rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: Determinants of transient and chronic poverty

||OLS with <br>|HCSE<br>|Transient p<br>|overty<br>|Chronic <br>|poverty<br>|
|---|---|---|---|---|---|---|
||(Depvar: me<br>|an cons)<br>|(Cond. quanti<br>|le: 0.75)<br>|(Cond. quan<br>|ile: 0.9)<br>|
|Variable|Coefficient<br>estimate|t-ratio|Coefficient<br>estimate|t-ratio|Coefficient<br>estimate|t-ratio|
|Single member hh (dummy)|0.02281|0.40|0.65579|-1.14|5.86248|6.27"|
|HH with couple, no kids (dummy)|0.00847|0.27|0.29873|1,19|2.15560|1.57|
|HH with couple& 1 kid (dummy)|0.00056|-0.03|0.01486|0.12|1.10826|4.64|
|HH with couple& 2 kids (dummy)|0.00109|0.12|0.12634|-1.28|0.30298|2.75"|
|HH with couple& 3* kids(dummy)|0.00649|1.09|-0.02680|0.49|0.10519|1.19|
|Village cadre hh (dummy)|0.05599|$.17°|0.26859|3.31"|0.13471|0.85|
|State empl. in hh (dummy)|0.05796|4,34°|0.29412|-2.09"|-0.64323|3.64|
|TVE empl. inhh (dummy)|0.02783|1.61|0.21828|0.59|0.84038|2.90"|
|Out oftown empl. in hh (dummy)|0.02674|2.107|~<br>0.07067|0.51|0.85864|6.50"|
|Ed. Ivl. of labor force - illit. (dummy)|0.12532|-3.69°|0.97346|0.93|1.01295|1.82"|
|Ed. lvl. oflabor force - prim. sch. (dummy)|-0.10304|-3.23°|0.77864|0.73|0.26291|0.57|
|Ed. Ivl. oflabor force - sec. sch. (dummy)|0.08277|-2.64°|0.66592|0.62|0.13829|0.31|
|Ed. lvl. oflabor force - high sch. (dummy)|0.06534|-2.06"|0.44150|0.40|0.00490|0.01|
|Prop. of illit. in hh|0.00338|0.13|0.34199|1.78"|-2.37357|4.75"|
|Prop. ofprim. sch. ed. in bh|0.00190|0.08|0.71619<br>—|4.66°|0.96659|4.19"|
|Prop. of sec. sch. ed. in bh|0.01728|0.62|0.95998|5.19"|0.59209|1.43|
|Prop. ofhigh sch. ed. in hh|0.01429|0.28|1.01308|1.80°°|1.20138|1.99"|
|Age ofhh head|0.00712|3.70°|0.00732|0.97|-0.03079|-1.61|
|Age? ofhh head|0.00007|3.11"|0.00003|0.40|0.00010|0.50|
|Head- illiterate (dummy)|0.05535|2.35”|0.14123|0.95|0.17548|0.52|
|Head - prim. sch. ed. (dummy)|0.06902|3.07"|0.27752|-1.98"|-0.42838|-1.60|
|Head - second. sch. ed. (dummy)|0.08307|3.67°|0.33256|-2.35"|0.98427|-3.25°|
|Head - high sch. ed. (dummy)|0.07310|2.95"|0.26650|-1.94"|0.96564|-2.79°|
|Spouse- illiterate (dummy)|0.00683|0.18|-0.25560|-1.07|-1.07750|0.64|
|Spouse - prim. sch. ed. (dummy)|0.01608|0.42|0.35716|-1.48|-1.40496|-0.82|
|Spouse - sec. sch. ed. (dummy)|0.00303|0.08|0.44121|-1.57|-1.83509|-1.09|
|Spouse - high sch. ed. (dummy)|-~0.01904|0.43|0.53844|-1.51|-3.19489|-2.01"|
|Spouse (dummy)|0.00029|0.01|0.11685|0.46|0.31398|0.18|
|Prop. ofpresch. kids in bh|0.10221|4.69°|0.68490|3.73"|2.69405|8.99"|
|Prop. ofkids 6-11 years in bh|0.02160|0.89|0.14330|0.65|0.06889|0.22|
|Prop.ofkids12-14inhh|0.07642|2.63°|0.17337|0.74|-2.08873|-5.08°|
whether child labor statistics depend on to whom the survey questions are asked (Dillon et al. 2012, Dammert and Galdo 2013, Janzen 2018). While the ILO guidelines for survey design and measurement of children’s work suggest that the child should answer the labor module him or herself (ILO 2008), substantial variation across household surveys exist. For instance, and although different surveys might entail different definitions of child labor, the Statistical Information and Monitoring Programme on Child Labour (SIMPOC) stand-alone child labor surveys collect labor information from both children and proxies, while the <mark>Multiple Indicator Cluster Surveys (MICS)</mark> gather labor information from the most knowledgeable adult member of the household, and UNICEF MICS surveys direct questions to the mother or primary caretaker. Furthermore, policymakers and researchers substantially rely on country-level labor force surveys (LFS), which provide in most cases either self or proxy reporting measures of child labor but not both measures due to the costs and logistics of fieldwork. A comprehensive review of LFS shows, for instance, that in half of the national-level LFS up to 50% percent of the responses are provided by proxy informants (ILO 2018). In Africa, the setting of our survey design intervention, the rate of proxy respondents ranges from 34% in Nigeria to 99% in Mali for the 15 to 24 age group (Desiere and Costa 2019).<sup>1</sup> The effect of the type of respondent on child labor statistics is not clear a priori if both respondents recall different types of activities with different errors. Child-reported information may be more accurate than proxy responses if a child knows best how she allocates her time, while at the same time, recall of past activities may be cognitively burdensome if the child may not fully understand what “work” entails or how to use recall-count strategies to track her activities and hours. On the other hand, a proxy respondent may be familiar with the children’s activities depending on the frequency and continuity of work activities, and the complementarity of child 3
-2 -

