rafmacalaba/datause-encoder-data huggingface dataset:
Table 4: Correlates of trust (ordered logit model)

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
||Institutional|Institutional|Institutional|Institutional|Institutional|Institutional|Institutional|Institutional|
||trust|trust|trust|trust|trust|trust|trust|trust|
|Satisfaction with|0.681***|||||||0.247***|
|security services|(0.0613)|||||||(0.0526)|
|Satisfaction with||1.003***||||||0.249***|
|social services||(0.0760)||||||(0.0854)|
|Satisfaction with|||0.740***|||||0.269***|
|water and sanitation|||(0.0399)|||||(0.0542)|
|Satisfaction with||||0.938***||||0.167**|
|economic performance (Index)||||(0.0788)||||(0.0709)|
|Integrity perceptions|||||1.476***|||0.638***|
|(Index)|||||(0.103)|||(0.108)|
|Equitable treatment||||||0.164***||0.126***|
|by the administration||||||(0.0417)||(0.0313)|
|Policy credibility|||||||0.698***<br>(0.100)|0.348***<br>(0.0584)|
|Region|X|X|X|X|X|X|X|X|
|Urban/rural|X|X|X|X|X|X|X|X|
|Gender|X|X|X|X|X|X|X|X|
|Income|X|X|X|X|X|X|X|X|
|Marital status|X|X|X|X|X|X|X|X|
|No. of HH members|X|X|X|X|X|X|X|X|
|Income|X|X|X|X|X|X|X|X|
|Education|X|X|X|X|X|X|X|X|
|Employment status|X|X|X|X|X|X|X|X|
|Observations|5,916|5,916|5,916|5,916|5,916|5,916|5,916|5,916|
|||Standard er<br>**|rors clustered <br>* p_<_0.01, ** p|by region in par<br>_<_0.05, * p_<_0.1|entheses||||
|||Data sourc|e: World Bank|survey on trust|(2021)||||

The results presented in Table 4 suggest that institutional trust in Morocco is tied to positive perceptions of both policy outcomes and processes. All regression coefficients reported in Table 4 are positive and statistically significant at the 0.01 level (except for economic performance at the 0.05 level). These positive coefficients indicate a positive relation between institutional trust and the di↵erent independent variables tested. They show that as perceptions of government performance, integrity or equity improve, trust in institutions also increases.

The consistently larger coefficients strongly suggest the larger e↵ects of the process-based variables. Specifically, regression results show that:

- Integrity appears to play a particularly significant role in feeding trust in institutions.

15
ing LSMS surveys for 18 countries after 2010.

# **4.1.1 Data challenges**

Dealing with the presence of a large number of zero-transport expenditures and including expenditures on durable goods in the total transport expenditure per household are the two main data challenges to consider before estimating the model.

**Large number of zero-transport expenditures** There is a substantial number of observations with zeros in all transport subcategories of private transport nondurables and transport services. A household is assumed not to spend on transport either when the answer is zero, or when a missing entry can be recorded as zero.<sup>8</sup>

**Include private transport durables** LSMS surveys contain information on private transport durable goods included in the durable goods section. Durable goods are a category of consumer goods that do not wear out quickly, and therefore do not have to be purchased frequently. Therefore, durables goods need a particular treatment. We follow the procedure from Deaton and Zaidi (2002) to calculate the annual value of transport durables using the following equation:

Where _StPt_ is the current value of durables ; _rt_ − _πt_ is the real interest rate; _δ_ is the depreciation rate. Because of missing values and outliers, we use the median value (12 %) of countries’ real interest rates for the survey year or the latest year available for all countries (Table A.2 in the Appendix). We then estimate depreciation rates for the countries for which the LSMS surveys contain information on purchase value, current resell value and age of durables using the following approximation: For all countries, we use the median value (8 %) to deal with missing values and outliers (Table A.2 in Appendix).

# **4.1.2 The determinants of transport expenditures**

We want to estimate the following specification:

> 8The missing expenditure entries can be recoded as zeros when there is a screening question on whether the household purchased the items over a certain period or owns the durables.

22
(SDG 6.4: _ensure sustainable withdrawals and supply of freshwater to address water scarcity_ ). Strategies in meeting these integrated targets can benefit from geospatial analysis of layers of digital data on groundwater quality and quantity.

