rafmacalaba/datause-encoder-data huggingface dataset:
across age, education, and gender. Second, taking Mexico as an example, we look at the pattern of mobility across time and how they vary across the business cycle. In both cases, existing theory provides potentially identifying restrictions.

# **III. Data**

To construct the time continuous matrices we employ three different surveys which compile information about labor status of workers and other relevant information.

# _Mexico_

The Encuesta Nacional de Empleo Urbano (ENEU National Urban Employment Survey) conducts extensive quarterly household interviews in the 16 major metropolitan areas. The questionnaire is extensive in its coverage of participation in the labor market, wages, hours worked, etc. that are traditionally found in such employment surveys. The ENEU is structured so as to track a fifth of each sample across a five quarter period. We have concatenated panels from the first quarter of 1987 to the forth quarter of 1999. For each individual contributed with tow transition pairs (from 1<sup>st</sup> quarter to the forth and second to the fifth.) giving rise to approximately 1,785.000 transitions, 810,000 for men and 975,000 for women.

# _Argentina_

In a similar fashion for Argentina, we use the Encuesta Permanente de Hogares (EPH Permanent Household Survey), a panel covering the area of the Federal District and surroundings (Gran Buenos Aires), which accounts for approximately 60% of total Argentina employment. The survey is conducted every 6 months (April/May and October) with a 25% rotation of the panel. As a consequence, each household is followed for two years at sampling intervals of six months. We employ panels from May 1993 to October 2001. The sample is notably smaller than the Mexican and Brazilian surveys and we can only study 29,000 transitions, 13,900 for men and 15,100 for women.

10
# **Welfare Dynamics in India over a Quarter Century: Poverty, Vulnerability, and Mobility during 1987-2012**

Hai-Anh H. Dang and Peter F. Lanjouw<sup>*</sup>

# JEL: C15, I32, O15

Key words: intra-generational mobility, welfare dynamics, imputation, synthetic panel, India, National Sample Survey

> * Dang (hdang@worldbank.org) is an economist with the World Bank, and is also affiliated with IZA, Indiana University, and Vietnam Academy of Social Sciences; Lanjouw (p.f.lanjouw@vu.nl) is a professor of economics with Vrije Unversiteit, Amsterdam. We would like to thank Chris Elbers, David Garcés Urzainqui, Himanshu, Abhiroop Mukhopadhyay, Rinku Murgai, Roy van der Weide, and participants at the ECINEQ conference (Paris), Monash University’s Prato Centre workshop (Prato), Sustainable Development conference (Ann Arbor), UNUWIDER conference (Helsinki), and VU-CSH workshop (New Delhi) for comments on earlier versions of this paper. We would like to thank UNU-WIDER and the UK Department of International Development for funding assistance through the “Inequality in the Giants” project and the Strategic Research Program (SRP) and Knowledge for Change (KCP) programs.
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The National Provident Fund reports only the number of members, i.e. persons that contributed at least once This was 1,078,914 members as of March 1982 and 1,099,778 as of March 1988 (including the deceased in both numbers). The absence of growth is notable in the face of labor turnover between the covered and uncovered workers population changes, but it may be related to the problems with individual accounts. The number of contributors in a given month or in a given year was unknown. The only keported figure is about contributing employers, which were close to 10,000 throughout the 1980s (live accounts)<sup>3</sup> . Although we know that about 10% of employers were from the public sector, there is no- information about the shae of members that worked in the public sector. The heavy involvement of the state in production in Zambia suggests that a large share were employees of parastatals. The law allows some .workers not to contribute to the provident Fund. These are casual workers ( those that work for less than one month), the self-employed, worker in cooperatives and domestic servants in rural areas. They can all become members voluntarily, but there -are no statistics regarding this.

