rafmacalaba/data-use-mentions-extended huggingface dataset:
= Market income – direct tax + direct transfers.

c. Final income = Consumable income + in-kind transfers for education and health.

d. Y = Individual income for the corresponding income concept.

e. Income group below birr 5,050: a calipered absolute poverty line that could produce the same number of poor people as the Household Consumption Expenditure Survey using “disposable income.” f. Income group between the absolute poverty line and 7,287, the 40<sup>th</sup> percentile of market income.

g. Income group between 7,290 and the official poverty line at June 2019 prices.

h. Income group above the official poverty line.

There appears to be less transition or movement across income groups from market to disposable income—the diagonal values are above 92 percent for both women and men. In movement from market to final income, however, there is some change in income groups. Although it appears that the majority do not change income groups along the transition from market income to disposable and then to final income, net fiscal policy allows a higher share of both women and men to make 18
17 _Figure 8. Unaccountedfor Water_ <!-- Start of picture text --> 600/o<br>40%/o<br>2 0 /o - -- - -- - - -- - - --- - - - -- - - - - - - - - - - - - - - ----- -- - -- - -- - - -- - - - - - - I-<br>1987 1988 1989 1990 1991 1992 1993 1994 1995 1996<br>Source: SEDAPAL Anmual Reports<br><!-- End of picture text --> Furthermore, the changes in management and incentives did not lead to any measurable improvement in total factor productivity (figure 9).

_Figure 9. Total Factor Productivity_ <!-- Start of picture text --> 1 20<br>1.00 -- - --- - - - - - - - -<br>0860<br>1990 1991 1992 1993 1994 1995 1996<br>Source: Authoe s calculations based on SEDAPAL Annual Reports<br><!-- End of picture text -->

> _ii. Methodology for Calculating the Welfare Effects._ What would have been the

net benefits to Peru had the concession with a private operator been signed and fully implemented? Using a partial equilibrium, cost-benefit methodology developed by Jones, Tandon and Vogelsang (1990), we compared the actual reform outcome with a counterfactual projected on the basis of the draft concession contract (CEPRI-SEDEPAL
_0.016<br>(1.003)|0.175<br>(0.087)<sup>_∗∗_</sup><br>[0.073]<sup>_∗_</sup>|0.132<br>(0.097)<br>[0.298]|0.218<br>(0.104)<sup>_∗∗_</sup><br>[0.040]<sup>_∗∗_</sup>|5053|
|(3) Satisfaction with village sanitation index|0.000<br>(1.000)|0.087<br>(0.043)<sup>_∗∗_</sup><br>[0.073]<sup>_∗_</sup>|0.087<br>(0.049)<sup>_∗_</sup><br>[0.298]|0.086<br>(0.050)<sup>_∗_</sup><br>[0.059]<sup>_∗_</sup>|5051|

_Notes:_ Treatment effects are estimated using Missing-Indicator ANCOVA, controlling for the community-level average of the outcome family index at baseline and matching-triplet fixed effects. Robust standard errors, clustered by clinic, are shown in parentheses. The household index is comprised of water sources for drinking, for other uses, the existence and type of toilet facilities and the actions households take to make water safe to drink. The village index contains the existence and types of water sources for drinking and general use as well as the existence of public toilets and waste facilities. The satisfaction index consists of questions about sanitation and health services offered as well as village cleanliness. Significance: * is significant at the 10% level,
we find significant gaps even when we compare workers with similar human capital levels and workers residing in the same urban regional or local labor market. Of course, these results should be taken with caution since there may still be unobservable factors that we cannot control for.<sup>48</sup>

**Growth.** The higher wages for jobs in UT relative to UNT imply that workers in UT are more productive than workers in UNT. If most urban areas in a country are consumption cities, this might have negative implications for aggregate labor productivity in the

> 46Indeed, only 54 countries have large enough surveys with earnings data and detailed information on the respondent’s type of residence and subsector, allowing us to distinguish UT and UNT workers.

