rafmacalaba/datause-encoder-data huggingface dataset:
- **Peters, Margaret E.** 2019 _a_ . “Bilateral Labor Agreements Dataset and Additional Replication Data for: Immigration and International Law.”

- **Peters, Margaret E.** 2019 _b_ . “Immigration and international law.” _International Studies Quarterly_ , 63(2): 281–295.

- **Peters, Margaret E, and Michael K Miller.** 2022. “Emigration and political contestation.” _International Studies Quarterly_ , 66(1): sqab088.

- **Rios-Avila, Fernando, Arne J. Nagengast, and Yoto V. Yotov.** 2022. “JWDID: Stata module to estimate Difference-in-Difference models using Mundlak approach.” _Statistical Software Components S459114_ , Boston College Department of Economics, revised 08 Apr 2024.

- **Roth, Jonathan.** 2024. “Interpreting event-studies from recent difference-in-differences methods.” _arXiv preprint arXiv:2401.12309_ .

- **Roth, Jonathan, Pedro HC Sant’Anna, Alyssa Bilinski, and John Poe.** 2023. “What’s trending in difference-in-differences? A synthesis of the recent econometrics literature.” _Journal of Econometrics_ , 235(2): 2218–2244.

- **Saez, Juan Sebastian.** 2013. “Using bilateral labor agreements to increase trade in services.” World Bank Group.

- **Shrestha, Maheshwor.** 2019. “Death scares: How potential work-migrants infer mortality rates from migrant deaths.” _Journal of Development Economics_ , 141: 102368.

- **Shrestha, Maheshwor.** 2020. “Get rich or die tryin’: Perceived earnings, perceived mortality rates, and migration decisions of potential work migrants from Nepal.” _The World Bank Economic Review_ , 34(1): 1–27.

- **Shrestha, Maheshwor.** 2023. “A Deeper Dive into the Relationship between Economic Development and Migration.” World Bank Policy Research Working Paper 10295, World Bank.

- **Sun, Liyang, and Sarah Abraham.** 2021. “Estimating dynamic treatment effects in event studies with heterogeneous treatment effects.” _Journal of econometrics_ , 225(2): 175–199.

30
<!-- Start of picture text --> 0.05<br>0.04<br>0.03<br>gg<br>x 0.02<br>=<br>a}S001S001<br>S<br>3 0<br>$ 0.01<br>%<br>oOzB5 0.02-0.03-0.03<br>-0.04<br>-0.05<br>1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text --> <!-- Start of picture text --> 0.05<br>0.04<br>0.03<br>g<br>x 0.02<br>=<br>a}S001S001<br>S33 0 nnn<br>gS -0.01<br>%<br>oOzB5 0.02-0.03-0.03<br>-0.04<br>-0.05<br>1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text --> <!-- Start of picture text --> 0.05 0.05<br>0.04 0.04<br>0.03 0.03<br>22<br>gg<br>x 0.02 x 0.02<br>= =<br>a}S001S001 a}S001S001<br><<<br>S<br>3 0 S33 0 nnn<br>$ 0.01 gS -0.01<br>% %<br>zB55 zB55<br>oOzB5 0.02-0.03-0.03 oOzB5 0.02-0.03-0.03<br>-0.04 -0.04<br>-0.05 -0.05<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Quarter Quarter<br>0.05 0.05<br>0.04 0.04<br>0.03 0.03<br>22ix 0.02 6xx 0.02<br>£ =<br>a}SS 0.01 a}SS 0.01<br>Fa80FS80<br>33<br>g -0.01 g -0.01<br>& &<br>35z=5 -0.02-0.03-0.03 z=355355 -0.02-0.03-0.03<br>-0.04 -0.04<br>-0.05 -0.05<br>1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8<br>Quarter Quarter<br><!-- End of picture text --> <!-- Start of picture text --> 0.05<br>0.04<br>0.03<br>ixx 0.02<br>£<br>a}SS 0.01<br>33<br>g -0.01<br>&<br>z=55<br>35z=5 -0.02-0.03-0.03<br>-0.04<br>-0.05<br>1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text --> <!-- Start of picture text --> 0.05<br>0.04<br>0.03<br>6xx 0.02<br>=<br>a}SS 0.01<br>3<br>g -0.01<br>&<br>5<br>z=355355 -0.02-0.03-0.03<br>-0.04<br>-0.05<br>1 2 3 4 5 6 7 8<br>Quarter<br><!-- End of picture text -->
in micro-data for emerging countries, a topic that has received very little attention in the literature.<sup>1</sup> We rely on high-quality matched employer-employee data from administrative records in Brazil and Uruguay, which allows us to obtain accurate measures of wages and to properly track worker mobility. The analysis is carried out using the maximum likelihood estimator first proposed by Altonji and Devereux (2000), and extended to consider DRWR by Goette et al. (2007). The estimator relies on a model of wage changes that jointly estimates the relevant parameters governing the process of DNWR and wage indexation. Importantly, the price index underlying the indexation process is endogenously determined by the model and allowed to be heterogeneous across workers. This permits ex-post evaluation of the relationship between this price index and different prices in the economy, which is crucial for understanding the sources of indexation. The model also allows for errors in the measurement of wages. This is potentially important, since measurement error in wage data has been shown to be a source of downward bias in studies of DNWR (Gottschalk, 2005).

