rafmacalaba/datause-encoder-data huggingface dataset:
in different segments of the Indian labour market, 1983–2012: exploring the glass ceiling or sticky floor phenomenon,” _Applied Economics_ , 2016, _48_ (43), 4098–4111.

- **Express, The Indian** , “Free bus travel for women in Punjab from today,” April 2021. https://indianexpress.com/article/cities/chandigarh/ free-bus-travel-for-women-in-punjab-from-today-7253260/, Visited 2022-11-17.

- **Fletcher, Erin, Rohini Pande, and Charity Maria Troyer Moore** , “Women and work in India: Descriptive evidence and a review of potential policies,” 2017.

- **Franklin, Simon** , “Location, search costs and youth unemployment: experimental evidence from transport subsidies,” _The Economic Journal_ , 2018, _128_ (614), 2353–2379.

- **Goel, Manisha** , “Inequality between and within skill groups: The curious case of India,” _World Development_ , 2017, _93_ , 153–176.

- **Gonzalez, Karla Dominguez, Ana Luiza Machado, Bianca Bianchi Alves, Veronica Raffo, Sofia Guerrero, and Irene Portabales** , “Why does she move? A Study of Women’s Mobility in Latin American Cities,” _World Bank, Washington, DC_ , 2020.

- **Goswami, Sweta** , “Pink tickets boost DTC women riders,” _Hindustan Times_ , August 2021. https://www.hindustantimes.com/cities/others/ over-76-of-dtc-passengers-were-women-in-last-five-months-101627755797751.html, Visited 2022-11-15.

- **ILO** , “World Employment Social Outlook–Trends for Women,” _Geneva_ , 2017, pp. 1–63.

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- **Khera, Purva** , _Closing Gender Gaps in India: Does Increasing Womens’ Access to Finance Help?_ , International Monetary Fund, 2018.

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22
matched the treatment into the control sample for each of the four combinations of race and parental level of education, and we determined the common support for each of these four comparisons as the overlap of the support of the control and treatment samples. Table A.3 above gives the common support and the number of observations in the common support for each of the types.

We tested the balancing property score using Stata. The optimal number of blocks was 11, and we had 54 explanatory variables, resulting in 594 tests. In 14 cases, the balancing property was rejected. As an additional test, we reran the logistic equation from table A.2 using the weighted sample. Only four coefficients out of 54 were significant. These results are encouraging.

Step 2: Construction of the cumulative distribution function Let I1 denote the set of individuals in the treatment sample, I0 denote the set of individuals in the control sample, and SP denote the region of common support. The number n0 gives the number of individuals in the set I0 ⋂SP . The outcome of individual _j_ in the control sample is Y0j, and the outcome of individual _i_ in the treatment sample is Y1i . Let _D_ = 1 for program participants and _D_ = 0 for those who do not participate in the program.

The purpose is to match each individual in the control sample with a weighted average of individuals in the treatment sample. The usual estimator of the average treatment effect thus becomes 30
|Income group control|NO|YES|YES|
|---|---|---|---|
|Constant|-0.056|-0.082|0.038|
||(0.067)|(0.087)|(0.064)|
|Number of observations|902|902|902|
|Countries|96|96|96|
||F(2, 95) = 4244<br>|<br>F(7, 95) = 5155|<br>F(8, 95) = 5233.70|
||Prob > F = 0.000|Prob > F = 0.000|Prob > F = 0.000|
||Arellano-Bond test for|Arellano-Bond test for<br>|Prob > chi2 = 0.357<br>|
||AR(1) Pr > z = 0.000|AR(1) Pr > z = 0.000<br>|Arellano-Bond test for AR(1)|
||Arellano-Bond test for<br>AR(2) Pr > z = 0.333<br>Sargan test of overid.<br>restrictions: chi2(127) =|Arellano-Bond test for<br>AR(2) Pr > z = 0.230<br>Sargan test of overid.<br>restrictions: chi2(125) =<br>98.73|Pr > z = 0.000<br>Arellano-Bond test for AR(2)<br>Pr > z = 0.207<br>Sargan test of overid.<br>restrictions: chi2(180) =|
||97.46|Prob > chi2 = 0.960|186.62|
||Prob > chi2 = 0.976||Prob > chi2 = 0.352|

