rafmacalaba/data-use-mentions-extended huggingface dataset:
Methodological weaknesses including use of a non-representative sample that cannot be traced back to a frame to allow sample weights to be calculated;<sup>13</sup> asking information on transactions and their formalization for one of the household’s plots only without having first listed all of them; and failing to cross-check households’ responses against official LAIS information to check that the respondents’ subjective notion of ‘registration’ indeed refers to the official process are likely to introduce large errors. Studies that use such methods (Biraro _et al._ 2015) can provide case study evidence to understand underlying processes but not figures that can assist decision-makers.

Data from the household sample (table A1) suggest that about a third of households are female-headed, their asset endowments are limited, and mean assets (excluding land) are at US$ 300. 94% of households own on average 0.68 ha of agricultural land (from 0.58 ha in the South to 0.91 ha in the East) with average parcel size of 0.18 ha (table A2) well below the 1 ha below which subdivision is legally prohibited. With 24% and 28% of the sample, respectively, reporting to have acquired or transferred out land (62% having purchased land, 46 % having inherited), land markets are active. This is consistent with some 1,800 parcels with an average size of 0.12 ha having been acquired—41% purchased, 29% inherited, and 16% rentedsince early 2012.<sup>14</sup> Perceived levels of tenure security are high and awareness of legal restrictions on subdivision seems low. A test of legal knowledge suggests high levels of awareness of gender-issues but little knowledge of legal provisions and procedural aspects for property transfers (table A3). Moreover, while 65% of women know that land acquired by couples married under the common property regime needs to be registered jointly, this is the case for only 46% of men, providing one
# **Are Microcredit Participants in Bangladesh Trapped in Poverty and Debt?**

## **1. Introduction**

Microfinance has succeeded in reaching the poor and women who lack access to mainstream financial institutions.<sup>1</sup> But its success in reducing poverty, a stated goal of microcredit programs, is less certain. The microlending system has created such innovations as group liability enforcements and dynamic incentives, claiming that many of the poor, including women and other vulnerable groups, could be lent to profitably, and beneficiaries could accrue both short- and medium-term welfare gains. But critics argue that, while microfinance may satisfy the unmet financial demand of the poor, the accrued gains in income and consumption may not suffice to affect poverty reduction or asset accumulation in a sustainable way.

There are two strands of empirical literature on the poverty reduction role of microcredit. The first strand of studies, which uses non-experimental research methods, observes that microcredit helps to promote social, human, and economic development in various ways (e.g., Dunford 2006; Hossain 1988; Shaw 2004; Panjaitan-Drioadisuryo and Cloud 1999; Chemin 2008; Khandker 1998, 2005; McIntosh 2008; Pitt and Khandker 1996, 1998; Pitt, Khandker, McKernan, and Latif 1999; Kevane and Wydick 2001; Imai, Arun, and Annim 2010; Boonperm, Haughton, and Khandker 2009).<sup>2</sup> A recent study, using panel data from 1997 to 2005, finds that larger benefits are realized from medium-term, rather short-term, participation (Islam 2011). Another study, using a long panel survey (1991/92–2010/11), also confirms that participants who remained with

> 1 According to the Consultative Group to Assist the Poor (CGAP), there are more than 200 million beneficiaries of microfinance worldwide.  In Bangladesh alone, about one-fifth of the country’s 150 million people—including threefifths of rural households—are members of microfinance institutions.

>
13 Through voluntary retirement SUNASS<sup>reduced the number of workers from</sup> 3,769 in 1988 to 1,359 in 1996, at the same<sup>time the number of connections was</sup> expanding, so workers per 1,000 connections<sup>fell from over 6 to 2. This reduction in the</sup> labor force was accomplished partly by removing<sup>redundant workers and partly by</sup> outsourcing activities that were previously<sup>done in-house, especially bill collection and</sup> maintenance, installation and repair of connections.<sup>As a result, labor costs fell sharply.</sup> Even though outsourcing raised administrative<sup>and maintenance expenses and electricity</sup> costs increased, overall operating expenses<sup>per cubic meter of water sold stayed fairly</sup> constant (Figure 2).

