rafmacalaba/data-use-mentions-extended huggingface dataset:
positive effects on the likelihood of school participation. For example, the Japan Fund for Poverty Reduction scholarship program in Cambodia, which awarded scholarships to girls from poor households who were in their last grade of primary school, increased the likelihood of school participation by 30 percentage points (Filmer and Schady 2008). Similarly, a CCT program in Turkey increased the likelihood of secondary school participation for girls from very poor households by 11 percentage points (Ahmed et al. 2007). And the Female Secondary School Assistance Project in Bangladesh, targeted to rural girls of secondary school age, increased the likelihood of girls’ school participation by 8 percent for each additional year of exposure to the program (Khandker, Pitt, and Fuwa 2003).

Similar evidence is available for Pakistan. For example, Cheema et al. (2016) examine the effects of a CCT program introduced in 2012 for beneficiaries of the Benazir Income Support Program (BISP). The CCT program benefit for households was conditioned on regular school attendance by children of primary school age. Using household survey data collected in 2016, and propensity score matching of BISP households in non-CCT program areas to BISP+CCT program households in CCT program areas, they find that the CCT program increased the likelihood of school participation.

The program that we study also has been the subject of earlier evaluations (Population Council 2007; Chaudhury and Parajuli 2010; Hasan 2010a, 2010b; Alam, Baez, and Del Carpio 2011), including some based on the same data that we use—annual rounds of government school censuses. In contrast to our study, however, these evaluations examine effects on girls’ education outcomes only in the early years of the program.

For example, using government school census data and a parametric triple-differences RD approach,<sup>3</sup> Chaudhury and Parajuli (2010) find that the program increased female

> 3 The triple-differences approach compares outcomes before and after program onset, between boys’ and girls’ middle schools (grades 6–8)
one hand, since 2010 urbanization and development in high-risk coastal zones has been occurring at rapid rates in many parts of the world. These trends are putting an increasing number of people in harm’s way. Human activities, such as ground water extraction, are exacerbating risks as land subsidence is increasing the extent and intensity of flooding. On the other hand, the increase in the estimated flood exposure headcount is methodological; the use of more accurate and higher resolution data, as well as the inclusion of pluvial flooding mean that this study captures flood risks more comprehensively than past studies.

In addition, the Global Assessment Report (UNDRR 2017) offers estimates of risks from five major natural hazards (including flood risks), though it focuses on monetary quantifications of disaster risks. Besides such global assessments, the recent availability of high-resolution flood and population maps has enabled countless local and national risk assessments, for instance in Tanzania, the United Kingdom, the United States, and Vietnam (Chakraborty et al. 2014; Erman et al. 2019; Fielding 2012; Braese et al. 2020).

# **Flood risks and poverty.**

Many analyses have explored the link between natural hazards – flood risks in particular – and poverty; a detailed review of this literature is offered by Hallegatte et al (2020). Studies at the country level illustrate the complex factors at play. For instance, Rentschler et al. (2020) show that people in coastal provinces in Vietnam are by far the most exposed to flood risks. In particular, the low-lying and densely populated Red River and Mekong Deltas display a concentration of people, economic activities, and assets. Coastal provinces also consistently have the lowest poverty rates in the countries. For instance, in Ho Chi Minh City, the country’s economic powerhouse, extreme poverty is nearly eradicated – and yet more people than anywhere else in the country live in flood hazard zones. In other words, high-income provinces and districts tend to be more exposed to flood risks
population, KGinf/<sup>θI</sup> , to capture the complementarity effect (as well as congestion effects) alluded to above:<sup>12</sup> PQ·IP/NGDP = IP[∆Ys/Ys-1, KGinf/Ys-1θI, ER·FP/NGDP, ER·FdebtTot/NGDP], (39) where total external debt, FdebtTot, is defined as FdebtTot = FdebtP + FdebtG. (40) Using equations (11) to (14), (24), (25), (29), (35), (37) and (39), it can be shown that the model imposes equality between private investment and private savings. The resulting equation, however, is not independent from the rest of the system. It can therefore be either eliminated or used for consistency checks.

