rafmacalaba/datause-encoder-data huggingface dataset:
Figure 1: Incidence by income decile of the LPG subsidy.

<!-- Start of picture text --> 1 2 3 4 5 6 7 8 9 10<br>Decile<br>Post reform Pre reform<br>Source: Tornarolli and Vazquez [13] .<br>100<br>80<br>60<br>40<br>20<br><!-- End of picture text --> is no evidence that a large number of poor households were unduly excluded. Therefore, we believe that on balance the incidence analysis based on _de jure_ eligibility provides a good approximation to the actual direct impact on households.

# _2.1. The puzzle_

While the reform benefited a large part of the population (and proportionally more the poor) it initially proved to be highly unpopular. A nationally representative survey conducted in late January 2011 showed that 70 percent of Salvadorans disapproved of the planned LPG subsidy reform. Criticism came from a variety of angles. Some critics stood to lose from the reform, such as LPG distributors, who would see the volume of sales decreased and who would eventually mount a short-lived and ineffective strike in late May 2011.

Other criticisms came from more unlikely quarters. In early February 2011 the Archbishop of San Salvador, Monsignor José Luis Escobar Alas, expressed his concern that “the poor may be left out” and asked the authorities to reconsider the reform plan. This was not an off-the-cuff remark. The Archbishop spoke to the press on a variety of issues regarding the LPG subsidy reform at least on three different occasions in February 9
- STIEGER, M., C. FLÜCKIGER, D. RÜEGGER, T. KOWATSCH, B. W. ROBERTS, AND M. ALLEMAND (2021): “Changing Personality Traits with the Help of a Digital Personality Change Intervention,” _Proceedings of the National Academy of Sciences_ , 118.

- STIEGER, M., S. WEPFER, D. RÜEGGER, T. KOWATSCH, B. W. ROBERTS, AND M. ALLEMAND (2020): “Becoming More Conscientious or More Open to Experience? Effects of a Twoweek Smartphone-based Intervention for Personality Change,” _European Journal of Personality_ , 34, 345–366.

- TABER, K. S. (2018): “The Use of Cronbach’s Alpha when Developing and Reporting Research Instruments in Science Education,” _Research in Science Education_ , 48, 1273–1296.

- WORLD ECONOMIC FORUM (2017): “The Future of Jobs and Skills in Africa: Preparing the Region for the Fourth Industrial Revolution,” World Economic Forum Executive Briefing.

- WORLD ECONOMIC FORUM, WORLD BANK AND AFRICAN DEVELOPMENT BANK (2017): “The Africa Competitiveness Report 2017: Addressing Africa’s demographic dividend,” World Economic Forum Insight Report.

- WRZUS, C., AND B. W. ROBERTS (2017): “Processes of Personality Development in Adulthood: The TESSERA Framework,” _Personality and Social Psychology Review_ , 21, 253–277.

21
Policy Research Working Paper 11065

# **Abstract**

Agency—the ability to define one’s own goals and make choices to achieve them—is often measured through questions that capture an individual’s direct involvement in decision-making. Yet not participating in decision-making may not always imply a lack of power. Drawing on the notion of effective power, this paper conceptualizes two forms of this power: effective power by proxy and effective power by influence or persuasion. The paper explores indirect ways of pursuing one’s goals when not directly involved in the decision, using unique mixed methods data from multiple members of rural households in Kilifi County, Kenya. The results indicate that traditional decision-making measures underestimate the agency of some individuals—particularly that of husbands and fathers, who can disproportionately rely on their preferences being met through their effective power by proxy, without bearing the time and cognitive cost of actively engaging in decision-making. The results also indicate that women’s agency varies by marital status and household composition. Focusing on decisions between married women and their husbands overlooks the dynamics of intergenerational households where other members, like mothers-in-law or sons, are involved in the decision-making process. The paper concludes by providing practical recommendations for measuring agency in surveys.

This paper is a product of the Gender Innovation Lab, Africa Region. 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 marya.hillesland@qeh.ox.ac.uk, cheryl.doss@tufts.edu, s.masino@westminster.ac.uk, martinaquerejeta@gmail.com, adonald@worldbank.org, gregory.t.seymour@census.gov, and clare.clingain@rescue.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _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
fend those you care about." In Bihar, respondents repeatedly asked for concrete examples to understand this question, leading us down a path where the module might differ significantly from how it is administered in other countries and contexts.