results when different institutions, like non-life insurance companies are considered. The literature is not clear on its assumption regarding causality between contractual savings and capital market development. A one-way or a two-way relationship is assumed, usually interchangeably. In this paper, we address the question of which relationship leads empirically. The evidence, including descriptive statistics as well as Granger causality tests is presented for OECD countries and some other countries such as Chile, Malaysia, Singapore, South Africa, and Thailand. The paper does not present a theoretical framework but explains with clear statements and intuitive examples the way in which we think the growth of the contractual savings sector promotes financial development.

**Table 1: Contractual savings ratio to GDP (percent)**

|**Counts**|**1980**|**1985**|**1990**|**1996**|
|---|---|---|---|---|
|Netherlands|66.90|93.65|108.11|148.19|
|United Kingdom<br>|38.81|74.77|86.90|141.72|
|Switzerland|70.00||88.5|131.38|
|United States|43.01|59.33|69.20|94.80|
|Canada<br>|30.29|38.08|47.80|64.59|
|Australia<br>|||33.49|57.52|
|Sweden||23.92|28.63|47.96|
|**Norway**|13.15|17.29|25.80|30.02|
|Belgium<br>||16.42|20.55|27.20|
|Korea, Rep.<br>|4.06|10.48|19.24|24.36|
|Germnany<br>|12.73|17.63|20.68|23.82|
|Austria|||13.28|21.35|
|Spain<br>||3.21|9.87|18.78|
|South Africa|39.27|55.93|78.13|126.01|
|Singapore||153.36|115.13|93.50|
|Chile|1.00||29.28|50.61|
|Malaysia<br>|20.08|35.65|47.18|51.02|
|Thailand|||**2.10**|**4.80**|

