rafmacalaba/data-use-mentions-extended huggingface dataset:
answer the research questions of interest. Moreover, the type of migrants captured in the data can vary from round to round. Therefore, empirical analysis is restricted to the 2007 and 2000 IFLS rounds for a consistent definition of migration. In the household-level data, the definition of households with international migrants refers to those families with parents, children, or spouses who are not co-residents and are abroad.<sup>6</sup> Given the way the questions about remittances are asked, in the data, households receiving international remittances are a strict subset of households with international migrants.

The individual-level panel, however, is less useful for this paper because not all individuals abroad are surveyed. An international migrant can only be defined as an individual who was present in the household in the previous IFLS round, but is abroad in the following round. The rate of migrants captured in this data is very small. Only descriptive statistics, without regression analysis, about individual migrants are reported.

Among households with migrants, the data allows further disaggregation by gender of the migrants. We can classify those families into (i) those with only female migrants, (ii) those with only male migrants, and (iii) those with both male and female migrants. The third category has very few households. In the regression analysis, we will combine categories (ii) and (iii) as households with mainly male migrants, for easy interpretation. Alternatively, the data also allows calculation of the female share among migrants, that is, for each household with migrants, what is the fraction of migrants that are female (mother, daughter, and wife).

# 4. **Descriptive Statistics**

Analyzing the differential impacts of female migrants requires a good understanding of the socio-economic factors of the migrants and migrants’ origin households in determining their international labor migration. This section presents descriptive examinations of the profiles of migrants and migrants’ origin households, including their trend over times. Some key characteristics of the migrants and migrants
poverty is a plausible candidate. Adding the poverty rate to equation (5), we find that the effect of excess demand becomes insignificant (prob.=0.19) while the poverty rate is significant (a coefficient of 0.828, with st. error of 0.333). A higher poverty rate may be associated with greater landlessness and hence a larger supply of casual labor, bringing down the wage rate.

# **5. Rationing and the participation of women**

Nationally, almost half (48%) of the employment as registered in the administrative data for 2009/10 goes to women.<sup>27</sup> This is very high for a country where a minority of women

> 24 The fit is slightly better using the log relative wage; using _Wi_ /<sup>_W_</sup> _i EGS_ instead the correlation coefficient is -0.520.

> 25 Using _Wi_ /<sup>_W_</sup> _i EGS_ as the dependent variable the estimated intercept is 1.466 (s.e.=0.132). We also tried a quadratic function of _ED_ but this did not improve the fit.

> 26  Note that exp(0.398)=1.489 is the implied ratio of the levels of wages.

> 27  While the administrative data are clearly inadequate for measuring aggregate demand for work, there is no

15
i.e., the four indices from the DVA, or the three alternative models).

The parameters are estimated via maximizing the log-likelihood: An additional constraint is added for the Lasso. Lasso regressions are useful for model selection by shrinking coefficients to zero. As an example, it is possible that the subjective probability estimate tracks the fundamental index over time. Lasso will penalize this and shrink one of the coefficients to zero. The general goal of Lasso is to minimize the sum of squared errors by including a penalty function: where _j_ is the _j_ th variable in the list of _K_ , _t_ is the time index, _y_ is default, and _x_ are the four components of debt in a matrix. The tuning parameter _λ_ controls the amount of

> 13We also run these tests over an in-sample period and a pseudo out of sample period where 2019 is used as the out of sample year.

22
(0.021)|(0.010)|(0.010)|
|1999–2004 dummy||-0.005||0.059***|
|||(0.023)||(0.008)|
|2005 and above dummy|0.269***|0.272***|0.076***|0.101***|
||(0.014)|(0.023)|(0.008)|(0.008)|
|Female* (1999–2004 dummy)||0.225***||-0.017|
|||(0.033)||(0.013)|
|Female* (2005 and above<br>dummy)|-0.068***|0.099***|-0.044***|-<br>0.050***|
||(0.021)|(0.032)|(0.013)|(0.014)|
|Hazard ratio|-0.002|-0.001|0.678***|0.680***|
||(0.051)|(0.051)|(0.045)|(0.045)|
|Constant|10.404***|10.389***|4.811***|4.787***|
||(0.125)|(0.128)|(0.151)|(0.151)|
|Observations|31,768|31,768|153,155|153,155|