_Figure 2. Operating Cost per Cubic Meter of Water_<sup>_Sold_</sup> <!-- Start of picture text --> 0.40<br>-<br>; 0.30 .<br>a 0 20 .10 l ;' ; s 0 II - e p<br><!-- End of picture text --> 1987 1988 1989 1990 1991 1992 1993 1994<sup>1995 1996</sup> Source: Author's Calculations based on data from<sup>SEDAPAL Annual Reports</sup> SEDAPAL also introduced a number of reforms designed<sup>to change management</sup> and personnel incentives in 1996. This included<sup>a performance-based pay system with</sup> specific targets for each employee and quarterly bonuses<sup>of up to10 percent of pay."'</sup> Partly as a result, the average annual salary of SEDAPAL's<sup>personnel began to increase</sup> in real terms, as can
workforces, and India is no different (Amin and Mattoo, 2008). We measure the general education level of the district’s labor force from the 2001 Census as the percentage of adults with a graduate (post-secondary) degree. Our results below are robust to alternatively defining a district’s education as the percentage of adults with higher secondary education.

Our second trait is the physical infrastructure level of the district. Functioning urban areas depend critically on their underlying infrastructure. Infrastructure likewise connects urban and rural parts of a district. The government of India is providing substantial financial resources for infrastructure investment (Ministry of Urban Development, 2008). The 2001 Census provides figures on the number of villages in a district which have paved roads, electric power access, telecommunications access, and access to safe drinking water. We calculate the percentage of villages that have infrastructure access within a district and sum across the four measures to create a continuous composite metric of infrastructure which ranges from zero (no infrastructure access in any village) to four (full access to all four infrastructure components in all villages).<sup>9</sup> We empirically assess the role of education and infrastructure for development without strong theoretical priors on their role, only noting their frequent emphasis by policy makers. We believe this agnostic approach is important as anecdotal accounts of India suggest multiple relationships are at play. For example, a natural baseline for education is that agglomeration economies or urbanization premiums are higher for skilled workers and industries. Observers note, however, that skilled workers may want to live outside of Indian cities to the extent that the amenities are lower in Indian cities than in the surrounding areas. Likewise, better infrastructure typically allows strong urbanization levels. However, cities in India often experience some of the largest infrastructure failures, and production of own electricity for organized sector firms is high in both settings. Thus better infrastructure capacity may allow establishments to move to rural locations.

Cost factors also play a critical role in location choice decisions for firms
once during the post-planting period (at the time of the information treatment) and once during the post-harvest period (together with the control group households in the same EAs). This assumption seems reasonable, for at least two reasons. First, Wollburg et al. (2021), studying earlier rounds of the same Malawi household survey, show that self-reported unconditional fertilizer quantity per plot is insignificantly associated with recall length and that self-reported farm labor activity (e.g., land preparation and planting, weeding and fertilizing) per plot is inconsistently and only modestly associated with recall length. Second, when we regress post-harvest self-reported fertilizer inputs against postplanting self-reports of the same variable among the treated households, the resulting coefficient estimate (0.955) is not statistically significantly different from one and the simple bivariate relationships explains almost half of variation, indicating a close correspondence between reports of the same early season input use regardless of whether collected post-planting or post-harvest.

25
2. It would be helpful if the American Express data could show actual IATA airport (not city) codes for the flights.<sup>39</sup> 3. Load factors by flight class should be investigated further, possibly through IATA which is likely to have the required information. For this report, our attempts to find actual load factors by class have proved fruitless because IATA is not making such data freely available to the public. The required data might however be available at a cost.

4. On-the-spot upgrades (coach to business, and business to first class) are not reflected in our data, and could further increase the correct carbon footprint.<sup>40</sup> 5. Moving toward a more detailed approach using the modified ICAO emissions calculator procedure applied here, and incorporating hopefully available load factors by class, would bring about more rigorous footprint estimates. An issue then is the increase in precision of the institution footprint, due to such more detailed information; relative to e g using industry averages for load factors and basic aircraft fuel consumption.

