rafmacalaba/data-use-mentions-extended huggingface dataset:
profitable returns to farmers, our estimate of nitrogen use efficiency implies that the net value of output produced from incremental fertilizer does not exceed the cost of the NAIVS program per farmer.

Goyal and Nash (2017) suggest that the potential for positive impacts of ISPs on fertilizer use is greatest when they are administered in areas where the private sector has been inactive and among households that cannot afford fertilizer at commercial prices (Jayne et al. 2013; Mason and Jayne 2013; Mather and Jayne 2015; Ricker-Gilbert et al. 2011). In contrast, we study the impact of NAIVS in a region where the private sector was already active and the program targeted households that were already purchasing fertilizers at commercial prices.

Corral et al. (in preparation) in Mexico and, in particular, Harou et al. (in preparation) using data from Tanzania provide another explanation for the low use of fertilizer. They provide plot-specific soil testing and find evidence of important within-village variation in soil nutrient deficiencies. As a result, the blanket NAIVS fertilizer recommendations may not be relevant for the majority of farmers in their sample given the diversity in soil nutrient limitations.

Columns 1 to 3 of Table 8 report welfare outcomes using the full sample. The dependent variable in column 1 is the share of the agricultural production sold to the market, column 2 reports the log of household income and column 3 reports the principal component analysis (PCA) of answers to five questions related to food security.

Column 1 shows that beneficiaries in lottery villages sell 19 percentage points more of their production compared to non-beneficiaries, and in non-lottery villages, beneficiaries sell approximately 25 percentage points more. Interestingly, non-beneficiaries in lottery villages report selling higher share of their output too (compared to non-beneficiaries in non-lottery villages) although the increase is only marginally significant (p-value is 0.128). While there are no differences in household income (column 2), beneficiaries in non-lottery villages report higher food security than non-beneficiaries, but there are no statistically significant differences between beneficiaries and non-beneficiaries
# _4.1.2 Demographics and the labor force_

As shown in Graph 8, registered Syrian refugees in neighboring countries numbered 5,663,675 at the end of 2018. A survey by UNCHR (2018) of a sample of 4,283 Syrian refugees in neighboring countries indicated that 76 percent intend to return to Syria one day. In the optimistic scenario of the voluntary mobility case, if we assume a 76 percent return rate, this would amount to 4.3 million returnees. We use the UN population statistics (United Nations 2017), which projects an average population growth of 2.5% over 2019‐2038 and builds in net migration into Syria of 4.21 million over 2020‐2035. Of this total, 66 percent return over 2020‐2025 (about 556,000 on average per year), 29 percent over 2025‐ 2030 (about 240,000 per year) and 5 percent over 2030‐2035 (about 46,000 per year).<sup>48,49</sup> For the moderate and pessimistic scenarios in the voluntary mobility case, we assume refugee return rates of 43 percent and 18 percent respectively based on the ratio of reconstruction‐related average funding per year in the first 10 years post conflict. For both these moderate and pessimistic scenarios, we still assume a similar time path as the optimistic scenario – with refugee returns rising to peak around 2023‐2024, and gradually moderating thereafter. Consequently, we obtain average population growth rates of 2.2 percent and 1.9 percent respectively over 2019‐2038 for the moderate and pessimistic scenarios. For all three scenarios in the forced repatriation case, we adjust the return rate to 100 percent (but keep the pattern of returns unchanged over time), thus giving us an average population growth rate of 2.7 percent over 2019‐2038.

We continue to draw on United Nations (2017) for projections of the working‐age population share, and ILO (2018)
Figure 4. Fuzzy RD Plots with Fitted Local-linear Polynomial <!-- Start of picture text --> Well-being (Index)<br>-200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 1<br>Rights and Services (Index)<br>-200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 1<br>Labor Outcomes (Index)<br>-200 -150 -100 -50 0 50 100 150 200<br>Arrival (days from June 8, 2018)<br>Sample average within bin Polynomial fit of order 1<br>.6<br>.5<br>.4<br>.3<br>.2<br>Well-being (Index) .1<br>0<br>-.1<br>4<br>3.5<br>3<br>2.5<br>2<br>1.5<br>1<br>Rights and Services (Index)<br>.5<br>0<br>1.5<br>1<br>.5<br>0<br>Labor Outcomes (Index)<br>-.5<br><!-- End of picture text --> _Notes:_ Each graph depicts the bias-corrected estimator 200 days around June 8, 2018, when the RAMV census closed, and the mean squared error (MSERD) optimal bandwidths. Confidence intervals are at the 95% significance level.

