rafmacalaba/data-use-mentions-extended huggingface dataset:
move among sectors of work. Individuals are matched by position in an identified household, sex, level of education, and age to ensure against generating spurious transitions. The analysis restricts itself to men aged 16-65 with a high school education or less. It also focuses on formal salaried workers and the "informal" self-employed, including owners of firms under 16 employees who do not have social security or medical benefits and are therefore not protected.<sup>4</sup> Only those who begin in formal salaried employment and move only once over five quarters into self-employment are retained, yielding a sample of 1087 workers. In the estimations, we employ predicted earnings in each sector as a measure of the "own" and "alternate" earnings, given the standard human capital variables, experience, experience squared, education, education squared. The return to accumulated capital (the opportunity cost of using savings to open a business) is the real 30-60 day deposit rate as calculated from the International Financial Statistics of the IMF deflated by growth of the consumer price index. We also test state dependence through introducing the lag of the independent variables in the regressions.

# **V. Results:**

Table 1 presents the results of the estimation of the model set out in section II.

**Table 1: Results from Rotating Panel Logit Regression**

||COEF.|S.E|COEF|S.E.|
|---|---|---|---|---|
|Wage (Salaried)|-.347|1.34|-.357|1.28|
|Wage (-1)|.132|1.41|X|l|
|Earnings (Self)|4.02|.249|4.03|.235|
|Earnings (-1)|_-.115_|.354||l|
|Interest Rate|-3.39 e-3|
The literature has identified several rationales for differentiated emissions policy. Many of these arguments rely on either political economy or rent-shifting arguments. Anger et al. (2006) develop a lobbying model that predicts differentiated emission taxes based on factors such as trade exposure. They find empirical support for the model using data from Germany. A large literature notes that governments have an incentive to manipulate environmental policy to improve their terms of trade or to give domestic firms a strategic advantage over foreign firms. These are rent-shifting arguments. Rauscher (1994) shows how this leads to differentiated pollution taxes across the tradable and non-tradable sector. One weakness of this approach is that governments have many instruments available to support domestic firms and if they do choose to engage in terms of trade manipulation, it is not obvious that environmental policy would be either a desirable or effective instrument (the theoretical literature resolves this difficulty by assuming no other instruments are available).

Another branch of the literature shows that differentiated emissions policy can have a role to play if pollution is transboundary and there are concerns about leakage. Most of this literature is driven by concerns about carbon leakage; i.e. that tightening up environmental policy in one country will encourage carbon intensive production to relocate to other countries. Hoel (1996) demonstrated how differentiated carbon taxes could mitigate carbon leakage.

Perhaps the most common rationale for a differentiated emissions regulation is to deal with concerns about the effects of regulation on international competitiveness. As discussed earlier, even if environmental regulation does affect the international competitiveness of an industry, this does not in itself yield an efficiency argument for sheltering the industry from environmental policy: countries have a choice of growth paths, and weak environmental policy provides an implicit subsidy to a less green growth 19
has been calibrated using the initial period’s data, it can then be used to gauge the country’s multidimensional inequality through time and, with additional assumptions, through space.

We illustrate the methodology using simulated data to allow specific inequalities, distributional means, and correlation levels to vary freely. Examples show the impact of each factor on multidimensional inequality for measures based on the Lorenz curve, the Gini coefficient, and other Lorenz-consistent measures. A second illustration using data from Azerbaijan examines the evolution of multidimensional inequality during a period of rapid income growth.

Previous studies have considered linear aggregation as one among several options, but to our knowledge this is the first paper that selects this structure based on the axiomatic properties of its associated measures. Likewise, a number of authors have linked multidimensional inequality to specific inequalities or to positive association, but none has the elegant simplicity of our decomposition, which highlights the fundamental role of Shorrocks mobility in representing a positive association in multidimensional inequality. In addition, the approach is unique in its use of normative weights and Lorenz curves to calibrate the measure in a base year, and then to judge subsequent changes accordingly. Finally, the paper is unusual in its focus on identifying multidimensional inequality measures that are especially useful in conducting policy.

