rafmacalaba/data-use-mentions-extended huggingface dataset:
productive capacity in villages. For goods that are imported locally and characterized by oligopolistic competition, prices could even decrease following the positive demand shock if local traders face fixed costs in a Bertrand competition model or if the demand shock causes more stores to sell the good, thereby increasing competition in a Cournot-Nash framework (Cunha et al, 2011; D’Aoust et al, 2013).

Few empirical studies have examined the price effects and the externalities of cash transfers on nonbeneficiaries. For example, using the data from Progresa in Mexico, Angelucci and De Giorgi (2009) and Barrientos and Sabatés-wheeler (2009) find that noneligible households in treatment areas show significantly higher levels of food consumption and asset holdings following the introduction of the conditional cash transfer program, compared to noneligible households in control areas. Studying the impact of the PAL program in Mexico, Cunha et al (2011) find that cash transfers in remote villages led to higher food prices, which in turn increased agricultural profits and household wealth. They also show theoretically and empirically that prices are lower following in-kind transfers, which affect supply whereas cash transfers affect demand.

This review of the literature shows that the impacts and externalities of the Burundian DDR are expected to depend on how ex-combatants used their allowances. The use of allowances ultimately depends on socio-economic constraints faced by beneficiaries and socio-economic 7
2018)).<sup>17</sup> We assume that females and males have identical human capital, which is a reasonable assumption in most countries, but we relax this assumption in the next section.

Our analysis focuses on the _difference_ between the estimated growth path with and without higher female employment, and hence the specifics of the calibration for each country are differenced out.

**Results.** The increase in GDP per capita (GDPPC) from closing the gender employment gap in MENA countries by 2050 averages around 31% (median 34%) and as is in between the short run and long run GEGIs in Figure 2 (green bars). The relative sizes of the effects are shown in Figure 3.<sup>18</sup> The boost to growth is about similar in the long-run GEGI and 2050 LTGM in countries with the smallest gains like Qatar, but are around 30ppts larger in the countries with the largest gaps like Yemen. Across all countries, the gains are about 50% (not 50ppts) larger in the basic 15For the commodity sector: Capital shares in oil and natural gas sectors are set according to GTAP 2004-14 averages (the other factor is reserves, which generate rents). Initial gross exports come from UN Comtrade, initial reserves come from BP Energy (most recent observation), discoveries are set to a historic average from BP-Energy data) and export prices are set constant, based on World Bank Commodity forecasts from 2021.

> 16The cross-country average is based on potential for future structural transformation based on current share of employment in agriculture: 0 _._ 2% + 5 _× AgEmpShare_ (to a maximum of 1.5%).

> 17Specifically, human capital is based on Learning-adjusted years of schooling and health (stunting and adult survival rates). Human capital embedded in each age cohort, but only boosts productivity for cohorts of working age. Human capital data is taken from the components of the World Bank Human Capital Index, with
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- Hamrita, T.K., Hoffacker, E.C., 2005. Development of a “smart” wireless soil monitoring sensor prototype using RFID technology. _Applied Engineering in Agriculture_ , 21(1), pp.139-143.

- Ilahiane, H., Sherry, J.W., 2012. The problematics of the “Bottom of the Pyramid” approach to international development: the case of micro-entrepreneurs’ use of mobile phones in Morocco. _Information Technologies & International Development_ , 8(1), 2-13.

- Lokanathan, S., De Silva, H., 2010. Leveraging mobile 2.0 in India for agricultural market access. _Available at SSRN 1618193_ .

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- Lio, M., Liu, M.C., 2006. ICT and agricultural productivity: evidence from cross country data. _Agricultural Economics_ , 34(3), pp.221-228.

- Helpman, E. 1988. _General Purpose Technologies and Economic Growth_ . Cambridge, MA: The Massachusetts Institute of Technology.