On the other hand, the annual reports insist that compliance was deficient; 'As highlighted in the previous report, shortage of transport adversely affected the mobility of inspectors. Consequently, the level of compliance was not as high as expected... During the year, the Board experienced a critical shortage of transport, especially at Read Office and stations along the line of rail. This shortage resulted in hindering the running of operations. Consequently,, the Board had to make do with the available obsolete vehicles in which it spent huge amounts of. money on maintenance and repairs." 32 Another dimension of coverage is related to the maximum earnings for contribution purposes. Over the 1980's, it evolved from 200 to 500 to 3,000 Kwacha per month as a response to inflation. We have exchange rates for the last two of these limits, which meant that the limit was _51.8_ U.S. dollars per month in 1990 and 62.4 U.S. dollars per month in 1991. From another point of view, in 1982183 the percentage of contributions that were calculated on a wage above 200 kwacha was 33.7%, but that rose only to 37.6% as of year 1988/89. In light of inflationary trends, this suggests that under reporting of salaries became massive in the private sector that was covered, and/or that real wages fell dramatically in the parastatal sector.

**31i Souces:** 17th **Anmnual** Report. page 9 and 22nd Annual Report, page S. 32 Zambia National **Provident** Fund Board, 22nd Annual **Report 1987188,** pages 7 and **8.**
statistically significantly higher if the person stated the risk of contracting the virus as the single most important travel attribute.

**_Table 1_** _: Factors statistically associated with becoming a new user of private motorized modes since the start of the pandemic (among people who currently use private motorized modes)_

|**Log likelihood = -6838.1909**|**Num**<br>|**ber of obs. = 1**<br>|**8,269**<br>|
|---|---|---|---|
||**Odds Ratio**|**Std. Err.**|**P>|z|**|
|**Gender(1=male)**|0.6856|0.0375|0.000***|
|**Age **|0.9851|0.0072|0.000 ***|
|**Education(1=some university)**|1.4394|0.0718|0.000***|
|**Change in employment status sincepre-pandemic**||||
|**Became virtual**|1.6548|0.1160|0.000 ***|
|**Became hybrid**|1.4794|0.0904|0.000 ***|
|**Became unemployed/stopped working**|2.2180|0.1941|0.000 ***|
|**Changedjobs**|2.5602|0.1991|0.000 ***|
|**No response**|1.4359|0.1542|0.001 **|
|**Trip start location(1=CABA)**|1.2763|0.0736|0.000 ***|
|**Recreation trip (1=yes)**|0.7013|0.0779|0.001 **|
|**Social trip (1=yes)**|0.7988|0.0671|0.008 **|
|**Health trip (1=yes)**|0.5445|0.0746|0.000 ***|
|**Most important factor affecting travel choices**||||
|**Comfort**|0.9519|0.0793|0.554|
|**Travel time**|1.0112|0.0826|0.892|
|**Risk of COVID-19 infection**|1.3797|0.1084|0.000 ***|
|**No response**|0.2017|0.2059|0.117|
|**Constant**|0.2035|0.0247|0.000 ***|

_Note: Constant estimates baseline odds_

**The decline in public transport use during the pandemic was particularly steep among higher income women** (and men) who could reduce their travel more because of higher job flexibility and opportunities to tele-work or study from home to stay safe. Meanwhile, lower-income residents had to keep traveling to get to work. Compared to low-income women, high-income women were also more likely to increase travel by walking and cycling, at least partly due to the concentration of higher income groups in CABA where non-motorized transport infrastructure is better – similarly to patterns found in other major urbanized areas such as Barcelona (Mejía-Dorantes _et al_ ., 2021) _._

# **4. DISCUSSION AND CONCLUSION**

**Transport planning and investment decisions rarely take into consideration gender-based mobility needs, especially at high spatial and temporal resolution.** Therefore, many publicly funded transport projects do not benefit everyone equally. In particular, they generally tend to benefit the fraction of the population – typically men from medium and high-income backgrounds (Department for Transport, 2022) – who travel long distances and use highways or more expensive rail journeys to access job opportunities further afield.
extend household members’ work and study hours and contribute to productivity and educational achievements (Cabraal and Barnes 2006; Roddis 2000; Wasserman and Davenport 1983; World Bank 2002c).

This paper aims to deepen our understanding of energy as one factor that underpins both economic development and poverty reduction by analyzing the relationship between energy and poverty in India. Its objectives are two-fold. First, we examine whether it is possible to establish a level of energy consumption at which people can be perceived as “energy poor.” Second, we investigate the relationship between income and energy poverty to determine whether reducing income poverty can help reduce energy poverty. The basic premise is that there is a level of energy consumption that is absolutely needed to sustain welfare, and this is called the energy poverty line. This minimum requirement of energy services may not necessarily coincide with the energy services consumed by those who are deemed “income poor.” The paper also postulates that, beyond this point, energy contributes to increased welfare. That is, energy is essential not only for supporting a decent quality of life but also for continued growth and productivity.

