rafmacalaba/datause-encoder-data huggingface dataset:
urban and educated women struggle with ACASI comprehension.

Our paper is also related to a broader recent literature about survey methodologies aimed at preserving respondent confidentiality; ACASI is only one of these.<sup>10</sup> Other methods rely on indirect responses, such as list experiments or randomized response techniques.<sup>11</sup> There is no consensus on the efficacy of these methods.<sup>12</sup> For example, Chuang et al. (2021) find logical errors in a list experiment and a randomized response technique focused on sexual and reproductive behavior. A recent set of evaluations compares list experiments with FTFI interviews specifically for measuring IPV and the evidence is mixed. While Ag ̈uero and Frisancho (2021) find no difference in IPV reporting between the two methods among urban microfinance borrowers in Lima, Peru, Cullen (2020) finds that the list method leads to substantially larger reporting of IPV among rural women in Nigeria and Rwanda.

The rest of this paper is laid out as follows. Section 2 describes the experiment and data collection. Section 3 presents our main results. Section 4 discusses evidence on potential pathways and heterogeneity. Section 5 concludes.

# **2 Data and Experimental Design**

## **2.1 Setting**

The ACASI experiment we analyze was done as part of an endline survey for a cash transfer RCT in Liberia and Malawi (the transfers were implemented by the NGO GiveDirectly, as part of a USAIDfunded study). The study takes place in Bong and Nimba counties in Liberia, and in Chiradzulu and Machinga districts in Malawi. The study includes 300 villages in each country, with half of the villages receiving cash transfers.<sup>13</sup> While we do not evaluate the transfers themselves in this study, one important detail is that villages were included in the study only if they fell below a

> 10Researchers have also tried other unconventional methods to measure IPV indirectly, such as asking female community leaders, but these efforts have not been very successful (Ag ̈uero et al. 2020).

> 11In list experiments, yes/no questions about sensitive behaviors are included in a list with other innocuous binaryresponse questions, and subjects report the number of items for which the answer is “yes” or “no”, which allows the researcher to back out the population level prevalence of a behavior without being able to identify whether a specific individual engaged in that behavior. The randomized response technique (RRT) bundles a question with a random event, such as a throw of the dice. Respondents report “yes” if either the truth is “yes” or if the die is a certain number.

> 12See H ̈oglinger and Jann (2018) and Lensvelt-Mulders et al. (2005) for reviews.

> 13The average transfer amount was $500, and was randomized between three amounts ($250, $500, and $750). In Liberia, transfers were also randomized between being paid as “lump sum” or quarterly transfers.

5
outflow from the fund. Then our aim is to answer the following questions for a sizeable oil discovery in a small economy that adds to the fund:

- What are the desired scale and pace of public investments over time?

- How should public investments be allocated over physical and human capital?

In order to answer these questions, we develop a multi-sector, small open economy Dynamic Stochastic General Equilibrium (DSGE) model that is based on Melina _et al._ (2016). We add to the latter, on the one hand, the accumulation of human capital, built via public expenditures in education and health expenditures; and, on the other, a richer array of fiscal options as far as the usage of natural resource revenues is concerned. These include Permanent Income (PIH) and Bird-in-Hand (BIH) based rules.

The model characterizes multiple types of public sector debt, multiple tax and spending variables, and a resource fund. The country produces a composite of traded goods and a nontraded goods using capital, labor, and its productivity is affected by governmentsupplied infrastructure, health and education. It is also endowed with natural resources, the production and prices of which are assumed to be exogenous. Since the time horizon is 20+ years, the model abstracts from money and all nominal rigidities.<sup>4</sup> The model has a number of important features specific to LIDCs. These are financially constrained households who do not have access to capital and financial markets and consume all of their disposable income each period; remittances received by households; a productivity effect of health and education; investment adjustment costs; international grants received by the government; public investment inefficiencies and absorptive capacity constraints, and finally a resource fund. It includes also standard distortionary taxes and investment adjustment costs.

We calibrate our model by using the available data from Kenya, which discovered an estimated 600 million barrels of oil in 2012 and is expected to start commercial production in the early 2020s. Although the proven reserves are relatively small in comparison to other oil producers, the revenues generated from them, which could reach 16 percent of GDP annually at peak, are likely to be significant for the Government of Kenya.