Table 4 investigates a possible direct relationship between debt and education expenditure. A system GMM is applied to estimate equation 4 using the debt stock-to-GDP ratio and the debt service-to-reserve ratio as proxies for debt. The results show that a higher debt-to-reserve ratio is associated with lower education spending. This is consistent with the results presented in Table 3. A higher debt level relative to reserves could mean that the government does not have sufficient capacity to face its debt burden. Government then attempts to control its fiscal imbalances through fiscal adjustments, leading to a decline of education expenditure.

Education expenditure tends to overreact to an increase of the debt burden. In other terms, the decline of education spending is proportionally larger than the increase of the debt burden. Table 4 shows that 1 percent increase in the external debt volume without any changes to the reserve levels could lead to a 2.9% decrease in the spending per school-age child. In other terms, education expenditure is vulnerable to external debt. This means that education expenditure may be one of the spending categories used by the government to adjust the fiscal deficits. Therefore, external debt poses a significant threat to education financing, especially in the current context of the rising debt levels caused by the pandemic. Protecting education finance should become a priority in the post COVID-19 era.<sup>4</sup>

> 4 UNESCO (2021) stresses the need to protect investment in education in the world in the context of the COVID-19 pandemic. https://en.unesco.org/news/protecting-education-funding-face-covid-19

8
5 (from ILO, 2011; 1-digit classification). It is well known, however, that both of these data sets are quite limited in their coverage of frontier countries. For this reason, a new data set was constructed, covering 12 economy-wide sectors during 2000–2010, using country data compiled from IMF desk inputs (see below for further discussion).

Appendix I provides greater details on the diversification indices and quality measures employed in this paper. Briefly, diversification is measured using the Theil index, which has the advantage of being decomposable into diversification along the extensive and intensive margins. Lower values of the index indicate _greater_ diversification. Quality measures are based on individual products’ unit values (that is, trade prices), but with important adjustments for differences in production costs, as well as for selection bias in the composition of international trade. Appendix II sets out a full list of the countries and regions analyzed.

# **III.   DIVERSIFICATION, GROWTH, AND VOLATILITY**

We start by examining the evidence on the links between diversification and growth. One result stands out: diversification patterns and growth are clearly related, although the relationship displays much heterogeneity. In particular, greater diversification is on average associated with faster subsequent output growth (Figure 1). The relationship holds both for the sample as a whole, and for Asian countries alone. Adopting a multivariate regression approach, output growth remains significantly associated with both initial diversification and initial product quality measures, even after controlling for a variety of standard growth determinants (Table 1). This conclusion is in line with a large literature, including Singer (1950), Sachs and Warner (1995) on the “natural-resource curse”, and Hausmann and others (2007) on the links between growth and product sophistication.

In a similar vein, diversification spurts (defined as in Papageorgiou and Spatafora, 2012) are associated with sharp subsequent growth accelerations (defined analogously to diversification spurts). This is especially true for non-fragile frontier countries. Conversely, growth accelerations are associated with subsequent increases in diversification among non-fragile frontier countries.
# **2. Recent Trends of Income Inequality in Latin America**

