rafmacalaba/datause-encoder-data huggingface dataset:
reported in Appendix Table A.5 are consistent with the 2SLS estimates in Table 1. The estimates in columns 7 and 8 of Table 1 show that the results for the full sample are driven by rural women. A one s.d. increase in the fraction of seats held by women in a district leads to a 0.07 s.d. increase in physical IPV against rural female constituents.<sup>14</sup> Panel B of Table 1 reports estimates from the first stage of the 2SLS regressions. The F-statistics are quite large, implying that the instrument has predictive power. The coefficient estimate of 0.90 does not statistically differ from one, implying that the share of female winners across all elections in a district varies almost one to one with the share of women who win in close elections against male politicians in a district.<sup>15</sup> However, the instrument is still useful to isolate the variation resulting from close elections against male politicians.

In Panel A of Appendix Table A.7, we examine the robustness of our physical IPV results for rural women using alternate specifications. Columns 1–3 show that our findings are robust to using polynomials of different degrees for the vote margin as controls. Columns 4–6 indicate that the coefficient estimates remain significant and positive when the bandwidth for close elections is reduced to 2 or 2.5 percent or when it is increased to 3.5 percent; if anything, the estimates become larger and more precise as we narrow the bandwidth. In column 7, we restrict the sample to districts in which there was at least one election between a female and a male politician since our identifying variation comes from these observations, finding very similar results. Columns 8–10 show that our results are robust to the addition of fixed effects for a woman’s age, removal of outlier districts where the share of female leaders exceeds 50 percent, or inclusion of controls for political party affiliations and the share of seats reserved for SC/ST.

# **4.2 Women’s Contraceptive Use**

Prior evidence shows that female leaders differ from male leaders in their policy choices (Pande 2003) and tend to invest more in infrastructure that is more directly relevant for women (Chattopadhyay and Duflo 2004). This suggests that female leaders may have

> 14This implies that the election of an additional female politician corresponds to a 0.09 s.d. increase in physical IPV experienced by women in rural areas of the district. In addition, the estimates reported in Appendix Table A.6 indicate no evidence of a statistically significant impact of women’s political representation on women’s risk of being subjected to psychological or sexual violence by their partners, although the coefficients are positive.

> 15This relationship has also been observed in previous studies (Bhalotra and Clots-Figueras 2014).

9
- How can we predict and model potential policy reforms using this study as a base?

**The analysis comes at a time when poverty in the Palestinian territories remains elevated after the COVID-19 shock and has become increasingly concentrated in Gaza.** Based on the PECS 2016/2017, about 29.2 percent of the Palestinian population lived on household consumption levels below the national poverty line of NIS 2,470 per month for a reference household of two adults and three children (equivalent to about US$7.92 in 2017 PPP per day per capita). About 20.5 percent lived on household consumption levels below the Upper Middle Income Class Poverty Line of US$6.85 (2017 PPP) per day per capita. However, poverty rates differed substantially by region: 13.9 percent of Palestinians in the West Bank (including East Jerusalem) and 53.0 percent of Palestinians in Gaza lived below the national poverty line (Figure 1-1).<sup>8</sup> Consequently, the Palestinian poor have increasingly concentrated in Gaza. GDP per capita measured in 2017 PPP dollars had been increasing steadily since 2004, but poverty-reduction has been achieved primarily in the West Bank. Poverty rates in Gaza remain persistently higher and increased markedly from 2011 to 2016-17. Inequality as measured by the Gini coefficient was relatively low at 0.30 nationally in 2016-17, and slightly higher in Gaza with respect to the West Bank. Since 2016-17, poverty rate estimates spiked during the peak of the COVID-19 pandemic, and while they have gradually declined, they have not yet recovered to pre-pandemic levels.

# **Figure 1-1 Poverty and inequality are higher in Gaza than in the West Bank**

Poverty and Inequality in the West Bank and Gaza, 2017 Panel a: Poverty by location Panel b: Inequality by location <!-- Start of picture text --> 53.0<br>0.3<br>0.27 0.28<br>29.2<br>13.9<br>West Bank Gaza National West Bank Gaza National<br>Gini coefficient (0-1)<br>Below national poverty line (%)<br><!-- End of picture text --> Source: World Bank calculations based on PECS 2016/17.

Note: Poverty calculated based on real consumption per capita. National poverty line establishes different thresholds based on different combinations of numbers of adults and children in the data. Gini calculated based on real consumption per adult equivalent.

