.15 and 3.16 and the employment figures by branch which are shown in table 7.5. The productivity ratios show a very clear US productivity advantage over both the other countries.

In the Brazil/USA comparison, the geometric ("Fisher") index of labour productivity varied between 33 per cent of the USA for wood and paper products to 76 per cent for food products, with a weighted average of 49 per cent for manufacturing as a whole. The average Mexico/USA productivity ratio is below that for Brazil/USA, namely 39 per cent, with a minimum of 22 per cent of the US level for wood and paper prJducts and a high of 48 per cent for food products.

We were also able to make a direct binary comparison of the Mexico/Brazil productivity relationship. The corresponding labour productivity ratios are presented in table 7.2. From this it appears that Mexican productivity is on average 83 per cent of the Brazilian level, with a range from 47 per cent for rubber and plastic products to 110 per cent for metal products.

**1** For Mexico, see INEGI (1985), vol. 1, p. 60; for the USA, see Employment and Earnings, December 1978, p. 85.
Expectedly, the following 16 countries are included in our comparison sample of developing economies for the variables from the PolityIV database (i.e., when the institutional variables are executive constraints or responsible government): Benin, Chile, Ghana, Hungary, Iran, Jordan, Kenya, Mali, Mexico, Nicaragua, Pakistan, Rwanda, Senegal, Syria, Tunisia, and Uganda.<sup>6</sup> We use 32 countries for the ICRG-data-based regressions (i.e., when the institutional variables are the control of corruption<sup>7</sup> ): Bangladesh, Bolivia, Cameroon, Colombia, El Salvador, Ghana, Guatemala, Haiti, Honduras, Hong Kong, India, Indonesia, Kenya, Mali, Mexico, Niger, Pakistan, Panama, Papua New Guinea, Paraguay, Philippines, Sierra Leone, Sudan, Syrian Arab Republic, Thailand, Togo, Trinidad and Tobago, Turkey, Uganda, Venezuela, Zambia, Zimbabwe. Table 2 reports standard descriptive statistics for these variables across the relevant samples of countries.

Table 3 reports our cross country regression results. These regressions incorporate year fixed effects to absorb global macro factors. Because of expected serial correlations for observations within the same country, the standard errors are estimated based on country clustering.

The institutional measures of good government that capture the strength of constraints on executive power are statistically significant positive determinants of FDI. In contrast, the two variables related to general government quality (i.e., control of corruption and the rule of law) are not associated with significantly more FDI. These results show that conditional on government tract record and general development levels, limits on executive power indeed attracts FDI, yet many other aspects of general government quality do not seem to matter. These results are consistent with the emphasis in Acemoglu and Johnson (2005) – investment and growth appear positively associated with measures that constrain public officials from acting arbitrarily.

> 6 In the case of executive constraints, Brazil is in, but Hungary is out.

> 7 The list of countries when the institution is measured as the rule of law is similar but not identical, and is available upon request.

18
**Figure 1: Female Labor Force Participation Rates by Age Groups: Rural and Urban Areas**

<!-- Start of picture text --> Rural Urban<br>1993‐94 2004‐05 2011‐12<br>80.0<br>1993‐94 2004‐05 2011‐12<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0 10.0<br>0.0 ‐10.0<br>15 to 24 25 to 34 35 to 44 45 to 65 65 plus 15 to 24 25 to 34 35 to 44 45 to 65 65 plus<br>years years years years years years years years<br>FLFPR<br>FLFPR<br><!-- End of picture text --> Source: Authors’ estimations.

While the above figure traces the trend in FLFP across age-groups over time, the following Table 7 quantifies the intertemporal change for each group. It suggests that the largest decline in the FLFP rate for rural areas occurred among the youngest age group, 15 to 24 years, by 17.1 percentage points during 1993-94 to 2011-12 (Table 7). As we proceed through the age groups, the decline in the FLFP rate appears to decrease, with the lowest decline being 2.1 percentage points for the age group of 65 years and above. In urban areas, however, the association between age groups and the decline in the FLFP rate over the years is similar, but more modest.

**<u>Table 7: Change in FLFP Rate in Rural and Urban India, by Age Group</u>**

||Changei|n Rural FL|FP Rate|Change|inUrban FL|FP Rate|Differe|ncein Diff|erences|
|---|---|---|---|---|---|---|---|---|---|
|Age|1993-05|2005-12|1993-12|1993-05|2005-12|1993-12|1993-05|2005-12|1993-12|
|15 to 24|-2.9|-14.2|-17.1|2.6|-6.3|-3.7|-5.5|-7.9|-13.4|
|25 to 34|1.4|-13.6|-12.3|3.5|-3.8|-0.3|-2.1|-9.8|-11.9|
|35 to 44|2.8|-12.9|-10.1|3.5|-6.5|-3.0|-0.7|-6.4|-7.1|
|45 to 65|2.8|-9.7|-6.9|0.5|-3.5|-3.1|2.3|-6.1|-3.8|
|65 plus|1.4|-3.5|-2.1|0.7|-1.7|-1.0|0.7|-1.7|-1.0|

Source: Authors’ own estimate based on NSSO data. Note: Diff-in-Diff in the last three columns are calculated by deducting the change in urban FLFP rate from the change in rural FLFP rate for respective years.