Our results indicate that Brazil and Uruguay present substantial differences in the nature of wage rigidities, and in their reaction to changes in the macroeconomic and institutional environment. In the private formal sector of Uruguay, the pegging of individual nominal wage growth to the rate of inflation falls dramatically over the sample period. Downward real wage rigidity affects on average 75 percent of the workforce in the period 1996-1999, reducing to a fairly stable 7 percent for the rest of the sample period (1999-2004). In parallel, the fraction of workers subject to DNWR increases from around 13 percent in the first three years to an average of 66 percent in the second part of the sample. Hence, disinflation is associated with a sharp movement away from wage indexation. However, this process does not lead the way to a frictionless wage setting regime. Instead, worker resistance to nominal wage cuts arises.

In contrast with Uruguay, the incidence of wage indexation in Brazil is fairly high and

> 1 A notable exception is Castellanos et al. (2004), who study downward nominal wage rigidity and indexation to the minimum wage in Mexico. Our paper differs from this study in three aspects. First, we allow for the presence of measurement errors in the estimation, an aspect that has received considerable attention in the literature in the last few years. Second, we do not impose wage indexation to follow a pre-determined price index. Instead, the price index considered in wage negotiations is allowed to be heterogeneous across agents, and is endogenously determined. Finally, our model is estimated year by year in order to track changes in the distribution of wage changes over time.

4
be contracting between 2004 and 2008, in stark contrast to the case in the other two countries. Finally, using growth of the bottom 40 percent as a measure of shared prosperity, Vietnam moved up and took the lead at 3.1 percent, followed by India at 2.0 percent, and finally the US at -1.3 percent. If comparison is made not across the countries, but within each respective country relative to the population as a whole, growth for the bottom 40 percent is stronger for Vietnam while it is weaker for India. On the hand, this growth is less negative than that of the whole population for the US (i.e., the mean consumption for the bottom 40 percent decreases less than that of the whole population).

We will come back to more discussion on each country in Section III.3. We discuss next the framework of analysis before considering a more disaggregated analysis of growth.

# **III. Analysis for Shared Prosperity**

A key feature with our proposed approach to measuring shared prosperity is that we construct a vulnerability line, which separates out the nonpoor population into two groups: those that are currently nonpoor but face a heightened risk of falling into poverty in the next period, and those that are secure (and can be denoted middle class).<sup>7</sup> Since this vulnerability line is built upon the existing poverty line, employing these two lines together can provide a more comprehensive and more consistent analysis.

# **III.1. Vulnerability Line: Definition and Estimation**<sup>**8**</sup>

> 7 The “middle class” defined in this way includes as well those households that would perhaps more reasonably be described as “rich”.  Note, however, that there is a general perception that household surveys do not typically capture well the richest segments of society.  For example, Szekely and Hilgert (2007) show for the case of surveys from Latin America that the richest surveyed households generally reported incomes that were roughly similar or lower in dollar terms to what a middle manager in the U.S. might earn.  The authors argue that inequality calculated from households survey data in the LAC region is likely to be seriously underestimated as a result of these findings.

> 8 This section provides a brief overview of the vulnerability lines provided in Dang and Lanjouw (2014). Interested readers are encouraged to read the cited paper for more details.