> 4The nominal side and New Keynesian features may be added if the model is used to study the short-run policy effects of fiscal management to resource revenue flows.

3
Lives and Livelihoods: Estimates of the Global Mortality and Poverty Eects of the Covid-19 Pandemic.<sup>*</sup> Benoit Decerf, University of Namur benoit.decerf(at)unamur.be Daniel G. Mahler, The World Bank dmahler(at)worldbank.org Francisco H. G. Ferreira, The World Bank and London School of Economics f.d.ferreira(at)lse.ac.uk Olivier Sterck University of Oxford olivier.sterck(at)qeh.ox.ac.uk JEL: D63, I15, I32, O15.

Keywords: Covid-19, welfare, poverty, mortality

> *Acknowledgments : We are grateful to Jean-Marie Baland, Guilhem Cassan and Christoph Lakner for useful discussions. We would also like to thank Simon Dellicour, Jed Friedman, Olivier Hardy and Damien de Walque for pointing us to relevant data sources. Funding from the Knowledge for Change Program through grant RA-P169493-RESE-TF0A8827 is gratefully acknowledged.
In this study, we use a factor analysis approach to construct composite indexes from observed indicators on gender relations reported in the IHDS. Factor analysis uses correlations among measurable (observed) indicators to infer a latent behavior. By creating a single score from multiple indicators, it offers insights into underlying trends that would otherwise be difficult to measure.

Factor analysis starts with all the variables assumed to measure different dimensions of a given concept. It reduces them to a smaller number of variables that are correlated with the original variables but are themselves orthogonal to one another. In arriving at a solution, factor analysis uses only the variance a variable shares with other variables; it then divides this common variance into factors (Child 1990; Conway and Huffcutt 2003; Thompson 2004). The factors obtained thus focus only on what is common to all variables.

In order to analyze these factors, factor-score correlation coefficients (also called _factor loadings_ ) are calculated using regression. The factor score is then estimated as the linear combination of products of the response on each of the original variables and the corresponding correlation coefficient, as shown in the following equation: scoredm = a1x1 + a2x2 + a3x3 + ⋯+ anxn (4) where, x1, x2, x3, ... , xn are original variables such as the ability to make decisions about one’s education, decide whether to have children, and so on; a1, a2, a3, ... , an are correlation coefficients between the score and each of the x ’s. Each of the correlation coefficients takes a value between 0 to 1; they show the extent to which change in one indicator is associated with the change in the overall score. A correlation factor of 0.70, for example, implies that the score increases by 0.70 percent with a 1 percent increase in the value of the original indicator (alternately, one could say that the original indicator increases by 0.70 percent with a 1 percent increase in the score).

> (2001) show that women’s bargaining power is captured by a combination of multiple aspects as opposed to a single factor.

9
> The promotion effort (P) is measured by the annual budget of each agency converted into

> US dollars. This conversion assumes that each dollar spent by the agencies has the same

> value across countries, which might introduce inconsistencies for cross-country

> comparisons. While the costs of certain promotional activities are independent of the

> IPA location (e.g., the cost of an advertisement in an international newspaper would in principle be the same for Ethiopia and Germany), some expenses vary greatly across

> countries. For these reasons, we also measure the promotion effort by the number of

> professional staff dedicated to investment promotion in each agency. This proxy does not

> vary with the exchange rate and accounts for different purchasing value across countries. However, it makes the strong assumption that labor (rather than money) determines the

> level of promotion effort in each country. Both variables -the IPA budgets and staff-

> have been extracted from the survey conducted by the Foreign Investment Advisory

> Service of the World Bank in early 2002.

> As explained earlier, the promotion effort has been separated into four categories so that

> we can capture better what IPA do. We follow Wells and Wint, 2001, and distinguish the following activities: investment generation (IG), policy advocacy (PA), investor servicing (IS), and image-building (IB). The budgetary resources allocated to each of these functions have also been identified using the FIAS survey.