# **7.  Poverty Analysis**

To link changes in consumption and poverty, and assess the effects of policy shocks on the poor, an attractive methodology from an operational standpoint is the procedure proposed by Agénor, Izquierdo and Fofack (2003), and further developed by Agénor, Chen, and Grimm (2004), in the context of the Integrated Macroeconomic Model for Poverty Analysis (IMMPA). Assuming that the focus is on consumption as a measure of poverty, applying this procedure would entail following five steps in the present case:

1.  From an existing household survey, extract the value of consumption (in current monetary units) for each household, and given the poverty line, calculate the initial poverty rate, using various standard indicators (such as the headcount index, the poverty gap, and so on).

> 12Pattillo, Poirson, and Ricci (2002, 2004) found a negative relationship between external debt and private capital formation in developing countries. See Agénor (2004 _a_ , Chapter 2) for a detailed review of the evidence. In line for instance with the results of Hermes and Lensink (2001)
unexplained wage _uwunagr_ for those who enter non-agriculture is calculated by taking the unexplained wage from agriculture and multiplying it by the ratio of the standard deviations of the residuals in the non-agricultural and agricultural sectors, respectively. For non-remunerated household members moving into non-agricultural sectors we simulate a residual.

In addition to labor income, we consider transfer and capital income as reported in the PNAD. Transfer income is scaled up or down with the GDP per capita growth rate and capital income with the change in the rental rate from the CGE model. The sum of all 25
Table 2: Estimates using the raw wage ratio

||_Homi_<br>_(1)_|_cides_<br>_(2)_|_Hospit_<br>_(3)_|_alizations_<br>_(4)_|_Rep_<br>_(5)_|_orts_<br>_(6)_|
|---|---|---|---|---|---|---|
|Raw F-M wage ratio|−.26|−.032|−.76|−.63|−1.1<sup>∗∗</sup>|−1.3<sup>∗∗</sup>|
||(.6)|(.6)|(.87)|(.72)|(.52)|(.5)|
|Controls|N|Y|N|Y|N|Y|
|Municipality FE|Y|Y|Y|Y|Y|Y|
|Year FE|Y|Y|Y|Y|Y|Y|
|Number of municipalities|841|841|841|841|841|841|

_Note:_ The outcome variables are in natural logs; 0.01 is added to each value. The control variables are the natural log of the population of women ages 15–49, the natural logs of male homicides, hospitalizations and reports, the number of households receiving cash transfers, divided by the population of women ages 15–49, the municipal per capita value of cash transfers, the ratio of females to males in secondary education completion, the share of the population ages 15– 49 that is female, the per capita number of health clinics, and the per capita number of hospital beds. Regressions are weighted by the average number of women ages 15–49 in 2011–16. FE = fixed
important prerequisite for designing appropriate political measures to deal with undeclared work. The differences between profiles of informal and formal workers might be country-specific, depending on social norms and corporate culture, on strictness or particular components of employment protection legislation, and on the sectoral composition of the economy. For instance, high firing costs and (relatively) high minimal wage are likely to push young workers disproportionally into informal sector. Lack of flexible working time arrangements in the formal sector might make informal work more attractive for students and married women. Booming construction (respectively, hospitality) sector likely increases proportion of males (respectively, females) among informal workers.

In Section 3.1 we compare (at the country and/or European region level) composition of formal and informal workers in terms of key personal characteristics and [main] job profile. Most of the time, we concentrate on employees with and without contracts. In order to have enough observations on this category for statistical inference at the country level, the ESS data which refer to 2004-2009 have been combined with the data of the Fourth European 22
# **_4.1 Key limitations of the LSMS-ISA data sets_**

Given the features of the LSMS-ISA program and based on our previous critical overview, we identify the following as the main limitation of LSMS-ISA data collection to empirically disentangle the climatemigration nexus: questionnaire design; sample size and statistical challenges; and respondents’ issues. In the rest of this section, we focus on each of these areas in turn.

