rafmacalaba/data-use-mentions-extended huggingface dataset:
elements of national welfare are used to calculate the contribution of agricultural development to national welfare.<sup>1</sup> The calculations of marginal welfare effects obtained in this paper suggest that agricultural development has had important positive effects on national welfare, especially in developing countries. Latin American and Caribbean (LAC) countries have also benefited from agricultural growth, but non-agricultural production has had marginal welfare effects that are similar in magnitude to those provided by agricultural activities. In contrast, the industrialized, high-income countries experienced marginal welfare gains from non-agricultural activities that are much greater than those derived from agriculture.

These findings were derived from econometric analyses that rely on international data dating back to the 1960s in most analyses, depending on data availability. The required data come from publicly available sources from the World Bank (World Development Indicators), the Food and Agriculture Organization (FAO) of the United Nations and Dollar and Kraay’s (2002) database.<sup>2</sup> The econometric analyses deal with key estimation challenges. First, we pay close attention to the issue of causality using econometric techniques that help identify the direction of causality between agricultural development and the performance of non-agricultural activities in the sense of Granger (1969). The Granger causality tests were estimated with panel-data techniques that rely on sequential instrumental variables in the context of general method of moments (GMM) estimators to identify the impact of agricultural growth on the variables of interest (Arellano 2003). When appropriate, we compare GMM IV estimations with results

> 1 Johnston and Mellor (1961) list five contribution of agriculture to the process of development: increase the supply of food for domestic consumption, release labor for industrialized employment, enlarge the size of the market for industrial output, increase of domestic savings, and earn foreign exchange.

> 2 See Appendix C for data definitions and data sources.

2
the instruments used with both the MENA and OECD datasets are valid. As to the strength of these instruments, we see that the values of the CDME statistics are higher than the respective critical values, thus rejecting the null hypothesis that the instruments used with both datasets are weak.

> 44 For details on the LIML method, see Rivers and Vuong (1988).

> 45 For more details on these characterizations, interested readers may refer to Stock and Yogo (2005).

25
0.228|0.656|0.596|0.678|

_Note:_ The table presents the results of estimating equation 1 for our outcomes related to the size of land used by producers for agricultural activity. Panel A shows the outcomes for the average plot managed by producers focused on commercial activities. Panel B does the same, but for the average plot managed by producers focused on subsistence activities. Column 1 uses as dependent variable the size of the land the producer owns. Column 2 uses the size of the total land the producer manages, which could also include rented land. Column 3 uses the area cultivated by the producer. Column 4 uses the share of the total area managed by the producer that the producer owns. Information in all columns comes from the Agricultural National Census of 2007. The unit of observation in all columns is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

35
wages by ten percentage points in urban areas. Bahar et al. (2021) provides evidences of asymmetrical effects of a large scale amnesty program that granted work permits to undocumented Venezuelans in Colombia that helped labor market outcomes of Venezuelan at the cost of Colombian workers. Morales and Pierola (2020) also finds heterogeneity in the impacts of Venezuelan refugees and migrants on native labor market outcomes in Peru with increasing likelihood of tertiary educated workers being employed in non-service sector and decreasing wage for secondary educated workers working in service sector.

The sudden influx of Venezuelan in 2018 in Brazil and their concentration in Roraima allows this paper to utilize regression discontinuity design and to identify the impact of the Venezuelan forced displacement as population shock and study its implication on the fiscal scenario and labor market outcomes.

# **4 Theory and Data**

The influx of displaced Venezuelans mainly through Roraima theoretically should create two types of shock - a population shock with a sudden increase in population of the state and an expenditure shock with host government or international community providing for housing, education and social assistance to the forcibly displaced. Annual data on population from IBGE (Brazilian Institute of Geography and Statistics) reveals that the population of Roraima jumped from about 522,636 in 2017 to about 576,668 in 2018, which amounts to about 10.3% increase in the population. Figure 4a, which shows the population in y-axis and the distance in years from 2018 as the x-axis, reveals that the growth rate of the population increased too, while population grew by about 5% between 2014 7
# **6. Results and discussion**

