rafmacalaba/data-use-mentions-extended huggingface dataset:
Column (3) uses the 'obstacle' variables as instruments. The estimate of the coefficient on uncertainty is again significant, lying over two standard errors below zero. It is very large in absolute value, indeed implausibly so. Nevertheless, a comparison with the OLS estimates provides further evidence that the effect of uncertainty is to reduce the likelihood that the governance structure is pure bilateralism. The Hansen _J_ -statistic provides no evidence that would lead one to question the exclusion restrictions for these three instruments, although it is wellknown that this statistic can have very low power. These are also weak instruments. However, if instruments satisfy the exclusion restriction but are weak, the estimated value of the coefficient can be expected to lie between the OLS estimate of the coefficient and its true value (Chao and Swanson 2005). Again, the comparison between the IV and OLS estimates (in columns (3) and (1)) suggests that the true coefficient is negative.

Column (4) uses all 4 instruments. Not surprisingly, given the use of the sector-region uncertainty averages, these are no longer weak instruments. For the same reason, the Hansen _J_ - statistic raises doubts about the exclusion restriction for this set of variables. The evidence in column (4), again, must be regarded as suggestive rather than definitive. The estimate of the coefficient on uncertainty is highly significant and nearly four standard errors below zero. It is twice as large in absolute value as the estimated OLS coefficient. The differences between the estimates in columns (4) and (1) is further evidence of a negative effect of uncertainty on the use of pure bilateralism.

It would strain credibility if we claimed that the results in Table 5 pin down the exact size of any coefficients. We are more confident that these results provide evidence for a negative sign of the uncertainty coefficient. As we have stated before, problems of endogeneity in estimating the determinants of inter-firm governance structures are legion and have hardly been definitively dealt 49
. A worker _ω_ is choosing between different transportation modes to travel from _n_ to _i_ . These transportation modes are grouped into different nests, for example public or private nests denoted by _G_ . Denote the set of transportation modes in _g_ , by Υ _g_ . The indirect utility of choosing transportation model _m ∈_ Υ _g ⊂G_ is:

> 40Holmes et al. (2014), Świ ̧ecki (2017), and Asturias et al. (2016) use a similar formula using hat algebra.

> 41Since this formula applies to the case in which the change in commuting/trade costs is infinitesimal, for the counterfactual analysis, I estimate and decompose the change in welfare using percentage changes and exact hat algebra.

> 42I calculated a weighted average of travel times across the different transportation modes using each transportation mode’s aggregate share for commuting and consumption from the travel survey data. Hence, in terms of workers’ utility, the assumption is that transportation modes’ preferences take a Cobb-Douglas form. This is a conservative assumption. For example, in the case of CES or random idiosyncratic shocks, workers will substitute more other modes of transportation for the subway after the transit shock.

25
with firm-level data, we would have information on the overall size or productivity of the firm, but we would lack information on the firm/product-specific ability. Finally, we have over four million observations for our regressions. For the analysis of the determinants of entry on export markets, firm-product-destination country data would hardly be tractable.

> 2The data are separately reported by firm type, including foreign-owned enterprises, Sino-foreign joint ventures, collective enterprises, private enterprises, and state-owned enterprises. The first two categories are considered foreign firms, and the other categories are considered domestic firms. Unreported regressions, available upon request, show that the results hold when restricting domestic firms to state-owned or private firms. In addition, the foreign export spillovers appear to emanate similarly from both fully-foreign and joint-venture

> 3The data also refer to a third category (“Others”) that groups other flows, such as aid, border trade, and consignment, together representing less than 1% of the total trade value per year. When considering the processing/ordinary trade distinction, this category is dropped. 7
, A., Testa, M. R., & Vignoli, D. (2015). Environmental aspects of internal migration in Tanzania.</mark> _<mark>Population and Environment</mark>_ <mark>,</mark> _<mark>37</mark>_ <mark>(1), 99-108.</mark> **[1]** <mark>Ortiz-Bobea, A. (2013). Is weather really additive in agricultural production.</mark> _<mark>Implications for climate change impacts Resources For the Future Discussion Papers</mark>_ <mark>.</mark> **<mark>[</mark> 3]** <mark>Overeem, A., R. Robinson, J. C., Leijnse, H., Steeneveld, G. J., P. Horn, B. K., & Uijlenhoet, R. (2013). Crowdsourcing urban air temperatures from smartphone battery temperatures.</mark> _<mark>Geophysical Research Letters</mark>_ <mark>,</mark> _<mark>40</mark>_ <mark>(15), 4081-4085.</mark> **<mark>[</mark> 3]** <mark>Pace, N., Sebastian, A., Daidone, S., Prifti, E., & Davis, B. (2022). Mediation analysis of the impact of the Zimbabwe Harmonized Social Cash Transfer Programme on food security and nutrition.</mark> _<mark>Food Policy</mark>_ <mark>,</mark> _<mark>106</mark>_ <mark>, 102190.</mark> **<mark>[</mark> 4]** <mark>Parnell, S., & Ruwani, W
same year, 10 percent of Mexican manufacturing firms were integrated to international markets via exports, 19 percent via imports, and 28 percent were importing intermediate inputs and exporting their final product (fully integrated).