**_Questionnaire design._** Although the LSMS-ISA questionnaires are usually highly standardized across surveys and countries, the information is sparse, and there are inconsistencies not just across countries, but also across different surveys within the same country. The lack of consistency in terms of the set of questions included to detect migration and/or define migrants is certainly the first element to be considered for a revision of the LSMS-ISA collection aimed at improving data collection on migration. It also explains why there are only a couple of cross-country investigations on the climate-migration links which uses LSMS-ISA, the analysis by Mueller et al. (2020) and the one by Di Falco et al. (2022). In both cases, due to comparable and precise information about migration not being available for all the countries, the studies have to rely on proxies to define different types of migrations. Specifically, Mueller and colleagues exploit questions regarding the absence of individuals during the follow-up survey to define temporary migration as whether an individual present at baseline reported migrating for at least one month in the previous twelve. Of course, without knowing, in most cases, either the destination or the reasons for migrating, they had to make a strong assumption on the equivalence between temporary absence from the household and outmigration. See below (subsection 4.2) for further discussion on this point. The study by Di Falco and colleagues focuses on long-term migration instead, and thus individuals are proxied as migrants if households report them to have left between two visits or waves of the survey and who were not observed to return to their household during the
f refers to the fiscal position.<sup>3</sup> Our measure of the fiscal position is the government debt-to-GDP ratio. While the literature has used a variety of measures, our choice is in line with theoretical models, where government debt is the modal state variable (as discussed below).<sup>4</sup> Since measures of the fiscal position are endogenous and move in tandem with the business cycle, we take lagged moving averages of our fiscal measures to control for business cycle effects.<sup>5</sup> The matrices determine the effects of structural shocks on the dynamics of endogenous variables in the VAR system. By conditioning the law of motion of the coefficients in ll these matrices on the fiscal position, as in Equation (2), we are allowing those effects to depend AA , ll= 1, ... , LL on the fiscal position. This scheme allows us to calculate impulse responses and hence estimates of fiscal multipliers conditional on a specific fiscal position.

As standard in the literature, we compute the cumulative fiscal multiplier at horizon T as the discounted cumulative change in output until horizon , as the discounted cumulative government consumption increases by one unit. That is, TT M ∑t <u>0</u> ∆g <u>g</u> , (3) ∑TT ∆g t= (1+rr)<sup>−tt</sup> tt TT llM ggllMMr (TT) = tt=0(1+rr)<sup>−tt</sup> tt where denotes the interest rate. We utilize the median short-term rate in the sample. From (3), the impact multiplier is obtained by setting rr and the long-run multiplier by setting T at an arbitrarily large number, which is taken to be (5 years). To calculate the fiscal multiplier using the coefficient estimates from the IPVAR, we first cumulate the discounted TT= 0 impulses of output
coverage, being available for approximately half of all rms. The coverage is similar for the self-employed and corporations. The ling of informative declarations is more concentrated than the coverage, meaning that an even small share of rms act as informants (results available upon request). Note that information reporters are slightly more likely to report their own costs rather than their own sales, as evidence by the fact that 54.3% of the D151 records represent sales records. Declaration D158 is similar to D151 in that sense, but has much lower coverage, given the specic nature of the transactions it covers (agricultural auctions). In our analysis, we thus use the sum of third-party information on sales/costs from D151 and D158. We henceforth refer to these reports as information reports by other rms or D151 reports.

Withholding by state institutions and nancial institutions, as reported in D150 and D153, has a

> 21Only the tax records for 2012-2014, and a small share of records for 2010 and 2011 have rm type indicators (selfemployed or corporation). During this period, we observe only a handful of rms switching rm type. We therefore use the 2010-2014 tax return data and the tax register to assign a rm type to the tax returns for 2006-2011. We drop returns for which we cannot determine the rm type with this strategy.

13
2013 but switch to group Y in 2014, we count them as members of group X in 2014 regardless of group X and Y’s treatment status. Our computation of total group size in 2014 is therefore not the equivalent of computing (size in the previous year + new members – dropouts), but instead (size in the previous year + new members + switchouts – dropouts – switchins), where switchouts are clients who left the group for another one, and switchins join the group from another one. In our group-level analysis, we follow the initial 2013 group composition and apply these definitions of dropouts and new members.