Furthermore, in any country or cultural context in which similar questions have been tried, respondents tend to exhibit what is termed as "social desirability bias"– that is, to answer some of the questions in ways which they think would win social approbation even if their true responses would be otherwise. For example, a question like "are you very careful or attentive when undertaking a task?" has been used to measure the personality trait of conscientiousness, but getting sincere responses to it is a problem, as evidenced by 100 percent of some respondents in our survey saying they are always careful. As we analyzed the data, we assessed whether/which questions worked as intended, to capture meaningful variation across respondents (even though the "levels" are subject to desirability bias). We report all our findings so that readers can reach their own interpretation and argue against the ones we offer. Part of the contribution of this work is methodological– finding better ways to measure these concepts, that may be crucial for strengthening public health systems, through trial and error.

We follow an established practice in the growing literature measuring public service motivation and integrity of grouping different sets of questions into indices. The underlying questions in each of the indices created are summarized below.<sup>9</sup> In this paper we focus on the following traits:

# • : **All Surveys**

1. **Integrity Index** (8 questions): Questions on agreement or disagreement with morally disengaged behavior (e.g. it’s OK to spread rumors to defend those you care about or it’s no big deal to pass someone else’s work as your own).

> 9All indices are created using Inverse Covariance Weighting (ICW) as described in Michael Anderson. "Multiple Inference and Gender Differences in the Effects of Early Intervention: A Reevaluation of the Abecedarian, Perry Preschool and Early Training Projects. Journal of the American Statistical Association. December 2008, Vol. 103, No. 484. This is similar to Principal Component Analysis (PCA), but overweights variables that provide "new information" (i.e. have lower covariance with other variables). All underlying data is coded such that higher value of indices are "more positive" outcomes.

17
To construct our final measure that captures both the presence of FDI in services sectors and plant usage of those services, we weigh the FDI penetration ratio in a services sector by the intensity of plant usage of those services in year _t_ . The ‘services FDI _K_ linkage’ measure is computed as: _FDIslit_ = ∑α _itk_ * _FDI kt_ , where _FDI kt_ is the FDI _k_ = 1 penetration ratio in services sector _k_ and α _itk_ is the intensity of usage of services from _j_ sector _k_ by plant _i_ in year _t._ We drop the manufacturing industry subscript _j_ from _FDIslit_ in what follows to emphasize the fact that this is a plant-level time-varying measure rather than an industry-level time-varying measure. The sum is computed over four groups of services: (1) electricity and water, (2) transport and communications, (3) financial, insurance, and business services, and (4) real estate.22 To validate our aforementioned working hypothesis, we generate 1000 groups of random plant-level time-varying services intensity weights, recompute our services FDI linkage variable using those random weights, and estimate our main specification 1000 times, following Keller (1998).<sup>23</sup> The average coefficient on the services FDI linkage obtained from this simulation is negative and insignificant suggesting that that the intensity of services usage by plants does matter for the estimated impact of FDI on the productivity growth of Chilean manufacturing plants, which will be shown to be positive and significant in Tables 1-4.

Our services FDI linkage measure draws upon the measures used by Javorcik (2004), Blalock and Gertler (2008), and Arnold et al. (2007a), but differs from those by

> 22 These groups of services are dictated by the availability of sectoral GDP from the Chilean Central Bank. Business services encompass advertising, legal, technical, and accounting services, licenses and foreign technical assistance, and other services. More details on the services FDI linkage measure are provided in the Appendix. 23 We follow Keller (1998) in drawing the random plant-level time-varying services intensity weights from a uniform distribution with support [0,1] using the pseudo random number generator of the GAUSS software.

16
recent improvements. Like sorghum, millet yields have grown dramatically in neighboring Ethiopia. These comparisons suggest that, by imitating successful policies implemented in countries that have seen rapid improvements in yield, Sudan too can increase agricultural productivity for all its farmers, from small-scale household farming to large-scale commercial enterprises.

Figure 7: Sorghum and Millet Yields in Top Producing African Countries <!-- Start of picture text --> (a) Sorghum Yields (b) Millet Yields<br>3000 3000 Ethiopia Niger<br>Ethiopia Nigeria Sudan Nigeria Sudan<br>2500 2500<br>2000<br>2000<br>1500<br>1500<br>1000<br>1000<br>500<br>500<br>0<br>0<br>Yield, kg/hectare Yield, kg/hectare<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br>1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> _Source:_ FAOSTAT 2018.