**The growing regional poverty disparity reflects geographic, administrative, and economic fragmentation.** East Jerusalem has been under Israeli administration and domestic law since 1967, and it

> 8 Figures and trends in this paragraph drawn from: World Bank (October 2022), World Bank (2022a), Palestinian Central Bureau of Statistics (PCBS) (2018), PCBS (2011), PCBS (2007), PCBS (2006), PCBS (2004).

6
43 Table 5: Financial Cost to the Country IMilons of dolam)

||Other claims<br>are<br>_risktess'|Other claims<br>are equal<br>in seniority<br>2|
|---|---|---|
|MEXICO|$2.189|<br>$8,368|
|PHILIPPINES|$451|<br>$3,151|
|COSTA<br>RICA|$193|<br>$907|
|**VENEZUELA**|**$2,444**|$5,583|
|URUGUAY|$53|<br>$407|
|**TOTAL**|$5,330|<br>$18,416|

# Source: Authorst calculations

- Costs to the country are the financial gains of the commercial banks, calculated as the post-operation market value of the commercial bank debt portfolio subtracted from the pre-operation market value of the commercial bank debt.

- **2** The costs as in the first cotumn plus the additional costs to the country as the result of the financial gains of other creditors assuming that all of them have the same seniority status as commercial barns. These financial gains of other creditors are calculate as the capital gains on existing debt (the difference between post-operation and pre-operation prices times the face value of other claims) and the financial loss associated with additiona **l** ending (using the post-operation stripped discount).
We also assessed if participants were immersed in the activities by asking: “How much did you feel the experience of the Venezuelan migrant was an extension of yourself?” Possible answers were: completely, quite a bit, a little, and none.<sup>20</sup> As Figure E.2 shows, a majority of individuals in both groups reported feeling that the migrant’s experience was an extension of themselves at least quite a bit or completely (64.5 percent for the game and 58.0 percent for the video). This suggests a high level of involvement in the activities.

Finally, we included questions to understand how the treatments affected participants’ emotions. We asked participants to choose a number from 1 (little feeling) to 10 (intense feeling) to evaluate the extent to which they felt compassion, distress, or happiness from participating in the online interactive game or watching the video. Figure E.3 presents the results, which show that both treatments induced higher levels of compassion and distress to a similar extent and lower levels of happiness; the game prompted lower happiness than the video.

# **5 Treatment Effects in Prosociality-Related Variables**

# **5.1 Altruism**

We find that both treatments increased altruism as measured through the results of the dictator game, where individuals were offered money and decided whether to keep it or donate it to a Venezuelan or nonmigrant NGO voluntarily. These results are in Table 2 in Panel A, whereas the raw data is in Figure D.1. Both the game and the video had a positive and significant impact on the probability of donation to a Venezuelan NGO of 10.6 pp and 11.1 pp, respectively. Given that the probability of donation for the control group was 66.7 percent, these effects are sizable and amount to an increase of approximately 15.9 and 16.6 percent, respectively. The treatments had a small and insignificant impact on the donation to a nonmigrant NGO, which implies that they generated changes in altruistic behavior toward Venezuelan forced migrants but not towards the general population. Moreover,

> 20This question was adapted from Behm-Morawitz, Lewallen and Miller (2015)’s _self-presence_ scale.

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||log(trade)|
|---|---|
|RTAm|0.280<sup>_∗∗∗_</sup><br>(10.27)|
|RTA_in-or-out_|0.103<sup>_∗∗∗_</sup><br>(4.56)|
|Country-Pair FE|Yes|
|Country _×_ time FE|Yes|
|Observations|796,107|
|R2|0.793|

Table 2: OLS estimates of regression equation (2)

# **4 Accounting for Global Value Chains**

To assess the contribution of Global Value Chains to the effects of regional trade agreements on trade flows, we augment the analysis from the previous section to include two measures of participation in Global Value Chains. As described in subsection 2.3, our first measure, denoted _share_ 1, is the share of intermediates in bilateral exports from the country of origin. The second measure, denoted _share_ 2, is the country of origin’s share of intermediates in imports from the region. The variable _share_ 1 captures the importance of a country as a supplier of intermediate goods to the Global Value Chains. The variable _share_ 2 captures the importance of the region’s part of the Global Value Chains in supplying intermediate goods to a given country. The augmented regression equation reads as follows: 13
Policy Research Working Paper 9994

# **Abstract**

The last expenditure survey released by India’s National Sample Survey organization dates back to 2011, which is when India last released official estimates of poverty and inequality. This paper sheds light on how poverty and inequality have evolved since 2011 using a new household panel survey, the Consumer Pyramids Household Survey conducted by a private data company. The results show that: (1) extreme poverty is 12.3 percentage points lower in 2019 than in 2011, with greater poverty reductions in rural areas; (2) urban poverty rose by 2 percentage points in 2016 (coinciding with the demonetization event) and rural poverty reduction stalled by 2019 (coinciding with a slowdown in the economy); (3) poverty is estimated to be considerably higher than earlier projections based on consumption growth observed in national accounts; and (4) consumption inequality in India has moderated since 2011.