- Jack W, Suri T. 2014. _“Risk Sharing and Transaction Costs: Evidence from Kenya's Mobile Money Revolution, American Economic Review,_ 104(1), 183-223 _._

- Jensen R. 2007. The Digital Provide: Information (Technology), Market Performance, and Welfare in the South Indian Fisheries Sector. _Quarterly Journal of Economics_ 122(3): 879-924.

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estimated coefficients, while _γ⇤_ represents the weighted average of the other coefficients. The first term in the above equation is the explained component of the wage gap, that is E, while the second term is the unexplained component, the C, which is the di↵erence in the return to the observed characteristics of Venezuelans and Brazilians, evaluated at the mean of the characteristics.

# **3.2.3 Social Protection Programs**

Providing social security to all irrespective of legal status is a constitutional obligation in Brazil. Social security in Brazil is organized in three main blocks, social welfare and pensions, which is contributory and social assistance and health, which is non-contributory. This paper focuses on social assistance programs that are administered and implemented jointly by the federal, state and municipal governments. The two largest social assistance cash transfer programs are _Bolsa Familia_ and the Continuous Benefit Provision (BPC). Social assistance programs are designed to target the vulnerable at each part of their life cycles. For example, _Bolsa Familia_ (PBF), which is a conditional cash transfer for households living in poverty and extreme poverty, targets not only families but also pregnant mothers, children and adolescents. BPC, Continuous Benefit Provision targets the elderly (those above 65 years) and the people with disabilities who cannot support themselves.

The Single Registry of Social Programs _(CadUnico)_<sup>_ ́_</sup> is a database that collects details about 15
## **WHOM WE ARE TALKING ABOUT**

As to the developing countries we have in mind, like the WTO Ministers in their 1996 Declaration, our concern focuses on the countries that are being marginalized. What we will argue will apply particularly to these countries - generally, the least developed countries - rather than to those developing countries that have already established themselves as major trading economies and active members of the WTO diplomatic community. Throughout the paper we will use the term, "least developed countries," to refer to all countries that are struggling to use the international trading system effectively - a list of countries approximated by the United Nations official list of least developed countries, but not necessarily identical to that list.<sup>2</sup>

## **WHAT WE ARE TALKING ABOUT**

Our analysis is based on World Bank project experience in customs reforrn, SPS, and intellectual property regulation. In each of these areas we reviewed Bank experience with three questions in mind:

1. What are the development problems in this area?

2. Does the WTO agreement correctly diagnose the development problems?

3. Does the WTO agreement prescribe an appropriate remedy?

"Appropriate" in the third question refers both to correct identification of the problem and to recognition of the capacities (resource constraints) of the least developed countries.<sup>3</sup>

## **STRUCTURE OF THE PAPER**

In the next section we will explain how implementation of such obligations differs from implementation of more traditional market access conmuitments such as tariff reduction or elimination of quantitative restrictions. In sections 3 and 4 we look briefly at least developed countries' participation in the Uruguay Round negotiations. In section 3 we point out that this participation, or lack thereof, has left these countries with no sense of ownership of the commitments they have undertaken. In section 4 we point out that in the Uruguay Round market access negotiations, the developing countries received no more than they gave - this in response
in household income, driven by an increase in mothers’ business revenues and fathers’ wage earnings. Lastly, a recent impact evaluation in the Democratic Republic of the Congo, which tested the opening of new childcare centers in rural areas, found that both women and their husbands increased their engagement in commercial activities, leading to gains in agricultural productivity, household income and women’s subjective well-being (Donald and Vaillant 2022).

# **5. Conclusion**

Existing research and data on Burkina Faso document high gender inequality in the country. However, gender gaps in economic outcomes and the root causes behind them have not been sufficiently explored. This paper investigates the underlying reasons for gender gaps in earnings in Burkina Faso, using recent household survey data to understand their change over time and the factors associated with them.

22
19 40% of the power in the wind into rotary power. In remote locations with mean monthly windspeed in excess of 3 m/sec, wind can be the most economic power source for water pumping.