Despite the heterogeneity that exists between the countries of Latin America, monitoring regional inequality is important in order to unveil two messages that are usually hidden in country-specific figures. First, regional inequality trends are a useful benchmark for country-specific inequality trends, as well as for comparisons with other regions in the world. Second, as intraregional migration and trade grow, measuring inequality between countries, as opposed to only within countries, becomes a more relevant and useful indicator.<sup>7</sup> Income inequality has fallen steadily in Latin America since the mid-1990s (World Bank 2011b; Lustig, Lopez-Calva and Ortiz-Juarez, 2013), with a more pronounced reduction during the 2000s. Income inequality in 2012 (measured by the Gini coefficient for total household per capita income using pooled country-specific data for 17 countries) was six Gini points lower than in 1995, decreasing from 57.8 to 51.8, and about four Gini points lower for almost all Latin American countries than in 2003 (Figure 1a). However, between 2010 and 2012, inequality reduction stagnated with an annual decrease of only 0.09 Gini points, in contrast to an average annual decline of 0.52 Gini points experienced between 2003 and 2010 (Figure 1b). These results are robust to different specifications.<sup>8</sup> Figure 1. Gini coefficient and annual change in Gini points in Latin America, 2003-2012 b. Annual change in Gini from previous year Source: Authors’ calculation from SEDLAC (CEDLAS and the World Bank).

Note: Since the Gini coefficient does not satisfy group decomposability, the regional Gini coefficient is computed based on pooled country-specific data for 17 countries. In order to test the robustness of the results, the unweighted average is also presented in the figure. The methodology to pool these datasets is explained in footnote 1 and the years used for each country in the weighted average are detailed in Table A2 in the annex.

The reduction of inequality during the period 2003 to 2010 can also be illustrated by the higher than average income growth rates of the lower end of the income distribution, as revealed by the growth incidence curve (GIC) (Figure 2a). While the top decile of the income distribution grew on average at

> 7 Migration between the countries in the region is a significant policy issue in several countries, for example Central American migration to Mexico, Haitian migration to the Dominican Republic, Nicaraguan migration to Costa Rica, and migration from Paraguay and Bolivia to Argentina.

> 8 The unweighted average of country-specific Ginis shows a steady decline from 53.8 to 48.3 Gini points between 2003 and 2012.

4
Weeding out obsolete land regulations would itself go a long way toward improving the housing consumption of lower income groups. Other actions will be needed, of course, such as investing in infrastructure and transport to increase the supply of land accessible from any point in a city in less than one hour.

## **_Define a supply-side policy program_**

Housing standards could be dramatically improved for the poor if many supply-side barriers were removed—or at least reduced—and if the spatial aspect of housing, particularly the transport cost for each location, is taken into account when designing a land and housing policy. Several actions are available in different sectors: _Transport._ Increase the land supply by expanding the reach of infrastructure and the speed and convenience of transport. Calculate current land supply based on the dominant mode of transport used by income groups.

_Management of public land holdings._ Audit and map government land holdings, and eventually reconvert some underused government land into salable land or use it as a public good (parks, for instance).

_Regulatory reform._ Remove some of the regulatory constraints on commercial and high-income groups that artificially increase their land consumption. Remove some regulatory constraints mostly affecting low-income groups to increase the number of households that can afford formal housing (reviewing minimum plot sizes, apartment sizes, parking requirements, and so on).

_Informal areas and slums._ Monitor the areas, densities, growth rates, and land use standards of informal areas. Consider formalizing these areas as parallel markets where land use rules are purely demand-driven instead of normative.

# **Urban planners need to take full responsibility for the timely delivery of buildable land to markets**

Urban planners have the merit of playing an operational role in city management; they are key actors in the design of infrastructure and urban regulations. They often consider their role as “designer” of more efficient and more “livable” cities. But they also often ignore the role of markets—and in particular the demand from households and firms for land and floor space. As a result, the plans they prepare lose relevance and fail to be implemented, particularly when they fail to monitor the growth of informal land markets that are the only source of land for the poor.

To play a more efficient role, planners should monitor the supply and demand for land and floor space. They should monitor the supply of developable land, defined by the area accessible through the transport network in less than one hour. They should ensure the elasticity of the supply of land by expanding the reach and the speed of the transport network when prices and household incomes are rising. Failure to do this inevitably results in high land prices that become unaffordable to the poor and even the middle class.