> 39 The scheduling data uses airport codes, such as IAD for Dulles Airport and JFK for John F. Kennedy International Airport. However, the American Express data often shows city pairs as, for example, WAS-NYC (for Washington, D.C. to New York City), which requires substantial additional processing of data in order to match these with the Diio scheduling data.

> 40 For example, most Lufthansa flights over 2,000 nautical miles are marked business class. However, with United Airlines roughly 4,400 flights out of 15,000 with stage lengths of 2,000 nautical miles or more are marked coach, most of which most likely have received on-the-spot upgrades at check-in. This is not registered in our data.

36
|Constant|5.47***|6.54***|6.98***|6.59***|
|---|---|---|---|---|
||(0.06)|(0.03)|(0.02)|(0.02)|
|N|22567|40687|24913|71149|
|F-test|445.89|251.08|297.67|1444.5|
|AdjustedR2|0.36|0.15|0.26|0.37|

Note: The grand mean effects of the industries are calculated; additional controls include age squared, weekly hours worked, and industry dummies. Standard errors are in parentheses. Statistically significant coefficients at the 10,5, and 1 percent level are indicated by one, two, and three asterisks respectively. High-skilled workers are those with upper secondary or above. Source: Author's estimates using data from Bangladesh LFS.

**Table** **<u>6. Mincerian Wage Premium Estimates, with T&A and female dummy interaction</u>**

|||Logarithmo<br>|f realwages<br>||
|---|---|---|---|---|
||(1)<br>Only2005|(2)<br>Only2010|(3)<br>Only2013|(4)<br>Only2016|
|Female|-0.592***|-0.123***|-0.108***|-0.179***|
||(0.0148)|(0.0109)|(0.00625)|(0.00558)|
|Textile and apparel|0.0560***|-0.00425
<okay – sg>

# **Foreign Wage Premium, Gender and Education: Insights from Vietnam Household Surveys**

## **Emiko Fukase**<sup>**1**</sup>

## **The World Bank**

JEL Classification: F16, F23, J31 Keywords: foreign ownership, gender, education, wage, Vietnam Sector Board: Poverty Reduction and Economic Management

> 1 Development Economics Research Group, World Bank. I would like to thank the late Robert E. Lipsey and Wim P. M. Vijverberg for their advice and support throughout this research; Will J. Martin and Jonathan R. Nelson for comments and suggestions; and the General Statistics Office (GSO) of Vietnam for providing the data. A shorter version of this paper will be forthcoming in _the World Economy_ . I am grateful to the editor of _the World Economy_ and an anonymous referee for very useful comments. Any remaining errors are mine.

1
, than in the Central region. Although these results illustrate the spatial heterogeneity in the impact of shocks across Sudan, differences in prevalence of shocks across states, region-specific characteristics such as the types of shocks most prevalent, and the common household livelihoods are likely to drive these results. For instance, as shown in Figure _5_ , prevalence of shocks in the Central region is fairly low compared to other regions. Also, in the Khartoum and Northern regions, floods/droughts (which as discussed earlier have the most negative impact on household welfare) were the most commonly reported shock in 2014/15 (see Figure 6b and Figure 6d). In addition, wages and salaries are the common livelihoods in these regions based on the 2014/15 data and as discussed below are among the livelihoods most affected by shocks. In the Darfur region, on the other hand, the high impact of shocks is possible driven by the high prevalence of shocks (around 60 percent of households reported at least one shock in 2014/15 as shown in Figure _5_ ) and the dominance of crop farming as the main livelihood.

> 16 The Central region consists of Al-Gezira, White Nile, Sennar, and Blue Nile states. The Darfur region consists of the North, West, South, Central, and East Darfur states. The Eastern region consists of the Red Sea, Kassala, and Al-Gadarif states. The Khartoum region consists of the state of Khartoum. The Kordofan region consists of the North, South, and West Kordofan states. The Northern region consists of the Northern and River Nile States.