Here, we develop, for the first time, multi-hazard groundwater risk maps for Bangladesh incorporating three hydrological indicators: groundwater arsenic, salinity and water storage. Our national-scale maps identify critical areas across the entire Bangladesh that may have both groundwater quality (arsenic and salinity contamination) and quality (water storage decline) issues – affecting access to drinking and irrigation water supplies. We have estimated exposure of population groups, particularly poor and socially vulnerable population, number of households with access to tubewells and piped water supplies, and the access to irrigation water supply to various levels of groundwater risks. These groundwater risk maps, and associated hydrological information and data will be made publicly available so that additional waterrelated indicators (e.g., _E. coli_ , access to sanitation services) can be incorporated to the models in future to develop further these national-scale groundwater risk maps to estimate population exposure.

# **2. Materials and Methods**

Water-related multi-hazard groundwater risk mapping at the national scale of Bangladesh is conducted in this study that applies a number of hydrological indicators or variables such as (i) shallow groundwater arsenic (As) concentrations, (ii) groundwater salinity (i.e., Electrical Conductivity (EC) of shallow groundwater), and (iii) the mean depth (2004–2013) to dryseason (December–April) groundwater levels (GWD). A brief description of the hydrological data is provided here but a broader description can also be found in the supplementary text.

## **2.1 Arsenic concentrations in groundwater**

We use the groundwater arsenic (As) data set from the national-scale survey of groundwater in Bangladesh (National Hydrochemical Survey) that was conducted jointly by the British Geological Survey (BGS) and the Department of Public Health Engineering (DPHE) (BGS and DPHE, 2001). According to the national hydrochemical survey, nearly 90% of these surveyed wells have an intake depth of <150 mbgl and are considered shallow wells (see Supplementary Texts for further details). Approximately 25% of these shallow tubewells were drawing groundwater with As concentrations >50 μg/L (Bangladesh drinking water standard); nearly 4
in treatment and control communities born before the opening of the nearest coffee mill, as the pre-treatment estimates are close to zero and statistically insignificant at the 95% confidence interval. However, we observe positive and (mostly) statistically significant estimates in the post-treatment period, suggesting an improvement in test scores for students in treatment villages born after the opening of a coffee mill.<sup>12</sup> Similar results are obtained for STEM and non-STEM test scores in panels (b) and (c) respectively. Additional event study analysis using the TWFE estimator (Figure A1 in the online appendix) yields similar conclusions. Overall, our event study estimates provide suggestive evidence in support of our identifying assumptions.

# **5.2 Effects by Gender**

Next, we explore the effects of early-life exposure to coffee mills on test scores across gender as shown in Table 4. Across the table, we observe a positive and statistically significant effect on test scores of both male and female students, although the effect size appears marginally higher for male than female students. In the top panel, the results show that early-life exposure to coffee mills is associated with a 0.09, 0.06, and 0.1 sd increase in test scores in all (aggregate), STEM, and non-STEM subjects respectively for male students. Meanwhile, the effect on test scores for female students in the respective subjects are 0.08, 0.05, and 0.09 sd. Thus the results suggest that exposure to coffee mills is beneficial to students regardless of their gender.

# **5.3 Robustness Checks**

Having established the positive effects of exposure to coffee mills on test scores, we explore in this section, the robustness of our baseline results to alternative hypothesis.

## **5.3.1 Alternative Measure of Exposure**

In this section, we present an alternate measure of exposure to coffee mills using the share of years between age 0 and 5 that a student was exposed to coffee mills.<sup>13</sup> In contrast to our baseline binary measure of exposure, this measure accounts for the cumulative exposure to coffee mills. It also allows us to capture the spillover effects on children born just before the opening of the coffee mill but still benefited from the economic impacts of the mills within

> 12The only exceptions are the estimates for the third and fourth events after treatment, which, despite being positive, are imprecisely (statistically) estimated.

> 13This is measured by using the same 2.5km distance buffer and then computing the number of years within the first 5 years of the students for which the nearest coffee mill was operational.

12
# **3.2. INTERNATIONAL TRADE**

Senegal’s export of services tends to be concentrated in ICT services and tourism. During 20052012, ICT services accounted for 31 percent of service exports, better than other WAEMU countries (Côte d’Ivoire and Mali) as well as non-WAEMU countries included in table 5. Indeed, Senegal’s services sector ranks among the most sophisticated in the group. However, this status of Senegal as the leading ICT-services producer and exporter has not translated into export of ICT goods. Although the country does better than the WAEMU comparator countries in terms of the share of ICT goods in total export of goods, it performed worse than the East African comparators – Kenya and Uganda – as well as South Africa. This lagging performance is perhaps a reflection of the lagging competitiveness of Senegal’s manufacturing sector.