Section II introduces the definitions and notation used in the paper. Section III presents different approaches to multidimensional inequality measurement and then establishes our main characterization result, identifying the subset of measures from Maasoumi’s (1986) two-stage approach that are axiomatically sound. Section IV provides expressions linking members of this class of multidimensional inequality measures to specific inequalities and Shorrocks mobility. Our calibration method is described in Section V, selecting initial parameters of the measure based on normative weights and Lorenz curves. Section VI illustrates our methods, while the final section concludes.

**II. Notation, Axioms, and Other Fundamentals** The data for measuring inequality are given in an array with rows and columns. The row lists data for person ; the column lists data for dimension
where δ<sup>∗</sup> _j_ k<sup>and δ∗</sup> _j_ l<sup>denote the equilibrium share of capital and labor allocated to sector</sup><sup>_j_respectively.</sup> The above equation is flexible enough so that the marginal effects of changes in capital and labor stock (through changes in participation and employment rates) on aggregate productivity can be isolated in addition to marginal gains from allocation of resources across sectors. Nonetheless, it is clear from equations 8 and 9, that the relative allocation of resources across sectors is independent of changes in stock of aggregate resources.5 Hence, given the focus of this paper on sectoral gaps in productivity, I abstract from marginal gains due to growth in stock of resources and consider only the marginal effects of reallocation of resources across sectors.6 Finally to take this equation to data, I normalize the final good as the numeraire. I discuss the data that I use to carry out the quantitative exercise in the next section.

# **3 Data**

The bulk of the data used in the analysis are taken from the World Development Indicator (WDI) online database. Specifically, I use the following series from the database: value added at sector level (provided in constant 2005 US dollars) and employment at sector level (provided as percentage of total employment). Apart from these six series, I use data from Global Trade Analysis Project (GTAP) to estimate the sector labor share of income at sector level.

The WDI data on sectoral share of employment are originally sourced from the International Labor Organization’s (ILO) Key Indicators of the Labour Market Database. This annual series on sectoral share is often constructed using different sources for the same country over time. The annual series starts from 1980 for some countries and there is a wide variation with respect to the annual observations available across countries. However, there are two major concerns with regards to using the data as
_natural resources_ as s̅� �1 �s̅�. Applying expression (15), the ratio of the growth rates of TFP with and excluding natural capital reduces to, In discrete time, the TFP growth rate with natural resources exceeds the growth rate calculated excluding natural resources if fixed capital is growing faster than natural resources. The difference in the growth rates increases with the share of natural resources in GDP. This result is qualitatively exactly the same as the continuous time case. The generalization of this equation for two or more natural resources is given in Annex II.

In presenting the empirical results of these calculations in the next section, it is important to note that the calculation of the average rate of TFP growth for a given country is calculated as the average annual rate of change (AARC) of TFP. To measure the AARC of TFP growth over n years we must calculate the geometric mean of g��t��1 over all years, then subtract 1. Here g��t� is the growth rate of TFP in year t: Because this is not a simple average of growth rates, we calculate the measure of variation in the TFP growth rate as the root-mean-squared (RMS) of the differences between the annual measures of TFP growth and the AARC. This notation signals that what is presented is not a classic standard deviation.

For calculating aggregates of TFP growth, such as for low-income countries as a group, or developing countries in South Asia, we measure a simple weighted average of the country figures, with country GDP in constant US dollars providing the weights.

# _Data sources_

Table 1 presents the data sources for the TFP growth calculations. For detailed information on the measurement of the natural resources appearing in the _Wealth of Nations_ database, the reader is referred to Appendix A of _The Changing Wealth of Nations 2018,_ or the detailed methodology documentation which can be found at <u>https://datacatalog.worldbank.org/dataset/wealthaccounting.</
their lives.<sup>3</sup> In this sense, urban residents of China with formal sector employment face

> 1Lee, Mason et al (2011) highlight the implications of population aging for sustainability of public and private transfer systems across Africa, Asia, Europe, Latin America and the United States.