24
sists of OLS regressions in which the potentially endogenous variables—entry costs, export costs, and own tariffs—are used sequentially as the dependent variable, while the exogenous variables from the baseline model along with the instruments are used as the independent variables. The residuals from these regressions are retained, and entered into the baseline Poisson formulation (21) as additional regressors (i.e., retaining the potentially endogenous measures of trade and entry costs). Conditional on the use of appropriate instruments, inclusion of the first stage residuals removes any endogeneity bias. A simple test of the joint significance of the residual terms can be used as an indication of the endogeneity of the variables being instrumented for: rejection of the null hypothesis indicates that there is a serious endogeneity problem. As usual, the most difficult aspect of this approach lies in identifying variables that satisfy the twin conditions for instrument validity: strong correlation with the potentially endogenous trade and entry costs, but orthogonality to current export diversification. Our strategy exploits variation in geography and colonial history to identify exogenous movements in the variables of interest. The idea is that a country’s economic and social institutions should be correlated with distance from the equator (Hall and Jones, 1999), as well as with the marks left by colonial rule on legal regimes and institutions (Djankov et al., 2002).73_ Thus we expect both instruments to be correlated with the currently observed levels of trade and entry costs, while being exogenous to current export diversification. In our first stage regressions (Table 4), we therefore include distance from the equator and binary dummies for colonization by Great Britain, France, Spain, Portugal, the Netherlands, and Russia in addition to the exogenous variables from the baseline model. The first stage R? is 0.4 for entry costs, and 0.3 for export costs and own tariffs, while F-tests of the null hypothesis that all instruments jointly equal zero are 5.47***, 1.88*, and 4.65*** respectively. While our instruments do a fair job of explaining the observed pattern of entry and trade costs, it would obviously be desirable in future work to try and identify stronger instruments. This is particularly true in the case of export costs, for which the F-test only rejects the null hypothesis at the 10% level. Nonetheless, <u>~ 231n fact, Djankov et al. (2002) use legal origin dummies,</u> not colonization, as instruments for market entry costs. However, those measures display insufficient variation in our sample, and so we prefer to use closely related data on colonization drawn from Mayer and Zignago (2006). 23
# **Table 6: Panel Regressions – Main result: Volume of small transactions for all crypto-assets**

|<br>Dependent Variable: Log monthly on-cha<br>|<br>in crypto-assets volum<br>|<br>e (transaction size<br>|<br><US$10K), US dollars<br>|||
|---|---|---|---|---|---|
|Variable|(1)|(2)|(3)<br>**All Countries**|(4)|(5)|
|Inflation (yoy)|1.032***|0.500|0.073|0.334|0.098|
|Change exchange rate (mom)|-2.685***|-0.878**|0.698**|1.159***|0.156|
|Change Bitcoin price (mom)||0.587***|0.498***|0.521***|0.491***|
|Change Ether price (mom)||0.292***|-0.035|0.139***|-0.004|
|US 10-year Yield (inflation adjusted)|||-0.394***||-0.431***|
|US Inflation Expectation Rate|||1.434***||1.398***|
|VIX||||-0.038***|0.001|
|Change gold price (mom)||||-2.008***|-1.486***|
|Pandemic dummy||1.023***|0.784***|1.566***|0.717***|
|N of Observations|3095|3095|3095|3095|3095|
|N of clusters (countries)|136|136|136|136|136|
|Adjusted R squared|0.010|0.452|0.643|0.551|0.649|
|Country-Fixed Effects?|YES|YES|YES|YES|YES|
|Variable|(6)|(7)|(8)<br>**EMDEs only**|(9)|(10)|
|Inflation (yoy)|1.175***|0.393|0.054|0.237|0.064|
|Change exchange rate (mom)|-1.756***|-1.106**|0.528|0.797*|0.307|
|<br>Change Bitcoin price (mom)||0.536***|0.360***|0.441***|0.370***|
|Change Ether price (mom)||0.249***|-0.025|0.120**|0.001|
|US 10-year Yield (inflation adjusted)|||-0.390***||-0.416***|
|US Inflation Expectation Rate|||1.413***||1.322***|
|VIX|||-|-0.037***|-0.002|
|Change gold price (mom)||||-2.199***|-1.564***|
|<br>Pandemic dummy||1.031***|0.810***|1.564***|0.785***|
|N of Observations|1837|1837|1837|1837|1837|
|N of clusters (countries)|85|85|85|85|85|
|Adjusted R squared|0.010|0.411|0.574|0.498|0.579|
|Country-Fixed Effects?|YES|YES|YES|YES|YES|