> 47Like Gollin et al. (2014), if we use national accounts data (source: United Nations (2020c); ISIC Rev. 3) and sectoral employment data (ILO (2022); ISIC Rev. 3.1) to estimate GDP per worker, we find for the sample countries much higher gaps around 120%-205%, depending on which sectors are included in UNT.

> 48If we use I2D2 data for the world instead, in order to increase sample size (thus adding developed economies), we find an unconditional gap of 24% (N = 91) and 16%-18% for the conditional gaps (N = 34-73).
8,655|10,118|10,866|9,585|
||[939]|[1,002]|[1,053]|[908]|
|Top 0.002%|12,224|12,973|13,766|12,744|
||[1,326]|[1,284]|[1,335]|[1,208]|
|Top 0.001%|17,731|19,167|19,764|19,951|
||[1,924]|[1,898]|[1,916]|[1,891]|

_Note:_ This table reports the thresholds to belong to each quantile in the tax records, for selected years, both in Lempiras and USD PPP (1,000s).

# **F Adjustment of survey agricultural income**

In this section we provide more details on the adjustments we make on the survey data related to self-employed agricultural income. As we document in Table A.1, in the period 2004 - 2013 the household survey we use included an additional module on agricultural income from self-employed activities. What we observe in the data is that reported income from those activities is extremely high in that period. Furthermore, the microdata available only includes total income from that module, and not a breakdown of all variables included in the survey, which limits our ability to further 64
Source:https://www.worldbank.org/en/topic/poverty/brief/poverty-and-distributional-impacts-of-covid-19potential-channels-of-impact-and-mitigating-policies In the case of Tunisia, the COVID-19 pandemic is likely to exacerbate the existing development challenges by potentially reversing the trend of poverty reduction in recent years with the risk of increasing the number of people falling below the poverty line and increasing the degree of poverty severity of those who are already poor. Recent evidence generated by phone surveys during the COVID-19 period, conducted by the National Institute of Statistics ( _Institut National de la Statistique_ or INS) in collaboration with the World Bank, highlight this disproportionate impact of COVID-19 on the poor and vulnerable Tunisians through the channels highlighted in Figure 1.

The first round<sup>1</sup> of the phone survey revealed that almost two-thirds of households interviewed were affected by COVID-19, either through rising food prices or through loss of employment. More specifically, a third of respondents feared not being able to afford food in the month prior to the survey. This concern was more evident for rural and poorer households. In addition, households seemed to have altered their eating patterns; especially poorer households reduced the quantities consumed or started consuming foods that they do not normally like. To cope with rising food prices or make up for the loss of employment, more than 25 percent of the households interviewed drew on their savings, 25 percent of the households received help or borrowed money from relatives and 15 percent used deferred payment of their obligations. Most of those employed prior to COVID-19 reported loss of job as well as lower income. Indeed, only a third of the respondents who worked before confinement were able to continue their work. Among those who were out of work during the survey period, only 40 percent received all or part of their salary. Family-owned businesses were also heavily impacted by the crisis.

During the second round of the survey, some progress was reported as the economy was partially able to respond to challenges associated with food shortages. Less than 40 percent of those
- 3 -

provided under one contract” (clause 2.5). Therefore, how to design a procurement plan is left to procurers or executing agencies.

In theory, whether to bundle or unbundle relevant contracts being auctioned is one of the most important policy choices for auctioneers. The multi-unit auction literature tends to favor unbundled procurements as long as competition is secured. If there are only two bidders for an arbitrary number of contracts, the auctioneer should bundle all the contracts to facilitate their competition against one another. Conversely, given a relatively large number of bidders, the auctioneer has a tendency to prefer to unbundle its contracts, which of necessity become relatively smaller (Palfrey, 1983). The choice of (un)bundling is also related to the cost of entry for bidders, which is interpreted as the extent to which two components are technically different. If the cost of entry is sufficiently large, separate auctions are more likely to be preferable (Chakraborty, 2006).

