rafmacalaba/data-use-mentions-extended huggingface dataset:
villages located in four strata across Dio ̈ıla. ERIVaS was conducted in three visits by semi-resident<sup>4</sup> enumerators.

During the first visit fieldwork, which was conducted in the post-planting period (SeptemberNovember 2017), each household received a light household questionnaire that elicited basic socioeconomic information, and this was augmented with an agriculture questionnaire that collected detailed information on farm organization, land tenure, crop cultivation and seed use, at either parcel- or parcelplot-level, depending on the topic. Following the random selection of the pure stand sorghum plot, the enumerator visited the plot location with the farmer, delineated the plot boundaries with the farmer and stored them on a handheld GPS unit, and set-up an 8x8m sub-plot for crop cutting, following the same protocol that was published by Gourlay et al. (2019) and that ensured the random placement of the sub-plot on each sampled plot. Each sub-plot was further divided into four 4x4m quadrants for which harvest was ultimately processed, weighed, and recorded separately.

During the second visit fieldwork, which was conducted immediately after the harvest (December 2017), the enumerators harvested the sub-plots with the help of the farmers and recorded the weight of the crop production immediately after the harvest and after additional drying at the enumerator’s residence. During the third visit fieldwork, which was implemented during the post-harvest period (February 2018), each household received an agriculture questionnaire that collected self-reported, parcel-plotlevel information on sorghum production and use of agricultural inputs, including fertilizer, household labor, and hired labor. Our analysis is based on 577<sup>5</sup> pure stand sorghum plots, with matching crop cutting and self-reported yield measures, augmented with plot- and household-level survey data and third-party geospatial data that are linked with georeferenced plot locations.

Intensive data quality controls and supervision measures were put in place to ensure that each crop cut sub-plot was placed in accordance with the desired protocol; was not managed by the farmer differently vis-`a-vis the rest of the
socioeconomic groups persist, and learning at the secondary level appears to be much weaker. In all other Central American countries, average learning outcomes at the primary level are low, and a host of promising reforms to increase autonomy, assessment, and accountability are at various stages of implementation in several countries. To the extent possible, rigorous evaluations of these efforts, such as the ones of teacher and principal trainings in El Salvador and Mexico, should be conducted and should include cost data to help inform policy making in the region and beyond.

To directly intervene among youth at high risk of dropping out, countries can consider developing early warning systems, and piloting comprehensive support programs for identified youth. Such an approach would be most promising in countries with strong administrative data, and would need to be targeted to the levels where dropout starts to occur at substantial rates. Given that dropout rates are so high, school or community-level programs might make more sense than individuallevel programs.

Reforming the targeting, coverage, and coherence of existing programs would also help. As discussed above, some conditional cash transfer and other support programs are not well-targeted, while others are well-targeted but are too small to cover the majority of the eligible. In addition, there may be opportunities to improve the design of existing programs to increase their impacts, based on insights from behavioral economics, for example by structuring the timing of transfers to maximize impacts on the decision to persist in school. Given that most countries have multiple social protection and school support programs aimed at overlapping populations, rationalizing and coordinating across programs may provide both increased impact and cost savings. Finally, based on the evidence that child labor is commonly used as a coping strategy in response to shocks in the region, rapid scale-up mechanisms for existing programs, in terms of expanding coverage or increasing support following shocks, could also play a role in reducing dropout.

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countries also greatly expanded their use of antidumping and countervailing duties (and investigations) to curb both agricultural and manufactured imports. In short, these general developments suggest that important increases in protection occurred over 1966-1986. This paper will attempt to establish if this was in fact the case and, if so, to quantify the magnitude of the change.

III. Empirical Results For an initial assessment of trends in the use of nontariff barriers, 1986 NTB frequency indices were derived for the major developed countries. These statistics, which show the percentage of four-digit SITC products affected by nontariff barriers (see equation (1)), were computed for all commodities as well as for major product groups (e.g., foodstuffs, agricultural raw materials, ores and metals, fuels and manufactures). Next, similar indices were computed for 1966. 11/ Since the 1966 and 1986 statistics were derived using a common list of nontariff barriers (i.e., all Type I and Type II measures shown in Table 1) they can be used to assess changes in the 11/ The 1966 NTB frequency indices were derived from Walter (1972, Table 2 on pages 341-2). Since these data were published in terms of two-digit SITC products they were aggregated to the major groups shown in Table 2 using a two stage procedure. First, the number of affected four-digit SITC products (N* ) in two-digit group i was computed using,