> The internal characteristics of the agency have been captured through the FIAS survey, including a) age of the agency, b) legal status (founded by law or decree), c) institutional affiliation and linkages with government (public, semi-public, autonomous, or private body), d) linkages with the private sector (financial contribution, frequency of meetings and inputs, degree of private sector representation on board), e) reporting arrangements

> (board, government body, prime minister or president), f) overseas offices, g) number of mandates on top of foreign investment promotion (e.g., export promotion, privatization programs,) and f) staff's characteristics and salary policy.

> We tested several indicators of the quality of the investment climate such as the ones developed by the World Economic Forum, Harvard, the Heritage Foundation,

10
2

# **1 INTRODUCTION**

Women are commonly considered to perform the bulk of work in African agriculture. Combined with new evidence of a non-negligible gender gap in agricultural productivity, this has motivated increased attention to raising agricultural productivity among African women.<sup>2</sup> Doing so is not only seen as important for empowering Africa’s women and improving the development outcomes of the next generation, but also as an important vehicle to increase Africa’s food supply, a key objective on the agenda of African and international policy makers (AGRA, 2012).<sup>3</sup> This paper revisits the first premise of this reasoning, i.e. that women perform the bulk of work in African agriculture. Systematic data on women’s labor contribution to agriculture are hard to come by. As such, it is no surprise that the widely shared notion that women in Sub-Saharan Africa (SSA) are responsible for 60 to 80 percent of the agricultural labor supplied, traces back to an undocumented, 1972 quote in a more general study of women’s contribution to development.<sup>4</sup> The statistical basis for these numbers has been questioned before (Jackson, 2005; Doss, 2014; Doss et al., 2011).

Taking the female share of the agricultural labor force as a proxy (calculated as the total number of women economically active in agriculture divided by the total population economically active in agriculture), FAO (2011) suggests that women’s labor contribution to African agriculture is slightly less than half. Using more reliable, but non-nationally representative case study evidence

> 2 See Backiny-Yetna and McGee (2015), Kilic et al., (2015), Aguilar et al. (2015), Oseni et al. (2015), Slavchevska (2015), O’Sullivan et al. (2014).

> 3 See also the 2003 Maputo Declaration, with the messages reiterated in the 2014 Malabo Declaration on Accelerated Agricultural Growth and Transformation by the African Union.

> 4 United Nations Economic Commission for Africa (1972, p. 359): “Few persons would argue against the estimate that women are responsible for 60-80 [percent] of the agricultural labour supplied on the continent of Africa.” A decade later, the Food and Agriculture Organization of the United Nations (FAO) posited that women constitute between 70 and 90 percent of the agricultural labor force in many sub-Saharan African countries (FAO, 1984). A later incarnation of the statement surfaced in a 1995 FAO Report: “In Sub-Saharan Africa, agriculture accounts for approximately 21 percent of the continent's GDP and women contribute 60-80 [percent] of the labour used to produce food both for household consumption and for sale.” A related assertion is that women produce 60 to 80 percent of the food in developing countries and 50 percent of the world’s food supply (Momsen, 1991).
In summary, declining labor shares are of second-order importance in quantifying the effects of changes in productivity gaps across sectors on aggregate productivity. Using disproportionate changes in labor shares increases the share of actual growth accounted for by changing distortions at the upper end.

# **6 Conclusion**

The goal of this paper is to examine the role of distortions in a historical perspective. Over the last few decades countries have seen sizable changes in GDP per capita over time. A fundamental issue of interest is to understand whether changes in distortions over time have been important drivers of growth or not. To quantify the effect of changes in distortions over time in accounting for actual growth, I develop a simple model of resource allocation in which these gaps arise due to distortions in the form of asymmetrical taxes on labor inputs across sectors. A model of resource allocation is essential in contrast to a shift-share analysis of aggregate productivity because productivity in both sectors change as a result of resources flowing from the low productivity sector to the high productivity sector.