4 By means of an explicit econometric model linking tariff reduction and household real income, De Hoyos (2005) finds that measuring NAFTA just as the reduction in tariff brought about by the agreement would lead to the conclusion that the agreement had almost no impact on real household incomes in the economy.

> 5Notice that this is not entirely true. For non-integrated firms to be completely isolated from direct linkages with foreign markets they would have to be part of a sector that does not suffer from import-competition and at the same time is not receiving FDI. Even using detailed data such as EIA, it is impossible to define if and to what degree a firm is in an “import-competing” sector. Hence the import competition channel will have an effect on integrated and non-integrated firms according to our definition. Nonetheless, a-priori, trade reform will have a smaller impact on non-integrated firms relatively to integrated firms.

9
Díaz-Serrano, Luis. 2010. “Do Legal Immigrants and Natives Compete in the Labour Market? Evidence from Catalonia.” _IZA_ Discussion Paper No. 4693.

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European Commission.
microeconomic survey data to assess the impact of cash and food aid assistance to refugees in camps on the local economy identified as host communities living in a 10 km radius from the camps. They find that each adult refugee receiving cash assistance increases the annual real income of host households by 205 to 253 USD, which is more than the value of cash assistance provided to refugees of 120—126 USD. They also find positive impacts on local trade and food aid, although the latter impact is smaller than the impact of cash assistance.

Therefore, both studies find a positive impact of refugees on household well-being of local communities, which is in line with the studies on Burundian and Rwandan refugees in Tanzania and the work on the Kakuma refugee camp in Kenya.

# IDPs in Sudan

The wars ravaging across Sub-Saharan Africa have led to the internal displacement of millions of people, particularly in countries such as Nigeria, Sudan, South-Sudan, the Democratic Republic of Congo and Somalia due to the prolonged nature of conflict in these countries. These populations are very difficult to study, or even count, because access to IDPs is limited due to war and restrictions are imposed by governments who often share responsibility for displacement. Population surveys are very scarce and published studies are rare. One exception is represented by two studies on internal displacement in the Sudanese region of Darfur, particularly the city of Nyala, a city that counted almost 3 m residents in 2010. This city became the epicenter of internal displacement in the Darfur region with up to 700,000 IDPs located in camps on the outskirts of the city or scattered around the city.

Alix-Garcia, Bartlett and Saah (2011) studied the impact of IDPs on local prices and find a significant association between the growth in IDPs and the rise in food prices. The impact varies across products, from an IDPs/prices elasticity of 0.4 for fava beans to 2.9 for wheat. This relation is found to be significant for key products like sorghum, wheat, fava beans and oil
forecast.<sup>5</sup> For global term spread, the dispersion is computed from the monthly Consensus Economics survey for each country of the G20 and aggregated using real GDP weights.<sup>6</sup>

- The balance of risks for each factor is captured by the skewness of its forecast distribution. A negative skewness indicates a balance of risks that is tilted to the downside. The skewness of the risk-neutral probability distributions of option price on S&P 500 returns as well as on Brent and WTI prices are approximated from the slope of their respective implied volatility curves, following the methodology of Mixon (2011).<sup>7</sup> For the term spread, the skewness is computed from the monthly Consensus Economics survey for each country of the G20 and aggregated using real GDP weights.

Several episodes of heightened uncertainty stand out from the analysis of these risk factors (Figure 1). The first one is the global financial crisis of 2008-09. Its unexpected severity was associated with financial market disruptions and a broad-based increase in volatility and risk aversion (Stock and Watson 2012; Allen, Bali, and Tang 2012). This was also reflected in the rising degree of uncertainty of all three risk factors. The second and third, albeit milder, episodes were around intensifications in the Euro Area sovereign debt crisis in 2011 and 2012, when financial market indicators also pointed towards a greater level of uncertainty surrounding global growth forecasts. Recent episodes of market stress, such as those associated with the Taper Tantrum in 2013, sharply declining oil prices since mid-2014 and the ongoing emerging market slowdown have also raised forecast uncertainty. Around these episodes, downside risks to growth have been more prevalent.