> 17 Although not presented here in the interest of brevity, the impact of shocks is larger on urban households than on rural households. Consumption per capita for rural households affected by a shock is on average 3.4 percent higher than urban households also affected by a shock.

36
>flows by development partners<br>(%)|Halve the proportion of<br>aid not disbursed within<br>FY for which it was<br>scheduled|
|8|Aid is untied|||OECD-DAC data|Continue progress over<br>time|
|9|Use of common<br>arrangements or<br>procedures<br>(program-based<br>approaches—PBAs)|||Program-based approaches<br>(budget support + non-budget<br>support PBAs)/total<br>assistance (%)|**66%**of aid flows are<br>through PBAs|
|10<br>a|Joint missions to the<br>field|||Number of joint<br>missions/total number of<br>missions (%)|**40%**|
|10<br>b|Joint country<br>analytic work|||Number of joint analytic work<br>/total number of analytic work<br>(%)|**66%**|
|11|Results-oriented<br>frameworks|||Bank‘s assessment based on<br>information from partner<br>countries|**38%**of partner countries<br>have sound results-<br>oriented frameworks|
|12|Mutual<br>accountability|||Partner countries have mutual<br>assessment reviews based on<br>their response|**All**partner countries have<br>mutual assessment<br>reviews|

_Source: World Bank (2011, p.4)_ 21
Figure 4 shows job losses by occupation classified with the 1-digit ISCO. Job losses varied from 15 and 16 percent for managers and professionals, respectively, to 37 percent for machine operators and assemblers, and up to more than 50 percent for workers in elementary jobs. Since the ISCO classification sorts occupations by job complexity, from managers to elementary workers (Vosters 2018), Figure 5 suggests that job losses were decreasing in the complexity of the job. In the following section, we work with the 41 groups of the 2 digit ISCO classification, which implies a richer analysis.

# **3.5 An Index for Work from Home**

In order to analyze whether the differential impact of the pandemic was related to the potential for remote work in the pre-pandemic situation, we construct a variable for potential for work from home. We proceed in two steps; first we construct a measure of WFH at the occupation level (Dingel and Neiman 2020), and then adjust this measure for home internet access using the answers to the HFPS (Garrote-Sanchez et al. 2021).

The first step follows the widely used methodology of Dingel and Neiman (2020). They construct a variable of WFH at the occupation level using data on occupational characteristics from O*NET for the U.S. Some of these characteristics are suggestive of the difficulties of working from home (e.g. working directly with the public, outdoor activity, exposed to the weather). When at least one of these characteristics is assessed as "important" or "very important", the occupation is classified as not compatible with WFH.

Occupations in O*NET are classified according to the Standard Occupational Classification (SOC) System. The next step in the methodology is to map the results in the SOC-8 digits classification to the 2-digit ISCO classification, the one used in this paper.<sup>8</sup> The number of groups in the latter classification is smaller, so for each ISCO category we typically have several SOC
the incidence of dismissals, which is endogenous to labor regulation. The same concerns hold for another available indicator, the number of detected violations per thousand employees. We do not use the other available measures (“all cases filed” and “pay cases filed”), because they are not specifically about hiring and firing disputes.

# **3.2 Firm‐level data**

Our regressions use panel data from the RUSLANA database (from Bureau van Djik, BvD). RUSLANA is an extensive data set that provides up to 10 years of financial, administrative, locational and managerial information on about 65,000 registered firms in Russia. The data are based on firms’ reporting to various administrative databases such as the tax directorate and social security.

According to BvD, every effort is made to ensure comprehensive coverage of these official databases, so that RUSLANA may include all registered and active firms. The fact remains that RUSLANA coverage is dependent on the extent to which firms report to such official databases. As we understand, a specific issue with coverage is that some firms ‘enter’ and ‘exit’ RUSLANA with a lag relative to their actual dates of market entry and exit, possibly due to lagged updating of official databases. The lag in entry into the database, in particular, indicates that RUSLANA has more selective coverage of newly established firms. The selection process is unobserved, in the sense that we lack information on why some new firms enter the database with a lag, while others do not. It could be a concern for our estimation if it varies systematically across provinces, and if the employment adjustment behavior of such selectively excluded 11 firms is systematically different from that of other firms.

