rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 1. Foreign aid distribution to Developing Countries by Region**

<!-- Start of picture text --> 0.50 140<br>0.45<br>120<br>0.40<br>0.35 100<br>0.30<br>80<br>0.25<br>60<br>0.20<br>0.15 40<br>0.10<br>20<br>0.05<br>0.00 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>Africa South America<br>Asia Middle East<br>Central & South Asia Oceania<br>All Developing Countries(recipients)<br>Share of total aid<br>aid received by developing countries(Bil UD$)<br>Total<br><!-- End of picture text --> Source: Our calculation based on OECD.STAT data base.

The literature on AfT is really quite new (e.g. Helble _et al._ , 2009; Hoekman _et al._ , 2010; Calì _et al.,_ 2011). In part this is because of data limitation; frequently, because it will be difficult to disentangle the impacts of AfT projects on trade, income and welfare. However, the literature on AfT can be traced back to that on foreign aid, whose role of foreign aid in the growth process and to reduce international inequalities in developing and least developed countries has been a topic of intense debate for several decades. This issue has been analyzed by researchers for decades due to its complexity in nature. It links with political relationship between donor countries and recipient countries, governance of public sector in recipient country, and how much the foreign aid is necessary and how long it should last.

The measurement of effectiveness of AfT, which is the core principle in the Paris Declaration
meters. Those users that pay do so on a volumetric basis after they have received the water.

During the rainy season, PJT I focuses on flood management, monitoring rainfall intensity and discharge along the Brantas River through the Flood Forecasting and Warning system. For basin wide monitoring of water quality, the Provincial Water Resource Services Office collects water quality data from the Balai PSDAs, as well as data from PJT I, and reports it each month to the Governor. PJT I monitors water quality, collects data, and reports to the Bappedalda Office ( Provincial Environmental Control Office), who, in turn, require concerned agencies and local police to enforce pollution control regulations and/or seek the legal remedies available.

PJT I is currently constructing a real-time water monitoring system on water quality. They monitor pollution from a central station in Lengkong Mojokerto, and have constructed a lab in Malang that is awaiting certification. According to the head of an advocacy organization that works on capacity building activities in the basin, 14 industries discharge polluted water in the Brantas, and thus far they have taken one to court using monitoring data from PJT I. However, though PJT I’s data is technically sound, their data has no authority without official certification, so currently the court tends to be in favor of factory data. Why such certification has not been issued by KLH or local environmental agencies is not clear.

# _Communication_

PJT I is very proactive in providing information concerning water resources management and issues. Staff disseminate brochures and participate in exhibitions each year for local government and in Jakarta. Some local governments invite them as speakers as well.

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>_USD_</sup> _odt_ is a depreciation of the USD, which we interpret as a depreciation in the exporter’s pricing currency (we come back below to the issue of currency denomination of prices and exchange rates in our estimated equations). The coefficient of interest _α_ is the elasticity of export prices to changes in the destination’s exchange rate and maps oneto-one onto an exchange rate pass-through elasticity into USD prices (pass-through is complete when _α_ = 0 and is zero when _α_ = 1).<sup>22</sup> Equation (3), as the rest of the specifications, is estimated by OLS with robust standard errors clustered at the dyad-product-year level. We will also report specifications using alternative clustering strategies (Section C.9).

**FE** _fpd_ accounts for all time-invariant firm, product or destination characteristics that might affect prices. **FE** _ot_ captures changes in economic conditions, in particular changes in costs at the origin country level – for instance supply shocks affecting prices across firms and correlated with exchange rates. We also systematically report results of specifications that include fixed effects in the firm _×_ product _×_ year dimension, i.e. those that capture entirely changes in costs at the firm-product level:<sup>23</sup>

> 21Note that as our dataset does not contain information on firm ownership, all firms in our sample are treated as independent entities. Thus in the presence of firm subscripts we omit the origin country subscripts.

> 22Note that _α_ may suffer from attenuation bias due to classical measurement error: because we use annual data, the exchange rates applied in our estimations are (potentially) not exactly the ones actually faced by the firms at the time they export. The only way to solve this issue would be to use higher frequency data which would allow us to match transactions with daily or monthly exchange rates. Indeed, Fosse (2012) shows using Danish data that moving from annual to monthly
infant and child mortality with these countries, as well as with the other BRICS countries, remain large (Figure 15).