This paper is a product of the Poverty and Equity Global Practice and the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:// www.worldbank.org/prwp. The authors may be contacted at ssinharoy@worldbank.org and rvanderweide@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
construction materials, and still others designing better buildings and doing other complementary (to machines) and value‐adding activities. Some did none of these things. Most people got better houses, different work and new goods and services not related to houses as countries moved up the technology ladder. Society’s wealth increased, even while some were left behind temporarily. Historically, workers have enjoyed a decline in the total number of hours worked to produce this varied and rising output. The last two centuries have seen average yearly working time halving from about 3,200 to about 1,600 hours per year (Maddison (1991) and Sangheon, McCann and Messenger (2007)); see Figures 1 and 2 below. Of course, averages and totals do not disclose the distribution of benefits or the short‐run disruptions in economic activity resulting in unemployment, nor does they give any idea of how the transitions may be managed to minimize loss to society.

# **Figures 1 and 2**

**Hours worked per worker versus total hours worked and per capita hours worked**

<!-- Start of picture text --> OECD<br>OECD Total<br>Hours Worked Per Worker vs. Per<br>Hours Worked per Worker vs. Total Hours<br>Worked Capita<br>2,000 1,500 2,000 1,000<br>1,000<br>1,800 500 1,500 0<br>1,600 0<br>Hours worked per worker<br>Hours worked per worker Total hours worked Hours worked per capita<br>EU Countries Total*<br>Hours Worker Per Worker vs. Per Capita<br>EU Countries Total* 1,720 750<br>Hours Worked Per Worker vs. Total Hours 1,700 740<br>1,750 Worked 350 1,680 730<br>720<br>340 1,660<br>1,700 710<br>1,640<br>330 700<br>1,650 1,620 690<br>320 1,600 680<br>1,600 310 1,580 670<br>1,560 660<br>1,550 300<br>Hours worked per worker Total hours worked Hours worked per worker Hours worked per capita<br>1970 1977 1984 1991 1998 2005 2012<br>Hours per worker 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 Hours, billions Hours per worker Hours per capita<br>Hours per worker Hours per capita<br>Hours, billions<br>Hours per worker<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> 4
The Gender Asset Gap Project found that the patterns of individual and joint ownership varied widely across the three countries and across assets. Savings were almost always reported as being owned individually, while land ownership patterns varied across Ecuador, Ghana, and Karnataka. In addition, the Gender, Assets, and Agriculture Program found that women in different parts of the world had different preferences for whether they own land and other assets individually or jointly.

Knowing the identity of the joint owners does not necessarily tell us whether the ownership rights are shared equally among them. Ownership may be joint but not equal. One analysis of land rights in Uganda finds that even when spouses jointly own land, women are reported as having fewer rights regarding land (Doss, Meinzen-Dick, and Bomuhangi, 2014). Additional work on the rights held by joint owners would provide useful insights into how the rights are shared.

Asset ownership is often used as an indicator of women’s bargaining power. However, little research has considered whether the level of bargaining power depends on whether the asset is owned individually or jointly. As a richer set of data becomes available, this will be an important area to explore.

# **4.3 Questionnaire Design**

There are two main approaches to the design of survey modules on asset ownership.

The first uses the asset as the unit of analysis, identifying the owner(s) and potentially the value of each asset. This approach is used by the Gender Asset Gap Project, MEXA, and the surveys supported by the LSMS-ISA. Consequently, the researcher can develop measures at the asset level, such as the share of land or livestock that is owned by women. In addition, if the owners are associated with a unique ID code that allows us to match them with their individual characteristics, one can identify which individuals within the household are owners, and develop incidence measures of ownership of assets by sex, age, marital status, and/or other individual attributes.

