rafmacalaba/datause-encoder-data huggingface dataset:
education of the women surveyed is 3.9 years. In rural areas it is lower at 2.6 years on average and in urban areas it is 6.2 years. The education of the husband is higher than that of the women on average, with 6.4 years as the total average, and 5.3 years in rural areas and 8.5 years in urban areas. The age at first birth is 19.3 years on average.

The pre-shock summary statistics for each of the countries is reported in Table 2. For many of the variables there is a significant difference between the treatment and control groups. These variables are controlled for in the regression analysis, and thus differences in variables in the predisaster period (and any subsequent difference in the changes over time) are accounted for. Variation in the pre-shock summary statistics may raise doubt with regard to what the differencein-difference dummies are picking up. Changes in education, age at first birth, or other time varying factors that may change differentially between the treatment and control groups may contaminate the effect of the earthquake on the number of children born. To check this concern, in Table 5 time variant variables are interacted with the difference-in-difference dummy variables. Further discussion on this is to follow, but the results indicate that there were no differential changes in education, age, age at first birth, father’s education and urban living, between the treatment and control groups. Thus while the treatment and control groups many differ over some of the control variables in the pre-shock period (as shown in Table 2), these socio-economic and demographic factors do not change differentially over the treatment and control groups over time.

In Table 3 the difference-in-difference reduced form estimation results of the impact of the earthquake on the number of children ever born are presented. In the first set of reduced form regressions (the total sample), the results indicate that the average number of children born to a woman has declined over time in both the treatment and control groups. However, in the total sample, we see that the decline in the number of children is greater in the control group than in the treatment group.

The positive difference-in-difference coefficient in the reduced form regression analysis in Table 3 implies that some factor (potentially the earthquake, but without sufficient controls we cannot 18
the only effect relevant for families that would have sent their daughters to middle school with or without the program. At the same time, for families with daughters out of school, the program’s condition on school attendance also induces a substitution effect since it reduces the price of education.

Both effects point toward an increase in the demand for education, particularly among credit-constrained households in environments with imperfect capital markets. On the one hand, if this demand is sustained over time, the higher utilization of education services can lead to higher school progression and completion. On the other hand, there might be perverse incentives or constraints that prevent program participants from accumulating more human capital. For example, families may try to keep their daughters in those grades that make them eligible for the transfers, in effect preventing them from progressing and graduating. Moreover, assuming no changes in supply, schools in program areas may become overcrowded due to the additional demand, which can hinder progression and completion. We seek to shed some light on the net effects of these conflicting forces in the context of the FSSP.

The analysis based on school-level data provides systematic evidence that the program helps girls progress through middle school. The point estimates from DD and RDD models indicate that the program raises enrollment not only in grade 6 but also in grades 7 and 8 for the same cohorts (Table 5).<sup>23</sup> For instance, cohort C sees higher percentage changes in enrollment in stipend than in non-stipend districts in both grade 7 in 2005-06 and grade 8 in 2006-07 (the second and third year of exposure to the program). The increases relative to enrollment in stipend versus non-stipend are 18 and 32 percent, respectively. Although the relevant cohorts (cohorts B to G) participate in the program at different times and for different periods, estimates of program effects on enrollment yield consistent results for all of these cohorts and grades over time (positive and significant impacts). The insignificant impacts in 2004-05 are expected since

> 23 The school-level approach measures changes in absolute enrollment between stipend and non-stipend districts. These changes, however, may be driven by a plausible mechanical relationship between higher fertility, population growth, and the number of girls enrolled given that stipend districts are poorer. Unfortunately, there are no school-level data available to do the estimation for a pre-program period and check if changes in enrollment in stipend district occurred even before the program began. Yet, the empirical models include a number of socio-economic covariates at their baseline values which are also likely to explain differences in fertility between stipend and non-stipend districts. Furthermore, the analysis at the household level looks at changes in progression and completion rates which, by definition, are not affected by the size of the cohorts.

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# **5 Conclusion**

Cross country differences in economic development are strongly influenced by the extent to which new technologies diffuse across nations. While it is often hypothesized that multinational corporations and international firm-to-firm relationships through global value chains play an important role in this process of international technology diffusion, understanding their role in the spread of new technologies has proved difficult because of the lack of detailed panel data on both the adoption of new technologies and various types of international-firm-to-firm linkages.

In this paper, we have assembled an unusually rich combination of granular information on online job postings with data on several types of international firm-to-firm linkages to study the role of multinational firms and global value chains in the cross-country diffusion of new technologies. Following a growing literature, we used the online job postings data to construct proxies for the spread of new technologies. We then examined the spread of 29 new technologies across 17 countries and 107,981 firms over the period 2014-2022. We combined this information with detailed panel data on international firm-to-firm linkages, which includes information on buyer-supplier relationships, innovation partner and affiliate. This made it possible to examine, for the first time, the role of these linkages in shaping the intensity and spread of technologies across countries, regions, and firms.