**Figure 15: India lags behind many of its comparators in reducing infant and child mortality**

<!-- Start of picture text --> (a) Infant mortality trend: India and selected (b) Child mortality trend: India and selected<br>comparators comparators<br>100 120<br>80 100 India, 112<br>India, 80 India, 75<br>80<br>60 India, 56<br>60<br>India, 39<br>40 India, 50<br>40<br>20 20<br>0 0<br>1994 2005 2014<br>1994 2005 2014<br>Nepal Bangladesh Nepal Bangladesh<br>Cambodia India Cambodia India<br>Vietnam Nicaragua Vietnam Nicaragua<br>(c) Infant, child and maternal mortality, 2014: the BRICS<br>50 200<br>IMR<br>Under‐5 MR<br>40<br>150<br>MMR<br>30<br>100<br>20<br>50<br>10<br>0 0<br>India China S. Africa Brazil Russia<br>Infant mortality rate Under 5 mortality rate<br>estimate<br>rate (per 1,000)<br>Infant, Under 5 mortality (per 100,000), modeled Maternal mortality ratio<br><!-- End of picture text --> _Source:_ World Development Indicators (WDI).

Relatively slow progress in sanitation in India has also meant that the country compares poorly with comparators including South Asian neighbors in access to sanitation and the incidence of open defecation (see Figure A‐ 6, Annex). In undernourishment among children of age 5 and under, stark differences across states mean that a number of
Understanding the determinants of female employability in Mexico is important to advance gender equality and women’s contribution to economic growth. Understanding what factors support women’s labor market inclusion and how these change through time can point to policies that help women benefit from economic growth (Klasen and Pieters, 2015) and avoid economic crises from erasing gains.

This paper aims to explain the increase in female employment in Mexico and to identify the main determinants underpinning women’s employability that allowed the narrowing of the gender gap in unemployment and labor participation. We take two approaches: to understand the demographic component, we decompose unemployment, gross employment, and labor force participation rates in 2005 compared to 2017 by age group and gender. Subsequently, we investigate the economic crisis and governmental policy implications on the probability of a woman being employed by estimating a probit model that considers individual and household characteristics, labor indicators as controls, and availability of childcare facilities as an explanatory variable.

We use data from the National Employment and Occupation Survey (ENOE), Census, and the National Childcare Facilities Directory for Working Mothers of the Social Development Ministry (SEDESOL) to estimate explanatory coefficients for 2007 and how they changed 10 years later. The Childcare Facilities for Working Mothers program (CFWM) granted a bimonthly cash transfer to mothers and children tutors without access to childcare facilities granted by their labor benefits. Eligible mothers were expected to work or study. We chose 2007 as the year to begin our study because it coincides with the program’s first 3
expected to directly increase both expected profitability _π_ as well as the ease of imitation _α_ , and evaluate the effect on the frequency of discovery.

This analysis is useful beyond the evaluation of the market failures hypothesis in that policy makers wishing to stimulate discovery activity must have some idea of what policy levers are effective. The relationship between discovery and development discussed in section 4 can be used to determine if a country is over- or under-performing in discovery activity relative to expectations, but it does not offer any policy guidance. For that, we must consider empirical support for the potential drivers of discovery.

Before discussing the variables we use to test for the presence of market failures, we again add to the basic model, this time by including export data, specifically the natural logarithm of 1993 exports and the average annual growth rate of exports between 1993 and 2001. We include these trade measures because, as discussed in section 3, penetrating a foreign market is itself an entrepreneurial event. Therefore, they are potential explanatory variables showing how closely related discovery and overall export growth are, and by extension how similar a discovery-promotion strategy and an export-promotion strategy might be. However, if one does not agree with the viewpoint that the penetration of a foreign market is itself a discovery, then including these export variables remains useful in that it corrects for the fact that we are not using domestic production data.

We consider four groups of explanatory variables to add to the basic model: education, absorptive capacity, ease of entry, and financial system development. Data definitions and 30
# **2 An empirical macro-economic framework for T ̈urkiye**

The underlying model is designed for data-driven climate policy analysis in T ̈urkiye.<sup>4</sup> It is calibrated and estimated using quarterly and annual data covering the period from 2000 to 2022. It comprises a high-skilled household sector, a low-skilled household sector, a core good sector, an energy sector, a public sector, and the Rest of the World (RW). Households use energy for consumption (on average around 48% of total final energy consumption) and firms for non-energy production (around 52%), which can be fuel, heat, or electricity. The energy sector consists of renewable energy (with a sample average of 0.9% of GDP and 17% of total energy consumption) and fossil energy (6.2% of GDP and 83% of total energy consumption). A carbon mining sector (3.4% of GDP) extracts and imports carbon (such as coal, crude oil, and gas) which is an input to fossil energy production. CO2 emissions arise from the combustion of fossil fuels and have fluctuated around 0.46 kilogram (kg) per 1 constant 2015 USD of GDP since the early 2000s.<sup>5</sup> Households, firms, the government, and the RW demand and supply finance, respectively. The model features various financial instruments. Households are assumed to be net creditors holding their wealth as bonds, deposits, and private equity. Firms are net debtors financing investments by borrowing from households and issuing equity. The government finances its deficit by issuing bonds. The RW is a net creditor from T ̈urkiye’s perspective. The public, corporate, and RW’s net debt-GDP ratios are moderate on average: around 36%, 57%, and -35% of GDP, respectively. Yet, these debt levels have increased sharply in recent years (OECD 2022). Despite a low net debt
line with the significant impacts found on self-employment in the short-term as discussed in the previous section.