Agriculture remains the mainstay of most of Sudan’s poor though agricultural productivity is low. Hence, increases in agricultural productivity are expected to reduce poverty. Little is known about householdlevel constraints to productivity gains. By examining the decisions agrarian households make, we hope to shed some light on why these yields are low and how to foster productivity growth for Sudan’s agrarian population. Understanding factors driving and hampering agricultural productivity is crucial for efforts to improve growth and reduce poverty.

This study will primarily use data from the Sudan 2009 NBHS and the 2014/15 NHBPS to provide insights into constraints to agricultural productivity to inform policy reform. This study explores the complexity of rural livelihoods in Sudan, with a focus on agrarian livelihoods, by highlighting regional variations in welfare, agricultural practices and productivity. This study also empirically investigates variation in household-level crop yields and relates this measure of agrarian productivity to household welfare as well as to key inputs and production variables. Factors contributing to variations in yield are identified by using household-level regressions. Focusing policy on easing these constraints to productivity (such as fertilizer/pesticide use, irrigation, crop choice, land title, and so on) can lead to improvements in productivity for the average agrarian rural households and welfare for all of Sudan.

Though the focus of this report is on rural and agrarian households, it is important to note that urban and rural welfare are closely related in developing nations such as Sudan. For example, rural livelihoods can improve by increasing agricultural productivity. Increased agricultural productivity has a direct positive effect on food producers—they can increase their own household consumption of the food they grow or they can sell some of their surplus in the market. In turn, the increased supply of food in the market could lead to a reduction in the price of food. This would increase the purchasing power and thereby the welfare of net food consumers, such as the urban poor. Just as increased productivity in the industrial sector can draw people away from rural areas, increased productivity in the rural economy 8
**Chart 1: Cumulative change in the index of Capital Flow Measures**

<!-- Start of picture text --> Capital flow measures on residents Capital flow measures on non-residents<br>2000q1 2005q1 2010q1 2015q1 2000q1 2005q1 2010q1 2015q1<br>CFMs on inflows CFMs on outflows CFMs on inflows CFMs on outflows<br>400 400<br>300 300<br>200 200<br>100 100<br>0 0<br><!-- End of picture text --> _Note:_ The chart displays the index of cumulative changes to CFMs over time. The red lines depict cumulative changes in CFMs on outflows and the blue lines show cumulative changes in CFMs on inflows. The left-hand panel shows changes in CFMs on Resident flows and the right-hand panel on Non-Resident flows.

The graphical impression of a trend towards liberalization is supported by the results of the regression analysis in Table 4. The table shows that all four cumulative indices of CFMs follow a linear and positive time trend. The coefficient is largest for resident outflows confirming that restrictions on resident outflows have been eased most rapidly over time.

**Table 4: Trend in the Cumulative Index of CFMs**

||R<br>Inflows|esident<br>Outflows|No<br>Inflows|n-resident<br>Outflows|
|---|---|---|---|---|
||0.053**|0.121***|0.051***|0.019*|
|Time trend|[2.49]|[4.34]|[3.21]|[1.74]|
|Observations|3,290|3,290|3,290|3,290|
|R-squared|0.09|0.24|0.14|0.04|
|# of countries|47|47|47|47|

Note: The table shows the coefficient of a panel regression of the respective country specific cumulative indices on a linear time trend. All regressions include country fixed effects. Robust t-statistics are reported in parenthesis. ***, ** and * indicate statistical significance at the 10%, 5% and 1% level.