56. Since 1979, significant operating experience with PV pumping has been gained through UNDP and USAID funded projects. Major projects include the Mali Aqua Viva Program, Desert Development in Egypt, Solar Pumping in Botswana, Remote Village Water Supply in India, and the UNDP Pump Test Project. Small PV pumping systems are in place throughout the Carribean, Africa, South and Southeast Asia.

57. In the developing world, wind pumps are produced in Kenya, Ethiopia, Mall, South Africa, India, Pakistan, China, Thailand, Philippines, Peru, Argentina and Brazil. Data on installed windpump capacity has not been gathered on a global scale. Nonetheless, as over 50 known windpump manufacturers were operating globally in 1983 (20 for more than 20 years),<sup>2</sup> '/ it is evident that the industry serves a significant global market.

<!-- Start of picture text --> Village Water Supply Power Source Costs<br>20 Liters/capita, head = 40m<br>Power source costs only ($/m3)<br>Diesel 0 15c/l<br>1.2 I Diesel 0 45c/1<br>l \ind 0 3m/sec<br>Wind W 0 4mn/sec<br>0.9 _ 3< PV 0 4kWh/mt2<br>-0 PV@ S kWh/zn2<br>0.8-x<br>0.3. I<br>100 300 500 700 900 1100<br>Village Population<br><!-- End of picture text --> Figure 8 Village Water Supply Costs: PV, Wind & Diesel IT Power LTD, Wind Technology Assessment Study, Vol 1, pp 16, February, 1983.
**Figure 6. Progressivity of fiscal interventions (Kakwani coefficient)**

<!-- Start of picture text --> 0.29<br>0.32<br>0.41<br>0.73<br>0.74<br>0.75<br>0.79<br>0.84<br>0.85<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal. Notes: DT = direct transfers; IK = in-kind benefits. The vertical line indicates the Gini coefficient considering market income plus pensions.

# 3.5 Incidence and concentration of taxes and transfers

## _Taxes_

The tax burden for the first MIPP income quintile is much higher than that for the rest of the population (Figure 7). Practically all taxes paid by the first decile are in the form of indirect taxes. Indirect taxes as a share of MIPP for the first quintile households are high (51.3%). F<sup>34</sup> This is likely a consequence of the existence of a high share of households with low MIPP who rely completely on direct transfers to finance their consumption. 34F<sup>35</sup> At the same time, the tax incidence for quintiles 2 to 4 is similar, though the tax burden increases slightly because the direct taxes burden increases more than the decline in the incidence of indirect taxes. Tax incidence rises in the fifth decile, mainly due to a more significant effect of direct taxes, even though the top 20% of the population pays less indirect taxes as a share of their market income plus pensions. The importance of property taxes (IPTU/ITR) as a share of the market income plus pensions is very low (0.2%).

34 According
# 1. Introduction

Banks dominate credit intermediation and savings mobilization in most Emerging Markets and Developing Economies (EMDEs). A key driver of lending interest rates is the lending-deposit interest spread, the difference between the lending and deposit interest rates, which captures the efficiency with which banks allocate society’s savings to its most productive uses. High lending rates and spreads pose a challenge for policy makers: they can affect monetary policy transmission, hinder private investment and job creation, inhibit financial development and inclusion, and can ultimately compromise financial stability.

This paper quantitatively analyzes the main factors that are correlated with lending rates and spreads in EMDEs. The empirical literature suggests that rates and spreads are driven by three factors: _macro-fiscal conditions_ , _banking sector characteristics_ , and the _business environment._ These affect four components that make up the spread: _bank operational costs_ , _risk premia_ , _quasi-taxes_ , and _returns_ .

Using data from 140 EMDEs, we show that the country median nominal lending rate and spread declined in EMDEs in the 15-year period between 2003 and 2017. However, country experiences differ: in the decade 2007-2017, rates and spreads have increased in 33% and 40% of the countries, respectively. The analysis also shows that rates and spreads have been consistently higher in Latin America and the Caribbean (LAC) and Sub-Saharan Africa (AFR) and are also a policy challenge in many countries in Europe and Central Asia (ECA), including Turkey and the Russian Federation. They are typically lower in East Asia and Pacific (EAP) and Middle East and North Africa (MENA), but exceptions exist (e.g., EAP: the Lao People’s Democratic Republic, Mongolia, Indonesia. MENA: the Arab Republic of Egypt, Lebanon, Tunisia).