Estimates in Panels B-D (Table 3) show that the treatment group tried and realized more projects than the control group up until 4 years after graduation, although ultimately they did not have more projects at the time of the endline survey. Panel B shows that the entrepreneurship track led to more project ideas and more attempts to realize these ideas overall. At the time of the endline survey, 67 percent of the treatment group reported having had a project idea in the past, significantly more than in the control group (58 percent). Overall, 28 percent of the treatment group tried to realize their past project idea, again significantly more than in the control group (19 percent). The higher share of attempts to realize a project in the treatment group led to more projects set-up; with 7 percent of the treatment group reporting having set-up a project, compared to 4 percent of the control group. On the other hand, the additional attempts also resulted in more failures in setting up projects in the treatment group (14 percent) compared to the control group (10 percent).

Panel C shows that the treatment group is not more likely to have an active project at endline, however. As mentioned above, a slightly larger share of program participants has realized a project at some point (7 percent versus 4 percent). However, program participants are no more likely to still have an active project in 2014 compared with the control group (4 percent versus 2 percent), or a project temporarily closed (2 percent versus 1 percent).<sup>17</sup> Finally, Panel D shows that while the program increased the likelihood that individuals had a project idea and tried to realize it, their success rates conditional on having an idea and trying to realize it did not increase. Conditional on having had an idea, significantly more program participants than non-participants (42 percent versus 33 percent) reported
―to price ceilings, thereby effectively entrenching subsidies (albeit smaller than at present).

In these situations, the government needs to decide how long it should take to raise prices to market levels, and how large each price increase should be. This task is obviously made easier if price adjustments are started in times of low world oil prices, as the government of China did in January 2009, when it switched to a new regime requiring more frequent price adjustments based a 22-day running average price of a basket of crudes. Several of the countries above now requiring large price increases had retail prices that were close to market-based price levels in January 2009. Having missed that opportunity, and given that oil prices are unlikely to collapse as spectacularly as they did in 2008 and 2009, governments may consider raising prices in small increments regularly until market levels are reached, as the government of Thailand has begun to do with LPG prices. However, if the starting prices are very low―a cup of coffee in Repúblicana Bolivariana de Venezuela is a hundred times more expensive than a liter of premium gasoline―large initial price increases may be warranted and find political acceptance.

Although rare, there are circumstances when overnight subsidy elimination may be feasible. One such case is a limited price subsidy program targeting only the poor, as with the LPG programs in Argentina and Panama. The Dominican Republic in September 2008 eliminated the LPG price subsidy and replaced it with bonagas cards issued to the poor; cash earmarked for LPG purchase is transferred to bonagas cards as part of social protection. Another scenario where overnight subsidy elimination may make sense is where black market prices have become the de facto end-user prices and are close to what would have been market prices, as with subsidized kerosene in Nigeria. In these situations, it would be informative to conduct price and quantity surveys to identify how much consumers actually purchase and at what price, because the _actual_ quantities purchased and prices paid determine the impact of higher official prices.
income employment shocks. We use the poverty line developed by the National Statistical Office (NSO, 2012), which is based on minimum subsistence requirements for consumption (see forthcoming Malawi Poverty Assessment), and household level panel data collected by the NSO, in conjunction with the World Bank, covering two time periods, 2010 and 2013. We augmented this data set with measures of current period rainfall shocks and measures of long‐term rainfall variability obtained from the National Oceanic and Atmospheric Administration (NOAA), and data on variance of maize prices obtained from Malawi Agriculture Statistics Bulletin, of the National Statistical Office. These additional data sources enable us to rely on objective measures of shocks, such as for rainfall, to capture the temporal variation in rainfall.