_Source:_ Calculations based on the Cambodian Socio-Economic Surveys 1996, 1999, 2004, 2007, 2008, 2009, 2010, and 2011,
countries, a convention that largely reflects the way most of them are administered.<sup>28</sup> In equation (6), _γ >_ 0 is our test for the combined hypothesis that NTMs are applied in non-discriminatory fashion but reduce competition increasing the fixed cost faced by producers.

**Applied tariffs** . To estimate the impact of tariffs on market structure (Testable prediction 2), we interact the exchange rate with the log of bilateral tariffs plus one. We use data on applied tariffs which vary across product-destination cells and take into account all preferential regimes. In order to make the exercise comparable to the one on non-tariff measures, for which we have no time-wise variation, we use a time-wise average of tariffs over the sample period.<sup>29</sup> The corresponding estimating equation is:

where **FE** is defined as before and Tariff _odp_ is the average tariff imposed by destination country _d_ on product _p_ imported from origin _o_ over the period. Prediction 2 implies an ambiguous effect of tariffs on foreign firms market shares and thus on their incentives to engage in PTM. How import tariffs in one destination market affect foreign firms market power is thus an empirical question. However, as previously discussed, since tariffs are discriminatory we would expect their rent-shifting effects to dominate and in net see them erode the market power of foreign exporters, i.e., empirically we would expect _θ_ to be negative.

# **5.2 Main results**

**NTMs** . Table 4 reports estimates from specification (6). In this table as in all the robustness exercises, we present estimations with origin _×_ year fixed effects (columns 1 to 4) or with firm _×_ product _×_ year fixed effects (columns 5 to 8). The number of observations is much lower than previously due to the incomplete availability of NTMs AVEs.<sup>30</sup> In spite of the reduced
Policy Research Working Paper 10642

# **Abstract**

This paper leverages Sri Lanka’s 2021 chemical fertilizer import ban to quantify the costs of restricted fertilizer access for agriculture, trade, and welfare. By leveraging trade and production data, satellite-based yield estimates, and event studies, the authors document sharp declines in fertilizer imports, agricultural output, and exports. A quantitative spatial model of trade and agriculture estimates the ban’s average welfare cost at 7.3%, with farmers, estate workers, and fertilizer-intensive regions bearing disproportionate losses. Model-implied partial-equilibrium elasticities line up with the experimental evidence, but once general-equilibrium price and wage adjustments are accounted for, the elasticities are much smaller—implying that using partial-equilibrium estimates to assess large, economy-wide fertilizer shocks (like those seen during recent conflicts) would substantially overstate their effects. Domestic and trade policy interact: by curtailing fertilizer use, the import ban effectively contracted the country’s fertilizer subsidy program and its implicit transfers from mobile workers to farmers, thereby mitigating the former’s welfare losses.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dghose@worldbank.org, epinheirofraga@worldbank.org, and afernandes@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development
**Figure 4. Increase in Labor Productivity and R&D Intensity by sector, Argentina, 2014–16**

<!-- Start of picture text --> Transport equip.<br>Leather Medical instrum.<br>Textile<br>Furniture<br>Electric equip. Other minerals<br>Others<br>Confections<br>Edition<br>Paper Other metals<br>Food & Beverages Machinery and equip.<br>Automobile Plastic<br>Wood<br>Metals<br>0% 0.5% 1%<br>R&D intensity<br>100%<br>80%<br>60%<br>Increase in output per worker<br>40%<br>20%<br><!-- End of picture text --> _Source_ : ENDEI 2014–2016.

_Note:_ Increase in output per worker = 2014–16 increase in value added over total employment; R&D intensity = expenditure in internal R&D over sales 2014–16.