# **3. Risk indicators and global growth**

To characterize the evolution of uncertainty around global growth forecasts, a similar approach to that proposed by Blix and Sellin (1998) and Kannan and Elekdag (2009) is used. More specifically, changes in the degree of uncertainty (
, mostly from Far-western and Mid-western regions of Nepal, have been migrating to India to work as daily wage laborers or security guards or in restaurants in Indian cities. Because of frequent migration to India over a long period, there are well-established migration linkages between districts in Nepal and Indian cities that help newer migrants find work in India (Seddon, Adhikari, and Gurung, 2002).

Because of the open border and historical linkages, India serves as the low cost and low return destination for Nepali migrants. A typical migrant worker to India pays Rs. 6,250 (USD 83) to migrate and find a job in India (Table 2). This amount is roughly over two months of per-capita consumption in Nepal. They earn, on average, Rs. 6,400 (USD 85) a month, of which they save almost two-thirds. The median migrant spends about 9 months in India per migration episode. This is consistent with the view that migration to India is seasonal and workers stay home during planting and harvest season and migrate at other times of the year. Almost a third of these migrants finance migration through their own savings while 60 percent take out a loan, mostly from village lenders and then from friends and relatives<sup>8</sup> .

On the contrary, migration outside India is a relatively recent phenomenon. Historically, people migrated to non-India destinations mostly through recruitment in the British Army. The numbers were small and directed to destinations like the UK, Hong-Kong, Singapore and Brunei. It was only in the late 1980s that Nepalis have started to migrate to other destinations for work. Foreign migration became easier after democracy was introduced in the country in 1990 and international travel was made easier and more systematic. Only in the mid-1990s, the

> 8Author’s calculations from Nepal Migration Survey 2009 and The World Bank (2011).

8
7

### 2. Predicting probability of severe COVID-19

<mark>To calculate the probability of severe COVID-19, we apply the odds ratios estimated in Ma et al to the proportion with the corresponding probability factor/comorbidity at the district level. lpl l(p p) = lpl(</mark> _<mark>2</mark>_ <mark>.</mark> _<mark>6</mark>_ <mark>)p p + lpl(</mark> _<mark>1</mark>_ <mark>.</mark> _<mark>7</mark>_ <mark>)p p + lpl(</mark> _<mark>2</mark>_ <mark>.</mark> _<mark>6</mark>_ <mark>)p</mark> _<mark>50</mark>_ <mark>+</mark> _<mark>intercept where psc = probability of severe COVID-19, pco = proportion with a comorbidity, psm =</mark>_ _<mark>where psc = probability of severe COVID-19, pco = proportion with a comorbidity, psm = proportion that smoke, p50 = proportion over 50.</mark>_ <mark>In order to apply this equation, we need the intercept value specific to Zimbabwe. This was set so that the mean district level probability of severe COVID was equal to that expected on the basis of the age-breakdown of the population. To estimate this expected probability of severe COVID, we applied the Imperial College London (ICL) clinical study age-specific estimates of probability of severe disease to the age breakdown data from the Zimbabwe census from 2012. This generated a mean probability of severe disease across the population in Zimbabwe of 2.9%. After testing a range of different intercepts, an intercept of -4.2 was chosen as this
products with measurement error, while products which do not result in a significant coefficient are the more accurate products. These results and the associated figures simply allow us to visualize the variability in the number of significant coefficients across these specifications of interest. And any variability in results is a sign that obfuscation/source combinations provide measures of weather and therefore measurement error exists.

# **4 Results**

Due to the large number of regressions and estimated values produced from our analysis, we do not present our results as one would for a traditional economics paper, with coefficients, standard errors, and stars signifying _p_ -values, all reported in tables corresponding to each regression. Instead, we present a series of figures, which allow us to evaluate the significance and magnitude, per our heuristics, of the effect of various sources of measurement error in remote sensing weather data. We start by examining measurement error due to extraction methods used to preserve the privacy of households. We then examine measurement error in relation to the choice of weather metrics to measure precipitation and temperature. Finally, we investigate the degree to which specific remote 20
18-65]|-0.062***|0.004|0.066***|
||(0.016)|(0.005)|(0.015)|
|Formal employment [18-65]|-0.024*|-0.035***|-0.011|
||(0.014)|(0.006)|(0.014)|
|Unemployment [18-65]|0.014***|0.013***|-0.001|
||(0.003)|(0.002)|(0.003)|
|Health insurance|-0.178***|-0.085***|0.092***|
||(0.020)|(0.010)|(0.017)|
|Number of disabilities|0.088***|0.033***|-0.055***|
||(0.012)|(0.004)|(0.012)|
|Teen pregnancy [14-19]|0.058***|0.002|-0.056***|
||(0.015)|(0.005)|(0.014)|

_Source_ : REDATAM INDEC Census Argentina 2010. _Notes_ : SE clustered at department level in parenthesis. * _p <_ 0.10, ** _p <_ 0.05, *** _p <_ 0.01.