In addition, based on a quantitative analysis of lending rates and spreads, we find that:

- First, less economically and financially developed countries tend to exhibit higher lending rates and spreads. These higher rates tend to be driven by higher spreads, not
the IMF and the World Bank. The link between deposit insurance and each of these factors is discussed in turn.

# _Prior Financial Instability_

Adopting insurance in a turbulent period may signal an alarm bell for depositors. Policy makers, therefore, may attempt serious banking reform only in relatively calm periods. Also, financial instability is often closely related to inflation. Because few explicit schemes index coverage for inflation, a program adopted in an inflationary period may not be a credible form of depositor protection. 2 If such credibility is important to policy makers, volatility measures such as inflation and the standard deviation of M2/GDP should be negatively related to the adoption of deposit insurance.

# _Post-Banking Crisis_

In the wake of a severe crisis, an explicit program may be necessary to restore depositors' faith. Unlike the pre-crisis instability described above, governments need not worry about the signal sent by adopting deposit insurance as the crisis has already occurred. One would, therefore, expect a positive relationship between past crisis and the adoption of explicit insurance.

- **12** Another hypothesis is that variation in M2/GDP is a signal of the ease with which depositors can exit a country's financial svstem in case of impending problems (exchange rate depreciation, real estate lending bubbles). Because exit is a form of self-insurance, the need for explicit insurance may be lower in countries that have active financial sectors as measured by variation in M2/GDP.

12
Figure A1: Survey period in context–contd.

<!-- Start of picture text --> CIV COL<br>80 0 100<br>0<br>-20 60 -20 80<br>-40<br>-40 60<br>40<br>-60<br>-60<br>40<br>20<br>-80 -80<br>20<br>-100 0 -100<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>CYP GAB<br>100 0 80<br>0<br>80 -20<br>-20 60<br>60 -40<br>-40<br>40 -60 40<br>-60<br>-80 20 -80<br>20<br>-100 0 -100<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>GEO GRC<br>0 100<br>80<br>-20<br>80<br>60<br>-40<br>60 0<br>-60 -20<br>-40 40<br>40<br>-80 -60<br>-80<br>-100 20 -100 20<br>03/01 05/01 07/01 09/01 03/01 05/01 07/01 09/01<br>GTM SLV<br>0 100 0 100<br>-20 80 -20 80<br>-40 60 -40<br>60<br>-60 40 -60<br>40<br>-80 20 -80<br>-100 0 -100 20<br
and fourth internal lag, and for contemporary electricity consumption using the third and fourth internal lag in the Difference GMM and System GMM regressions. Included control variables are share of imports and exports in GVA, share of manufacturing in GVA, and the log of capital and labor.

In the services sector, the two-step system GMM results also show a significant positive relationship between sectoral electricity consumption and value added, with an estimated elasticity of 0.029 when using data for all countries (see Table 3, column 3). In addition to the two-step system GMM, all other estimations produce positive and significant coefficients with varying magnitudes (see Table 3, columns 1-3). Both the Hansen test and autocorrelation tests indicate the exogeneity of the chosen instruments in the system GMM, while the Hansen test rejects the validity of the instruments for the difference GMM 16
reshore activities.