We first established the following stylized facts about the geographic and firm-to-firm diffusion of new technologies. First, we find that technology hiring has become more dispersed across countries and regions over time. Second, while technology hiring has disseminated more across firms over time, it remains highly clustered in multinational firms and their value chains. Third, the share of technology hiring is still significantly higher in richer countries. Fourth, there is evidence that proximity to invention sites (pioneer cities) contributes to accelerate technology diffusion across countries and regions.

We have then explicitly examined the role of different types of firm-to-firm linkages in the cross-country diffusion of new technologies. The results point to a dual trend. On the one hand, there is a strong technological advantage for firms located in pioneer cities in the US, as revealed by the fact that technology postings tend to be concentrated in these firms for several years. However, we also document that the technology advantage of these firms tends to diminish over time. On the other hand, we provide evidence that firm-to-firm linkages play an important role in the international diffusion of technologies. In particular, the results reveal that buyers and innovation partners of pioneer firms experience the most significant technological benefits. In contrast, being an affiliate of a pioneer firm does not emerge as a statistically significant driver of the diffusion of new technologies across countries. Once we control for firm ownership, we find that external firm relationships drive the positive results.

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remittances were affected by the shocks, suggesting their widespread impact. During the same time period, the fraction of households depending on informal income sources increased. Overall, household monthly consumption expenditures took strong downward hit and the reduction was highest among the urban households.

# **6.  Empirical Approach**

Income diversification affects consumption stability and the overall welfare of households. The level and the type of income diversification depend on the accessibility and availability of different income sources and the type risk households are responding to, which may in turn depend on household’s geographic location, access to factor and labor markets, human and social capital, and recurring policy changes. Empirical studies show that educational attainment and infrastructure access are strong determinants of diversification (Barrett, et al., 2001a, 2001b; Block and Webb, 2001). In this section, we empirically investigate the impact of income diversification on household welfare. At the same time, we also examine the determinants of income diversification taking into account several household characteristics variables.

Let Djt be a measure of income diversification for a household in region j (rural, urban) and at time period t (1990/91, 1995/96). A model that contemporaneously determines income diversification and per capita consumption (Cjt) as a function of explanatory variables Xjt, and Zjt can be given as: where Xjt is a vector of explanatory variables common to both (3) and (4); Zjt contains those variables that affect income diversification but affect per capita consumption only indirectly through their effect on income diversification (e.g. transitory income factors). The vector Xjt includes household demographic variables in age, sex and education classes as well as asset holding. We include regional dummy variables in estimating (3) and (4) in order to account for regional differences in income generation that may affect income diversification as well as the level of consumption expenditures. The explanatory variables are either directly obtained from the Zimbabwe Income, Consumption, and Expenditure Surveys (ICES) of 1990/91 and 1995/96 or derived from it with the exception of the rainfall variables. In order to facilitate comparison of the estimates obtained, the construction of the dependent variables is identical and similar sets of explanatory variables are used for both 1990/91 and after 1995/96 households.

Since the above system of equations is endogenous, we estimate the parameters by using instrumental variables approach. A two-stage least squares (2SLS) instrumental variables regression can be used to produce consistent estimates if the system is properly identified (Davidson and Mackinnon, 1993). Two seasonal (planting and harvesting) rainfall variables with a lag are used as identifying instruments for income diversification. These variables are standard deviations of seasonal rainfall by a year on the grounds that more variable rainfall would lead to 8
|lll<br>it<sup>× d</sup>it<sup>× LRLRL_IltFlAitI</sup>j<br>|||(0.030)|
|---|---|---|---|
|<br>llt<br>∗|0.404***|0.426***|0.441***|
|<br>|(0.005)|(0.005)|(0.005)|
|Observations|1,093,779|967,268|842,560|
|R<sup>2</sup>|0.310|0.320|0.313|

***, **, * imply significance at the 1%, 5% and 10% respectively. SE, adjusted for clustering at the HS6-year level, are reported in brackets.

Column 1 presents the results regarding access to finance. The coefficients on the interaction of the BRER with the access to finance ratio are negative, which means that the elasticity of export with respect to the BRER rises as the ratio of outstanding loans to exports of the sector increases. The triple interaction, also including the depreciation dummy, is not statistically different from zero, revealing no specific impact of access to finance on the responsiveness of exports to depreciations of the BRER. Jointly, the coefficients imply that sectors with higher persistent access to finance are more responsive to BRER depreciations, as shown in Figure 2. At the mean, an additional 10 percent in the ratio of loans over exports is related to a 4.7 percent larger BRER elasticity. That is, for the sector exporting linen, with a ratio of outstanding loans to exports of 0.41, the export elasticity with respect to bilateral exchange rates is at 0.06 percent, while for fats and oils, with a ratio of loans to exports of 0.91, the elasticity is almost a third larger, at 0.08 percent. It is possible, however, that sectors with larger export responses to RER depreciations secure more financing in Pakistan, introducing endogeneity. Hence, we use two largely exogeneous indicators of supply constraints, to check for robustness – a structural indicator on financial dependence (due to Rajan and Zingales (1998)), and labor intensities.