7
Holl, A. and E. Viladecans‐Marsal, 2011. Infrastructure and Cities: The Impact of New Highways on Urban Growth, Universitat de Barcelona‐Institut d’Economia de Barcelona Working Paper.

Hochman, O., Pines, D. 1982. Adjustment costs and the spatial pattern of a growing open city. Econometrica 6: 1371–1391.

Hotelling, H., 1929. Stability in competition. Economic Journal, 39: 41–57.

Hsu, W‐T and H. Zhang. 2014. The Fundamental Law of Highway Congestion: Evidence from Japanese Expressways, Journal of Urban Economics, 81: 65‐76.

Imai, Haruo. 1982. CBD hypothesis and economies of agglomeration. Journal of Economic Theory 28:275–299.

Jacobs, J. 1969. The Economy of Cities. Random House.

Jedwab, R. 2013. Urbanization without Structural Transformation: Evidence from Consumption Cities in Africa. Mimeo, George Washington University.

Jedwab, R. 2011. African Cities and the Structural Transformation: Evidence from Ghana and Ivory Coast. Mimeo, Paris School of Economics.

Johansson, B. and J.M. Quigley. 2004. Agglomeration and networks in spatial economics. Papers in Regional Science 83: 165‐176.

Joshi , K.K. and T. Kono. 2009. Optimization of floor area ratio regulation in a growing city. Regional Science and Urban Economics, 39: 502‐511.

Jun, M. (2004). The Effects of Portland’s Urban Growth Boundary on Urban Development Patterns and Commuting. Urban Studies, 41(7): 1333–1348 Kim, S., 1995. Expansion of markets and the geographic distribution of economic activities: the tends in U.S. regional manufacturing structure, 1860–1987. Quarterly Journal of Economics 110: 881–908.

Kolko, J. 1999. Can I get some service here? Information technologies, service industries and the future of cities. Processed, Harvard University.

Kolko J. 2010. Urbanization, agglomeration, and coagglomeration of service industries. In: Glaeser E (ed) Agglomeration economies. National Bureau of Economic Research, Cambridge Kono, T. and K.K. Joshi. 2012. A new interpretation on the optimal density regulations: Closed and open city. Journal of Housing Economics, 21(3): 223‐234.

34
women’s access to finance. The influence of gender norms on intra-household dynamics such as access and control of income and expenditure may also play a role. As a result, women may exhibit – voluntarily or involuntarily – a lower demand for financial services than men (Johnson, 2004). Research in rural Paraguay shows that women are more knowledgeable about financial institutions and loan requirements when they control a larger share of family assets and when their husbands do not oppose them taking out loans (Fletschner and Mesbah, 2011).

While a growing literature examines household finance and especially households’ borrowing and savings decisions,<sup>2</sup> systematic data on household and individual use of financial services remains limited. The absence of such data contributes to the scarcity of empirical literature investigating the link between access to finance and gender at the individual level. One exception is the paper by Aterido, Beck, and Iacovone (2011) that uses individual-level survey data from FinMark Trust (Finscope) for nine countries in Sub-Saharan Africa to examine the gender gap in financial services. They find that the lower use of formal financial services by women in nine Sub-Saharan Africa countries can be explained by gender differences in education and income levels, formal employment, and being the head of household. The evidence on a gender gap in the use of informal financial services is more mixed; the study finds that while in some countries women are more likely to use informal services, the reverse is true in other countries. One shortcoming of the FinMark data, however, is that surveys were carried out over a span of six years, 2004 to 2009, and may not be appropriate for cross-country comparisons.

Much of the literature on the gender gap in access to financial services has focused on access to credit in the context of financing entrepreneurial activities, rather than on household

> 2 For a detailed literature review, see Beck, et al. (2008a) and references therein. Campbell (2006) also provides an overview of the household finance field.

4
the quality of the other services under study, namely telephone faults (per 100 mainlines) and electric power transmission and distribution losses in % of output, both from WDI. Other general data include (from WDI, unless mentioned otherwise) measures of GDP per capita, gross fixed capital formation, primary and secondary school enrollment (from Barro and Lee, 2000), primary and secondary schooling expenditures, government stability and Corruption (from Political Risk Service, International Country Risk Guide), life expectancy, M2/GDP (as a measure of financial development), imports/GDP and inflation.