41
DNK RUS (2009)<br>DEU MEX (2006)<br>ITA FIN<br>NOR SWE (2009)<br>CZE DNK<br>SVN (2009) NOR<br>SWE (2009) NLD<br>POL (2008) PRT<br>IRL (2009) Energy CZE<br>SVK POL (2008)<br>PRT BEL (2009)<br>LUX Environment AUT<br>GRC (2008) NZL (2008)<br>BEL (2009) IRL (2009)<br>AUT GRC (2008)<br>GBR (2009) HUN<br>HUN CHE (2008)<br>NLD SVN (2009)<br>ISL (2009) SVK<br>USA (2009) ISR<br>RUS (2009) EST (2009)<br>ISR LUX<br>CHE (2008) ISL (2009)<br>0 1000 2000 3000 4000<br>0% 5% 10%<br>Source: OECD, Research and Development Database, May 2011. Note: Government budget appropriations or<br>outlays for R&D measures the funds committed by the federal/central government for R&D. It can be broken<br>down by various socio-economic objectives, defined on the basis of the primary purpose of the funder, including<br>control and care for the environment as well as energy.<br><!-- End of picture text --> 21 To the extent that the figures in Box 4 are comparable, Brazil with green R&D expenditures of $300 million would be between the Russian Federation and Mexico.

18
**Figure 10. Percentage Rates of Backward and Forward Integration to Manufacturing GVCs by Sources and Destination of Imports and Exports of Intermediate Inputs: Group of Benchmark Countries (1995 vs. 2015**

<!-- Start of picture text --> 100<br>90<br>85.3<br>80<br>76<br>70 67.6<br>60<br>50 52.5<br>40 43.1<br>30 32.6<br>20<br>10<br>0<br>-10 West Europe East Asia China India USA Germany<br>-20<br>Backward Integration (2015) Forward Integration (2015)<br>Δ GVC (2015/1995) Δ Backward Integration (2015/1995)<br>Δ Forward Integration (2015/1995) GVC (2015)<br><!-- End of picture text --> _Source_ : Authors computation using data from EORA database.

**Figure 11. Percentage Rates of Backward and Forward Integration to Manufacturing GVCs by Sources and Destination of Imports and Exports of Intermediate Inputs: Africa, Non-Resource Rich Group of Countries (1995 vs. 2015)**

<!-- Start of picture text --> 100<br>90<br>80<br>70 72<br>60<br>50<br>40 41.7<br>34.2<br>30 30.5<br>20 18.1<br>10<br>0 2.6<br>-10 West Europe East Asia China India USA Germany<br>-20<br>Backward Integration (2015) Forward Integration (2015)<br>Δ GVC (2015/1995) Δ Backward Integration (2015/1995)<br>Δ Forward Integration (2015/1995) GVC (2015)<br><!--
This finding implies the existence of a trade-off: while financial deepening plays a vital role in stimulating economic growth, it can also contribute to higher CO2 emissions per dollar of GDP. Importantly, we find evidence that some dimensions of a country’s institutional environment mitigate this trade-off. Specifically, more robust environmental regulation, stronger rule of law, and a financial system that is relatively more market- than bank-based have helped countries mitigate the unconditional adverse effect of financial deepening on CO2 emissions per dollar of GDP. We find no significant role of foreign bank ownership in managing the trade-off. Moreover, our results show that countries with an initially lower carbon intensity of production can mitigate the effect of financial deepening on CO2 emissions per dollar of GDP by improving their general institutional environment as proxied by the rule of law index. There is also somewhat more limited evidence that having a relatively more market-based financial system can help mitigate the potential trade-off. By contrast, countries with an initially higher carbon intensity of production could manage the trade-off from financial deepening by improving their environmental regulations. Our results hold up in robustness checks, including using an alternative measure of financial deepening, restricting the sample to episodes of credit booms, addressing the Nickell bias and cross-sectional dependence, and using alternative empirical specifications.

The remainder of the paper is organized as follows. Section 2 describes the data and presents some stylized facts. Section 3 outlines the empirical methodology, while section 4 discusses the results. Section 5 includes several robustness checks. Section 6 concludes the paper.