**Source:** 1998 OECD Institutional Investors Statistical Yearbook and WB institutional investors database.

The paper is organized as follows. Section II, presents the key propositions on the links between contractual savings and capital markets development. Their effects and implications for the economy as a whole are analyzed in terms of growth, term structure of interest rates, capital structure, regulation, and comparative impact on developing versus developed economies. Section III, discusses the role of contractual savings in the structure of the financial sector. In particular, it distinguishes between the effects of contractual savings, mutual funds and non-life insurance development. Section IV, presents a descriptive analysis of the data, which confirms that there is a positive relation between contractual savings and capital markets development. Section V, analyzes the causality between contractual savings and non-life insurance companies and market
24 _Prime-age women in urban areas with children display lower participation rates than those who have no children_ (Table 14). Indeed, 41.4 percent of women living in households with no children participate in the labor force, against 37.3 percent of those with children. Although participation rates are higher than those observed in rural areas, the gap imputable to childcare appears to be slightly wider in urban centers. The overall differences in participation between men and women in urban areas cannot however be attributed to childcare only: 89.9 percent of males aged 25 to 49 are in the labor force compared to 41.4 percent for women of the same age without children. Finally, females aged 50 to 64 are more likely to work when living in a household with children than not (respectively 37.5 percent and 27.4 percent). This is probably to compensate for the income loss generated by prime-age women’s decision to opt out of the labor force when they have children.

**Table 14. Urban Labor Force Participation by Gender and Family Status* (in %)**

||||_Female_<br>||
|---|---|---|---|---|
||_Male_|_All_|_With_<br>_children_|_Without_<br>_children_|
|_Age group_|||||
|_15-24_|35.8|17.8|17|19.5|
|_25-49_|89.9|38.4|37.3|41.4|
|_50-64_|84.6|32.4|37.5|27.4|

Source: EPCV 2004. Figures are displayed in percentage. * Whether or not there are children under 10 living in the household.

_Half of the women working in urban centers are self-employed,_ which is substantially higher than the proportion of self-employed men among male urban workers (50 percent compared to 29 percent, see Figure 11). Wage earners amount to 31 percent of female employment (53 percent for males). Unpaid family labor or apprenticeship is comparable in urban centers for men and women (3 percent). It is however much lower than that observed in rural areas.

**Figure 11. Types of Employment in Urban Areas by Gender (in %)**

<!-- Start of picture text --> Females Males<br>Self-<br>employed Self-<br>50% employed<br>29%<br>Unpaid family<br>worker/<br>Unpaid family worker/appre Wage earner31% apprentice3% Wage earner<br>ntice 53%<br>3% Day laborer<br>Day laborer 15%<br>16%<br><!-- End of picture text --> Source: EPCV 2004
Gaur (1992) showing the effect of FYM use on the yields of major crops;<sup>38</sup> these should also be approximately valid for neighboring Pakistan. In the year of application, one metric ton of FYM per acre would increase the yields of any of the four major crops (wheat, rice, cotton, and sugarcane) by 1.5-1.75 percent. Based on the leasing coefficient from a linear model otherwise identical to speci fi cation (3) in Table 4, leased plots received on average about 0.6 metric tons less FYM per acre than owned plots in the 2000-01 agricultural year. Therefore, by converting a leased plot into an owned plot, major crop yields would rise by about one percent in the fi rst year. Taking into account the carryover effects of FYM to future years (see fn. 6) would give a cumulative yield gain of around 2-2.5 percent. For poor tenant households in rural Pakistan, even modest gains such as these will have important income effects.

To be sure, these are just gross impacts; they do not account for the cost of FYM application, but they do give some sense of the magnitudes involved. Banerjee et al. (2002) fi nd that a tenancy reform in India, one that fell far short of giving share-tenants full ownership rights, increased crop yields on the order of 50-60 percent. At least part of this increase, they argue, can be attributed to higher investment due to improved tenure security. Our fi ndings suggest that investment — more precisely, noncontractible investment — may only be a small part of the story. However, we reiterate, this conclusion rests critically on what other noncontractible investments (e.g., land maintenance activities) tenants can potentially undertake and how responsive these investments are to changes in the security of tenure. Filling in these gaps in our knowledge is an important area for future research.

> 38 These estimates are more accurate than could ever be obtained from economic data because they are quite literally taken from experiments, in which everything else is held constant. The experimental effects reported are for FYM use on top of a standard application of chemical fertilizer.

