rafmacalaba/datause-encoder-data huggingface dataset:
Given the increasing interest and analysis on multi-product firms in trade, we provide in addition to the measures in the Database a set of companion matrix tables by country and year showing the distribution of exporters and of total exports by the number of products exported and the number of destination markets served. The matrix tables consider the following categories for the number of products and the number of destinations: 1, 2, 3, 4-10, 11-20, 21 or more.<sup>31</sup> Some examples of the matrix tables are provided in Appendix 4. All matrix tables can be downloaded at http://data.worldbank.org/data-catalog/exporter-dynamics-database.

To illustrate the content and diversity of measures included in the Database, Table 2 presents a summary of a representative set of measures in the Database –average and median exporter size, share of top 5 percent firms, number of exporters, number of HS 6-digit products exported per firm, number of destinations served per firm, entry exit and survival rates– as well as total exports per country. We focus on averages per country for the period 2006-2008 which is the period most commonly covered across countries (and captures trade performance before the global financial crisis that started at the end of 2008).<sup>32</sup> Table 3 presents the same set of measures in the Database but for groups of HS 2-digit sectors, where the measures for each sector group are obtained as averages across all countries that export that particular group of HS 2-digit sectors, again focusing on the period 2006-2008.

Some interesting cross-country patterns emerge from Table 2. First, there is tremendous variation across countries in the export base (number of exporters) and the average exports per firm. Developed countries tend to exhibit the larger numbers of exporters. Among developing countries, the largest numbers of exporters are found in Turkey and Mexico followed by South

> 31 These matrix tables are available only for the developing countries for which we have the customs exporter-level data. We are unable to provide matrix tables for the developed countries in our sample.

> 32 For countries for which data is available for only 1 or 2 years within the period 2006-2008 we compute the average based on those years. For Kuwait and Portugal the data coverage does not include that period. Hence for Table 2 and for Figure 1 as well as for the Figures in Appendix 3 we use averages for the period 2009-2010 for Kuwait and averages for the 2003-2005 period for Portugal.

18
particular size firm-specific effects are estimated. Studies on wages and productivity in developing countries identify firm size as another significant determinant of wages and productivity (Tan and Batra, 2000). As noted by Brown and Medoff (1989), with all other factors being equal, large employers pay more than small employers. One way to explain this wage differential is that larger firms employ higher quality workers because of the greater capital intensity and capital-skill requirements of larger establishments.

This paper is organized by first introducing the data. Second, the descriptive statistics are presented. Third, the methodology is described. Fourth, the determinants of wages and productivity are analyzed by first analyzing employee characteristics and then by analyzing firm characteristics. The effects of variables on wages and productivity are compared. The wage and productivity effects of training treated as endogenous are also discussed. The final section offers the conclusions of these findings.

# 2. Data

The data used in this paper come from the National Survey of Employment, Salaries, Technology and Training (ENESTYC) and the National Survey of Employment to Workers in the Manufacturing Sector (ENTRAM)<sup>2</sup> . The data include observations from 1993 and 1999. INEGI (Instituto Nacional de Estadistica, Geografia e Informnatica) has compiled the ENESTYC and ENTRAM. The ENESTYC gathers rich information on training, technology, wages, employment, forms of labor contracting, and internal plant organization of Mexican manufacturing firms. In 1993 the ENTRAM interviewed 7,619 employees from 575 firms in the ENESTYC survey. About 15 randomly selected workers from different occupational categories were interviewed in each company in 1993. In 1999 the ENTRAM interviewed 6,259 employees from 722 firms in the ENESTYC survey. About 10 randomly selected workers from different occupational categories were interviewed in each company in 1999.

The wage data was obtained from ENTRAM, as reported by the worker and converted to real 1997 pesos. The productivity per worker measure was calculated from

> 2 For a full description of these surveys see Appendix A

4
interacted with the difference-in-difference variables. In column (8) with the full set of controls we see that the marginal effect of number of children who have died is not different across the treatment and control groups over time.

In Table 4b, column (8) from Table 4a is presented again to serve as a point of reference. In columns (9), (11) and (12), the results for the individual countries are presented. From column (9) we see that in India there was no differential fertility response in Gujarat relative to the rest of India following the earthquake in Gujarat. In Pakistan (column (11)) and in Turkey (column (12)), however, we see that there was a positive fertility response following the event of the earthquake in addition to the response to child mortality. For both countries, the response to child mortality is close to one (0.77 children for every child who died in Pakistan, and one for one in Turkey) and that in the treatment areas there was a positive fertility response in addition to the mortality response of around 0.2 children.