Table 5 below shows that the slope of the trend of capital account liberalization depends on its initial openness. We estimate initial capital account openness by the level of the Chinn-Ito index at the beginning of our sample in Q1:1999 and include an interaction term between the Chinn-Ito index and the time trend in the regression. The results show that while the coefficients on the linear time trend remain positive and significant, the interaction effects are all negative and significant. This means that countries that had a more closed capital account, indicated by a lower value of the 11
|P004107|1983|1985|0 Closed|Korea,Republic of|
|---|---|---|---|---|
|P004107|1983|1986|0 Closed|Korea,Republic of|
|P004107|1983|1987|0 Closed|Korea,Republic of|
|P004107|1983|1988|0 Closed|Korea,Republic of|
|P004107|1983|1989|7 Closed|Korea,Republic of|
|P004108|1983|1985|0 Closed|Korea,Republic of|
|P004108|1983|1986|0 Closed|Korea,Republic of|
|P004108|1983|1987|0 Closed|Korea,Republic of|
|P004108|1983|1988|0 Closed|Korea,Republic of|
|P004108|1983|1989|6 Closed|Korea,Republic of|
|P004109|1983|1985|0 Closed|Korea,Republic of|
|P004109|1983|1986|0 Closed|Korea,Republic of|
|P004109|1983|1987|0 Closed|Korea,Republic of|
|P004110|1984|1985|0 Closed|Korea,Republic of|
|P004110|1984|1986|0 Closed|Korea,Republic of|
|P004110|1984|1987|0 Closed|Korea,Republic of|
|P004110|1984|1988|0 Closed|Korea,Republic of|
|P004110|1984|1989|5 Closed|Korea,Republic of|
|P004111|1984|1985|0 Closed|Korea,Republic of|
|P004112|1984|1985|0 Closed|Korea,Republic of|
|P004112|1984|1986|0 Closed|Korea,Republic of|
|P004112|1984|1987|0 Closed|Korea,Republic of|
|P004112|1984|1988|0 Closed|Korea,Republic of|
|P004112|1984|1989|8 Closed|Korea,Republic of|
|P004113|1984|1985|0 Closed|Korea,Republic of|
|P004113|1984|1986|0 Closed|Korea,Republic of|
|P004113|1984|1987|0 Closed|Korea,Republic of|
|P004113|1984|1988|0 Closed|Korea,Republic of|
|P004113|1984|1989|6 Closed|Korea,Republic of|
|P004113|1984|1990|7 Closed|Korea,Republic of|
|P004114|1984|1985|0 Closed|Korea,Republic of|
|P004114|1984|1986|0 Closed|Korea,Republic of|
|P004114|1984|1987|0 Closed|Korea,Republic of|
|P004114|1984|1988|0 Closed|Korea,Republic of|
|P004114|1984|1989|7 Closed|Korea,Republic of|
|P004116|1985|1987|0 Closed|Korea,Republic of|
|P004116|1985|1988|0 Closed|Korea,Republic of|
|P004116|1985|1989|2 Closed|Korea,Republic of|
|P004116|1985|1990|3 Closed|Korea,Republic of|
|P004117|1985|1985|0 Closed|Korea,Republic of|
|P004117|1985|1986|0 Closed|Korea,Republic of|
|P004117|1985|1987|0 Closed|Korea,Republic of|
|P004117|1985|1988|0 Closed|Korea,Republic of|
|P004117|1985|1989|4 Closed|Korea,Republic of|
|P004118|1985|1985|0 Closed|Korea,Republic of|
|P004118|1985|1986|0 Closed|Korea,Republic of|
|P004118|1985|1987|0 Closed|Korea,Republic of|
**Table 2: Employment in the sugar sector, 2002 (number of workers)**<sup>_a_</sup>

||**Br**|**azil**|**No**|**rth**|**Centra**|**l/South**|
|---|---|---|---|---|---|---|
||Cane|Sugar|Cane|Sugar|Cane|Sugar|
|**Age group**|||||||
|10 to 17 years|1571|1035|297|629|1274|406|
|18 to 29 years|160488|175613|36613|94075|123875|81538|
|30 to 49|169916|185080|39903|90424|130013|94656|
|50 and above|35645|35245|9516|18050|26129|17195|
|**Total**|**367620**|**396973**|**86329**|**203178**|**281291**|**193795**|
|**Education**|||||||
|Illiterate|46567|70722|33722|63489|12845|7233|
|8th grade completed|297810|273239|50190|128320|247620|144919|
|High School degree|20095|43238|2099|9543|17996|33695|
|College degree|3148|9774|318|1826|2830|7948|

> _a_ Source: Moraes, 2004.

32
15 the home (Schultz 1988).'<sup>0</sup> Marital status thus can influence labor market participation, but not an individual's market productivity.<sup>1</sup> 1

# 2.3. Male-Female Wage Differences

A standard procedure to measure the male-female wage gap is to fit equation (6) by ordinary least squares to a sample of male (m) and female (f) workers: and where: Bm and Bf are the vector of unknown coefficients, including the intercepts, to be estimated; X. and Xf are, respectively, the vector of males' and females' observed characteristics; and em and ef are, respectively, the males' and females' individual specific error. A property of ordinary least squares is that the regression lines pass through the mean values of the variables so that The hats denote the estimated values of the coefficients.

By simple manipulation of (9) and (10) the male-female wage gap function can be written as l°Ideally, one would like to include such spouse characteristics as age and education, instead of marital status, to explain labor market participation. This information is not available for unmarried persons.

"Note that marital status may influence productivity and participation in the informal sector (self-employment) because of the compatibility between informal work and marital status.
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Borjas, George J. and Marta Tienda (eds) 1985. “Hispanic in the U.S. Economy.” New York: Academic Press.

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