The current paper discusses pros and cons of enlarging contract packages in public procurement. With detailed procurement data from rural road projects in Nepal, it aims at examining the optimal size of road contracts from different perspectives. It estimates the firms’ bidding strategy with the endogeneity of bidder participation taken into account. Unlike the existing empirical auction literature, this paper also casts light on the ex post contract performance, which could be affected by the size of contracts. The remaining sections are organized as follows: Section II summarizes pros and cons of enlarging the size of public contracts from a general point of view. Section III describes our data and Section IV develops the estimation methods. Section V discusses the main estimation results and policy implications. Then Section VI concludes.
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
Fazel, M., Reed, R. V., Panter-Brick, C., & Stein, A. (2012). Mental health of displaced and refugee children resettled in high-income countries: risk and protective factors. _The Lancet_ , _379_ (9812), 266-282.

- <mark>Frasquilho, D., Matos, M. G., Salonna, F., Guerreiro, D., Storti, C. C., Gaspar, T., & Caldasde-Almeida, J. M. (2015). Mental health outcomes in times of economic recession: a systematic literature review.</mark> _<mark>BMC Public Health</mark>_ <mark>,</mark> _<mark>16</mark>_ <mark>(1), 115.</mark>

- <mark>Frijters, P., Johnston, D. W., & Shields, M. A. (2014). The effect of mental health on employment: evidence from Australian panel data.</mark> _<mark>Health Economics</mark>_ <mark>,</mark> _<mark>23</mark>_ <mark>(9), 1058-1071.</mark>

- Heckman, J. J., Stixrud, J., & Urzua, S. (2006). The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. _Journal of Labor Economics_ , _24_ (3), 411-482.

- Johnston, D. W., Schurer, S., & Shields, M. A. (2013). Exploring the intergenerational persistence of mental health: Evidence from three generations. _Journal of Health Economics_ , _32_ (6), 1077-1089.

- Kessler, R.C., Barber, C., Birnbaum, H.G., Frank, R.G., Greenberg, P.E., Rose,
**Qualitative data** We collected longitudinal qualitative data to better understand the context, intervention, and mechanisms. First, a Liberian research assistant acted as a participant-observer during the Phase 1 therapy. Second, we interviewed facilitators for their impressions of the intervention and participants. Third, three Liberian research assistants conducted semi-scripted interviews, 14 pre-treatment and 130 post-treatment, with 66 men in the sample.<sup>29</sup> Interviews covered job satisfaction, investments, economic challenges, plans, antisocial behaviors, and perceptions of the interventions.

# **5 Empirical strategy and estimation**

We estimate intent-to-treat (ITT) effect on outcomes, _Y_ , via the OLS regression: where _TherapyOnly_ , _CashOnly_ , and _Cash_ & _Therapy_ are indicators for random assignment to treatment arms: therapy only, cash only, or both therapy and cash. We control for a vector of baseline characteristics, _X_ , and fixed effects for each of the _j_ randomization blocks, _γj_ . _Yij_ is the average of the two proximate survey rounds (e.g. the 2- and 5-week surveys for short term effects). To reduce sensitivity to outliers, we top-code continuous variables at the 99th percentile. We test sensitivity to alternative approaches in Appendix E.1.

**Key outcomes and multiple comparisons** Our theory emphasizes five major outcome families: (1) anti-social behaviors, including crime and various forms of aggression; (2) income; (3) skills of self regulation and control; (4) economic time preferences; and (5) anticriminal and antiviolent values. We identified the latter three as prime mechanisms. For each of the five outcomes we typically have multiple measures (e.g., earnings, assets, and consumption as measures of income). To reduce the number of hypothesis tests, we combine related measures into mean effects summary indexes, one for each major outcome.<sup>30</sup> We do

> correlates of attrition. Of the 298 non-responses (of
5 percent. Similarly to our robustness check on measurement errors, we apply this robustness check to the 2019 crosssection of the underlying household data.