# **3.3. Sample**

We rely on a sample of 93 developing or emerging countries. Of these 40 are classified by the World Bank as low income, 25 as lower middle income, 19 as upper middle income and 9 as high income. Note that this last category includes Hong Kong, Korea, Singapore and a number of oil producing countries. Overall, 16 East Asian and Pacific countries are included: China, Fiji, Hong Kong, Indonesia, Korea, Lao PDR, Malaysia, Mongolia, Myanmar, Papua New Guinea, Philippines, Singapore, Thailand, Tonga, Vanuatu, Vietnam.

# **3.4. Techniques**

In what follows we present two types of estimations. First, we perform simple cross country estimations based on the collapsed data set for 1971-1995 or 1984-1995 alternatively, using the rate of growth of GDP per capita as dependent variable and standard controls (initial level of GDP, investment, proxies for human capital). In each case, we instrument potentially endogenous infrastructure indicators and perform related tests. We also test specifications with different set of regional dummies (specific East Asian dummy, income groups), and the alternative infrastructure indicators mentioned above.

Then, we present panel regressions on 5-year subperiod averages with the same dependent variable. This frequency should result in enough variations in infrastructure indicators to allow the use of fixed effects. Following best practice in this type of exercise, we compare fixed vs. random effects and perform instrumental estimations. Finally, we use Arellano-Bond dynamic panel techniques.

17
<!-- Start of picture text --> Figure 11. Generation costs (USc/kWh)<br><!-- End of picture text --> <!-- Start of picture text --> 9<br>8<br>7<br>6<br>5<br>4<br>3<br>2<br>Generation cost with import parity gas prices<br>1<br>Generation cost with current gas prices<br>0<br>2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025<br>USc/kWh<br><!-- End of picture text --> Given that renewables have close to zero short‐run marginal costs, investment and financing costs become the most crucial factors in defining generation costs. Correspondingly, the most important assumptions for least‐cost optimization planning concern the assumptions on capital and financing costs for solar generation. Table 3 allows for a comparison of potential solar generation costs with generation costs. The uptake of solar is highly sensitive to solar prices. We estimate that solar costs have to decline to around 6.5USc/kWh for solar to economic in the Bangladesh context given regional capacity factors, reserve requirements, and grid generation costs. This will essentially require the capital cost of solar to drop below $1,000/kW for a weighted average cost of capital of 5%. Given the paucity of land and the need to import vast majority of the capital, realistic chance of doing projects at GW scale is likely to be limited at least in the near term. This certainly aligns well with the lack of sufficient degree of interest on the ground to mobilize projects during 2016/2017.

Table 3. Cost of solar power generation under different assumptions (USc/kWh)

|||||**Co**<br>|**st of financi**<br>|**ng solar (%)**<br>||||
|---|---|---|---|---|---|---|---|---|---|
|||**9%**|**8%**|**7%**|**6%**|**5%**|**4%**|**3%**|**2%**|
||**1.5**|13.0|12.2|11.4|10.7|9.9|9.2|8.5|7.9|
|**W)**|**1.4**|12.3|11.5|10.8|10.0|9.3|8.7|8.0|7.4|
|**S$/**|**1.3**|11.5|10.8|10.1|9.4|8.8|8.2|7.6|7.0|
|**(U**|**1.2**|10.7|10.0|9.4|8.8|8.2|7.6|7.1|6.6|
|**olar**|**1.1**|9.9|9.3|8.7|8.2|7.6|7.1|6.6|6.1|
|**of S**|**1.0**|9.1|8.6|8.1|7.5|7.1|6.6|6.1|5.7|
|**ost**|**0.9**|8.4|7.9|7.4|6.9|6.5|6.1|5.6|5.3|
|**al C**|**0.8**|7.6|7.1|6.7|6.3|5.9|5.5|5.2|4.8|
|**pit**|**0.7**|6.8|6.4|6.0|5.7|5.3|5.0|4.7|4.4|
|**Ca**|**0.6**|6.0|5.7|5.4|5.1|4.8|4.5|4.2|3.9|
||**0.5**|5.2|5.0|4.7|4.4|4.2|4.0|3.7|3.5|