# **2. Data and stylized facts**

- **2.1. Data**

We use an unbalanced panel dataset of 125 advanced and emerging economies covering 19902019.<sup>1</sup> The frequency of the data is annual with an average time span of 20 years per country. The list of countries included in our sample is reported in Table A.1 in the Appendix. We aim to include all countries for which data is available so that we
# **1. Introduction**

The COVID-19 pandemic has caused unprecedented global employment losses of 114 million jobs worldwide in 2020 compared to the previous year (ILO, 2021). Kenya, as well as other parts of the world, has been strongly affected as restrictions on personal mobility have severely disrupted economic activities.<sup>2</sup> Although losses have been alleviated through welfare support programs, hard-hit groups remain: children, youth, women, low-paid and low-skilled workers (FAO, 2020; Hill & Narayan, 2020; Chetty, Friedman, Hendren, Stepner, & Opportunity Insights Team, 2020; ILO, 2021; Josephson, Kilic, & Michler, 2020).

Displaced populations are particularly vulnerable as they arrive in host destinations without initial assets or established business connections and often lack access to official documentation to establish themselves in formal markets or access formal financing. Refugees living in Kenya face working and movement restrictions which severely impact their ability to participate in the labor market and limit their livelihood opportunities (Zetter & Ruaudel, 2016). Refugees were experiencing high levels of poverty, food insecurity as well as low school attendance and labor market participation rates.<sup>3</sup> Yet despite their large numbers and contributions to the local economy, quality data on refugee communities is scarce.<sup>4</sup> In particular, while qualitative assessments or cross-sectional quantitative analyses have been conducted in the past (MacPherson & Sterck, 2021; Sterck & Delius, 2020; Alix-Garcia, Walker, Bartlett, Onder, & Sanghi, 2018), reliable longitudinal studies on refugee job market outcomes are not readily available, severely restricting the ability to assess the impact of a crisis like COVID-19 on refugees.

This study uses the Kenya COVID-19 Rapid Response Phone Survey (RRPS) to investigate the socioeconomic implications of the COVID-19 pandemic on labor market outcomes of urban national and refugee communities in Kenya. Refugees in Kenya live in densely populated refugee camps inhabiting more than 200,000 inhabitants and
> Of the students who did not continue education in the initial months (3 percent), the major reasons cited were irregular power supply, unavailability of TV or internet at home, and unavailability of the TV channels that broadcast lessons.

Furthermore, the Government of Uzbekistan provided strong support to schools and teachers to ensure continuity in learning. According to the Responses to Education Disruption Survey (REDS) conducted by IEA in 2021, a large majority of teachers (around 95 percent) felt supported by national and provincial education authorities (UNESCO 2022). This percentage is the highest among all countries that participated in REDS and participant countries included upper-middleand high-income countries like Denmark, the Russian Federation and Slovenia. Additionally, 91 percent of teachers engaged in remote teaching reported that their school provided the office infrastructure to assist with teaching from home (highest percentage among participating countries). The majority of schools reported providing internet access for some or all students (85 percent), digital devices for some or all students (61 percent), and virtual learning environment or learning management system (79 percent). These percentages are significantly higher than other lower-middle income countries and closer to the results of upper middle- and high-income countries. Similarly, more than 80 percent of students reported receiving individual or group feedback from their teachers on all or almost all of their schoolwork (the highest percentage among countries participating in REDS) and a majority communicated with school staff and teachers regularly. Apart from feedback, the highest percentage of students (greater than 90 percent) reported receiving support from teachers in terms of interest, encouragement and adaptation of schoolwork to individual needs. The results from the REDS support the hypothesis that the national

> 1 UNICEF Rapid Assessment – telephone survey: June – July 2020, SISQE-UNICEF online poll (through SISQE web portal) – June – July 2020, U-Report Poll (SMS, Facebook, Telegram, App for smartphones): May 2020, Listening to the Citizens of Uzbekistan – May 2020.

> 2 There are slight variations in
sup>65</sup> . Findings to date include

- (a) For an 87 country sample, increased information transparency is associated with lower average inflation rates (at least for countries without an exchange rate peg – where the issue is much less relevant. This result is cet par on a number of macro-economic and institutional variables.);  and

- (b) For a 63 country sample, increased information transparency is associated with lower variability of an inflation-output index.

Both the studies cited use the Fry et al index to measure information transparency.

> 63

> See Fracasso, Genberg and Wyplosz (2003), p.33 and Table 4.2.

> 64 See discussion by Fracasso, Genberg and Wyplosz op cit, p36.

> 65

> See Geraats op cit, p. F559 and her discussion of the studies (a) by Chortareas et al (2001) and

> (b) by Cecchetti and Krause.