The data set we use is the 2012 ‘version’ of RUSLANA, meaning that it was last updated by BvD in 2012, and spans the 1999‐2011 period. Due to relatively poorer availability of employment data in years prior 12 to 2003 and in 2011, we limit our analysis to 2004‐
# Introduction

The goal of this background paper is to provide a detailed analysis of the link between poverty and armed conflict using the newest available conflict and poverty data both at the country level and at the regional level. We will first review the literature on the complex relationship between poverty, conflict, crime and other co-determinants. We then develop a measure based on the literature on the role of conflict risk and human capital accumulation and use this measure to investigate the relationship between conflict and poverty.

Before we go on to discuss the role of armed conflict in poverty, it is important to understand the limitations of the present background study. We will approach the role of conflict in poverty through two commonly used data sets, the Uppsala Conflict Data Programme (UCDP) and the Armed Conflict Location & Event Data Project (ACLED), both of which track violent conflict around the world. The UCDP uses the most stringent definition of conflict by focusing on events that result in battle-related deaths. According to the definition used, battle-related deaths result from the use of armed force between warring parties in a conflict, be it state-based or non-state. This implies a focus on specific, organized, extreme levels of violence, i.e. it does not include riots for example. ACLED is more lenient in its definition of a conflict event, but also gives its coders specific guidelines targeted at capturing political violence.

We relate these conflict data to poverty through an analysis at the country and region level, i.e. we do not analyze the experience of households or neighborhoods in a city and we do not study crime. This produces an important omission as interpersonal violence, like homicides, constitutes the vast majority of violent deaths. Hoeffler and Fearon (2015) stress, for example, that for every civil war battlefield death, roughly nine people are killed in interpersonal disputes. The relevance of interpersonal violence and crime for economic activity is beyond doubt. However, as we will explain in the following section, these effects are not showing
x|x|

NOTES: Hazard ratios from specification (2) estimated using a Cox proportional-hazards model on the mother-year sample. The dependent variable is an indicator for birth to a given mother in a given year. Sample includes all mothers who have ever given birth, for each year from their year of marriage to the year of interview. Standard errors in parentheses are clustered by state. State-year fixed effects of the model are included by stratifying the Cox PH model by state-year. Data: NFHS. *** 1%, ** 5%, * 10%.

60
As we will show in this paper, market reforms have been another important source of another important source of change in environmental performance. The reforms have shifted production away from state-owned-enterprises and toward larger plants. With these changes have come substantial increases in productive efficiency and o decreases in the cost of abating pollution, both important sources of declining pollution intensity. These two factors may have had as great an impact on industrial pollution as changes in direct regulation.

<!-- Start of picture text --> Atmospheric ~~~Sulfur Dioxide. Emissions _ andI I<br>Atimospheric Concentration in Chinese Cities, 1991-1993<br>cn 2.5<br>C 2<br>C<br>O0<br>3 3.5 4 4.5 5 5.5 6<br>S02 Emissions (Log)<br><!-- End of picture text -->

**Figure 1: S02 Emissions and Air Pollution in Chinese Cities**

Although the general trend is positive, there remain great regional disparities in industry's environmental performance. In provinces with stricter regulation and a strong commitment to economic reform, many industrial facilities have already shown the way to a cleaner future. However, the legacy of the past remains strikingly apparent in thousands of highly-polluting plants. With industry expected to maintain rapid growth during the next 20 years, a steep decline in pollution intensity will be necessary just to keep emissions constant.

The consequences of current pollution are tragically apparent in public health statistics for polluted areas. As we will show, the best available evidence suggests that approximately 4,000 people suffer premature death from pollution-related respiratory illness each year in Chongqing; 4,000 in Beijing; and 1,000 in both Shanghai and Shenyang.<sup>2</sup> If current trends persist, we project large cumulative losses in human life through 2020. Figure 2 illustrates the grim stakes: Beijing could lose nearly 80,000 people, Chongqing 70,000 and