By focusing on Mexico, this article also contributes to three strands of literature that have been largely concentrated on high-income countries. First, it relates to the emerging literature of the economic impacts of automation. Notable articles in this area include Acemoglu & Restrepo (2017), who find negative impacts of automation on LLM outcomes in the US, and Graetz & Michaels (2017) who find positive impacts of automation on labor productivity and TFP, and no effects on total employment using country-sector level data for a sample of 17 high-income countries. Chiacchio et al. (2018) apply Acemoglu and Restrepo’s (2017) methodology to six European countries and find strong negative impacts of automation on employment. Using a similar approach, Dauth et al. (2017) find that while automation in Germany had a negative impact on manufacturing jobs, the total effect was zero as jobs were created in the services sector. They also find negative impacts of automation on wages of middle-skill workers. There are two recent studies that examine the impacts of automation in Mexico. Faber (2018) and Pedemonte et al. (2018) examine the labor market impacts of exposure to US and domestic automation across Mexican municipalities. However, unlike this article, they do not use trade data at the local level and instead rely on a Bartik-style variable to allocate national-level exports to each locality. At the same time, municipal-level data do not necessarily provide an appropriate approximation of local labor markets. Thereby, the existence of labor migration across municipalities may introduce a bias to the estimated labor market impacts. Finally, they use data from Census samples instead of the full counts, which may lead to noisy labor market indicators for small municipalities.

Second, this article contributes to the literature on the technological drivers of offshoring (see, for example, Abramovsky & Griffith (2006); Bartel et al. (2014); Fort (2016)). Finally, this article is linked to a large body of literature on the impacts of offshoring
0.285**|
||(0.109)|(0.129)|(0.107)|(0.122)|
|HH SES in 2008|0.038|0.022|0.006|-0.027|
||(0.024)|(0.027)|(0.021)|(0.026)|
|Constant|0.208|0.349|-1.739***|-0.297|
||(0.199)|(0.222)|(0.183)|(0.219)|
|Observations|3,285|3,285|3,264|3,264|
|Adjusted R-squared|0.175|0.222|0.014|0.038|
|<br>Sample|All|All|All|All|
|<br>Village FE|No|Yes|No|Yes|
|<br>Province FE|Yes|No|Yes|No|
|Number of clusters|423|423|423|423|

* p<.10  ** p<.05  *** p<.01

_Notes_ . The dependent variable in columns (1) and (2) is the Mathematics score, computed using Item Response Theory with a two-parameter logistic (2PL) model. The items included in the model are those from the tablet-based computer-adaptive test. The dependent variable in columns (3) and (4) is a socioemotional skills index computed using principal-component factor analysis on 15 Big Five items and 8 Grit items. The sample for this long-term follow-up is 3,294 respondents. Among them, some did not complete the computer-adaptive test, and some answered “Don’t know” to the socioemotional skills questions. Therefore, the sample is 3,285 respondents for the cognitive index and 3,264 respondents for the socioemotional skills
percent report three) and is also signicantly higher than the next category (1.59 percent report six). My model is not likely to capture this pattern. I therefore assume that the probability interval corresponding to the ve bean category is larger, implying that some of the women who would allocate 4 or 6 beans to the likelihood of being HIV-positive under the rule described above, report instead 5 beans. The probability of observing 5 beans is assumed to be

# 4.2 Fertility

The parameters of the utility function are estimated using the data on pregnancy outcomes. As described in Section 3.6, the solution to a woman's optimization problem is to become pregnant at period _t_ if the preference shock, _εpi_ ( _t_ ) , is bigger than the critical value _ε_<sup>_∗_</sup> (Ω<sup>_d_</sup> _i_<sup>(</sup><sup>_t_)</sup><sup>_,t,_type</sup> _i_<sup>).Conditionalonthewoman'stypeandtheobservableelementsof</sup> the woman's state space at time _t,_ the probability of the woman's choice to become pregnant is given by Conditional on the set of initial conditions and the unobserved type, the probability of observing a sequence of belief reports is independent from the probability of observing a sequence of fertility outcomes. This is because the set of initial conditions and type map deterministically into the sequence of life-cycle beliefs.

# 4.3 Actual Infection Process

Given the assumption of no symptoms, the state space does not contain actual HIV status beyond any HIV test results that women have received. It is therefore not necessary to recover the actual hazard process for estimation of the decision-model parameters. For each possible 24