# **3.3 The role of education in explaining differences in outcomes**

Given that the level of education is an important determinant of socioeconomic outcomes (see, for example, Angrist and Krueger, 1991; Lleras-Muney, 2005), we study whether the differences in these outcomes disappear once we control for the level of education (we
. Since the training is typically conducted in the informal sector, they are also potentially better placed to prepare youth to transition into that sector, which accounts for about the majority of employment opportunities in many African countries (Filmer & Fox, 2014).<sup>3</sup> . However, there

> 1 For example, data from the nationally representative 2014 Ghana Demographic and Health Survey (DHS) show that almost 70 percent of primary-age students were attending school, while only 38 percent of secondary-age students were attending secondary school (Ghana Statistical Service (GSS), Ghana Health Service (GHS), and ICF International, 2014). The financial constraints in many African countries, including Ghana, will be less binding in the near future due to the introduction of free secondary schooling.

> 2 Previous program evaluations have focused extensively on formal vocational training programs, with limited research on apprenticeship programs. These studies have shown mixed results on the effectiveness of training programs in increasing youth employment. In his literature review, McKenzie (2017) finds that the estimated treatment effects from training programs range from no change to an eight percent increase.

> 3 For example, 85 percent of jobs in Ghana are in the informal sector (Honorati & Johansson de Silva, 2016).

1
income countries. To minimize the role of measurement error, we exclude households in the top and bottom 0.5% of the status quo expenditure distribution in all our analyses. All statistics derived from household surveys presented in the remainder of the paper are weighted using survey weights. For the relatively few surveys for which survey weights are not available, we simply assume each household has the same weight.

Expenditure shares are reported in Table 2. We show averages for six major expenditure aggregates, namely Staple Agriculture, Non-Staple Agriculture, Manufactured Goods, Non-Traded Goods, Other Goods, and Home Consumption. Expenditure on food is the dominant expenditure category, accounting on average for 45 percent of all household spending across countries, which is not surprising since the bulk of countries in our sample are low income countries with an average poverty rate of 35 percent (using national poverty lines) and an average GDP per capita of US$ 1879. This focus on poor countries also helps explain why home consumption is important, accounting for an average budget share of 17 percent across countries and for more than a third of all expenditure in Ethiopia, Madagascar, Mali and Uzbekistan. Spending on manufacturing goods on average accounts for 17 percent of overall household expenditure, and spending on non-tradables accounts for 15 percent.

Average income shares for staple Agriculture income, Non-Staple Agricultural income, Wages, Family Enterprise Income, Other income, and Own Home Production are reported in Table 3. Wage income is the single most important source of income, accounting on average for 29 percent of household income across countries, and for 39 percent if we weigh countries by their GDP, suggesting that the importance of this source of income increases with development. The value of autoconsumption accounts for 23 percent of household income. Profits from running farms and other family businesses account for 17 and 13 percent of household income, respectively. These averages hide important heterogeneity across countries, which reflects differences in structural features of their economies and heterogeneity in survey design (including coverage of different sources of incomes
.05||
|<br>If yes, credit 5-7-9||0.03|0.00|0.00|0.04||0.01||
|<br>If yes, fertilizer||0.87|0.57|0.78|0.91||0.89||
|<br>If yes, seed||0.33|0.29|0.34|0.34||0.46||
|<br>If yes, other||0.11|0.24|0.08|0.11||0.03||
|<br>No. of obs.||514|110|79|325||345||

_Source:_ Own computation based on PSG records. _Note:_ The types of support other than PSG was a multi response question and hence they do not add up to one, and the other category includes cash grant. Other purposes for which SAR was used include checking information on land parcels or prices. _Δ_ Sig is the result of a t test for _ΔPSG_ - _ΔNon-PSG_ = 0 where _ΔPSG_ and _ΔNon-PSG_ is the difference between values in 2023 and 2022, for PSG participants and eligible non-participants, respectively.

19