In sum, aggregate data from Argentina suggest there might be important inefficiencies in the role of knowledge in the production function, reducing private investments in R&D and constraining the impact of knowledge investments on productivity. This poses the question of what role the lack of key complementarities plays in the effectiveness of innovation investments and their returns. In other words, what is the role of knowledge complementary factors and market complementary factors in pushing upward or holding back the returns to private investment in innovation and what could the firm do to make the most of those factors?

# **3. Conceptual Framework**

The relationship between innovation and firm performance has been largely studied, primarily using the extended knowledge production function approach pioneered by Griliches (1979). A key element in the estimation of production functions is the need to control for the endogeneity of investment (in knowledge stocks or physical capital stocks) and productivity (
_Yijt_ = _β_ 0 + _β_ 1 _Femaleijt_ + _β_ 2 _SchoolClosedjt_ + _β_ 3 _SchoolClosedjtxFemaleijt_ + _β_ 4 _AcademicBreakjt_ + _β_ 5 _AcademicBreakjtxFemaleijt_ (3) + _γ_ 1<sup>_′Xijt_+</sup><sup>_γ_2</sup><sup>_Vjt_+</sup><sup>_γ_3</sup><sup>_lnGDP_2019+</sup><sup>_δr_+</sup><sup>_αt_+</sup><sup>_εijt_</sup> where _Yijt_ is the outcome of interest for firm i in country j at time t. _Femaleijt_ is a dummy variable equal to 1 if the business owner is female; 0 if male. _SchoolClosedjt_ is a dummy variable equal to 1 if schools were fully or partially closed in the country due to COVID-19 at time t of the survey wave. _Xijt_ is a vector of controls of firm i which includes controls for sector of business, firm size and age, role in the business, and demographics of the business owner, including marital status and types of dependents. In order to control for other factors that may be influencing both school closure and firm outcomes, we also control for _AcademicBreakjt_ and _AcademicBreakjtxFemalejt_ that indicates whether the country j was in a scheduled academic break at the time of survey wave t. _Vjt_ is a set of mobility controls in country j at time t. _lnGDP_ 2019 is the natural log of the Gross Domestic Product (GDP) per capita in country j for the year 2019 i.e. before the pandemic. _δr_ indicates region-of-the-world fixed effects, _αt_ are time fixed effects in the form of survey wave dummy variables. _εijt_ is the error term. We use robust Eicker-Huber-White (EHW) standard errors throughout.

Finally, we look at whether the school closure shock is either mitigated or accentuated by gender norms through the OLS estimation of equation 4: Variables are defined as in equation 3, with the addition of norms from the 2019 round of the FoB data set.
# **3.  The Political Geography of Methane Monitoring**

# **3.1 The Spatial Distribution of Methane Emissions**

Methane emissions come primarily from fossil fuel extraction and processing, livestock grazing, irrigated rice production and waste disposal. Table 4 shows that the spatial distribution of methane emissions sites is hugely skewed. Of 6.5 million cells in the EDGAR 10 km global raster, only 142 (0.002% of total cells) account for 10% of global methane emissions and 21,057 (0.325%) account for 50%.<sup>7</sup>

**Table 4:  Spatial clustering of global methane emissions**

|EDGAR<br>Decile|Cells|Cum.<br>Cells|Cum.<br>%|
|---|---|---|---|
|1|142|142|0.002|
|2|578|720|0.011|
|3|1,775|2,495|0.039|
|4|5,817|8,312|0.128|
|5|12,745|21,057|0.325|
|6|22,364|43,421|0.670|
|7|37,919|81,340|1.255|
|8|64,334|145,674|2.248|
|9|124,529|270,203|4.170|
|10|6,209,797|6,480,000|100.000|

Figure 4 displays the global distribution of the 21,057 cells that account for 50% of methane emissions. The map reveals widely-scattered cell clusters in all global regions except South and East Asia, which also have very large clusters in some subregions. Figure 4 motivates a cluster-concentrated strategy, because the cost-effectiveness of emissions-reducing interventions rises with spatial clustering.<
**Figure 4** :  Costa Rica’s Exports by Destination, 2000 versus 2008 (US$)

<!-- Start of picture text --> A 77% increase<br>in total exports<br>A 130%<br>increase in<br>exports to CA<br><!-- End of picture text --> According to calculations by SIECA, for the region overall,<sup>11</sup> since the signing of the Central America Free Trade Agreement, the share of intraregional exports in total export has fluctuated between about 26 and 30 percent in US$ FOB terms, reaching the highest share in 2008 and then decreasing slightly in 2009 (Figure 5). In fact, over the past five years, the region was the second largest trading partner for the individual Central American economies.