33
literature on how firing costs can affect unemployment, depending on labor supply and demand elasticities (see, e.g., Lyungquist 2002; Garibaldi and Violante 2005).

The fourth and final control in this study covers the ratio of private credit by deposit money to GDP, covering the years 2000 through 2016 and downloaded from the Financial Development and Structure Database. This control helps identify the partial correlation between unemployment and the incidence of the digital economy when the proxy for the latter is the share of the adult population that reports paying bills over the internet. In fact, as shown in the Appendix tables, in most of our estimation samples there is a positive correlation between internet payments and bank credit.

# _D. Instrumental Variables_

As discussed further below, to further assess the partial correlation of interest, we also estimate a series of IV models. These models use two additional variables as instruments, with their validity and power assessed in the results section. One is the number of fixed telephone lines per capita averaged over a pre-sample historical period, 1975-1999. The conjecture is that countries that had laid out the infrastructure for fixedline telephony in the late 20<sup>th</sup> century are also countries with a high coverage of internet use and potentially of digital payments. Since unemployment during 2000-17 is unlikely to have caused fixed-line coverage during 1975-99, it might be a suitable IV. In addition, we explore IV-estimations with dummy variables that identify the legal origin of each country. These variables appear as IVs in articles that study the relationship between financial development and economic growth. The seminal article in this vein is Levine, Loayza, and Beck (2000). The set of dummy variables includes British, French, German and Scandinavian legal origins. Lastly, given the results of the OLS estimations discussed 11
**Figure 3. Decomposition of Poverty Rates. Urban Areas**

<!-- Start of picture text --> Oriente vs Costa Sierra vs Costa<br>30 30<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>Geo graphic Co ncentratio n Unco nditio nal Unco nditio nal Geo graphic Co ncentratio n Unco nditio nal Unco nditio nal<br>No rmal No rmal<br>Oriente Urban Costa Urban Sierra Urban Costa Urban<br>Figure 4. Decomposition of Poverty Rates. Rural Areas<br>Sierra vs Oriente Costa vs Oriente<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Geo graphic Co ncentratio n Unco nditio nal Unco nditio nal Geo graphic Co ncentratio n Unco nditio nal Unco nditio nal<br>No rmal No rmal<br>Sierra Rural Oriente Rural Costa Rural Oriente Rural<br>Poverty Rate Poverty Rate<br>Poverty Rate Poverty Rate<br><!-- End of picture text --> _Source:_ Own calculations based on ECV Survey.

In sum, returns explain about 90 percent of the differences in log welfare ratio between the urban areas of Oriente and Costa and about 70 percent between the urban areas of Sierra and Costa (Figure 5). The differential between the rural areas of Sierra and Oriente is explained by returns and characteristics, while the differences between Costa and Oriente are mainly due to characteristics.

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in the area, while in a low malaria area immunity does not exist; and (3) the infection in a low-malaria area is likely to be more severe due to the lack of exposure to the disease. Therefore, I focus on the part of Senegal discussed earlier that is at a pre-elimination stage. Within this area, I focus on ve of the lowest malaria districts where data are disaggregated at the health post level and available for every health post in these districts. Malaria data are not available at this high spatial resolution for any of the other low malaria districts. The data cover 117 health posts. The appendix provides a map of the ve health districts, which I subdivide into areas based on the location of the health posts and cell phone towers. Health posts in close proximity were grouped together forming 36 health post catchment areas.

I use incidence data based on data collected from each health post on all new cases in the reporting month.<sup>12</sup> The use of incidence data is one thing that separates this paper from some of the previous work that relies on endemicity data. The endemicity data are gathered from parasite rate surveys in which a random subsample of the population is tested for malaria parasites. When the malaria prevalence is very low, the likelihood of having a positive case becomes very small. Therefore, when focusing on a low-malaria setting to understand impact of mobility, incidence is a more reliable measure (Alegana et al. 2013, J. M. Cohen et al. 2013).

PNLP's work has led to a system that provides high quality data on malaria incidence across the country. In Senegal, if an individual feels sick, usually experiencing a fever, chills and fatigue, she will go to the closest health post where she will be tested using a rapid diagnostic test (RDT) due to her symptoms. If she tests positive, she will be provided with medication for free to treat the disease. Therefore, all incidence data used in this paper comes from suspected cases that have been tested and are positive for malaria based on the test. For the rest of the country, these incidence data are available monthly at the health

> 12The data used to measure malaria incidence comes from the PNLP and PATH, a non-prot organization working with the PNLP to ght malaria in Senegal through its Malaria Control and Elimination Partnership in Africa (MACEPA).