**Under this threshold, the vulnerability rate in El Salvador is much higher and our main messages reinforce themselves.** Vulnerability is high under a 25 percent vulnerability threshold, with an overall rate of 44.2 percent. Moreover, all of our messages reinforce themselves under this vulnerability threshold (for a detailed overview of the results, see Annex 3). First, the rural population is more affected than the urban one (0.672 versus 0.307). Next, vulnerability is more risk- than poverty-induced, with an overall rate of 4.0. Additionally, this holds for both rural and urban areas. In addition, vulnerability is mainly based on idiosyncratic and not covariate shocks. The variation of vulnerability is high in this case. We can therefore conclude that our main results are robust to alternative thresholds.

18
. Hamilton, B. Jessen-Thiesen, A. Kaya, H. Kruse, E. Mensah, and P. Woltjer. 2021. “The Economic Transformation Database (ETD): content, sources, and methods.” WIDER Technical Note 2/2021, United Nations University World Institute for Development Economics Research, Helsinki, Finland. 2.1

- de Vries, G., M. Timmer, and K. de Vries. 2015. “Structural Transformation in Africa: Static Gains, Dynamic Losses.” _Journal of Development Studies_ 51:674–688. 2.1

12
The first agglomeration rationale is that proximity to customers and suppliers reduces transportation costs and thereby increases productivity. This reduction in shipping costs is the core agglomerative force of the new economic geography (e.g., Fujita et al. 1999). Where customers and suppliers are geographically separate, firms must trade off distances. While transportation costs have declined dramatically over the past two centuries, the fundamentals remain important even in advanced economies. For example, much of the clean energy production in the United States based upon biomass is located near sources for the raw inputs.

To test the importance of this mechanism, we measure the extent to which districts contain potential customers and suppliers for a new entrepreneur. We begin with an input-output table for India developed by India‘s Central Statistical Organization. We define Inputi←k as the share of industry i's inputs that come from industry k, and Outputi→k as the share of industry i's outputs that go to industry k. These measures run from zero (no input or output purchasing relationship exists) to one (full dependency on the paired industry). These shares are calculated relative to all input-output flows and are not symmetrical by design (Inputi←k≠Inputk→i, Inputi←k≠Outputk→i).

Following Glaeser and Kerr (2009), we summarize the quality of a district d in terms of its input flows for an industry i as Inputdi = - ∑k=1,...,I abs(Inputi←k – Edk/Ed), where I indexes industries. This measure simply aggregates absolute deviations between the proportions of industrial inputs required by industry i and district d's actual industrial composition, with E representing employment. The measure is mostly orthogonal to district size, which we separately consider, and a negative value is taken so that the metric ranges between negative two (i.e., no inputs available in the local market) and zero (i.e., all inputs
Our preferred source of growth data is annualized growth in real GDP per capita from national accounts, as reported in the World Development Indicators (WDI). When such data are not available for the whole period, we complement it with annualized growth in real GDP per capita from the WEO. In two economies, data are also missing from WEO (Syria, and the West Bank and Gaza). In these cases, we rely on GDP data from the Economist Intelligence Unit.

Beyond 2018, we use three different growth scenarios: (1) that each country grows according to its annualized growth rate from national accounts for the last 10 years for which we have data (2007-2017); (2) that each country grows according to its annualized growth rate from national accounts for the last 20 years (1997-2017); and (3) that each country grows according to its annualized projected growth rate from 2018-2023, which currently is the last year for which WEO has growth projections. The simulations relying on the 20-year historic growth rates (1997-2017) may be optimistic, as the rapid growth experienced in the early 2000s is showing signs of slowing down. For example, Rodrik (2014) suggests that the rapid growth experienced by emerging economies in recent decades is unlikely to persist indefinitely and that convergence will slow down in coming decades.