**_Overall effects_**

Table 1 shows results from our 2SLS estimation using, as a dependent variable, the two measures of livelihood diversification – diversification of activities as a primary and secondary occupation (Panel A), and the two measures of agricultural commercialization – sales of crop and livestock products (Panel B). The table also presents OLS estimates (with controls) for comparison. Both dependent variables and refugee inflow are Inverse Hyperbolic Sine (IHS) transformed, so the coefficients can be interpreted as elasticities.<sup>24</sup> The OLS estimate results are lower in magnitude than the 2SLS, indicating that the OLS estimate would underestimate the effect of refugee presence.<sup>25</sup> Here, we will discuss the estimates from the 2SLS.

The 2SLS results show statistically significant and intuitively signed coefficients for diversification of activities as a secondary occupation and sale of livestock products, both with and without controls. The estimates with and without controls are similar, showing the results are robust to the exclusion of controls.

Regarding livelihood diversification, we find that a 1% increase in refugee inflow leads to more than 1% (2.5 and 2.7% without and with controls, respectively) increase in diversification of activities as a secondary occupation. We find no effect on diversification of activities as a primary occupation across model specifications. Putting the results together suggests that refugee presence significantly increases availability of livelihood activities and households in host communities that pursue these activities as a secondary occupation (because of the positive elasticity effect on secondary occupation) while retaining their primary activity (because of the robust null effect on primary occupation). This can be attributed to the nature of primary and secondary activities in the study areas, where many individuals engage in activities that require skills or livelihood assets as a primary occupation rather than a secondary occupation. For instance, about 7.6% of the individuals who have a primary occupation engage in
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changing control: Women’s empowerment and targeted transfers,” _The Economic Journal_ , 128, F609–F639.

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- Baland
# **7. HOUSEHOLD DECISIONS SHAPE DEVELOPMENT OUTCOMES**

While Sudan’s formal institutions, informal institutions, and markets exert a great deal of influence over women’s and youth’s ability to accumulate human capital endowments and seek, obtain, and maintain employment, individuals’ positions in their households, for example, wife versus husband, their relations with their immediate and extended family members, and, critically, their ‘perceptions’ of these positions and relations may be equally important in shaping their economic opportunities. In this section, women’s and youth’s voices and bargaining power are discussed in the context of their ability or inability to engage in income-generating activities. This section draws heavily on the qualitative research that was conducted in Sudan between May 2019 and March 2020 employing KIIs and FGDs in Khartoum, Madani, and Nyala (details are provided in Annex 1).

## **Stifled personal and professional aspirations**

Decisions made at the household level are informed by the external context and household-level needs and preferences, and they have an impact on individual-level human capital accumulation, agency, and economic opportunities (World Bank 2012b). While the latest data show that a similar share of young women and young men enroll in tertiary education, the prevailing norm is that this is more important for men: more than a third (36 percent) of male Arab Barometer respondents believe that university education is more important for males than females, compared to almost a fourth (24 percent) of female respondents (Arab Barometer 2018). The decision as to whether a girl or a boy should drop out of school is generally decided at the household level. In other words, it is not just rural Sudanese girls and boys who are making decisions about their futures, but their families, especially their fathers. It is not just rural Sudanese girls whose personal and professional aspirations may go unrealized but those of many Sudanese youth as well.

When asked what they wanted to achieve in their adult lives (
38|0|1|0.202|0.147|(0.243)|
|Physical punishment is effective (%)|265|0.37|0.48|0|1|0.341|0.390|(0.413)|

_Notes:_ This table shows summary statistics of the main outcomes used in this paper. Panel A shows statistics of main outcomes, which were constructed as described in table A8 in the Appendix. Panel B presents statistics on measures of sensitive information using different sources (knowledge incorporation surveys, baseline, and follow up data). Construction of these sensitive items and data details are presented in table A9 in the Appendix. Differences in number of observations to estimate the consistence measures are due to availability of data in each survey. For example, 305 participants completed the midline survey and 358 has baseline information in our sample. However, to estimate consistence, we only have information in both samples for 207 respondents.