In the whole sample (column (8)) we may be concerned that the differential fertility response is driven by migration effects. For example, following a disaster, it may be that individuals with smaller families have a higher propensity to move away from the affected region than larger families. Quite simply, smaller families take up less space, and friends and relatives in other parts of the country are more easily able to assist a smaller family than a larger one. To overcome this issue using the DHS, I utilize information on the length of time the respondent has been living in the same village or city. With a sample of individuals who have always lived in the same village or city, I run the same analysis as that in column (8). As data on length of stay in the same village or city does not exist for Pakistan, the analysis is conducted for India (column (10)) and Turkey (column (13)). This restriction of the sample, however, is not a perfect control for migration. Individuals who moved as a result of the earthquake are excluded from the sample. The benefit of this sample restriction however, is that we are comparing like with like across treatment and control and within this sub-sample migration is not an issue.

Taking Turkey as the example, column (13), we see that for the sample of individuals who have always lived in the same place, there is a positive fertility response in addition to the fertility response to mortality. The coefficient measuring this response, post*near, is larger for the sample 20
The success of Korea's economic growth is often attributed in large part to these outward oriented policies. As can be seen from Table 1, exports grew rapidly, reflecting major expansion in the production of labor intensive manufactures (textiles, apparel, plywood and footwear) in which exporters were believed at this time to have a significant comparative advantage. The annual growth rate of exports in volume terms was about 30 percent between 1961 and 1972, and real GNP grew at an annual rate of 8.2 percent. The manufacturing sector was the dominant force in this export growth; manufactured exports were 18.2 percent of total exports in 1961 but reached 88 percent by 1972.

The expansion of manufacturing in domestic product (from 8.9 percent in 1961 to 20 percent in 1972)8 also induced a shift in the labor force from agriculture and other primary industries, where output per worker was low, to manufacturing and other activities, where it was higher. Table 2 indicates that 63.1 percent of the working population was in agriculture in 1963. This proportion steadily declined to 50.6 percent by 1972. The percentage of workers employed in manufacturing increased from 8.7 percent in 1963 to 14.2 in 1972; total employment increased by about 38 percent between 1963 and 1972. Hence, the expansion of non-agricultural employment was achieved both by sectoral shifts of labor and by an increase in total employment. The share of employment in the social overhead -apital and service sectors also increased from 28.2 percent in 1963 to 35.2 percent in 1972.

In the early 1970s, the government began to change the direction of policy away from general export promotion to irds sectoral development, focusing on heavy and chemical industries (HCI). This change in policy Al This data **ia** Era, the Economic **Planning** Board (1982), Table 3-15d.
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# Major roads

## Selected cities

— ca. 1992 @® GQtarget cities — ca. 1872 BH Darjiling @ Patna
The figure below shows the annual infrastructure investment requirements against actual investments disaggregated over 2014-2020 for the countries included in the sample.

**Figure 3. Infrastructure investment requirements Vs. Actual for the subset of EMDE countries that were included in the estimation**

<!-- Start of picture text --> 900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>EAP SAR LAC SSA ECA MENA Total<br>Actual 356 67 40 27 35 46 571<br>Needs 212 304 140 56 60 47 819<br>Amount, in 2011 US$(billion)<br><!-- End of picture text --> Source: Authors’ calculation for the countries included in the estimation (See Note in Table 2).

In order to present figures for all EMDE countries,<sup>14</sup> we extrapolated the results of investment requirements and spending as percentage of GDP for the countries not included in our estimation due to data limitations.<sup>15</sup> We apply the estimated regional investment requirements (as percentage of GDP) to the countries not included in the estimation. Figure 4 illustrates infrastructure investment requirements for EMDE countries and disaggregates the results by region. The infrastructure investment requirements for EMDE countries is $836 billion per year over 2014-2020. The estimated actual spending is $580 billion per year. This estimated gap only includes the following sectors: roads (paved and unpaved), ports, railways, water, sanitation, waste water management, electricity (generation and distribution), telephone and mobile lines.