Results show that many households in Malawi are vulnerable to poverty, though as with many other studies of rural areas in other countries, much of vulnerability is due to chronic poverty. Nonetheless, risks – particularly rainfall and employment shocks – are also important in explaining why poor households remain poor, and why some non‐poor households are more likely to fall into poverty in the next period. The results also underscore the importance of having access to long‐term measures of variability, as opposed to relying on spatial variation as a proxy for temporal risks. In particular, rainfall patterns in 2013 were relatively better than generally observed over the period 1983‐2012. Using the information from the longer‐term rainfall data to generate expected shocks better captures the number of households vulnerable to falling into poverty. Of the additional explanatory variables included in the vulnerability analysis, both household wealth and agricultural asset indices are the most important in protecting households from falling into poverty and reducing the severity of the fall when shocks occur.

The paper contributes to the literature in two main ways. First, the data set includes explicit information on a number of shocks hypothesized to affect vulnerability, including longer‐term, objective measures of rainfall variability and current period rainfall shocks. Many previous analyses have limited
The country-year weights are constructed by taking three-year rolling averages of oil imports to GDP. Combining these weights with oil prices, our main index for country _i_ and year _t_ is constructed as: The weights, Ω _i_ , _Oil_ , _t_ ,are calculated as: Although the main results in this paper are based on oil shocks constructed with real oil prices and three-year rolling average weights, all results are also robust to the use of nominal prices, and five-year rolling weights.

The next data source is the Commodity Terms of Trade Database introduced in Gruss and Kebhaj (2019). These data consist of country-specific commodity price indices, based on a set of 45 individual commodities. The formula for the gross import price index is: In the above specification, _Pj_ , _t_ refers to the logarithm of the real price of commodity _j_ in time period _t_ . Ω _i_ , _j_ , _t_ denote the commodity-country-specific weights by which the prices are weighted. The country specific weights are based on the ratio of gross imports to GDP. Rolling weights use a moving average of the last three time periods before _t_ to construct Ω _i_ , _j_ , _t_ . The commodity price indices are available at both an annual and monthly frequency over our sample period. We use the annual data for our main specification and turn to monthly data when considering the polling data. We show that our results are robust to using rolling as well as fixed weights.

Table 1 provides summary statistics for the key variables. The average oil shock in election years in real terms is -0.16%, and 0.14% in nominal terms. In slightly more than half of the elections (51%), the incumbent party remains in power.

6
of actual expenditures. Observed household expenditures at time t can be decomposed into the "permanent" (P) component of expenditures (the part which is time-varying but is expected at time t to persist), a "transitory" (7) component of expenditures (time varying and expected not to persist), and a measurement error term (v): _el_ = _eh_ + _eh_ + Vh If the three variances (u<sup>2</sup> ) are uncorrelated, then ratio of measurement error (or "4noise') to total variance is: _a2 /(aI +a2 +al)_ How large is this noise to signal ratio in measured expenditures? A wide variety of evidence suggests that in cross sectional surveys measurement error is somewhere between 1/3 to **Y/2** of the total variance (see appendix for evidence with these data sets).<sup>7</sup> Measurement error is typically ignored in poverty analysis for three reasons.

First, pure mean zero measurement error does not affect the aggregate poverty rate.

**6** While the total sample size of Mini-Susenas is smaller than 100 Village Survey, the former is nationally representative while the latter is not.

' The method used to estimate measurement error in the appendix is similar to that used by Luttmer (2000).

11
Figure 5: Labor Income by household type, % Figure 6: Main source of income reported by difference from baseline households (% of households) <!-- Start of picture text --> 50<br>Quintile 5 45<br>40<br>Quintile 4 35<br>30<br>Quintile 3 25<br>20<br>Quintile 2 15<br>10<br>Poorest 5<br>0<br>Quintile 5<br>Quintile 4<br>Quintile 3<br>Quintile 2<br>Poorest<br>-64 -63.5 -63 -62.5 -62 -61.5 -61<br>Total Male headed Female headed<br>Rural<br>%<br>Other sources<br>Urban<br>Non-agricultural business Agricultural activity Paid employment Help from others<br><!-- End of picture text --> <u>Source: Author’s calculations from HFPS 2020</u>

# 3.2. Impact on female-headed households

Around 23 percent of Chad’s households are female-headed, divided equally among rural and urban areas. Male-headed households tend to be two-parent households, whereas 70 percent of the female heads in Chad are either widowed, divorced, or separated (Figure A. 2 in the Appendix). Female-headed households face particular financial constraints related to often being single-income households and having a larger dependency ratio.<sup>11</sup> Based on ECOSIT 4, female working members of female-headed households are more likely to work as non-salaried non-farm workers or workers in non-family farms than female workers in male-headed households likely to work as family farm workers or be owners of family farms. There is an over-representation of female-headed households among the poorest urban quintile compared to male-headed households, and an under-representation in the highest urban quintile despite this group being the recipient of most of the household transfers of remittances from abroad<sup>12<