25
# **Do Labor Statistics Depend on How and to Whom the Questions Are Asked? Results from a Survey Experiment in Tanzania**

Elena Bardasi The World Bank Kathleen Beegle The World Bank Andrew Dillon International Food Policy Research Institute Pieter Serneels University of East Anglia Key words: Labor statistics, Survey design, Tanzania, Field experiment JEL code: J21, C83, C93 Word count: 8,343

* We would like to thank Economic Development Initiatives, especially Joachim de Weerdt, the supervisory staff, enumerators and data entry teams for thorough work in the field. We also thank Gero Carletto, Louise Fox, Annette Jäckle, David Newhouse, Dominique Van de Walle, and seminar participants at IFPRI, ASSA annual meetings, IZA/World Bank Conference, CSAE University of Oxford, and the World Bank for very useful comments. This work was supported by the World Bank Research Support Budget and PREM-Gender. All views are those of the authors and do not reflect the views of the World Bank or its member countries. Corresponding author is Andrew Dillon a.dillon@cgiar.org

1
> We organize industries into four types ( _k_ s), based on a classification developed in Lall [1999].  At one end of the spectrum are what resource based industries, involving primary processing of agricultural products and natural resources. This sector includes the food and tobacco industries, wood processing, tanneries, and precious stones.

Although most industries in this sector are relatively labor intensive, their distinguishing characteristic in the context of trade is that the competitiveness of a locality or a country in their production stems from the availability of specific natural resources. Relatively labor intensive, low-entry barrier industries in which competitiveness depends more on the relative price of unskilled labor than on the cost of raw materials constitute low tech industries, including textiles, garments, leather goods, furniture and fabricated metal products. Then there are the medium or high technology industries that are as a rule more capital and skill intensive and, more importantly, are characterized significant to large R & D expenditure that also make entry costs concomitantly higher. Our grouping of industries based on this classification is provided in Appendix 2.

# C. <u>Identification of agglomeration economies</u>

We just described a model of firm location choice, where a firm chooses a location for production based on its valuation of the local business environment and agglomeration economies derived from own industry concentration. The choice of variables described in Section II is derived from theoretical priors and various empirical studies. However, in addition to these observed attributes, it is possible that firms also 20
# **3 Empirical Strategy**

Our goal is to estimate the effect of female leaders on the prevalence of IPV experienced by women. Since there may be unobservable factors correlated with both the likelihood of having a female winner and the prevalence of violence against women in a given district, the OLS estimates may suffer from selection bias. For instance, less genderbiased districts may be more likely to elect female leaders and have a lower incidence of violence against women. Therefore, to identify the effects of exposure to female leaders in a causal manner, we employ an IV strategy based on the existence of close elections between male and female candidates in a given district. Given that the identity of the winner in a close election is quasi-random, we use the fraction of constituencies in a district won by a woman in a close election against a man as an instrument for the fraction of constituencies in a district won by a woman.

Our identification strategy closely follows previous studies (Clots-Figueras 2012; Bhalotra and Clots-Figueras 2014) and is illustrated in Figure 1, which plots the fraction of constituencies won by female politicians in a district against the margin of victory in male–female elections. A positive margin denotes that a female politician won. Panel A of Figure 1 uses all elections where the top two candidates are a male and a female, while Panel B uses only districts that had a single male–female election. In both panels, there is a clear discontinuity: the fraction of seats in a district that are won by female candidates increases by 15–20 percentage points when a woman wins a close election against a man in the district. This is not surprising since the average district has 5 to 6 constituencies.<sup>10</sup> We estimate the following specifications using two-stage least squares (2SLS):

> 10Appendix Figure A.1 shows no evidence of manipulation around the cutoff point.

6
Policy Research Working Paper 9953

# **Abstract**

Wildfires throughout western North America produce smoke plumes that can stretch across the agricultural regions of the American Midwest. Climate change is likely to increase the number and size of these fires and subsequent smoke plumes. These smoke plumes change direct, diffuse, and total sunlight during the crop growing season and consequently influence yields of both corn and soybeans. The analysis in this paper uses a twelve-year panel of county-level yields from all counties east of the 100th meridian combined with measures of exposure to smoke plumes of low and high density during the growing season. It shows that low-density plumes enhance yields, likely by increasing in the fraction of diffuse light, while high-density plumes decrease yields. Because there are more low-density plumes today, the net effect is a slight increase in yields on average. As climate change makes wildfires larger and more frequent, the overall impact of smoke on yields is expected to be substantially more negative.

