rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8262

# **Abstract**

This paper uses the dramatic expansion of access to the Internet in China to analyze the impact of the Internet on firm performance. The paper combines firm-level production data with province-level information on Internet penetration to examine how the rollout of the Internet across Chinese provinces between 1999 and 2007 influenced firms’ export behavior. The econometric strategy enables identifying the impact of the Internet on firm performance in China. The paper shows that the rollout of the Internet boosted manufacturing exports of firms in China, even before the rise of major e-commerce platforms in the country such as Alibaba. The paper takes a closer look at why, focusing on three questions: what aspects of firm performance were affected, what types of firm communication were facilitated, and what dimensions of the new communication medium were relevant? The paper finds that the Internet not only enhanced trade, but also improved overall firm performance. The results are consistent with improvements in communication with buyers and input suppliers. The benefits arose not just from better communication, but from establishing a visible virtual presence, and were enhanced by, but not contingent on, access to broadband.

This paper is a product of the Trade and International Integration Team, Development Research Group. 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://econ.worldbank.org. The authors may be contacted at afernandes@worldbank.org, amattoo@worldbank.org, mschiffbauer@worldbank.org, and HNguyen4@imf.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/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
The impact of commodity _j_ on the differences between the two indexes is then determined by the difference in the price change of commodity _j_ and the average rate of inflation Ltt−1, and by the difference in the shares of commodity _j_ in the two baskets.

# **4 Data**

Two data sources were used to construct household-specific price indexes and analyze the impact of applying them in measuring poverty and inequality. The first source is price data on 12 consumption components corresponding to the two-digit Classification of Individual Consumption According to Purpose (COICOP) specification (UN 2018).<sup>4</sup> The time series of country-specific price indices by the COICOP two-digit groups covering the period from January 2010 to the latest month available for 2022 were obtained from the CPI data repository of the International Monetary Fund (IMF 2022a) for 47 countries in ECA (all except Kazakhstan, the Kyrgyz Republic [after January 2022], the Russian Federation [after February 2022], Tajikistan, Turkmenistan, and Uzbekistan [before January 2020]). The COICOP two-digit group price indices for Kazakhstan and the Kyrgyz Republic (after January 2022) were obtained from the countries’ national statistical offices; data for Russia, Tajikistan, Turkmenistan, and Uzbekistan (before January 2020) were not available.

The second set of data corresponds to household-level consumption expenditure. This information comes from microdata from the household budget surveys of 23 countries in ECA for the latest available year. These surveys are nationally representative and include detailed information on consumption expenditure for each household. Aggregates in these surveys are harmonized by extracting variables with the same definitions and employing standardized classification and aggregation methods to achieve maximum inter-country comparability. We partition the total household consumption expenditure into the 12 two-digit COICOP components and calculate the share of total consumption expenditure allocated to each component for each household. We also compute aggregate country-level shares. In the few cases in which consumption shares at the country level were missing, we imputed them from previous rounds of the survey.<sup>5</sup>

> 4 We focus on the two-digit COICOP classification to achieve better intercountry comparability and because of the lack of information in most of the ECA household budget surveys on the representativeness of the sample results based on more granular product classification.

> 5 <u>Table A1, in Appendix, indicates the year of the latest available survey and the sample size for each country.</u>

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professionals earn a premium compared to employees. On the other hand, in many high income country employers and professionals face a penalty compared to employees, and in high income countries where employers and professionals earn a premium compared to employees, the premium is small.

# _By Region of the World_

In Table 4 we report the results of the estimates of penalties (-) and premiums (+) by region of the world. As we have noted earlier, over 90% of our sample of surveys comes from either Latin America or Europe and Central Asia. Outside of Latin America and Europe and Central Asia, the largest group of surveys in our sample comes from Sub-Saharan Africa. We therefore focus on these regions in our regional analysis. We find some similarities between regions, but also some interesting differences, which suggest that care should be taken when generalizing the results from studies in Latin America to other regions of the world.

In all regions for which we have data (Latin America, developing Europe and East Asia, and Sub-Saharan Africa), non-professional own-account workers pay an earnings penalty compared to formal employees. On the other hand, while employers and professionals earn statistically significant premiums compared to employees in Latin America and SubSaharan Africa, this is not true in Europe and Central Asia. In both high income and developing European and Central Asian economies there is no statistically significant difference in the earnings of employers and professionals compared to formal employees. Similarly, informal employees earn significantly less than formal employees in Latin America, but not in the developing middle income European and Central Asian economies. Taken together, these results suggest that informal employees are particularly disadvantaged in Latin America (but not in the developing economies of Europe and Central Asia), while employers and professional own-account workers do much better in Latin America and Sub-Saharan Africa than in Europe and Central Asia. As Latin American and the developing countries of Europe and Central Asia have similar GDP per capita, it is not likely that level of development explains these regional differences. Regional differences may be due to different legal and regulatory environments, an issue we examine in more detail in a companion paper (Gindling, Mossaad and Newhouse, 2015).

# _For Urban Workers Only_

Many analyses of self-employment and labor market segmentation focus on urban and nonagricultural labor markets. Therefore, it is useful to examine the results for only urban workers and see if they are consistent with results found using data for all workers (urban plus rural).<sup>9</sup> Table 5 replicates Table 3 using data for only urban workers.