Tourism receipts were 16 percent of total exports during 2005-2012. Although higher than in Côte d’Ivoire and Mali, this performance was lower than those of the East African comparators. It is also clear from the table that Senegal is not very competitive in travel services. While history and location endows the country to become a travel and tourism destination, setbacks in transportation and lack of investment in the tourism sector have not allowed realization of the potentials.

**Table 5: Service Export Indicators 2005-2012**

||ICT service exports<br>(% of service<br>exports, BoP)|ICT goods exports<br>(% of total goods<br>exports)|Travel services (%<br>of service exports,<br>BoP)|International<br>tourism, receipts<br>(% of total exports)|
|---|---|---|---|---|
|**Senegal**|**31.49**|**0.53**|**39.81**|**16.24**|
|Côte d'Ivoire|30.76|0.37|11.40|1.26|
|Mali|28.64|0.13|55.32|11.25|
|Gambia|-|0.42|63.65|29.52|
|Kenya|-|0.81|25.00|18.29|
|Uganda|12.77|5.23|61.06|20.93|
|Nigeria|2.34|0.01|17.26|0.72|
|South Africa|11.32|1.15|64.64|10.57|

Source: World Bank Apart from ICT services, there has also been a complimentary shift of Senegal’s exports toward technology-intensive goods. Table 6 shows the transition of merchandise export structure over the period 1995-2012. Over the period, there was a slight shift in export structure toward manufactures 15
Policy Research Working Paper 8134

# **Abstract**

This paper revisits the decades-old puzzle of the inverse plot-size productivity relationship, which states that land productivity decreases as plot size increases. Existing empirical studies on the inverse plot-size productivity relationship define land productivity or yields as self-reported production divided by plot size. This paper considers an alternative approach to estimating yields based on crop cuts. The cropcut method entails measuring and harvesting randomly selected subplots by trained technicians, and is recommended by the Food and Agriculture Organization for the accurate measurement of crop production. Using data representative of rural Ethiopia, the analysis indicates that the inverse relationship is strong when based on self-reported production, but disappears when based on crop-cut estimates. The inference from these findings is that the inverse relationship is an artifact of systematic overreporting of production by farmers on small plots, and underreporting on larger plots. The paper also discusses how rejecting the inverse plot-size productivity relationship has significant implications for the inverse farm-size productivity relationship.

This paper is a product of the Development Data Group. 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://econ.worldbank.org. The authors may be contacted at at samdesiere@gmail. com or djolliffe@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
# **Table 1: Characteristics of Quantitative and Qualitative Methods as Used in Impact Evaluations**

||**Quantitative Methods**|**Qualitative Methods**|
|---|---|---|
|Research<br>Questions|<br>Usually derived deductively (e.g.,<br>from knowledge gaps in the<br>literature); seek to demonstrate<br>‘precise’ causal effect (impact) of x<br>on y for relatively large populations;<br>can also draw on qualitative insights<br>to refine questions for specific<br>contexts|<br>Usually derived inductively (e.g., by<br>refining questions as they emerge in<br>situ); focus on process concerns—how<br>outcomes were attained, how different<br>types and combinations of mechanisms<br>generated different outcomes for<br>different groups|
|Data<br>Collection|Use data collection methods such as<br>surveys with closed ended questions;<br>this standardizes but limits the depth<br>and variability of the information that<br>is obtained|Use data collection methods such as<br>focus groups to capture in-depth,<br>context-specific information; are also<br>used to ensure that questions in surveys<br>are worded and sequenced in ways that<br>all parties understand (‘construct<br>validity’)|
|Evaluation<br>Design|Use techniques to diminish selection<br>bias (and other confounding factors);<br>seek to ensure representativeness and<br>comparability of project and non-<br>project samples to enhance quality of<br>statistical inference (‘internal<br>validity’)|Can help to discern and discuss issues<br>that are ‘unobservable’ statistically<br>(including identifying good instruments);<br>weaknesses in ‘breadth’ and<br>representativeness are compensated for<br>by strengths in ‘depth’ and<br>understanding of causal mechanisms|
|Analysis and<br>Interpretation|Quantifies the magnitude of impact<br>to try to determine_whether_an<br>observed aggregate outcome can be<br>causally attributed (probabilistically)<br>to the intervention; but even the most<br>‘rigorous’ (‘well-identified’) analysis<br>rarely provides warrant for inferring<br>that similar results will obtain<br>elsewhere (or at larger scale)<br>(‘external validity’)|Is best suited to informing discussions<br>regarding_how_,_why_and_for whom_a<br>given intervention worked (or not); thus<br>can help explain (and foster learning<br>from) variation in outcomes and/or<br>implementation processes, and usefully<br>contribute to discussions about the<br>possible generalizability of given<br>findings to novel contexts, populations<br>and scales of operation|