*** p<0.01, ** p<0.05, * p<0.1. Standard errors clustered at the country level. All independent variables are lagged by one month, except the pandemic dummy. Bitcoin, ether, and gold prices expressed in US dollars. The gold price is expressed per Troy ounce. A constant is estimated, but not reported.
program rather than a three-year intervention as was originally planned. Hopes for broader social transformation, raised by Feigenbaum et al. (2013), however were unfortunately not corroborated in this particular study. There are several potential reasons for these different results. Clearly, country and program context differ. Their focus on microfinance group members in contrast to our search for community-wide effects among both members and non-members of SHGs probably played a role as well. In addition, we adopted a different measurement strategy that may also contribute to these different results. We chose to use lab-in-the-field activities to measure social capital and pro-social attitudes in addition to network surveys and other self-reported measures.

These negative findings on the broader social capital impacts of the program should be no reason for discouragement. The program’s improvements to the poor’s savings behavior and association through SHGs are important achievements in themselves. If we include the possible increase in incomes though meat production as a result of the SHGs producers groups, the case for these programs is even stronger. Finally, we must mention that we conducted this study less than three years after the pilot program’s launch. Given more time, the increased association among the poor in SHGs may produce the hoped-for community-wide gain in social capital.

# **5. References**

Avdeenko, Alexandra and Michael J. Gilligan. 2015 International Interventions to Build Social Capital: Lab-in-the-Field Evidence from Sudan. _American Political Science Review_ . 109(3).

Barrett, S. 2005. _Environment and statecraft: the strategy of environmental treatymaking_ . New York: Oxford University Press.

Beaman, L, Karlan, D and Thuysbaert, B. 2014. Saving for a (Not So) Rainy Day: A Randomized Evaluation of Savings Groups in Mali. _NBER Working Paper_ , 20600.

Berg, J, Dickhaut, J and McCab, K. 1995. Trust, Reciprocity, and Social History. _Games and Economic Behavior_ , 10(1), 122-142.

Cardenas, JC and Carpenter, J. 2008. Behavioral Development Economics: Lessons from Field Labs in the Developing World. _Journal of Development Studies_ , 44:337-64.

28
population experiencing multiple deprivations has declined particularly rapidly in rural areas. However, the analysis also documents that deprivation in some dimensions is still quite high and there are still a large number of households experiencing one out of any three selected deprivations.

The rest of the report is organized as follows. The next section describes the methodology and the data. Section 3 assesses progress in various dimensions taking one indicator at a time. Section 4 presents results of overlapping deprivation analysis using Venn diagrams for sets of three indicators. Section 5 discusses the results with emphasis on the disconnect between the MPI analysis and progress in various dimensions of poverty in Ethiopia. Section 6 concludes.

# **2. Data and poverty dimensions**

The indicators considered in the analysis are selected based on their relevance to the country’s policies and the MDGs. Accordingly, a total of 11 indicators are identified covering education, health, water, sanitation, access to information, and gender equality as well as measures of monetary poverty. Table 1 presents the definitions of the indicators and how households are counted as deprived in each dimension. All the indicators are defined at the household level, which is the unit of analysis.

The study uses the Household Consumption and Expenditure (HCES) survey and the Welfare Monitoring survey (WMS) data collected in 2000, 2005, and 2011. Starting in 1996 with the first WMS, these two nationally representative surveys are conducted every five years.<sup>1,2</sup> The number of households in the WMS sample is 26,072 in 2000, 36,352 in 2005 and 28,032 in

> 1 WMS has been conducted together with HCES in 1995/96, 1999/2000 and 2004/05 and has also been carried out alone in 1997 and 1998.

> 2 The 2000 WMS does not cover the non-sedentary population in Afar and Somalia Regional States.

4