Using sector level data from WDI database and labor share estimates from GTAP database, I estimate distortions in Industry and Services relative to Agriculture for a number of countries that span a spectrum of world income distribution. The main finding of the paper is that there is limited evidence supporting a dominant role of changes in distortions in accounting for actual growth. For the median country, implied growth is less than 2.5% of actual growth across all periods. However, changes in distortions can account for larger share for some countries. To the extent that reductions in distortions represent a _lower bound_ on possible gains because large reductions in barriers are yet to be realized, I perform a second counterfactual in which barriers across countries are reduced to what is observed in rich countries. Once again, I find only modest gains in productivity for most countries. The findings of the paper are also robust to changes in labor shares over time. However, disproportionate changes in labor shares across sectors somewhat increases the share of actual growth accounted for by changes in distortions for some countries. The analysis suggests that a simple reallocation of resources across sectors without any corresponding changes in TFP and resource accumulation will not deliver large gains in aggregate productivity as suggested by observed large gaps in productivity across sectors.

22
to 70-80%<sup>17</sup> in the Slovak Republic. However, it should be taken into account that these surveys are most likely not fully comparable and that access capacity may be located largely in sectors for which there is limited demand.

This brings us to a more likely explanation of higher output rigidity in Macedonia, the gap between domestic demand and production, in other words, narrow production base. Around 60% of Macedonian industrial production is accounted for by sectors with rigid capacity constraints (electricity, oil) or export-oriented sectors which operate at almost full capacity (iron and steel, and textiles).

Further, the size and structure of both economies differ considerably. While Macedonian economy is dominated by small (even micro) entities, most of the value added in the Slovak Republic is generated by large enterprises (see Table 8) which probably have sufficient excess capacity or greater ability to expand production with limited cost implications (see also Figure 2 panel 2), and better exploit the economies of scale.

**<u>Table 8: Structure of economies by size of entities (as % of total)</u>**

||Nu<br>Small|mber of ent<br>Medium|ities<br>Large|Num<br>Small|ber of empl<br>Medium|oyees<br>Large|V<br>Small|alue adde<br>Medium|d<br>Large|
|---|---|---|---|---|---|---|---|---|---|
|Slovakia|93.1|5.5|1.4|28.7|22.5|48.8|25.1|17.3|57.6|
|Macedonia|99.0|0.6|0.4|63.8|13.6|22.6|47.5|13.0|39.5|

_Source_ : For Slovakia: European Business, Facts and Figures, 2007 Edition - Eurostat Statistical book (p.30), data refers to 2004, for Macedonia: author calculations based on National Accounts data from the State Statistics Office.

_Note:_ Data refers to non-financial business economy A considerably more favorable business environment enables Slovak businesses to react faster and cheaper to increased demand. The Slovak Republic ranks 32<sup>nd</sup> on the 2008 Doing Business indicator compared to Macedonia’s 75<sup>th</sup> ranking (despite a considerable improvement from 2007, and from before when the country ranked around 100<sup>th</sup> ). Overall, the better business environment provides Slovak businesses with more and cheaper financing, less corrupt and faster public administration, and more efficient

> 17 Current level of Capacity utilization in manufacturing industry, EUROSTAT, available at: http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=bs070

23
The representative household faces the real budget constraint at every period t: where the income of the representative household comes from the provided labor service _h_<sup>_s_</sup> with a wage _w_<sup>_s_</sup> (in each sector s), and real risk-free domestic bonds _B_ remunerated at a rate _r_ . The household’s expenditures go to its consumption basket, bonds, and the lump-sum taxes _T_ charged by the government.

The representative household allocates total consumption between non-agricultural and agricultural consumption goods. The CES consumption bundle reads as follows: where _μ_ denotes the substitution elasticity between the two corresponding types of goods, while _φ_ is the fraction of agricultural goods in the total consumption basket. The consumption price index is determined accordingly by: Finally, demand for each type of good is a function of the total consumption index and its relative price:

# _Agriculture sector_

The economy is populated by a unit mass _i ∈_ [0 _,_ 1] of entrepreneurs, where a fraction _n_ operate in the agricultural sector (farmers).<sup>4</sup> The agricultural output is produced with the Cobb-Douglas production function: where _y_<sup>_A_</sup> is the outcome of farmers’ activity that combines an amount of land _l_ , physical capital _k_<sup>_A_</sup> , and labor _h_<sup>_A_</sup> inputs. The production is subject to a weather shock through a function Ω � _ε_<sup>_W_</sup> _t_ � described in detail below and an economy-wide technology shock _ε_<sup>_Z_</sup> that follows an AR(1) process. The parameter _ω_ is the elasticity of output to land, _α_ stands for the physical capital share in the agricultural production, whereas _κA_ is a technology parameter endogenously determined in the steady state.