Using qualitative and mixed methods can enhance the robustness of the underlying model of causal inference (i.e., improve internal validity) and thereby diminish the influence of various sources of bias (e.g., selection bias, by observing ‘unobservable’ factors shaping program 5
program to identify the relationship between the price of sex work and condom use and thus estimate the compensating differential for safe sex. This instrument allows us to eliminate a serious source of bias common to estimates of compensating wage differentials - biases introduced by simultaneity and unobserved productivity (Garen 1988, Hwang, Reed and Hubbard 1992).

In many ways the market for sex work is simply another labor market. Sex workers in the red light area of Sonagachi in Calcutta who are the focus of this paper are almost always part of a brothel under the ownership of a madam or pimp. They are required to pay fifty per cent of their earnings as rent and "protection" to the person controlling the brothel. The market is quite competitive with over 4,000 sex workers working in 3 70 brothels servicing about 20,000 clients a day (AIIH&PH, 1997). Calcutta is one of the world's largest cities with an estimated population 13 million of which 31 per cent are migrants. This results in a male dominated sex ratio with 0.83 females for every male in the population that in turn causes the demand for sex work to be consistent and high. Sonagachi is the oldest and best established red-light area in Calcutta and has been in existence at least for 150 years. It is located close to Calcutta University which provides a steady source of clients, and like many other older Calcutta neighborhoods consists of a dense network of narrow, winding streets lined by two and three story buildings. The brothels are supported by a number of restaurants, teashops, bars and other businesses that serve sex workers and their clients in the area.

Sex work is a highly stigmatized activity. Women who enter the profession are rarely able to participate in mainstream social activities and see themselves as "fallen." In most instances they are cut off from their families, even though some continue to send them remittances. Children of sex workers cannot find spouses from mainstream society and have to marry within the community - which is also indicative of a sex worker's status as an outcaste. In another survey conducted in Sonagachi in 1992, sex workers were asked the reason why they entered the profession. Table 1 summarizes the responses by income categories of sex workers showing that the majority of sex workers (49 per cent) became sex workers because of acute poverty. This includes situations where destitute parents were compelled by circumstances sell their daughters to pimps or their agents, or where widowed women are forced into it to subsist after the death of their husbands. In an ethnographic study of Sonagachi, Sleightholme and Sinha (1997) suggest that a large number of women enter the profession in order to escape violent husbands, or to support themselves after being abandoned by their husbands. This is substantiated in Table 1 that reports results from an earlier survey of Sonagachi conducted in 19923. It shows that 1.6 per cent of women entered the profession because of family disputes. Despite the highly stigmatized nature of the

> _3_ We have not been able to obtain these data and are therefore unable to further analyze them.

S
**lxvi** INTRODUCTION

Table 19 Population Aged 0-14, 15-64, and 65 and Over for All Countries and Economies, 198S-2025 (percent)