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contract with the government that the manager reports that is expected in bribes to obtain the contract, Individual or family ownership of the fi rm is a dummy variable that equals one if the largest shareholder of the fi rm is either an individual or a family, Internal ownership of the fi rm is a dummy variable that equals one if the largest shareholder of the fi rm is either the management or the employees, Government ownership is a dummy variable that equals one if the largest shareholder of the fi rm is the government or a government owned fi rm, Corporate ownership is a dummy variable that equals one if the largest shareholder of the fi rm is either a domestic or a foreign fi rm, Bank ownership is a dummy variable that equals one if the largest shareholder of the fi rm is a bank or an investment fund, Other ownership includes other types of fi rm ownership.

Data on the enforcement of labor regulation in 2002 comes from the Ministry of Labor. The federal system of labor inspections, which is part of the Ministry of Labor, has the objective of enforcing all the laws and regulations, including international conventions, related with labor and employment relations and contracts (Law 55.841/65). The inspectors of the federal system of labor inspections verify the enforcement of laws and regulations, including those related with heath and security at work. In particular, they try to avoid labor informality verifying that workers are formally registered with the labor authorities, i.e., that they have a work permit (or carteira de trabalho). The Ministry of Labor is a decentralized structure with a regional branch in each of the 27 Brazilian states (“Delegacia Regional do Trabalho” or DRT). Within each of these branches, there are several administrative units, or subdelegacias. The concept of subdelegacia is administrative and does not correspond to any geographical unit.<sup>47</sup> In particular, a “subdelegacia” includes more than one city (or municipio). In each subdelegacia there are several regional officies, of which one is the headquarters.

The inspector responsible for each subdelegacia, or the subdelegado, reports to the inspector responsible for the regional branch, the delegado. The labor inspectors (or “Auditores Fiscais do Trabalho”) are affiliated only with one subdelegacia. In general, each inspector

> 47Brazil is divided into 5 regions (North, Northeast, Center-West, Southeast and South) and 27 “unidades de fomento”.

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# **Exhibit 3.7 – Trading activity for Bloomberg-covered stocks**

_Cumulative distribution of % of non-trading days for selected countries, 2008 (upper left graph) Cumulative distribution of % of non-trading days in Egypt, 2004-08 (upper right graph) Average non-trading days for selected countries, (lower graph)_ <!-- Start of picture text --> Number of Number of<br>stocks, % total stocks, %<br>100% 100%<br>90% 90%<br>80% Egypt 80% 2008<br>70% Abu Dhabi 70% 2007<br>60% Amman 60% 2006<br>50% Casablanca 50%<br>2005<br>40% Kuwait 40%<br>30% Manama 30% 2004<br>20% Muscat 20%<br>10% Qatar 10%<br>0% 0%<br>Non-trading Non-trading<br>days, % days, %<br>Country 2004 2005 2006 2007 2008<br>Abu Dabhi NA 72.2% 31.1% 30.7% 25.4%<br>Amman 32.0% 24.0% 24.8% 21.8% 19.1%<br>Cairo 38.2% 31.6% 27.3% 16.1% 11.8%<br>Casablanca 42.3% 34.6% 28.1% 21.4% 18.2%<br>Kuwait 20.4% 19.8% 25.8% 21.8% 19.1%<br>Manama 67.6% 65.2% 65.0% 59.5% 50.0%<br>Muscat 59.5% 56.9% 59.6% 51.6% 45.4%<br>Qatar 11.9% 6.7% 4.9% 4.0% 6.4%<br>0.0% 2.5% 5.0% 7.5% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0% 0.0% 2.5% 5.0% 7.5% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% 100.0%<br><!-- End of picture text --> _Source: Bloomberg; World Bank staff analysis Note for all graphs: 2008 information is up to and including June 20_<sup>_th_</sup> _._ This analysis shows that the incidence of highly illiquid stocks has decreased from 30 percent in 2004 to 10 percent in 2008, implying that EGX is now one of the most liquid markets in MENA. However, this finding might be misleading, since Bloomberg covers only 201 stocks, while EGX reports the existence of 375 stocks as of mid-2008. Assuming that Egypt’s stocks not reported in Bloomberg are highly illiquid, the fraction of illiquid firms would have instead decreased from 85 percent in 2004 to about 50 percent in 2008, making Egypt a poor regional performer (Exhibit 3.8). Regardless of this assumption, EGX liquidity has strongly improved since 2004, with the greatest advances being made in the most intense delisting periods―2005-06 and 2006-07.

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