As presented in the figure below, South Asia has the highest investment gap. The opposite occurs in East Asia and the Pacific as China drives up the average. In that region, investments actually exceed requirements to keep up with economic and

> 14 The definition of EMDE country list comes from two parts: Emerging market countries come from IMF World Economic Outlook Update: http://www.imf.org/external/pubs/ft/weo/2012/update/02/index.htm, and Developing economies list come from WBG: <u>http://data.worldbank.org/about/country-and-lending-groups.</u>

> 15 List of countries not included in the regression due to limited data: EAP : American Samoa, Kiribati, Korea, Dem. Rep., Marshall Islands, Micronesia, Fed. Sts, Myanmar, Palau, Samoa, Solomon Islands, Timor-Leste, Timor-Leste, Tuvalu, Tonga, Vanuatu; SAR: Afghanistan, Bhutan, Maldives; LAC: Belize, Bolivia, Grenada, St. Lucia, St. Vincent and the Grenadines SSA: Botswana, Cabo Verde, Côte d'Ivoire, Eritrea, São Tomé and Principe, Seychelles, Somalia ECA: Kosovo, Macedonia, FYR, Moldova, Montenegro, Serbia MENA: Djibouti, Lebanon, Libya, Tunisia, West Bank and Gaza

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very high rates of open defecation, especially in the central and southern areas, excluding the coastal regions. The remainder of the country to the north displays very low rates. Accordingly, the state with the highest rates is Kwara, where 63% of the population practices open defecation, while the practice is least prevalent in Kano, at just 2%.

# **5. CONCLUSION**

In order to design targeted policies, access to geographically specific information is crucial. However, this information is usually derived from representative surveys, whose sampling techniques are meant to save on costs while ensuring the representativeness of the population, but only permit a limited degree of desegregation, so the inferences are not extended to outliers. Geospatial models can help address these limitations by generating predictions for areas where information is lacking. In this paper, we implement a model-based geostatistical (MBG) prediction of access to specified water and sanitation services in Nigeria. Using information from households and water points and water schemes gathered as part of the National Water and Sanitation Survey 2015, as well as an array of geo-spatial covariates, we generate layers of information for seven key indicators of access to WASH, at a spatial resolution of 1x1 km.

The availability of these spatially detailed estimates provides a new trove of important information to support the targeting of programs advancing water and sanitation access in Nigeria, and offers more detailed, granular estimates, for tracking progress toward the SDGs.

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## **Figure 2: Climate and Environmental related Natural Disasters and Non-performing Loans**

This figure exhibits the relationship between NPL ratios (right axes) with the number of disaster events during five year intervals (left axes). Except for Latin America & Caribbean (LAC) and South Asia (SA), we generally observe an downwards trend in NPL ratios versus an overall increase in disaster numbers across all regions.

<!-- Start of picture text --> Europe & Central Asia<br>32<br>[FSI] Non-performing Loans Net of Provisions to Capital<br>iv) a call for international assistance 200 30<br>iii) an official declaration of a state of emergency<br>ii) 100 or more people affected 100 28<br>i) 10 or more people reported killed<br>0 26<br>East Asia & Pacific Latin America & Caribbean North America<br>2 12<br>400 30 200 100<br>10<br>0<br>200 20 100 2 50 8<br>10 6<br>0 0 4 0<br>Sub-Saharan Africa South Asia Middle East & North Africa<br>16 200 18 6.4<br>200 60<br>150 14 150 16 6.2<br>40<br>100 12 100 14 6.0<br>50 50 12 20 5.8<br>10<br>0 0 0<br>1999- 2004- 2014- 2009- 1999- 2004- 2009- 2014- 1999- 2004- 2009- 2014-<br>2003 2008 2018 2013 2003 2008 2013 2018 2003 2008 2013 2018<br><!-- End of picture text -->

# **4 Empirical methodology**

To provide econometric estimates of the average impact of climate change, natural disasters or environmental related events on key financial soundness variables, such as the non-performing loans (NPL) ratio, we use the local projection method (LPM) as in Jorda (2005). This methodology identifies the effects of an event on dependent variables over various horizons by direct computation of impulse response functions without specification and estimation of the underlying multivariate dynamic system. It offers numerous advantages over the Vector Auto-Regression(VAR) methodology, including easy implementation with simple regression techniques, robustness to misspecification, easier joint or point-wise inference and easier accommodation of highly nonlinear and flexible specifications. Identification is further helped by the relative exogeneity of the occurrence of the climate-related natural disaster events with respect to developments in the financial sector, at least in the short run, where our focus is primarily on physical risks.