This paper is a product of 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 abehrer@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
From the perspective of a multinational that operates in Europe, it is Central European countries that offer cultural and linguistic similarities, greater ease of ensuring compliance with European regulations, e.g. pertaining to privacy, as well as high levels of technical ability. GE has become one of the largest investors in Hungary over the past 12 years moving a number of business processes to that country, in particular to support GE units across Western Europe. Russia too has a large pool of technical talent but needs to overcome difficulties created by weak infrastructure and language barriers. Boeing was the among the first companies to locate in Russia; today Russian aeronautics specialists in seven cities are designing parts on the 777 aircraft.

This optimistic scenario confronts one possible problem: political opposition in importing countries and pressure for trade barriers. What looks like a healthy job creation process in India and other developing countries may be seen as a “white collar job outflow” in the industrialized countries. The figures referred to above and other examples (e.g. forecasts that as many as 3.3 million jobs in the United States and 2 million jobs in western financial services will be lost over the next decade, and 200,000 in the United Kingdom by 2008; as well as more specific episodes, e.g. British Telecom announcing the creation of 2200 jobs in India at the expense of British call centers) are reportedly alarming some unions and politicians in the industrialized economies and political opposition is visible<sup>20</sup> .

Figure 3

> 20 See ”The New Global Job Shift: The next round of globalization is sending upscale jobs offshore”, in: _Business Week_ , Cover Story, February 3, 2003, “Call Centers: The Revolution Revs Up”, in: _Financial Times_ , March 10, 2003, “Services Go East”, in: _Financial Times_ , August 7, 2003. See Kirkegaard (2003) for a critique of these anecdotal figures and how they do not correspond to official data from the US Bureau of Economic Analysis.

8
# **<u>Applications of the FDM ‐ Potential for modal shift on the Eastern Corridor</u>**

The current transport cost for the Eastern Corridor is US$11.6 billion (refer Table 5:). Initial results show that this can be reduced by US$1.6 billion if rail‐friendly freight along the corridor is returned to rail, according to the current design, i.e. freight with origin‐destination pairs along the corridor (refer Table 7:). Many of the freight flows currently using roads on the corridor are better suited to rail transportation (refer Figure 22). These flows are typically high‐density, long‐haul flows of less time‐ sensitive commodities that originate or are destined to locations close to the rail line. Specific reasons why these flows are not currently on rail relate to issues of efficiency and capacity on the Eastern Corridor rail line. Attracting these flows would require the rail line to commit and invest in the upgrade of its services and the design of a targeted commercial strategy. It has however become clear that a dedicated corridor focus can solve corridor capacity problems, but does not _per se_ address problems with port limitations, connectivity or integrated logistics planning.

The Eastern Corridor has the potential to play a far more prominent role in unlocking logistics efficiencies and trade potential in the landlocked states north of West Bengal. It can also become a robust and prolific hinterland corridor for the two terminals of the Port of Kolkata to enable port‐led development in the surrounding states. A key element of the proposal is to redesign the Eastern Corridor where it terminates in Kolkata by routing it via a logistics hub. Feasibility studies are required to determine the optimal location of a logistics hub. The logistics hub will require a high‐volume, high‐ speed connection to the rail line at Dankuni, a high‐volume, accessible truck terminal, and efficient intermodal transfer facilities. Capacity for other supply chain services such as storage, customs clearance and value‐added services would also be beneficial but is not required right from the start. The logistics hub also needs to be connected to KDS directly, circumventing the city‐center of Kolkata completely. A rail or inland waterways shuttle is suggested as a high‐speed, high‐volume solution. An accessible, high‐speed connection from the logistics hub to HDC will also be necessary, but this may very well remain a road solution. If these conditions are met, a further US$2.1 billion of costs can be saved (these savings are detailed in Table 7:). Modal shift on the Eastern Corridor will therefore only contribute 44% of the total potential of $3.7 billion saving that can be achieved by an integrated corridor design.

37