3.3 The relationship between GDP/capita growth and welfare growth from surveys A challenge with using growth rates of GDP/capita is that prior evidence has shown that only a fraction of growth observed in national accounts is passed through to the growth observed in household surveys (Ravallion, 2003; Deaton, 2005; Pinkonvskiy & Sala-i-Martin, 2016). Estimating this fraction across our entire sample is fairly straightforward. One would simply regress the annualized growth in the survey means on the annualized growth in real GDP/capita, under the constraint that the intercept is zero, s s G , and use as the fraction of growth in GDP/capita that is passed-through to the welfare observed in
2023|0.386|0.380|0.396|0.385|0.000|0.000|
|Year 2024|0.337|0.363|0.289|0.367|1.000|1.000|
|Number of observations|270,762|173,651|97,111|25,719|9,166|2,135|

_Source:_ Own computation based on land monitoring data reported by SGC. _Note:_ All distances are in km.

24
where _wageis_ is the monthly real wage of worker _i_ in year _s_ .

The sample used to construct the Yagan wage measures excludes the observations of workers with the maximum monthly wage in each year.

# **B. Estimation of Firm Wage Premia**

We follow Abowd et al. (1999) to decompose worker wages into a worker component and a firm component, focusing on Brazil’s worker panel dataset over the pre-GFC period (2004-2007). The specification we estimate is given by: where _w_ is the real monthly wage for worker _i_ employed by firm _j_ in year _t_ , _αi_ is a worker fixed effect that reflects unobserved efficiency and other worker capabilities that are transferable across employer firms, _φj_ ( _i,t_ ) is a firm fixed effect that captures a premium or discount paid by the firm to all its workers (above or below average wages), _γt_ are year fixed effects, _Xit_ is a vector of time-varying worker characteristics (age and age squared), and _uijt_ is an error term. The firm fixed effects in this equation are identified based on workers that transition across employer firms. Worker mobility is thus crucial for identification of these firm fixed effects.

The estimation of the equation above by OLS results in estimated worker fixed effects that we use as a proxy for the worker unobserved ability. We split the worker sample into workers that are in the first, second and third deciles of the estimated worker effects.

42
parameters. The results are extremely close to the results presented in table 3.4 so are not reproduced here. We have verified that in the household model as well, the same three parameters

> 54 Results, based on the national accounts model, for EV as a percent of consumption by decile are: decile 1, 5.7; decile 2, 5.7; decile 3, 6.1; decile 4, 6.2; decile 5, 6.5; decile 6, 6.6; decile 7, 7.0; decile 8, 7.3; decile 9, 7.5; decile 10, 8.0. With the data reconciled based on the national accounts data, the Gini coefficient is .403 in this benchmark equilibrium, and it increases to .405 after our WTO accession scenario.

75
Table A.23: Relationship between FAT survey variables and log of wages from administrative data for Brazil

|Variable|(1)<br>log(sales per worker)|(2)<br>GBF|(3)<br>SSBF|
|---|---|---|---|
|ln(Wage) RAIS|0.882***|0.400***|0.299***|
||(0.157)|(0.111)|(0.101)|
|Observations|592|675|674|
|R-squared|0.346|0.364|0.800|
|Controls:||||
|Sector FE|Y|Y|Y|
|Region FE|Y|Y|Y|
|Size-group FE|Y|Y|Y|
|Age|Y|Y|Y|
|Exporter|Y|Y|Y|
|Foreign owned|Y|Y|Y|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Average wage information for each establishment is obtained from the 2017 _Relação Anual de Informações Sociais_ (RAIS) merged with the Firm-level Adoption of Technology (FAT) data used in this exercise, including sales per worker, the technology adoption index ( _MOSTj_ ) for GBF and SSBF, and firm characteristics used as controls. Regressions estimated using establishment-level sampling weights. Robust standard errors in parenthesis.

firms in RAIS that are part of our universe for the State of Ceará, in Brazil<sup>41</sup> . We then perform a t-test to compare the differences. Table A.24 shows that the differences are not statistically significant.

Overall, these ex post checks appear to validate the quality of the data collected.

> 41The variables are number of workers, average wages