The farmers’ land _l_ is subject to both economic and meteorological conditions. The agricultural production process between _t-1_ and _t_ might be affected by the unexpected realization of the weather _ε_<sup>_W_</sup> through a simple damage function:

> 4Entrepreneurs could switch from one sector to another according to a stochastic AR(1) process.

11
Table 1: Summary Statistics

||Gi|rls|Bo|ys|
|---|---|---|---|---|
||Mean|S.D.|Mean|S.D.|
|Grade-for-age<br>(=100*education/[age-61)|76.64|30.33|<br>74.24|30.59|
|Education<br>(years)|4.48|3.14|4.49|3.14|
|Time allocated<br>to schooling<br>(hrs per week; 1994<br>only)1|22.72|8.86|22.20|<br>8.69|
|=1 if attended<br>school<br>full-time<br>(i.e 25 hrs per week)|0.80|-|0.79|-|
|Time allocated<br>to housework<br>(hrs per week) 5|11.48|10.81|<br>6.76|7.33|
|Time allocated<br>to self-employment<br>activities<br>(hrs/week)<br>S|2.79|7.62|3.69|9.06|
|=1 if individual<br>works<br>in self-employment<br>activities|0.19|-|0.23|-|
|Time allocated<br>towage activities<br>(hrslweek)<br>1|0.50|5.21|2.26|0.23|
|=1 if individual<br>works in wage activities|0.01|-|0.06|-|
|Age in 1994<br>(in years)<br>Y|11.83|3.69|12.00|<br>0.28|
|=1 if household<br>is female-headed|0.08|-|0.09|-|
|=1 if household<br>is of native<br>origin|0.14|-|0.15|-|
|Value<br>of household<br>wealth<br>per capita<br>(real value)x<br>10 3|5.21|15.07|<br>4.91|0.82|
|Number<br>ofchildren<br>under<br>4years|0.59|0.76|0.58|0.86|
|Number<br>ofadult females<br>between<br>15 & 60|1.94|1.04|1.60|0.30|
|Number<br>ofelderly<br>females,<br>over 60|0.10|0.30|0.09|0.30|
|Household<br>size|6.47|1.97|<br>6.43|2.18|
|Adult wages<br>(cluster<br>median)|4.24|0.59|4.24|0.58|
|Female<br>unemployment<br>rate(cluster<br>mean)<br>1|0.87|0.12|0.87|0.12|
|=1 if household<br>source<br>of energy<br>isfuelwood/coal|0.39|-|0.38|-|
|=1 if water source<br>is outside<br>the household.<br>|0.20|-|0.21|-|
|School<br>expense<br>(cluster<br>average)xl0-<br>3|0.22|0.33|0.23|0.33|
|Time to school<br>(cluster<br>average)|16.77|14.58|16.64|<br>7.52|
|Educational<br>level of oldest<br>prime age female<br>in household|5.58|4.12|5.50|4.23|
|Birth order<br>(=1 for youngest)|2.65|1.36|<br>2.73|1.57|
|=1 ifrural|0.38|-|0.39|-|
|=1 if department<br>isUma|0.20|-|0.23|-|
|=1 if department<br>is Costa|0.23|-|0.24|-|
|=1 if department<br>is Selva|0.22|-|0.23|-|
|=1 if department<br>is Sierra|0.35|-|0.30|-|
|=1 if child was sick in household<br>t|0.19|-|0.21|-|
|=1 if adult was sick in household|0.39|-|0.39|-|
|Sample|975||||

S In the seven day period prior to the survey. M The sample isrestricted to children between the ages of 6 and 18.

r The proporti **o** n f prime age women in cluster who were working less than 20 hours per week. t In the 30 day period prior to the survey.

33