# Transmission analysis

The transmission network will need to be upgraded and re‐designed to some extent to enable the delivery of power from the four corners of the country to the Dhaka region in an efficient manner. In 2015, roughly 40% of power was generated and consumed in the Dhaka region. By 2025, peak demand in Dhaka is projected to grow from 3.4 GW to 8.1 GW, whereas local supply will remain essentially the same as in 19
number of contexts where mobile phone data are being made available for humanitarian purposes (Milusheva et al., 2021). For example, in just the past few years, mobile money was used to make cash transfer payments in countries including Bangladesh (Ali & May, 2021), Ghana (Karlan et al., 2021), Liberia (USAID, 2021), and Malawi (Paul et al., 2021). Mobile phone _data_ has been used to guide cash transfers in Colombia (Gentilini et al., 2020), the Democratic Republic of Congo (Gentilini et al., 2021), Pakistan (Gentilini et al., 2020), and Togo (Aiken et al., 2021).<sup>1</sup> – The context of our empirical analysis identifying ultra-poor households in Afghanistan – is a particularly challenging environment for data collection and program targeting, as 62% of the households classified as not _ultra_ -poor still fall below the national poverty line. In such environments, when traditional options for targeting are not feasible, these methods may provide a viable alternative for identifying households with the greatest need. Given the policy relevance of these results, we conclude our analysis by discussing important ethical and logistical considerations that may influence how CDR methods are used to support targeting efforts in practice.

# **2 Data and Methods**

## **2.1 Targeting the ‘Ultra-Poor’**

Our empirical analysis relies on survey data collected as part of the Targeting the UltraPoor (TUP) program implemented by the government of Afghanistan with support from the World Bank. The TUP program was a “big push,” providing multi-faceted benefits to 7,500 ultra-poor households in six provinces of Afghanistan between 2015 and 2018 (Bedoya et al., 2019). Our analysis uses data from the baseline and targeting surveys from an impact evaluation of the TUP program conducted in Balkh province.

**Ultra-Poor Designation** Eligibility for the TUP program was determined based on geographic criteria,<sup>2</sup> followed by a two-step process including a community wealth ranking (CWR) and a follow-up in-person survey. CWRs were conducted separately in each village, coordinated by a local NGO and village leaders, in collaboration with the government team.

> 1The anti-poverty program implemented in Togo and described by Aiken et al. (2021) was based on the methods developed and evaluated in this paper. Due to the time-sensitive nature of the COVID-19 response described in Aiken et al. (2021), the two academic articles are in circulation concurrently.

> 2The poorest villages were identified by the availability of veterinary services, financial institutions, and social services, and being relatively accessible (Bedoya et al., 2019).

4
cost-effectiveness of workfare, namely an allocation of the same budget but assigned instead as transfers in cash or in kind to those holding BPL cards.

# **3. Data and methods**

For the purpose of this study, we collected two rounds of data in a household panel structure from 150 villages spread across rural Bihar. The first round (R1) was implemented between May and July of 2009 and the second (R2) during the same months one year later. These periods were chosen for being lean periods for agricultural work, and were thus expected to be peak periods for BREGS. Both rounds included questions with recall over the previous 12 months. The year preceding the first survey period witnessed severe floods during the monsoon (July-August of 2008) in some districts falling in the catchment area of the Kosi River. In contrast, rainfall was scanty during the 2009 monsoons and drought was declared in many districts during the second survey period.

A two-stage sampling design was followed, based on the 2001 Census list of villages. In the first stage, 150 villages were randomly selected from two strata, classified by high and low BREGS coverage based on administrative data for 2008/9. In the second stage, 20 households per village were randomly selected, drawing from three strata based on an initial listing of all village members and a few selected attributes. This stratified approach ensured that the sample included both scheme participants and households with likely participants. We have used the appropriate sample weights to reflect the sampling design.

The surveys collected information on a range of household level characteristics including demographics, socio-economic status (including asset ownership and consumption), employment and wages, political participation and social networks, as well as information on BREGS participation and process-related issues. Two household members, one male and one female, were interviewed about their participation in BREGS, experience of BREGS at the most recent worksite and knowledge and perceptions of the program, the village labor market and the role of women. In addition, in each village, key informants were interviewed about physical and social infrastructure in the village, and access to government programs.

In total, 3,000 households and approximately 5,000 individuals were interviewed in both rounds. The balanced panel comprises 2,728 households and 3,749 individuals. The overall attrition rate for households between the two rounds is 8% and is not concentrated in any 8