Source: LCSSD Economics Unit calculations based on WITS data, 2010

**Figure 5** :  Central America’s Intraregional and Total Export Flows, 2005-2009 (US$’000)

<!-- Start of picture text --> Total Exports<br>Intraregional Exports 29.5%<br>27.2% 26.5%<br>26.6%<br>27.2%<br><!-- End of picture text --> Source: SIECA According to the model results produced for the current paper, the size of the domestic exporting markets and the distance between trading partners are the main factors affecting bilateral trade in Central America. Given a 1-percent increase in GDP of the exporting country, it is expected that exports from that country to the rest of the region increase by nearly 1.7 percent. Conversely, a 1-percent average increase in distance between each country and its export partners is associated with reductions in expected exports by 1.6 percent.

In contrast to the EU15, in Central America, the “gravitational pull” (proxied by the Importing Countries
severe for LDCs, because even the most comprehensive firm datasets exclude large segments of the productive sector in these countries. We aim to address this bias. Using official employment/household survey data, we characterize the (employment-weighted) size distribution of the universe of productive units from which workers obtain their income in 45 countries, including advanced economies and LDCs, particularly (but not only) those in Latin America. In doing so, we also quantify the magnitude of the mentioned bias in firm-level datasets. Finally, taking advantage of the nature of our data, where the unit of observation is the individual, and the information is representative of the entire private economy, we characterize the relationship between the business size distribution and individuals’ earnings for the overall working population. By working population, we mean the complete set of individuals in the (private) economy who declare obtaining income from productive activities, either as employees, self-employed workers or firm owners.

> 1 The literature has documented the concurrence of increasing wealth concentration and income inequality, falling labor shares in national income, and increasing market power in advanced economies over the last decades. Piketty (2014); Piketty et al. (2017); Saez and Zucman (2020a,b); Autor et al. (2020); Karabarbounis and Neiman (2013); De Loecker and Eeckhout (2018); De Loecker et al. (2020); Yeh et al. (2022).

2
257.8|
|GIS rural accessibility|% of rural pop within 2 km from all-season<br>road|19.7|**16.8**|22.9|
|Overengineering of network|% of main road network paved despite low<br>traffic volumes|15.0|**65.0**|20.0|
|Paved road traffic|Average annual<br>daily traffic|1,408.2|**736.6**|2,558.3|
|Unpaved road traffic|Average annual<br>daily traffic|54.2|**45.2**|14.9|
|Paved network condition|% in good or fair condition|67.9|**83.0**|82.0|
|Unpaved network condition|% in good or fair condition|61.4|**25.0**|57.6|
|Perceived transport quality|% firms identifying as major business<br>constraint|27.4|**10.6**|4.8|

_Source:_ Gwilliam and others 2008, derived from AICD national database downloadable from http://www.infrastructureafrica/aicd/tools/data _Note:_ GIS = geographic information system.

8
of socioeconomic status for the student and peers. First, I use parental education, defined as the average between the two parents when information for both is available. The four socioeconomic groups are defined in relation to levels attained: incomplete primary, complete primary, complete secondary and complete tertiary. For the regression testing the existence of heterogeneous effects, I compute the average years of education.<sup>16</sup> For reference, I also include in the tables the results using the variable books but only for observations for which there is also information of parental education. This is to help distinguish between difference in results arising from the use of alternative proxies of socioeconomic status and those from a smaller sample, given the presence of non-random missing values. The second proxy of socioeconomic status is based on assets and access to services at home. I construct an asset index as the first principal component of a factor analysis. The boundaries of the socioeconomic groups are such that the distribution of students is similar to that derived from the number of books, with 60 percent of pupils in the lower two groups.