# **Electricity Consumption and Economic Growth**

At the international level it is well established that there is a high correlation between energy consumption and economic growth, although the direction of causality is unclear. Countries with high levels of income also used high levels of energy. For instance, for each person in the United States energy consumption is 10 times the amount that is used in India. Energy consumption also increases with economic growth as well. For India, economic growth has been quite high in recent years. This trend has been mirrored within the energy sector, and this is especially true for electricity consumption. Between 2000 and 2007 India’s economy grew nearly 77 percent and this was matched by a 60 percent increase in electricity consumption (Figure 1).

3
returns throughout all quantiles. The size of an investment at inception has a positive, albeit small, elasticity on annual TVPI, except in the regression for the 90th percentile (column 9), where returns at the top end of the distribution are negatively associated with size at inception. Duration is also positively associated with returns, except for the top returns, where this association becomes negative. The highest returns are linked to smaller, short-lived investments.

Results from Panel B, where PME-MSCI is used as the measure of equity returns, reveal almost no statistically significant association between returns adjusted by PME-MSCI and the macroeconomic variables. This lack of significance suggests that the macroeconomic factors influencing annual returns do not affect IFC investments differently than their market equivalents. In other words, while macroeconomic variables such as GDP growth, exchange rate changes, or inflation may drive fluctuations in absolute returns, IFC’s relative performance—measured against the PME-MSCI benchmark—remains unaffected by these factors. This finding indicates that IFC investments behave in line with the broader market in terms of their sensitivity to macroeconomic conditions. IFC’s ability to mitigate risks or leverage opportunities presented by macroeconomic factors appears comparable to what is observed in the market. The coefficient on duration in column 9 indicates that IFC returns with longer duration will outperform the MSCI market equivalent.

Table B1 in the appendix presents three alternative estimations: OLS, RREG, and winsorized. OLS is the standard OLS estimation, RREG is the robust regression that screens for outliers and then performs Huber iterations followed by biweight iterations, as suggested by Li (2006). Winsorized estimation involves capping values of TVPI and PME-MSCI at the 10th and 90th percentiles before estimating OLS. Instead of removing outliers altogether, winsorization replaces values above the upper percentile threshold with the value at that threshold and values below the lower percentile threshold with the value at that threshold. Results are qualitatively similar to those reported in table 2.

Table 3 explores whether there are differences in these associations by type of investment vehicle. The positive association with GDP growth starts from the median return of direct investments. The coefficients for the interaction with funds are insignificant. Conversely, the negative association with RER depreciation occurs mainly in direct investments. Table B2 in the appendix suggests that macroeconomic variables have no heterogeneous association by investment vehicle with returns adjusted by PME-MSCI. Only duration and size at inception are positively and negatively associated with PME-adjusted returns in the 90th percentile.

18
a positive _B_ is equal to _b_ = 1 − _FB_ (0). An increase in _b_ raises the supply of prospective entrants. Such increase could be due to an influx of socially motivated individuals in the economy; to an increase in social appreciation for charters’ contribution; or to a reduction in the cost of preparing an application, locating facilities, developing a business and instructional plan, learning about successful charters, etc. For brevity we refer to an increase in _b_ as a reduction in entrants’ application costs.

# **5 Data and Estimation**

Estimation proceeds in three stages, in which we estimate demand-side parameters _θ_<sup>_d_</sup> , supply-side parameters _θ_<sup>_s_</sup> and proficiency rate parameters _θ_<sup>_q_</sup> respectively. We describe the data and estimation below.