- (3) _N4*._ - fi x N4i

where fi is Walter's published two-digit frequency index and N4i is the number of four-digit SITC groups in the two-digit class. Next, the affected and total four-digit products (N4i and N& ) were summed to the levels shown in Table 2 and their ratios used to estimate the aggregate frequency indices. The matched 1986 indices were directly computed at these levels using the UNCTAD Data Base on Trade Measures.
# **1.** **<u>Introduction</u>**

In recent years a strong belief that ‘entrepreneurship’ is a crucial driver of economic growth for both developed and developing nations has emerged among both scholars and policy makers (see, for instance, Audretsch, Keilbach and Lehmann, 2006; Koellinger and Thurik, 2012; and, for a comprehensive survey, Van Praag and Versloot, 2007). However, moving from macroeconomic scenarios to the micro foundations of entrepreneurship, since the seminal contribution by Baumol (1990) we have known that ‘Shumpeterian innovative entrepreneurs’ coexist with ‘defensive and necessity entrepreneurs’, the latter being those who enter a new business not because of market opportunities and innovative ideas, but merely because they need an income to survive. For obvious reasons, this kind of ‘survival-driven’ self-employment is particularly diffused in developing countries (DCs) (see Naudé, 2009 and 2010), where poverty and lack of formal opportunities in the wage sector often push a large number of people into ‘entrepreneurial’ activities ranging from street vending to traditional and personal services (in most cases within the informal sector of the economy, see Ihrig and Moe, 2004; Maloney, 2004; Sonobe, Akoten and Otsuka, 2011).

Empirically a world-wide research project, the ‘Global Entrepreneurship Monitor’ (GEM), has been collecting survey data using standardized definitions and collection procedures on potential and actual entrepreneurship since 1999, and now covers 60 developed and developing countries (see Zacharakis, Bygrave and Shepherd, 2000; Reynolds _et al._ , 2005; Acs, Desai and Klapper, 2008). This project reports the rates of business start-up and of self-employment across different countries of the world, but makes it clear that these statistics comprise both ‘opportunity-motivated’ entrepreneurs and those driven by necessity, the latter being defined as those who have started their own firms as a consequence of the following personal situation: “because they cannot find a suitable role in the
options available in each labor market, workers have an option to move to the residual labor market. Empirically, unemployment, home employment, and working in the informal sector all belong to this choice.<sup>20</sup> Each worker compares the option to move to the residual labor market with all the other job options available in each of formal labor markets.<sup>21</sup> We denote the choice to be in the residual labor market by _j_ = _I_ . The residual labor market is assumed to offer only one job option, and thus we do not distinguish a job from a labor market for the residual labor market, i.e., _kI_ = _I_ without loss of generality. We also assume that there is no job switching cost, _δ_ , if a worker decides to move into the residual labor market.<sup>22</sup>

**Timeline of events** . Each period _t_ consists of two stages in the following order: (1) production stage, and (2) preparation and mobility stage. In the production stage, workers start the period _t_ attached to a job _j_ , perform the task _τj_ associated with _j_ for production, and receive a market-level real wage _wt_<sup>_kj_.Next,thepreparationandmobilitystagebeginswithproducersdeterminingthemass</sup> of tasks _Tt_<sup>_k_and assigning tasks to positions for the next period.Workers then sample jobs for the next</sup> period and learn about the random iid costs shocks associated with each of them, the current job, and

> 20As explained in the previous section, the RAIS database covers only formal sectors. We introduce the residual market to account for the informal labor market that is not covered in the data that we will use to quantify the model.

> 21To be consistent with the way the residual market is empirically defined later, we will use the terms “formal” and “non-residual” interchangeably to denote the baseline labor markets.

> 22We could also assume
lagged dependent variables and an intercept term and where _p_ is the lag length, _B_ is an matrix of coefficients, and M is a _N ×_ 1 vector of residuals. The model includes real GDP, consumer inflation, central bank policy rates, nominal effective exchange rates, and oil prices. The model is estimated on quarterly data from 1991Q2 to 2021Q1 and includes a constant. We estimate the model using Bayesian techniques and the Minnesota prior with hyperparameters on the first lag coefficients at 0.8, on overall tightness at 0.1, and cross-variable weighting at 0.5. A total of 12,000 iterations are run, with the first 2,000 discarded.

To identify demand and supply shocks, the following sign restrictions are imposed: _ε_ where a positive structural supply shock ( ) is defined as that which raises output, decreases inflation, increases oil prices and appreciates the exchange rate. A positive demand shock raises economic growth, inflation, and oil prices. A positive monetary policy shock is defined as that which decreases economic growth and inflation. Sign restrictions are imposed for the first two periods. The model is based on data for 14 economies weighted using equity market capitalization in US dollars. In the robustness section we look at the role of weighting strategy and alternate policy rates have on supply and demand decompositions.