50
Table 8: Short-term impacts of _Mais Educação_ on educational outcomes for schools that joined in 2010

|Dependent variable|Dropout rates|Test score<br>mathematics|Test score Portuguese|
|---|---|---|---|
||(1)|(2)|(3)|
||Panel A: Impacts on fifth-gr|ade variables||
|Dummy_Mais Educação_*|year 2011<br>0.064|–1.431**|–0.676|
||(0.164)|(0.710)|(0.653)|
|_R_ <sup>2</sup>|0.749|0.864|0.850|
|Observations|7,536|7,536|7,536|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|
||Panel B: Impacts on ninth-gr|ade variables||
|Dummy_Mais Educação_*|year 2011<br>–0.238|–0.682|–0.775|
||(0.290)|(0.727)|(0.781)|
|_R_ <sup>2</sup>|0.796|0.847|0.804|
|Observations|6,886|6,886|6,886|
|Dummy year 2011|Yes|Yes|Yes|
|School fixed effects|Yes|Yes|Yes|

_Sources:_ Authors’ calculations using MEC/FNDE administrative data, _Prova Brasil,_ School Census, and INEP.

_Note:_ Treatment group are schools that joined _Mais Educação_ in 2010 and remained in the program until 2011. This table reports coefficients from the ordinary least squares estimation of equation (5) in the paper. The dependent variable is in column (1) is dropout rates, in column (2)
payouts, based on a long span of historical rainfall data, combined with contract specifications from a previous paper by us. We also present information on actual payouts on policies sold in recent years, again based on administrative data generously provided to us by BASIX.

# 6.1 Putative historical distribution

In a previous paper, Giné et al. (2007), we use approximately three decades of daily historical rainfall data to estimate a putative distribution of insurance returns, based on 11 different contracts offered to farmers in Andhra Pradesh in 2006. We estimate payouts for each year of our rainfall data, assuming the 2006 contracts had been available during that year. Each of the 11 contracts we study is linked to rainfall data from the Indian Meteorological Department (IMD). The history of past rainfall data also comes from the IMD.

The estimated distribution of returns is presented in Figure 6.6, below. The x-axis for the graph is “payout rank,” which ranks payouts in increasing order of size, expressed on a scale from 0 to 1. Figure 6.6 plots payout amount against payout rank. (The minimum payout is zero, the maximum payout is 1,000 rupees.) The calculated distribution presented in the figure suggests that returns on the rainfall insurance are highly skewed. The payout is zero up to a payout rank of 0.89 (i.e. the 89th percentile), indicating that an indemnity is paid in only 11% of phases. The 95th percentile of payouts is around 200 rupees. In about 1% of phases, the insurance pays an indemnity of 1,000 rupees, which is the maximum payout for each of these 11 contracts. Thus, the policies appear primarily to insure against extreme tail events, with around half of the value of indemnities being generated by the highest-paying 2% of phases. [Figure 6.6 here] Giné et al. (2007) also calculate the ratio of expected payouts on rainfall insurance relative to premiums. We estimate
or no

> 24 The classification of employment is based on the response to a question “for whom has [name] worked?” in the VHLSS 2004: those who responded to work in “foreign-invested sector”, “state”, “collective economic sector” and “private economic sector” are classified as “formal”; “informal wage” workers are assumed to be represented by those who responded to “work for other households”; “self-employed” includes those who answered “selfemployment” and “working in his family owned company”. For 2002, based on the VHLSS 2002, those who responded to work for “government agencies, police, military”, “communist party, social organization”, “state owned enterprises”, “other state economic sector”, “collective economic sector”, “private capital economic sector”, “state capitalist economic sector” and “foreign shared enterprise” are referred to “formal”; “informal wage” and “self-employed” are represented those who responded to be ”self-employed” and “work for other households” respectively.

> 25 The hours worked per week are computed by combining the hours worked for the first and second jobs.

15
**Similarly, the number of emigrants over the population in the state of origin (emigration rates) ranges from 1.7 to 6.4 percent.** Mexico City, Campeche, Guerrero, Tabasco, and Quintana Roo show the highest internal emigration rates (6.4, 6.0, 5.7, 5.3, and 5.3, respectively). The lowest emigration rates are observed in Guanajuato (1.7 percent), Nuevo León (1.9 percent), and Yucatán (1.9 percent). Furthermore, there are significant gender gaps in internal emigration rates in some states, especially Mexico City, Colima, and Quintana Roo (Figure 6).