## **5.1 Data**

Our data include 65 markets (13 grades times 5 years) and _J_<sup>_S_</sup> =281 campuses, for a total of _J_<sup>_D_</sup> =1,269 school-year observations and _J_<sup>_X_</sup> =8,112 school-grade-year observations. It also includes _J_<sup>_C_</sup> =153 neigborhood-year observations. Recall that we observe the following school characteristics: type (public, charter, Catholic, other religious, private non-sectarian), location, grades covered, focus, student body composition by race and poverty status, tuition (for private schools) and proficiency rates (for public and charter schools). In the data some characteristics (such as student body composition) change over time. Similarly, household choice sets change as well as some schools enter, exit or change grade coverage. Since we have tuition only for school year 2002, we assign the same value to all years. Lacking direct information on the number of children eligible for each grade, we estimate market sizes _Mgt_ s as described in Appendix A.2. Then, for each market we make _ns_ = 100 draws (each one corresponding to a hypothetical child) from the joint distribution of child race, poverty status, income and location.<sup>24</sup>

## **5.2 Demand Estimation**

To estimate _θ_<sup>_d_</sup> we use Generalized Method of Moments (GMM) and match market shares at the school-grade-year level (“share moments”), student demographic composition at the school-year level (“demographic moments”), and average fraction of students attending charter schools, average distance traveled to public schools, and average distance traveled to charter schools at the neighborhood-year level (“neighborhood moments”). While typical BLP consists of share moments, we augment the GMM objective function with the other moments.

24We assume two ages per grade (for instance, ages 5 and 6 in kindergarten), and draw an equal number of children of each age per grade. Given the low fraction of white and hispanic students in the population, we stratify our sample by year, grade and race.

22
SUMMARY OF MEASURES IN SAL PRESIDENT'S REPORTS THAT ADDRESS THE SOCIAL IMPACT OF ADJUSTMENT PROGRAMS

|Year|Country|Measures||Measures fo|r the Poor||Other|
|---|---|---|---|---|---|---|---|
|||for the<br>Newly<br>Unemployed|Social<br>Expenditure<br>Reallocations|Targeted<br> <br>Subsidies|Tax or<br>Labor Market<br> <br>Reforms|Targeted<br>Projects|Measures|
|FY86|Chile I|-|x|||**x**|-|
||Malawi III||-|-||-|**x**|
||Senegal II|x|x||x|-|-|
||Guinea I|x|-||x|-|x|
||NigerI|-|x|||**x**|-|
||Burundi I|-|_|||||
|FY87|Gambia I|x||||||
||CARI|x|-||-|**x**||
||Chile<br>II|-|x||x|x||
||Panama II|x|x|||||
||NepalI|-|-|x||x|x|
||Ghana<br>I|x|x|-||x|-|
||Guinea-Bissau<br>I|x|x|-||x|-|
||Senegal<br>III|x|x|-|x|x|-|
||Mauritania<br>I|x|x|-||x|-|
||Dominica<br>I|-|-||||x|
||Uruguay<br>I|-|x|-|-|x||
||Zaire<br>I_||x|-|-|x|x|
||Sao Tome &|||||||
||Principe<br>I|x|x|-|-|x||
|FY88|Congo I|x|x|-|-|||
||Chile III|-|x|x|-|x||
||Togo III|x|x|-|-|**x**|-|
||Gabon I|x|x|-|x|-|-|
||Burundi<br>II|-||-|**x**|x|-|
||CAR II|x|x|-||x|-|
||Tunisia<br>I|A|x|x||x|-|
||Guinea<br>II|x|x|||x|x|
|FY89|Honduras I|-|x|||||
|(First|<br>MoroccoI|-|x|x|x|x||
|Half)|Costa Rica II|-|x|||x||
1986; Alexander 2000). Figure 1 illustrates a summary of the traditional quality framework.

Yet, all these approaches are confined by analysis of the segmented education system. The findings and inferences from those studies are largely detached from the realistic complexities of education. When applied to diagnose educational problems and reforms, these amount to “treating the symptoms rather than the disease” prescriptions,<sup>2</sup> and often do not make a difference in student learning achievement.