> 2Recent research by demographers at the US Census Bureau suggests that the old age dependency rates in 2020 will reach 22, 19 and 13 percent, for Korea, China and Indonesia, respectively, and by 2040 these rates will rise to 53, 40 and 25 percent (Kinsella and He, 2009). A preliminary release from China’s 2010 census informs us that 13.3 percent of China’s population is now over 60 as opposed to 10.3 percent in 2000, while the size of the future workforce has dwindled, with individuals under 14 accounting for 16.6 percent of the population, down from 23 percent in 2000 (National Bureau of Statistics, 2011).

> 3New initiatives are currently underway in rural China. A government-subsidized contributory rural pension piloted in 2009 will be rolled out to cover all rural counties over the next three years. In cities, a new pension scheme,

2
of the 54th month, than with the actual wealth distribution. The effect on sales over the course of the adoption process, on the other hand, is rather small. With an equal asset distribution, simulated sales never exceed predicted actual ones by more than 7 percent. Moreover, when we focus on differences between simulated and predicted actual sales of more than 3%, simulated sales never lead predicted actual ones by more than five months.

According to Figure 6, a similar picture emerges for the dynamics of inequality. While the inverted U contracts by about 20% toward the origin, the change in the general pattern of inequality as measured by the Gini can hardly be judged economically significant.

A second set of simulations investigates two extreme scenarios. The first one assumes that each household in the sample holds only the smallest observed wealth, that is each house is assumed to be worth Rs. 20,000. The second one, in contrast, assumes that each household in the sample holds the highest observed wealth, that is each house is assumed to be worth Rs. 500,000. The results for this set of simulations together with the predicted actual values are set out in Figures 7 and 8. We thus consider situations in which all households are either tightly credit-constrained or virtually do not face a credit constraint at all. The mean income and inequality paths for the first simulation very closely follow the respective paths generated from the actual asset data, which suggests that the observed income pattern accompanying the introduction of the new technology closely resembles a situation in which all households are substantially credit-constrained.

The results for the second simulation, where the credit constraint is released for the entire sample, are more striking. The dotted lines in Figures 7 and 8 suggest that with uniform asset wealth the adoption process is completed in just five months. As a consequence, the village enjoys a substantially higher mean income for about two years by which adoptions in the simulated data lead predicted actual ones. This result suggests that a community in
RZ (1998) find evidence consistent with the hypothesis that industries that rely more heavily on external finance grow faster in countries with a better-developed financial system. They use data on a panel of 42 countries and 36 industries over the 1980s. Furthermore, RZ show that the effect of financial development on the industrial growth runs mostly through growth in the number of establishments rather than through growth in the average size of establishments. So financial development improves disproportionately the prospects of young firms in industries that rely heavily on external finance.

Besides confirming the RZ findings with alternative measures of financial development and alternative instruments to deal with endogeneity, this paper explores whether industries with a high need of external finance grow faster in economies with bank- or market-based financial systems. Furthermore, we assess the financial services and legal-based views. Specifically, we study whether (1) the overall level of financial development and (2) the legal codes and enforcement mechanisms governing financial transactions importantly determine industrial growth patterns. Following RZ, we then decompose industry growth into the growth in the average size of establishments and the growth in the numbers of establishments to evaluate the importance of the degree to which financial systems are bank-based or market-based, financial development and its legal determinants for the sources of growth. For conciseness, we use the term "financial structure" to refer to the degree to which a country's financial system is bankbased or market based. We define our indicators of financial structure in such a way that higher values imply a more market-based system.

Econometrically, we use the following regression to assess the impact of financial development and financial structure on industry growth.