The dependent variable is the cumulative change in NPL and other financial sector variables between time _푡_ − 1 and _푡_ + _푗_ , measured as difference in levels. The explanatory variables include the event dummies as well as the lags of both the dependent variables and the event dummies. For a specific type of event, the explanatory variable of interest is an episode which equals 1 if the event occurred at least once in a particular country in a year and 0 otherwise. For each horizon (year in our case) _h_ = 0 _,_ 1 _,_ 2 _,_ 3, the empirical model is given by: where _훼_ ( _h_ ) _푗_ and _휏_ ( _h_ ) _푡_ are country and year fixed effects, respectively, and _휖_ ( _h_ ) _푡,푗_ is an error term. The coefficient of interest _훽_ ( _h_ ) is the impulse response function of the dependent variable with 8
between community, parents and school that they were intended to play (World Bank 2012; Pradhan et al. 2012).

This low level of participation was partly due to inadequate parental knowledge about the program. A survey conducted by the Ministry of Education and Culture in a nationally representative sample of parents in 2009 found that, while parents were generally aware of the existence of the program, they were much less knowledgeable about the detailed workings of BOS—knowledge necessary to actively participate in school affairs and hold schools accountable for the use of BOS funds. Even general knowledge about the existence of the program was far from universal, with only about 60 percent of parents aware of its overall objective. Knowledge of more detailed information like the eligible uses of the BOS grants was even lower, with less than 30 percent being knowledgeable about such information, the reporting process and the amount of per-student allocations (World Bank 2012).

# **_2.2 The information interventions_**

In order to increase awareness of the program, in 2011 the Ministry of Education and Culture with the support of the World Bank, developed a Social Marketing and Information Campaign (SMIC) that included national, district and school-level interventions. The campaign included nationwide coverage (television and print media), district interventions (including local media, and district events) and different methods of delivering information to reach parents at the school level. The school-level interventions we evaluate here were part of this larger nationwide campaign. The main messages of the campaign were consistent across levels and types of interventions but the level of detail in the information provided was much more limited in the TV and radio advertisements than in the printed materials. Throughout, however, messages were deliberately simple: (1) What is BOS? (2) How much does the program provide per student to each type of school? (3) What can the resources be used for? And (4) how are parents expected to participate in managing the program?

We focus here on four approaches that were implemented in an experimental way in order to evaluate their relative effectiveness at reaching parents and affecting their behavior: (1) Physically giving information to parents through their children (which included two variants—a one-page letter and a colorful “pocket book”), (2) using a series of SMS messages to contact 7
pandemic, the federal Ethiopian government put in place several measures including restrictions on movement of people, school closures and shelter in place orders. These culminated in the declaration state of emergency on April 8, 2020. Prior to that, a range of restrictions had been put in place including bans on overcrowded public transport and large gatherings, closure of primary and secondary schools, and closure of universities and colleges on March 16, 2020. Domestic travel restrictions were put in place on March 21, 2020; and halt in the movement of people along all borders was imposed on March 22, 2020. A mandatory quarantine for international travelers was imposed on March 23, 2020, followed by stay at home/shelter in place orders on March 23, 2020 (EPHI, 2020).

As of June 2020, all respondents (99.8 percent) had heard of COVID-19. The initial source of information was primarily mass media. Just over half of respondents indicated that they first learned about COVID-19 either on the radio (51 percent) or television (8 percent). Neighbors (18 percent) and family members (4 percent) were also noteworthy sources of initial information. On average, respondents could describe 1.8 symptoms of COVID-19 and 93 percent could identify at least one symptom. Slightly more than one-third (36 percent) reported that they did not leave their homesteads during the previous week. On average, respondents reported taking 3.2 actions to reduce the likelihood that they or someone in their household would contract COVID-19 and only 5 percent reporting taking no actions at all. The most common actions were washing hands for 20 seconds or more (82 percent), and conditional on going out, avoiding shaking hands or kissing when greeting others (77 percent) or avoiding large gatherings or queues (66 percent).

We asked respondents how worried or stressed they were because of the coronavirus pandemic, using a scale from one (Not worried/stressed at all) to 10 (Extremely worried/stressed). Two-thirds of respondents (68 percent) reported that they were extremely worried, 10 percent said they were not worried at all with the rest reporting stress levels scattered between these. We then asked which aspect of the coronavirus crisis had had the greatest impact on the respondent and her household? (Enumerators read these out and asked the respondent to indicate which item had the greatest effect). Aggregate responses are shown in Figure 5. Market closure, fear of getting infected by the virus, high food prices and loss of income were the most important effects of the pandemic on livelihoods. More than 24 percent of respondents reported market closure as the greatest impact of the pandemic. Responses disaggregated by self-reported stress levels (not 9