**Figure 1: Traditional Quality Framework**

<!-- Start of picture text --> Economic, political,<br>and social context<br>Education<br>Policy<br>Policy actions Schools<br>Students<br>Specific Priori knowledge<br>Endowment and<br>programs and behaviors of<br>Behaviors of Systemic reforms Teachers<br>Students<br>Schools<br>Parents<br>Families<br>The Education<br>System<br>Its organization and<br>institutionality<br>Learning<br><!-- End of picture text --> Source: Vegas and Petrow 2007: 66 As an alternative, this paper suggests a systems approach to view the quality of education, echoing the motif of the World Bank Education Strategy 2020 (World Bank 2011). A key assumption is that analysis of the quality of education has to shift from a factor-dependent approach to a more systemic approach, including an increasing engagement of key quality drivers and prioritizing significant quality elements in the delivery of education services.

> 2 This is a Chinese saying used in Chinese herbal medicine, originally expressed as “ _zhi biao bu zhi ben_ ”, which means that the prescribed medicine treats the symptom of the disease while neglecting the cause of the disease.

3
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project. Expenditures on El Cajon accounted for about half of the total public investment expenditures during 1981-85. As a result, during 1981-85, over 702 of public investment expenditures were allocated to infrastructure, while 7Z went to productive sectors and 23Z to social sectors.

**Table 12:** Public Sector Invoetm.nt **Expenditures.** 1981-87

(X **of** CDP)

||1981|1982|1989|1984|1985|1986|**1987**|
|---|---|---|---|---|---|---|---|
|Public Sector|**90.**|**9.8**|**11.5**|<br>11.9|**9.1**|**7.1**|**7.6**|
|o/w Pubi**c**Sector||||||||
|**Enterprises**|**(5.9)**|(6.2)|(7.9)|**(8.**<br>1)|(6.2)|**(8.**)|(8.9)|

Source: **Ministry of Planning.** F.2. Public Investment Program for 1987-90 61. The authorities prepared a Droposed investment program for 1987-90. Based on Government projections, the proposed program aims at recovering the share of public investment expenditures to an annual average of 112 of GDP during 1988-90. To achieve this, the authorities plan to start a number of new projects during the proposed program period.

62. Table 13 shows the shares of the new and ongoing projects in the proposed investment program. As indicated in the table, about 702 of the proposed investment program would consist of new projects. The composition of the program would also change, with the productive (i.e. mostly in agriculture) and social sectors increasing their share of the total, while infrastructure's share falling.
Producer and consumer rice prices have declined by an average of 22 to 27 percent, although in some regions the price change exceeds one-third (Table 2). It is not clear to what extent the observed decline in rice prices is a result of higher productivity, although there is a strong correlation between rice production volume and rice prices in Bangladesh. Thus, Dhaka wholesale price of rice was consistently below the import parity price except for a brief period in 1998 and 1999 following production shortfalls after a flood and a drought (Dorosh 2004).

**Table 2.** **<u>Change in the price of rice paddy and consumer prices, 1995-96 to 2000.</u>**

||Producer price of rice<br>(% change)|Consumer price of rice<br>(% change)|
|---|---|---|
|Northwest|-22%|-18% (-20%)|
|Ganges Floodplain|-23%|-20% (-22%)|
|Coastal|-28%|-23% (-28%)|
|North Central|-15%|-19% (-21%)|
|Meghna Floodplain|-31%|-30% (-36%)|
|East Hills|Na|-34% (-42%)|
|All Bangladesh|-23%|-22%(-27%)|

Note: The East Hills region is excluded because it is a minor area of rice cultivation and is not statistically representative. Numbers in brackets are averages from household-level data set. Source: calculated from the community-level panel using 1995-96 and 2000 HIES.

Rather significant variation of the extent of price change in different communities, observed in Table 2, is caused by different demand and supply conditions and the degree of integration into national and international markets for rice in these communities. The discrepancy between the two price indexes and a strong correlation between domestic rice prices and production volume suggests that rice can be regarded as a non-tradable commodity in Bangladesh. Hence, we assume in this paper that the decline in rice prices is largely a result of growth in production rather than international price fluctuations.

# **4. Ex-ante simulation of the welfare effects of rice price decline**

Rice is a major source of income in rural Bangladesh. While crop income constitutes 18 percent of total income, revenues from rice production alone are 13 percent of total household income. Rice accounts for 75 percent of the total crop production value and 63 percent of total crop sales, and cultivated area under rice ranges from 63 to 84 percent of total cultivated area in the country (World Bank 2002b: Annex p.33). Rice is also the 12