|CoLmtry or economy<br>and age grouip|1985|1990|1995|2000|2005|2010|2015|2020|2025|
|---|---|---|---|---|---|---|---|---|---|
|World||||||||||
|0-14|33.7|32.6|32.0|31.2|30.0|28.7|27.5|26.6|25.7|
|15-64|60.4|61.4|61.7|62.2|63.2|64.4|65.1|65.3|65.3|
|65+|5.9|6.0|6.3|6.6|6.8|6.9|7.4|8.2|9.0|
|Africa||||||||||
|0-14|45.3|45.3|44.8|43.9|42.8|41.5|39.7|37.6|35.5|
|15-64|51.7<br>|51.7<br>|52.2<br>|53.1<br>|54.2<br>|55.5<br>|57.1<br>|58.9<br>|60.7<br>|
|65+|3.0|3.0|3.0|3.0|3.0|3.1|3.2|3.4|3.8|
|East Africa||||||||||
|0-14|45.7|45.9|45.5|44.9|43.9|42.7|41.0|39.0|37.0|
|15-64|51.4|51.3|51.7|52.2|53.2|54.4|56.0|57.7|59.5|
|65+<br>|2.9|2.9|2.8|2.8|2.8|2.9|3.0|3.2|3.5|
|Botswana||||||||||
|0-14|48.5|44.8|40.3|34.6|32.5|30.3|27.8|25.6|24.0|
|15-64|47.9|51.6|56.2|61.9|63.8|65.8|67.8|69.3|69.8|
|65.<br>|3.7|3.5|3.5|3.5|3.7|3.9|4.4|5.2|6.1|
|Burundi||||||||||
|0-14|43.4|45.5|47.4|47.6|46.1|43.7|42.0|40.6|38.8|
|15-64|52.9|51.3|49.7|49.7|51.3|53.8|55.5|56.7|58.1|
|65+|3.7|3.1|2.8|2.7|2.7|2.5|2.5|2.7|3.2|
|Comoros||||||||||
|0-14|48.1|47.2|48.8|47.7|45.6|44.0|42.3|40.2|37.6|
|15-64<br>|47.9<br>|49.9<br>|48.2<br>|49.7<br>|51.5<br>|53.2<br>|54.9<br>|57.0<br>|59.4<br>|
|65.|4.1|2.9|3.0|2.7|2.9|2.8|2.9|2.9|3.0|
|DJlbouti||||||||||
|0-14|45.7|44.8|44.8|44.3|43.5|42.0|40.5|38.6|36.5|
|15-64<br>|52.0<br>|52.8<br>|52.7<br>|53.1<br>|53.8<br>|55.0<br>|56.3<br>|57.8<br>|59.6<br>|
|65.|2.3|2.4|2.6|2.7|2.8|2.9|3.2|3.6|3.9|
|Ethiopia||||||||||
|0-14|44.2|43.8|43.3|44.4|44.0|42.8|40.7|38.9|37.2|
|15-64|53.1<br>|53.5<br>|53.9<br>|52.7<br>|53.1<br>|54.2<br>|56.1<br>|57.6<br>|59.1<br>|
|65+<br>|2.7|2.7|2.8|2.8|2.9|3.1|3.2|3.4|3.7|
|Kenya||||||||||
|0-14|52.3|52.9|51.6|50.1|48.8|47.7|46.1|44.0|41.7|
|15-64|45.2<br>|44.9<br>|46.3<br>|47.9<br>|49.2<br>|50.4<br>|51.9<br>|53.8<br>|55.9<br>|
|65+|2.5|2.2|2.1|2.1|2.0|2.0|2.0|2.2|2.4|
|Lesotho||||||||||
|0-14|41.9|42.7|42.8|41.7|39.9|37.8|35.6|33.2|30.7|
|15-64|53.8|53.1|53.1|54.2|56.1|58.2|60.5|62.6|64.7|
|65+<br>Madagascar|4.3|4.2|4.1|4.0|4.0|3.9|4.0|4.3|4.6|
|0-14|46.3|46.7|45.9|44.1|42.3|40.8|38.8|36.3|33.5|
|15-64|50.5|50.2|51.1|53.0|54.9|56.6|58.5|60.6|62.9|
|65+|3.3|3.1|3.0|2.9|2.8|2.6|2.7|3.1|3.6|
|MaLawi||||||||||
|0-14|47.0|47.8|48.5|49.2|48.8|47.7|46.1|44.8|43.4|
|15-64|50.4|49.6|49.0|48.4|48.8|50.0|51.5|52.9|54.2|
|65+|2.6|2.6|2.5|2.4|2.4|2.4|2.4|2.4|2.4|
|Mauritius||||||||||
|0-14|32.6|30.7|28.0|26.0|25.3|24.4|23.4|22.5|21.6|
|15-64|63.2<br>|64.6<br>|66.9<br>|68.4<br>|68.8<br>|69.4<br>|69.4<br>|68.8<br>|67.6<br>|
|65+|4.3|4.7|5.1|5.7|6.0|6.3|7.1|8.8|10.7|