The unprecedented nature and size of the COVID-19 shock presents possible challenges to the effective modeling of the pandemic, especially for the historical decomposition used in this paper. However, in order to deal with the significant change in volatility, the VAR model includes stochastic volatility in the error structure as in Cogley and Sar8
deficit on a fixed exchange rate regime we assume that at time 0 agents receive information that future deficits will be larger than they originally believed. The government's intertemporal budget constraint implies that a speculative attack is inevitable. This is true regardless of the government's initial level of foreign reserves or its initial debt position. Once the government learns that the present value of future deficits has risen, the only choices left to it are when and how to raise the seignorage revenues required to meet its intertemporal budget constraint.

We analyze the date of a speculative attack, i.e. the date at which the economy switches from a fixed to a floating exchange rate regime, under two assumptions. First, we assume that the government follows a threshold rule according to which the fixed exchange rate regime is abandoned in the first period that net government debt reaches some exogenous upper bound. Second, we consider the case in which the only constraint faced by the government is its intertemporal budget constraint. In both cases we depart from the speculative attack literature by distinguishing between the time of the speculative attack and the time at which the government implements the new monetary policy required to balance its intertemporal budget constraint. Disentangling these two events considerably enriches the dynamic implications of the model.

Using versions of the model calibrated to Korean and Thai data, we show in Section 4 that the speculative attack occurs after the information about higher future deficits arrives but _before_ the new monetary policy is implemented. An econometrician looking at the data would see an exchange rate crisis - but he would not see large deficits, high growth rates of money or high rates of inflation prior to the speculative attack. The econometrician could well conclude that the attack was a multiple equilibrium phenomenon. In fact it reflects fundamentals: high prospective deficits.

While the government cannot prevent a speculative attack, it can affect its 4
findings are quite similar regardless of the distribution: women are 40% of taxpayers in the top 1% – the share is stable between the top 1% - 0.1%, and then starts to decline at higher incomes until the range 20-30% within the top 0.01%.

These shares of female presence among the top 1% of earners are high by international comparison. Atkinson _et al._ (2018) document that the share of women in top 1% are consistently in the range of 15-25% in several high-income countries. In Sweden, Boschini _et al._ (2017) document that the share of women in the top 1% in 2013 was 23% when capital gains were included but only 16% when it was not included. Neef & Robilliard (2021) document that the share of women in the top 1% of wage earners is in the range of 20-30% in Brazil, US, France and Costa Rica. Our estimates of 40% female participation within the top 1% are therefore higher than in all those settings.

These differences between female participation among top earners in Honduras and other countries could be driven by several reasons. First, most of the countries for which granular data on top earners exists are much richer than Honduras – the threshold to be at the top 1% in Sweden in 2013 was approximately USD 140,000 PPP, for example, five times more than in Honduras where that value is stable around USD 30,000 since 2014. An yearly income of USD 140,000 PPP would be equivalent to being at the top 0.05% of the distribution (using tax records income) in Honduras. In that sense, it is not strictly comparable to consider gender gaps among the top 1% in countries with vastly different income levels.

Boschini _et al._ (2017) also document that women showing up at the top of the distribution in Sweden are particularly likely to have received large capital gain incomes, which are often
estimated coefficient is much smaller for the full IHS4 sample and loses significance if only owner-respondents are considered (Table 6). The estimated effects of land rights on cash crop production are insignificant throughout. The loss of significance is consistent with the notion that relying on one respondent per household only increases measurement error that is not eliminated for those claiming to be a (co-)owner of a parcel.

Allowing for heterogeneous effects by gender (Table 7) accentuates the differences between the two surveys: compared to point estimate of 0.16 for the impact of female bequest rights on manure use, estimates for both IHS4 samples are insignificantly different from zero for bequest and sale rights. For planting of cash crops, where the IHPS sample suggests a positive and significant coefficient on male bequest rights with an estimate of 0.20, the results from the full and restricted IHS4 samples imply insignificant effects of male rights and negative and significant effects of female bequest rights.

Finally, interacting gender-specific rights with the inheritance regime indicators (Table 8) produces results that are similarly inconsistent with those obtained from the IHPS sample. While the estimated coefficients on women’s or men’s bequest rights for manure application and cash crops are insignificant in matrilineal communities, a negative and marginally significant effect of male bequest rights on cash crop adoption 13
Fewer firms receive subsidies in the form of exemptions on value-added taxes. These exemptions increased until 2003, then declined. It is clear from these tables that income tax holidays were a more pervasive form of incentives until the 2008 tax reform.