In the context of MEXA, Kilic and Moylan (2016) find that questionnaire design affects reporting on personal ownership of and rights to assets. When subject to a questionnaire with a sole focus on respondents’ personal ownership of assets in Arm 5, female respondents identify themselves as (overall and joint) owners of dwelling, livestock and financial assets at a substantially higher rate compared to their female comparators in households in Arms 1 through 4 in which one or more respondents may have been subject to a questionnaire with a joint focus on respondents’ as well as other household members’ ownership of assets. For instance, within the sample of female respondents, compared to 19
Figure 2: Timeline of Training and Survey <!-- Start of picture text --> Household and CF Household and CF<br>Midline Survey Endline Survey<br>Feb.2012-Apr.2012 Feb.2013-Apr.2013<br>CF (preharvest) (preharvest)<br>Baseline Survey May.2012-Aug.2012 May.2013-Aug.2013<br>Jun.2010 (postharvest) (postharvest)<br>2010 2011 2012 2013 2014<br>Demonstration season Adoption season Adoption season<br>Rainy season Rainy season Rainy season<br>Oct.2010-Apr.2011 Oct.2011-Apr.2012 Oct.2012-Apr.2013<br>EAs and CFs EAs and CFs<br>First training Second training<br>Oct.2010 Nov.2012<br><!-- End of picture text --> Notes: EA = extension agent. CF = contact farmer.

29
large-scale conflict and violence; larger numbers of civil society organizations and private firms; larger and more decentralized governments; and more social consensus around the norm of treating people equally and without regard to their personal social connections and positions. The core argument of NWW is that these differences between countries with open access orders (for which they propose a rough and ready threshold of GDP per capita of $20,000) and those with closed access orders are no accident. The goal of the book is to develop an explanatory logic that demonstrates how these characteristics are mutually reinforcing and give rise to the dual equilibria.

In the limited access order (or the “natural state”), a category into which NWW collect all human societies prior to 1800 and almost all low- and middle-income countries today, social order arises when elites overcome a security dilemma by agreeing to share the future revenues that arise from a peace pact. The state provides third-party enforcement for a variety of intra-elite bargains and exchanges; but because the state itself is no more than an intra-elite bargain among the dominant coalition, it is inherently unstable, and liable to break down as pact participants continually reappraise the value of social cooperation as new technologies develop and exogenous shocks arise. The core problem is that the capture of state authority is the main route to economic advancement, and as a consequence, the stakes of political competition are very high. Some natural states are able to secure more stability for elites by establishing intra-elite relations on predictable law (predictability for elites can generate additional rents), creating organizations that endure beyond the lives of their members (thus becoming “perpetually lived organizations”), and consolidating political control over the military. Upon crossing various “door-step conditions”, some of these “mature” natural states then make the transition to open access orders.

In open access orders, the state permits wide latitude for all individuals to establish new organizations, especially limited liability corporations and political parties. The emergence of more and various kinds of organizations erodes the rents that elites had divided among themselves in limited access orders. It also has the effect of separating political institutions from personal identities, inducing further specialization of labor and technological innovation through a process of Schumpeterian competition, and consolidating political control over the military. It exposes every organization – political, economic, social, and military – to potential competition if it assumes too much political power or captures excessive rents. Essentially, the emergence of open access to civil society, economic, and political organizations generates a large number of veto points in a society, and across almost every 15
findings by Clemens et al. (2007), who estimate similar specifications for a number of social development outcomes—albeit for much longer time spans (1960–2000). The authors conclude, first, that for the majority of developing countries, the attainment of most MDG targets was unrealistic to begin with. Second, they state that the expectations created by the MDG process were unreasonable, and that only under optimal circumstances and policies would poor countries be in a position to achieve the progress necessary to meet the MDGs.

**Figure 2: Transition Paths of Net Primary Enrollment and (Under-Five) Child Mortality**

<!-- Start of picture text --> 100 300<br>250<br>80<br>200<br>60<br>150<br>40<br>100<br>20<br>50<br>0 0<br>-50 -40 -30 -20 -10 0 10 20 30 40 50 -50 -40 -30 -20 -10 0 10 20 30 40 50<br>Primary enrollment with adjustment speed estimated using all Child mortality (U5)<br>observations<br>Using only observations with initial enrolment lower than 70<br>percent<br><!-- End of picture text --> Source: Author’s compilation based on the estimates reported in the Appendix. Note: The horizontal axis refers to time _t_ .

A less deterministic view that gives less weight to past experience as a benchmark would probably state that inputs into social services and human development outcomes (see equations 3 and 6 above), even as part of the MDG process, have not been sufficient. Furthermore, the above regressions refer not only to the past—our estimates refer to a more recent past than those by Clemens et al. (2007)—but also to average paths. The extent to which countries deviate from this average path is reflected, for example, in the R-squared of the above underlying regressions, reported in the Appendix. Despite their rudimentary specification, the regressions for child mortality rates exhibit astonishingly high explanatory power. They explain approximately 70 percent of the variation. While Clemens (2004) reports similarly high R-squared for primary enrollment based on a sample of more than 100 countries and data from 1960–2000, the R-squared for our enrollment equations including all countries is fairly low with only 0.10. This suggests considerable variation around a time path 11