Figure 6. Mexico City, Campeche, Guerrero, Quintana Roo, and Tabasco are the states that account for the highest emigration rates _Percentage of internal migrants by state of origin (emigration rates), 2020_ <!-- Start of picture text --> 8.0 Men Women All<br>7.0<br>6.0 6.46.05.75.3 5.3<br>5.0<br>4.0<br>3.0<br>1.9 1.8 1.7<br>2.0<br>1.0<br>0.0<br>States of origin<br>Percentage<br>CDMX CAM GRO TAB QROO VER BCS COL SIN NAY MOR OAX ZAC DGO TAM CHS MEX HGO QRO BC MICH PUE SLP TLA SON COA JAL AGC CHH YUC NL GTO<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020. Abbreviation codes in Annex 1.

**There is a high correlation between net internal migration flows and labor market outcomes.** Figure 7 displays the net internal migration balance (i.e., immigration minus emigration flows) in 2020.
**Table 1:  Data used for the analysis**

|**Region**|**Country**|**Slowdown in**<br>**GDPgrowth**|**Source**|**Period**|**Frequency**|**Note**|
|---|---|---|---|---|---|---|
||Argentina|-6.9|Household survey (EPH)|2006 Q4 -<br>2009 Q2|Quarterly|-Urban only<br>-rotational panel<br>-self employed and<br>family worker combined<br>|
||Brazil|-4.7|Monthly Survey of<br>Employment (PME)|1/2006 -<br>8/2009|Monthly|-Urban only<br>-Rotational panel<br>-No agricultural sector|
|**LAC**|Chile|-5.7|National survey of<br>Employment (ENE)|2006 Q1 -<br>2009 Q3|Quarterly|-Urban only<br>-No earnings info|
||Costa Rica|-5.4|Multi –purpose<br>Household survey (EHPM)|2006-2009|Annually||
||Mexico|-11.2|National Survey of<br>Occupation and<br>Employment (ENOE)|2007 Q1 -<br>2009 Q1|Quarterly|-Rotational panel|
|**EAP**|Indonesia|-1.7|Indonesian Labor force<br>Survey (Sakernas)|Feb 2006 -<br>Feb 2009|Biannually|-Earnings only for wage<br>and salary workers|
||Philippines|-4.5|Philippines Labor force<br>Survey|2006 -<br>2009|Biannually|-
8 This pattern of rural‐urban disparity is repeated in countless national studies using national poverty lines. There is far less evidence, however, on such a “poverty gradient” between secondary towns and cities. One of the reasons is that sample sizes in household surveys are not large enough to permit such disaggregation within the urban population. In the best known study in this area, Ferre, Ferreira and Lanjouw (2012) use small area poverty estimation methods to examine the poverty gradient, and to assess whether there is in fact a “metropolitan bias” in eight countries: Albania, Brazil, Kazakhstan, Kenya, Mexico, Morocco, Thailand and Sri Lanka. They analyze the gradient for both money metric poverty measures and data on access to public services, and find that: “In all cases, poverty is lowest and service availability is greatest in the largest cities – precisely those where governments, the middle‐classes, opinion‐makers and airports are disproportionately located.” (Ferre, Ferreira and Lanjouw, 2012, p. 353) Leaving to one side the theory implicit in the above statement, which they characterize as “metropolitan bias” to contrast with Lipton’s (1977) “urban bias”, there is still the dynamic question to answer. Levels of poverty may be lower in areas of high urbanization, but what is the role of urban agglomeration in poverty reduction? Ravallion, Chen and Sangraula (2007) conduct some cross‐country regression analysis to try and answer this question, using both the incidence of poverty and a poverty gap measure: “...we regressed urban and rural poverty rates on the urban population share including additive fixed effects (a dummy variable for each region or country): that is, the mean level of poverty at a given urban population share is allowed to vary by region or country....Both poverty measures tend to decline as the urban population share rises, although the effects are generally smaller (but more significant) for the
Rayons of Republican Subordination (RRS) were severely affected by the war and the accompanying terror, including assassinations, hostage-taking, rapes, murders and robberies.<sup>3</sup> The government was unable to contain the conflict independently and negotiated for outside political and military assistance, provided by Russia and Uzbekistan from 1992 to 1999. Some regions in Tajikistan, such as Khatlon, the Regions of Republican Subordination (RRS) and the country capital Dushanbe, were greatly affected by the conflict, while other

> 1 This section heavily relies on the description of the Tajik armed conflict provided in Shemyakina (2007). 2 University of Uppsala Conflict Database. (Accessed: April 2010.) 3

> Based on the “Vechernii Dushanbe” and “Narodnaya Gazeta” news material for 1991-1999.

7