Historically in India, the education of female children, especially at the secondary and higher levels, was conventionally discouraged due to the perceived social role of females as caregivers.

This social role was equally applicable for before and after marriage— leading to gender 12
headline inequality measures to examine patterns of upward and downward relative mobility, and to construct profiles of the upwardly and downwardly mobile.

Our focus in this paper is to enquire into income mobility _within_ generations (intragenerational mobility). Here one is asking whether long-term incomes are possibly distributed more equally than current incomes, and whether the process of inequality change reflects the experience of winners and losers (see further discussion in Jantti and Jenkins 2015).<sup>5</sup> We ask whether there is much mobility across our survey years, and whether there has been an increasing tendency for households to see their relative positions in the consumption distribution change over the quarter-century since the late 1980s. We further explore whether we can identify population characteristics that are more commonly aligned with upward or downward movement in the consumption distribution, and whether there is evidence of change over time in the pertinence of those population characteristics.

As noted above, studies of intragenerational mobility at the all-India level have relied on IHDS panel data to describe mobility in terms of income. The IHDS survey was conducted in 2004/05 and again in 2011/12. It is a nationally representative survey with a sample size of just over 40,000 households, and it features a panel structure that allows one to track households across this interval. Moreover, the IHDS rural sample can also be linked to a sample surveyed in 1993 by the NCAER in their Human Development Profile of India (HDPI). Thus, there is scope for some assessment of mobility patterns between 1993/94 and 2004/05. Key issues that arise with respect to the analysis of mobility based on the IHDS (and HDPI) are, first, the analysis is based on income rather than consumption (even though most discussion about

> 5 Another direction is to study mobility across generations: asking questions about the extent to which children’s living standards are associated with those of their parents, and often interpreting findings as offering a window on the extent of equality of opportunity in society (i.e. the extent to which children’s fortunes are unrelated to the circumstances of their parents). The nationally representative data that allow an investigation of intergenerational income mobility are simply not available for India (although see van der Weide and Vigh (2018) for an analysis of intergenerational educational mobility in India).

8
**Are Cities the New Growth Escalator?**

# **Enrico Moretti, University of Californina, Berkeley**

**Keywords** :  growth, labor, jobs, economic policy, local government, cities, economic development

**JEL codes** :  R11, R19

This paper is part of a collection of papers prepared for the World Bank's Sixth Urban Research and Knowledge Symposium, October 2012. Financial support for this paper was provided by the Urban Development and Resilience Department and the Research Support Budget of the World Bank.
## _Taxes and Transfers and their Effect on Poverty_

The degree of poverty reduction from direct transfers is not sufficient to fully offset the poverty-increasing effect of taxes. Taxes have the strongest impact on poverty, and indirect taxes lead to higher overall increase in poverty than do direct taxes. VAT has the highest poverty-increasing effect, increasing poverty by about 1.76 percentage points. On the direct tax side, social security contributions and health insurance together have the highest poverty-increasing effect, increasing the poverty rate by respectively 0.13 and 0.33 percentage points. Despite direct transfers reducing poverty, the degree of poverty reduction is limited given the modest generosity of support through this channel. Direct transfers (pre-COVID) reduce poverty by only 0.06 percentage points.

Figure 15: Marginal contribution of taxes and transfers to poverty reduction <!-- Start of picture text --> Salary tax<br>SSC<br>HI<br>Property tax<br>MoT tax<br>Rental tax<br>Registration tax<br>All contributions<br>All direct taxes<br>All direct taxes and contributions<br>VAT<br>Specific tax, excise<br>Accommodation tax<br>Public lighting tax<br>All indirect taxes<br>Total direct transfers<br>Electricity subsidy<br>-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5<br>Point change<br><!-- End of picture text --> Source: Authors’ calculations based on CSES 2019/20 and fiscal data.

# **Heterogeneity of Effects across Deciles**

The burden of taxes and benefits varies across market income distribution deciles. In absolute terms, the burden of direct taxes, in particular salary tax, concentrates at the upper end of the income distribution (Figure 16A). In relative terms, the direct tax burden as a share of _market income_ does not vary considerably across the distribution except for the poorest and richest deciles (Figure 16B). However, cash transfers are not sufficient to offset the burden of direct taxes across all deciles of the distribution except the poorest. As such, the net cash position for most households is negative before considering indirect taxes and subsidies.

After indirect tax and subsidies, the net cash position is negative for each market income decile. Cash transfers do not offset the tax burden. While in absolute terms, households in the upper end of the income 24