# **III. Estimation and Results**

# **A. Baseline Results**

We begin the analysis by estimating the model described in equation (1) using ordinary least square (OLS) with and without firm fixed effects. Columns (1) and (2) of Table 3 are estimated with the dependent variable as the log of the firm‘s deflated output. To study the impact of FDI spillovers on the performance of domestic firms, we are interested in how FDI invested in other firms affect the domestic firms located in the same sector. Therefore, the key parameters in the above specification are  7 ,  8 and  9 .

One possibility that has not been explored in the literature on vertical and horizontal linkages is that foreign investment shares are proxying for different trade policies across sectors. Protected sectors may be more likely to receive foreign investment as these firms may be motivated to relocate in order to circumvent tariff or non-tariff barriers (―tariff-jumping‖ foreign investment, which leads to immiserizing effects as modeled by Diaz Alejandro (1977)). In this case, the gains from foreign investment could be under-estimated due to omitted variable bias.

To control for the effects of trade policies, we have created a time series of tariffs, obtained from the World Integrated Trading Solution (WITS), maintained by the World Bank. We aggregated tariffs to the same level of aggregation as the foreign investment data, using output for 2003 as weights. We also created forward and backward tariffs, to correspond with our vertical FDI measures. Table 1 and Table A-5 show basic summary statistics for these tariff variables. During the sample period, average tariffs fell nearly 9 percentage points, which is a significant change over a short time period. While the average level of
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included a nationally representative sample of around 1,000 households per country in the second and fourth rounds and around 1,500 households per country in the third round. The second round covers 35 countries, the third round 34, and the fourth round 39. The 30 countries that were included in all three rounds used in our study include all the countries that were part of the former Soviet Union except Turkmenistan; Czechia and the Slovak Republic; and Germany, Türkiye, and the remaining countries in Central and Eastern Europe that transitioned from a planned to a market economy during the 1990s.<sup>10</sup> Several countries of Western Europe, the Middle East, and North Africa were included in some (but not all) rounds.<sup>11</sup> For these countries, we estimate intergenerational mobility using GDIM data (discussed later in this section).

The main variable is the educational attainment of the survey respondents and their parents, expressed in years. The second round of the LiTS recorded the educational attainment variable in years; in the third and fourth rounds, educational attainment was recorded in completed levels of education. A country-specific correspondence table between levels of education and years of schooling was used to transform this categorical variable into years of schooling <u>(UNESCO Institute of Statistics 2021). These correspondence tables are based on 2010 data and may not</u> accurately reflect the correspondence between years of schooling and levels of education for past years, particularly for individuals who completed their education several decades ago. The differences are likely to be minor, and, as detailed later, we control for them.<sup>12</sup> Information on the number of observations with non-missing information per country and birth cohort is included in the appendix.<sup>13</sup> 10 The 30 countries included in the three rounds of the LiTS used in our study are Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia
through physical weakening due to inadequate nutrition for example rather than through nonadherence.

The paper is organized as follows. In the next section, we present the dataset. In section 3, we look at the impact of the food crisis on the welfare of households in the sample, differentiating by their HIV status. Changes in adherence to treatment between 2007 and 2008 will be studied in section 4. We then assess the impact of welfare changes on the frequency of visits to the health facility of HIV/AIDS patients in section 5 and on the evolution of their treatment outcomes in sections 6.

# **2. The data**

We use data collected in 2007 and 2008 in 4 provinces of Mozambique (Maputo City, Maputo Province, Sofala, Manica).The survey was designed to collect data in order to assess the impact of the Treatment Acceleration Project (TAP). The TAP is a World Bank project carried out in three countries of Africa: Mozambique, Burkina Faso and Ghana, which scaled-up access to HAART. The project began in 2004 and had a 4-year duration.

The HIV/AIDS patients of the survey were identified at the health facility where they received treatment and were interviewed at home along with the rest of the household. The questionnaire includes information on consumption, time use, labor force participation and earnings of not only the patient, but of all members of the household. It also included questions on adherence to treatment, health of adults and children, anthropometric measurements, and quality of life. We will refer to these households in which there is one HIV/AIDS patient identified as “HIV households” in the rest of the paper.

A group of comparison households was included in the sample, in which there was no identified HIV/AIDS patients, to control for general trends in socio-economic circumstances. The comparison households were randomly selected in the neighborhood of the HIV households of the sample.

6