Table 8: Robustness Analysis

||Heterogenous|Class composition|Co|mbined efect|s|
|---|---|---|---|---|---|
||increasing|not strictly monotonic|UM largest|L-LM vs U|UM vs U|
|Public schools|✓|✓|✓|✓|✓|
|Parental education|✓|✓|✓|✓|✓|
|Assets (PCA)|✓|✓|(✓)||✓|
|Math|✓|✓|✓|✓|✓|

Table 8 summarizes the results; details can be found in the Appendix. In all cases, the main conclusions remain. Crucially, they imply that increasing the degree of segregation of children on economic grounds will harm worse off students test scores while increase the attainment of richer pupils
# 1 Introduction

Increasing domestic and international mobility has magnied the devastating consequences of infectious diseases: more than 11,000 deaths from Ebola, 440,000-1,300,000 cases of Zika infections in 13 countries, and most recently, more than 125,048 infections and 4,613 deaths from COVID-19<sup>1</sup> across 117 countries ( Bogoch et al. 2016, WHO 2020a, WHO 2020b). Negative externalities from mobility are also relevant for long-standing diseases that we are aiming to eliminate. For example, Venezuela, the rst country certied by the World Health Organization (WHO) for eliminating malaria in its most populated areas in 1961, experienced a dramatic resurgence of the disease in 2016 in part due to migrant workers in the mining region becoming sick, traveling home, and spreading the disease to their home villages and cities (Casey 2016). This paper uses a case study of malaria in Senegal to demonstrate how to harness big data to causally estimate the size of this externality of movement and apply the results towards more eective policy targeting. The methods can be applied more broadly to inform policies related to mitigating spread of infectious diseases.

The economics literature identies malaria eradication as having important impacts on adult income and consumption (Bleakley 2010, Cutler et al. 2010, Venkataramani 2012), real estate wealth (Hong 2011), longer term health including chronic disease and disability (Hong 2013), test scores and educational attainment (Barofsky, Anekwe, and Chase 2015, Lucas 2010, Venkataramani 2012).<sup>2</sup> Ninety-nine countries have been certied by the WHO as malaria free; however, Sub-Saharan Africa, which accounted for 93% of all malaria deaths in 2018, has only had a single successful case of elimination (World Malaria Report 2019). While previous work has studied malaria prevention/treatment in short-term settings, focusing on the pricing of malaria interventions (Jessica Cohen and Dupas 2010, Jessica Cohen, Dupas
Given that national corporate law and state-ownership laws vary, countries are characterized by different types of national legal forms. For instance, in Indonesia SOEs according to national state-ownership law are denoted with two different legal forms: Badan Usaha Milik Negara (controlled by the central government) and Badan Usaha Milik Daerah (controlled by the local government). Similarly, in Azerbaijani SOEs are defined as public interest entities (PIEs), in Mozambique as public enterprises and shareholding companies, and in Rwanda as corporate entities recognized by national law as an enterprise in which the state exercises ownership. In some cases, national legal forms of state ownership include state corporations as well as regulators or administrative agencies with some commercial activities (e.g., parastatals).

Given that many SOEs are corporatized and the large heterogeneity in corporate and state-ownership laws, national legal forms cannot be solely used as a variable to identify SOEs. However, the use of national legal forms is essential to ensure that non-corporatized SOEs are included in the BOS database. As most non-corporatized SOEs do not have a shareholder structure, these companies do not provide shareholding percentage information in ORBIS’s links file. As a result, these companies will not be identified as SOEs using an approach that relies on linkages to public authorities in ORBIS, as these linkages are not present. For this reason, we incorporate into the identification of SOEs as well as the construction of ownership structures the information pertaining to national legal forms. In this manner the identification approach ensures that all companies -even under different legal forms- are included in the database when they satisfy the criteria of the SOE definition. For this, national legal forms were retrieved from an ORBIS variable with the same name and then cross-checked with local country teams with the legal expertise to corroborate that those were aligned to the definition proposed in section 2. Once those legal forms were verified, we identified the companies with legal forms associated to state ownership by law as non- corporatized SOEs. In addition, some of these companies