Second, we collect a multi-module household survey among OAF clients in our study sample in May–August 2013.<sup>20</sup> The instrument was designed to generate quantitative data on agricultural production, participation in agricultural extension services, perceptions of agricultural extension providers, and socioeconomic characteristics over the course of two visits.

# 3.2   Farmer characteristics

OAF clients in the sample areas took loans averaging $56 (RWF 38,641) in 2013. Female clients take significantly smaller loans, $48 on average for 2013, compared to $60 for men.

19 OAF FOs serve our study area. One FO is responsible for 12 farmer groups on average (minimum 2, maximum 18), a total of 134 clients (minimum 20, maximum 209), and are overseen by 5 FMs. FMs manage an average of 6 of the sampled FOs each (minimum 2, maximum 9).<sup>21</sup> Of the sampled households, 73% are male-headed. Household heads are on average 45 years old, and most (77%) have less than a primary school education. The household head is the OAF client in 86% of households. Compared to male OAF clients, female clients are

> 20 Fieldwork was implemented by an independent survey firm. 21

> Statistics come from OAF administrative records and refers to all OAF clients in
that the ex‐ post impact of shocks on consumption is a “reduced form” approach which evaluates the impact after households have used every strategy available to them to mitigate the shock (diversification, asset sales etc – see below for further discussion, as well as Porter, 2012). If such strategies include opting for low‐risk, low‐return activities (Dercon, 1996), then risk also carries an ex‐ante cost, which cannot be measured with this particular approach. However, this cost can be difficult to quantify and is very data intensive. It is also less compatible with a perspective of quantifying realized financial losses to be compensated ex‐post, if for example index insurance were offered to households.

Poverty, for the purpose of policy analysis, is most often conceptualized as a level of consumption or expenditure that is below some pre‐specified level that is considered the minimum for an acceptable standard of living (e.g. based on a basket of food to reach a calorific minimum plus other basic expenditures). Other outcomes such as ownership of household assets, or human capital have also been used in this context. Much poverty analysis up to the turn of the Millennium had been in a static context, but the World Development Report 2000/01 highlighted issues of risk and a hitherto neglected stochastic component of poverty, noting that poor people have low incomes, but also higher risks, and fewer resources to cope with shocks, should they hit.

> 3 The potential to use probabilistic catastrophe risk models outside of the context of insurance, has been recognized in recent years. This has resulted in developments such as the Pacific Risk Information System<sup>3</sup> and the CAPRA<sup>3</sup> Program which both apply the probabilistic catastrophe risk modelling framework developed by the insurance markets for disaster risk management. A similar framework has also been applied to estimate food security needs through the Africa RiskView platform. See http://pcrafi.sopac
and (ii) a reduction in demand due to adverse shocks to household incomes, increased economic anxiety and the associated increase in precautionary savings and reduced economic activities due to the spread of the pandemic. To understand the extent to which supply-side shocks and demandside responses drive our results, we use data on government responses to the pandemic to further inform our analysis. These data are collected on a real-time basis by the Assessment Capacities Project (ACAPS) and cover major travel restrictions, social distancing, and lockdown measures for 192 countries.<sup>7</sup> The data are compiled from various publicly available sources on the internet, including governments (official sites and embassies), media, United Nations agencies, and other organizations. Whenever available, the ACAPS prioritizes official/ governmental sources in collecting and assembling these data. Because each government measure is dated, we construct time-varying indicators of government responses, mainly social distancing and lockdown measures.

# **2.2 Economic Sectors, Services, and Associated Google Query Terms**

In addition to the relevance of search terms to demand in specific service categories, the quality of the information relayed by Google search data also depends on the comparability and representativeness of selected search terms. To make the search terms comparable across countries, more general terms such as "hotel" and "restaurant" are used to capture changes in demand for hotel and restaurant services. We use two search terms for each service category to enhance the robustness of the search query results. For instance, we measure temporal variations in demand for transport services, more specifically air travel, using "flight" and "airport" search terms. Slowdowns in the transport sector have ramifications for the tourism sector. Such spillovers, combined with social distancing and lockdown measures, could put a brake on tourist flows. We track the search terms "tourist" and "museum" to gauge changes in demand for tourism services. We use "mall" and "shopping" to measure changes in demand for the
17-21.