30
single subperiod of the survey year: the more expenditures are concentrated, the less significant is the distortion.

At the same time, by using nominal expenditure we avoid the distortion induced by the fact that the _temporal deflator may not correctly reflect the dynamics of relative prices_ during the survey year. A correct estimate of the price of commodity _j_ should be given by: j where CPIt is a temporal price index specific to commodity _j_ . If all prices increase at a uniform j h j h growth rate, then CPIt = CPIt and uvj (CPIt ) = uvj (CPIt). However, if the price of commodity _j_ j h h j increases more than the general price level, then CPIt > CPIt ⇒uvj (CPIt) > uvj (CPIt ) and this implies that by using the real consumption aggregate to calculate unit values _we overestimate the market price_ for commodity _j_ .

The general recommendation is that if _i)_ the dynamics of the general price level during the survey year is sustained, and _ii)_ the official temporal CPI can be assumed to capture the dynamics of the most important commodities consumed by the households, the analysts should use the strategy “CPI first, SPI next”, that is, first adjust the nominal consumption aggregate for intra-survey temporal price variation and, then use the adjusted expenditures to estimate a spatial price index that can be used to further adjust households’ expenditures.

# **6 An empirical illustration**

In this section, we illustrate the empirical implications of choosing a single index (SI) versus multiple index (MI) approach in spatial (and temporal) price adjustment, using data from the Islamic Republic of Iran’s 2019 Household Expenditures and Income Survey (HEIS).

# **6.1 Construction of single and multiple price indexes**

Similar to most expenditure surveys, the Islamic Republic of Iran’s HEIS only collects information on expenditure and quantities consumed for food items. Unit values obtained from reported food expenditures and quantities allow for the estimation
|ant at 5%<br>0.380|level ***<br>0.306|Significant at 1%<br>0.428|

_Note:_ * Significant at 10% level ** Significant at 5% level *** Significant at 1% level. t-statistics are in parentheses. Size is population size.

With a descriptive picture of the level of service export sophistication in the three European country groups, we will now look at the extent to which their level of sophistication is associated with their level of economic growth.

# **4 RESULTS FROM GROWTH REGRESSIONS**

We now turn to the analysis of the relationship between service export sophistication and growth, and in particular if there are any differences in how the level of sophistication affects growth in Europe compared to the rest of the world. Mishra, Lundstrom and Arnand (2011) analyses the relationship between _Service EXPY_ and economic growth through a number of estimations using fixed effect panels and GMM techniques. The data includes up to 103 countries over the time span of 1990-2007, and the result show a strong positive association between service sophistication and growth. The result stands given a number of different model specifications.<sup>9</sup> In this section we use the same approach but take a closer look at Europe.

The baseline empirical growth model consists, in addition to the variable of interest, of four determinants of economic growth - initial income level, rates of physical and human capital accumulation, trade openness and institutional quality. We use data on income per capita from WDI. To account for differences in human capital accumulation we use latest available data on years of schooling from Barro and Lee (2010). We use M2 (as percent of GDP) as a proxy for

> 9 Apart from the control variables presented in the base model, they control for Goods EXPY, test various time periods and country groupings and drop outlier such as India, and the result still holds.

16
# **7. Estimation of standard errors**

The field protocols used by BBS to collect the HIES 2016/17 data are based on the concept of terms. Each term consists of 20 days, the time needed for a team of two enumerators to cover an entire PSU (20 households). The 12-months of data collection are divided across 18 terms (18*20 days=360 days). As the 18 terms of data collection planned cannot be divided exactly into quarters, PSUs in terms 5 and 14 will need to be randomly split to avoid sampling bias so that each quarter corresponds to exactly 4.5 terms. This random split is conducted by randomly allocating PSUs in term 5 to quarters 1 and 2, and those in term 14 to quarters 3 and 4.