8
# Acknowledgments

This paper draws on contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Heinrich Bofinger on air transport, Michael Minges on information and communication technologies, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in the relevant World Bank teams; notably Yusupha Crookes (director of the Africa Regional Integration Department), Andrew Roberts (Africa Regional Integration Department), Gael Raballand (transport), and Charles Kunaka (International Trade Department). The paper benefited from feedback provided by REC staff at a May 2010 Workshop organized by the Infrastructure Consortium for Africa in Tunis and at other meetings.

v
income from estate/trusts; ( _iv_ ) social security or railroad retirement; ( _v_ ) supplemental security income; ( _vi_ ) public assistance or welfare payments; ( _vii_ ) retirement, survivor or disability pensions; and ( _viii_ ) other regular sources of income, such as veterans payments, unemployment compensation, child support or

> 6 Using the MLD index, the within-group inequality component could be seen as a proxy of inequality due to individual effort. Unfortunately, this measure contains residual elements arising from non-observed circumstances, luck and other measurement errors, which prevents us from using this term in the empirical analysis. 7 The data cover 1 percent of the population for the years 1960-70 and 2010, and 5 percent of the population for the years 1980-2000.

6
the land owned by the household in the past and the present, and the household's income, along with a set of village dummy variables.

# Data and Statistical Analysis

The data used in this paper are from a census survey of an endogamous sub-caste of potters spread across three villages, Halli, Beedu and Ooru<sup>8</sup> , located within 70 miles of each other also the state of Karnataka. Only 20 per cent of the adults in this community actually practice pottery, and most are day laborers with small-scale farming and silk rearing the other major activities. Halli and Ooru are multi-caste villages located within commutable distances of large towns. Beedu, a village consisting entirely of potters, is located in a "forward" district with relatively good schools and a long tradition of providing basic social services.

Table 3 provides some summary statistics about the sample. The average annual household income is 14,471 Indian rupees - about $1644 in PPP adjusted US dollars ($482 at the exchange rate prevalent at the time) - a little over half of Indian per capita household in PPP dollars, and well below the Indian poverty line of 45 rupees per person per day. The average land holding is very low at 0.59 acres. Education is also low, with the average maximum schooling of a person within a family at 4.54 years (the mean years of completed schooling among household heads is much lower at 1.6 years). Despite the low levels of education the villages have recently undergone a fertility transition with the average household consisting of 3 adults and 1.5 children.

Tables 4 shows that festival expenditures rise with income indicating that they are a "normal" good. They also rise with the maximum level of education in the family, the number of young children, and the number of girls of marriageable age. Thus, we can infer that private incentives are important determining factors. The amount spent on festivals by neighbors is not significant indicating that demonstration
Table 4: Effect of savings default on bank transactions

||(1)<br>Day of<br>transfer|(2)<br>+3 days|(3)<br>+7 days|(4)<br>+14 days|
|---|---|---|---|---|
||Panel A: Depo|sits|||
|Savings Default|23452.552***<br>(378.585)|23680.378***<br>(621.169)|23932.107***<br>(698.161)|22586.859***<br>(1256.521)|
|Observations|474|474|474|474|
|Mean of dependent variable in control group<br>R-squared|1636.75<br>0.90|2190.82<br>0.78|2741.40<br>0.81|5122.17<br>0.80|
||Panel B: With|drawals|||
|Savings Default|-17937.270***<br>(649.696)|-19966.633***<br>(565.638)|-20456.032***<br>(703.387)|-21086.793***<br>(1264.565)|
|Observations|474|474|474|474|
|Mean of dependent variable in control group|-9.02|-254.79|-886.54|-2426.92|
|R-squared|0.68|0.81|0.75|0.50|
||Panel C: Net D|eposits|||
|Savings Default|5524.446***<br>(721.615)|
products under investigation. There are only three countries with detailed countervailing duty data in the database thus far:

**Table 5.1 : Antidumping User Countries with Detailed Data in the Database**

|**Country Code**<br>**(3-letter CTY)**|**Country**<br>**Name**|**Excel Workbook File**<br>**Name**|**Spreadsheet Names within**<br>**Country Workbook**|**Available**<br>**Years of**<br>**Data**|**Current**<br>**Version**<br>**(last updated)**|
|---|---|---|---|---|---|
|CAN|Canada|CVD-CAN-v1.0.xls|CVD-CAN-Master ,<br>CVD-CAN-Products|1985-2005|v1.0<br>(8/2005)|
|MEX|Mexico|CVD-MEX-v1.0.xls|CVD-MEX-Master ,<br>CVD-MEX-Products|1990-2003|v1.0<br>(8/2005)|
|USA|United<br>States|CVD-USA-v1.0.xls|CVD-USA-Master ,<br>CVD-USA-Products|1980-2005|v1.0<br>(8/2005)|

38
controlling for spatial price variation<sup>15</sup> – governorates in which income levels are lower would have a relatively lower incidence of households reporting that their income is sufficient to satisfy needs. On the other hand, as shown in Figure 4, while average household income is by far the lowest in Idleb governorate, the share of households in Idleb that report income being sufficient to satisfy household needs is the third highest after Damascus, Aleppo and Ar-Raqqa. This finding, coupled with background information of high levels of both cash and in-kind humanitarian assistance being provided to households in Idleb underscore the limitations of a partial welfare metric not comprehensive of in-kind income receipts.

_Figure 3: Contribution to total household monetary income, by source_ <!-- Start of picture text --> IDLEB<br>QUNEITRA<br>TARTOUS<br>AS-SWEIDA<br>RURAL DAMASCUS<br>ALEPPO<br>HAMA<br>DAR'A<br>LATTAKIA<br>DAMASCUS<br>HOMS<br>DEIR-EZ-ZOR<br>AR-RAQQA<br>AL-HASAKEH<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Labor Remittances (domestic) Remittances (Intl.) Pension Cash Assistance<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on HNAP June2021 Demographic and WASH survey data _Figure 4: Average household income and share of households reporting income sufficiency, by governorate_ <!-- Start of picture text --> 500,000 45%<br> 450,000 40%<br> 400,000 35%<br> 350,000 30%<br> 300,000 25%<br> 250,000<br>20%<br> 200,000<br> 150,000 15%<br> 100,000 10%<br> 50,000 5%<br
effect may be compounded by employers, who presumably expect less career commitment from their female employees.

The other group of Northern European countries differ from the Scandinavians in that they have lower FLFP levels but also less generous policies to encourage participation. As for the other four country groups, there is a clear correspondence between their macro- and micro-levels results; this suggests the existence of a bifurcation at the level of developed countries; in this case increases in FLFP do not lead to declines in inequality. Existing labor market regulations and the fiscal policies play an important role in this respect. In the case of these countries, the higher returns of top deciles suggest that the _glass ceiling_ is less present, and that women can reach top positions. Less generous pro-participation policies limit women’s access in the lower deciles and lead to a much lower participation level than the Scandinavians. Results hold even when considering surveys 5 years older (see table A4 in the Appendix).

When introducing the PAM index as an additional instrument (Table 5) and restricting the sample, the results do not vary substantially. Only in Ireland the impact of FLFP becomes insignificant while in all other countries the signs, significance and country ordering remain practically the same as in Table 4. Overall in developing countries the impact is pro-inequality while with the exception of the United Kingdom and the Netherlands, the FLFP impact is pro-equality in developed countries.

# 5. Conclusions

Over the past few decades developing countries have seen a rapid increase in the number of women taking an active role in the labor market; however, this picture is highly heterogeneous. For many developing countries, increasing FLFP levels remains a priority (United Nations, 2000; World Bank, 2012), as well as a challenge because, as documented in this paper, increasing FLFP levels in many of these countries can produce undesired distributional effects if it is not adequately combined with measures targeted towards women in the lower deciles of the income distribution.

As a matter of fact, our results confirm that