Fasani, Francesco and Jacopo Mazza (2020) “Being on the frontline? Immigrant workers in Europe and the COVID-19 pandemic”, _IZA Discussion Paper no. 13963._ IOM (2020) “Effects of COVID-19 on Migrants – Main Findings – Survey in Central America and Mexico”, June, - <u>https://kmhub.iom.int/sites/default/files/publicaciones/surveyeffects_of_covid 19_june_2020_final.pdf</u> [accessed January 22, 2021].

Motyl, Matt, Joshua Hart, Tom Pyszczynski, David Weised, Molly Maxfield and Angelika Siedel (2011) “Subtle priming of shared human experiences eliminates threat-induced negativity toward Arabs, immigrants, and peace-making”, _Journal of Experimental Social Psychology_ 47(6): 117984.

OECD (2020) “Managing international migration under COVID-19”, June 10 update. <u>http://www.oecd.org/coronavirus/policy-responses/managing-international-migration-undercovid-19-6e914d57/ [accessed January 22, 2021].</u> 20
# **VI. CONCLUSIONS**

Using manufacturing sector data, this paper examined the determinants of wages and productivity differentials in Mexico. We used both worker and firm characteristics in our model specification, to see whether the wage differentials were justified by the productivity differentials.

First, men are better paid relative to women because they are significantly more productive. However, their salary increase does not fully compensate for their higher productivity. There is no evidence of gender discrimination in Mexico since wage differentials are explained by equal or larger productivity differentials, though this is a topic for further research.

Second, the wage premium increased with additional years of schooling. Additional years of schooling were also shown to increase productivity. Wages associated with years of schooling do not, however, have similar significant positive effects: workers with upper secondary education were found to be 84 percent more productive (than the base case), and workers with higher education were found to be 282 percent more productive. Yet, they earn 44 percent and 137 percent more respectively. On average, the workers are not able to fully appropriate the productivity gains of education through wages. This issue deserves further research beyond the scope of this paper.

Third, workers with more experience are more productive in the same proportion that they earn more in medium and large firms, meaning they are fully compensated. On the other hand, workers in micro and small firms are paid more than what their productivity would merit. Those with more than 10 years of tenure are 50 percent less productive and earn only 20 percent less than those with less than three years of experience. Workers with three to 10 years of experience are equally productive, but earn 20 percent higher wages in micro and small firms. These findings should be investigated in more detail.

Fourth, both employees and employers benefited the most from external formal training. For both types of training, the productivity differential is larger than the wage differential, meaning that employers receive a higher proportion of the benefits of any 19
city|54.54|54.69|54.13|55.13|53.96|55.24|
|**Panel B: Area shares**|||||||
|Share cultivated area|0.774|0.803|0.694|0.804|0.704|0.902|
|Share grassland|0.198|0.170|0.275|0.168|0.264|0.086|
|Share forested area|0.021|0.021|0.022|0.021|0.023|0.009|
|Share built-up area|0.002|0.002|0.002|0.002|0.002|0.001|
|Share other land|0.005|0.005|0.007|0.005|0.007|0.002|
|**Panel C: Soil indicators**|||||||
|Bulk density (g/cm^3)|1.310|1.311|1.306|1.308|1.306|1.296|
|Total nitrogen (g/kg)|4.145|4.074|4.342|4.083|4.340|4.137|
|Soil pH)|6.770|6.762|6.791|6.764|6.786|6.772|
|Organic carbon (g/kg)|5.674|5.710|5.575|5.707|5.551|5.743|
|Number of observations|884,936|648,292|236,644|382,653|151,474|79,316|

_Source:_ Own computation based on land monitoring data reported by SGC and complemented by administrative and remotely sensed data