The HIES 2016/17 sample was designed so that each of the 132 sub-strata includes at least 4 PSUs during the entire four quarters of planned data collection. The allocation of PSUs was conducted in such a way that each quarter includes at least one PSU per sub-strata to ensure that each of the quarterly poverty estimates includes observations from each of the 132 substrata. This implicitly means, however, that standard errors for quarterly poverty rates and welfare statistics cannot be estimated for sub-strata that have only one PSU assigned. This is not an issue when estimating annual poverty rates or annual welfare statistics, as each of the sub-strata includes at least four PSUs, but this poses a challenge when trying to estimate standard errors for quarterly statistics.

To address this issue, we consider four alternative ways of estimating standard errors for quarterly poverty rates and welfare statistics based on the HIES 2016/17. In the first option, we propose to use the method called the “centered approach”. This method calculates the standard error for the sub-strata based on the distance from the grand mean rather than the substrata means. In the second method, we propose to use the “certainty method”. This method assumes that a sub-strata with a single PSU do
value approach of delaying irreversible investment decisions when one knows that new information will arrive over time Dixit et al. (1994). The climate change adaptation challenge appears to be a prime example. Future research should explore how the value of holding a real option affects the timing of firm irreversible investments in location specific capital.

# **3.2 Adapting to the Uncertainty of Extreme Rare Events**

There are two components of extreme weather uncertainty: (a) the incidence of uncertainty: regarding where, when, and whether an extreme weather event will occur, and (b) the impact of uncertainty: how severe is the impact of the weather event’s effect conditional on an event occurring.

Business-related uncertainty is known to affect firms (Bernanke, 1983), however, the salience of such uncertainties in the context of climate change are only now being recognized (Fabrizio, 2012; Dorsey, 2019). Uncertainty about future climate shocks affect investments in innovation and technologies as well as decisions regarding other aspects of production such as input choice, location of firm and the sourcing of materials (Jia et al., 2022).

How firms and investors respond to uncertainty related to climate change also has implications for adaptation. Firms’ expectations about future weather events or shocks may influence their decisions concerning sector level innovation as well as the choice of inputs, spatial sourcing of material inputs, location of production and so on which in turn affects their productivity. Yet, these dimensions have been examined mostly in response to actual climate changes rather than uncertainty per say. One exception to this is a study by (Huang et al., 2018) that uses Global Climate Risk Index to capture likelihood of losses from natural hazards at the country level, which was found to be associated with firms’ lower and more volatile earnings and cash flows. These firms also tend to hold more cash and pay less cash dividends, suggesting that more exposed firms tend to hedge more against cash flow volatility and illiquidity due to higher
The impact of ENTRE is heterogeneous, with significantly larger effects for students at higher risk of dropout, students in large schools, and boys. In particular, ITT (LATE) estimates show that the program reduced dropout by about 2.9 (6.1) percentage points for the students with the highest risk, 2.1 (4.6) percentage points for students in large schools, and approximately 1.9 (4.4) percentage points for boys.

Our multi-arm design sheds some light on the potential mechanisms driving the results. The results indicate that the bulk of the impact seems to be explained by the component providing information on _how_ to prevent dropout (as opposed to providing information on _who_ is at-risk of dropping out or small behavioral nudges to try and keep dropout prevention top-of-mind for principals). In a setting like Guatemala, where school actors have limited knowledge of and support on dropout prevention strategies, and school principals—who play a leading role in these initiatives— are usually regular teachers without any specific leadership or management preparation, the training and guidelines on simple strategies to prevent dropout appear to be highly impactful. This component was also the most intensive in terms of time and resources (representing approximately 92% of the cost of the program).

Self-reported data from school principals also helps to further unpack the mechanisms driving the results. This data shows that about two months after the training, principals in treated schools perceived higher returns to schooling, ranked dropout as a higher priority, and provided more targeted support to at-risk students. The impact on targeted support and prioritization are not only large in quantitative terms but also persist in the next academic year (with a smaller but still economically and statistically significant impact).

Finally, the results suggest that school actors have gender-biased perceptions of who is at risk of dropout (significantly underestimating the relative likelihood of dropout for girls) and that providing objective information on these probabilities seems to have corrected these biases (and distributed the benefits of the program more