There are no noticeable differences between tables 3 (urban plus rural) and tables 5 (urban only). The results for urban workers are similar to the results for all workers together.

> 9 Results for non‐agricultural workers only are similar to those presented for urban workers only.

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**Figure 5: Demographic change contributes positively to growth in all countries but South Africa where the working-age population is growing slowest and the aged share are growing fastest.**

<!-- Start of picture text --> Real GDP per capita growth rates, 2015-50 (average annual, percent)<br>3.5<br>Baseline No demographic effects<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>Botswana Namibia South Africa Lesotho and Swaziland<br><!-- End of picture text --> Source: Authors’ simulation results In contrast, demographic change can be found to be a drag on South Africa’s growth over the next 35 years, consistent with other recent simulation analyses for the country.<sup>13</sup> In the case of South Africa, the working-age population is already relatively high, and so the labor supply is not growing as fast as in other countries. More importantly, the working-age population is growing more slowly relative to the total population. Under constant employment ratios, the labor supply would thus be growing more slowly in the baseline than in the _no demographic effects_ scenario where labor supply grows at the rate of the total population. In the baseline, the population share of elderly is also growing faster than in any other African country, except for Mauritius (Figure 6). This has negative impact on savings as a share of GDP, and subsequently on investment. As the aged dependency ratio rises, there is a greater drag on savings and subsequently investment. However, in the _no demographic effects_ scenario, changes in the aged dependency ratio have no impact on changes in savings behavior, and subsequently on investment.

Demographic change and realization of demographic dividends both have substantial impacts on poverty reduction success in SACU. Botswana, Namibia, and South Africa are able to almost eradicate extreme poverty, by reducing their poverty headcount rates by 11.6, 16.9 and 13.4 percentage points, from 2012 rates of 13.5, 19.7 and 16.2 percent, respectively, by 2050 (Figure 7). Lesotho and Swaziland are also able to reduce their poverty headcount rates by two-thirds over the same time period. When the sources of the poverty reduction are considered, demographic change shifting the distribution of households alone was responsible for almost 20 to 60 percent of poverty reduction, while realization of the demographic dividend was responsible for another 10 to 20 percent. In Swaziland, 75 percent poverty reduction was due to

> 13 Ahmed et al. (2016) and World Bank (2015c).

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taxes, direct transfers, and indirect taxes drive the changes in poverty from market income plus pensions to consumable income. The impact of indirect taxes practically wipes out the poverty gains for moderate poverty and has a sizable effect on the extreme poverty reduction. The higher poverty reduction in extreme poverty is due to direct transfers being concentrated in the first quintile (see next section).

Fiscal interventions lead to an important reduction in both the poverty gap and the squared poverty gap. For the extreme poverty line, the poverty gap reduces to half and the squared poverty gap to a third, from the MIPP to consumable income. So, even though the fiscal system does not reduce the headcount, it does alleviate the situation of the poor.

**Table 3. Poverty headcount ratio by income concept**

||**Market income**<br>**pluspensions**|**Disposable**<br>**Income**|**Consumable**<br>**Income**|
|---|---|---|---|
|**Extreme poverty**||||
|Headcount Index|14.9%|7.1%|9.0%|
|Poverty Gap|10.5%|2.8%|3.6%|
|Squared Poverty Gap|8.9%|1.3%|1.8%|
|**Moderate poverty**||||
|Headcount Index|32.8%|26.0%|32.2%|
|Poverty Gap|19.1%|11.5%|14.4%|
|Squared Poverty Gap|14.5%|6.9%|8.6%|

Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal. Notes: The moderate poverty line used is half a minimum wage of 2019 in Brazil: BRL 499, while the extreme poverty line is BRL 178.

# 3.3 Concentration of taxes and transfers

Two aspects stand out regarding the concentration of fiscal interventions in Brazil: direct transfers and direct taxes. Half of the direct transfers go to the first income quintile: this group includes the population in extreme poverty and part of those in moderate poverty (Figure 5). This could be a reason for the reduction in extreme poverty being higher than that in moderate poverty. We also observe that the richest income quintile pays almost all direct taxes and the proportion of indirect taxes paid by the highest income quintile is close to their share of market income plus pensions. Meanwhile, the share of direct transfers, education, and health is higher than the participation in market income plus pensions for the first three quintiles. When we consider education in-kind transfers, it is higher for the first quintile and gradually decreases until the fifth quintile, which receives about 12% of total education expenditures. The distribution of health benefits, in turn, is similar to that of education but with a slightly lower percentage for the bottom 40% of the population and slightly higher for the top 40%.

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per billion (ppb) of arsenic; 2) based on the Government of Bangladesh (GoB) standard of less than 50 ppb of arsenic; and 3) based on a combination of the WHO and GoB standards by categorizing three risk levels as low risk (0-10 ppb), medium risk (11-50 ppb), and high risk (51+ ppb). After controlling for a range of demographic, social, and economic characteristics of households, the results from the logit regression models show that arsenic contamination is significantly and negatively associated with the overall ECDI measurement, and specifically on outcomes within the physical, social-emotional, and learning skills domains. Further, there was a clear dose-response relationship, where those children with exposure to higher concentrations of arsenic had relatively worse developmental outcomes. Moreover, children from non-poor households significantly had better results on the EDCI than children from poor households when exposure status of arsenic was comparable. However, non-poor children who are exposed to arsenic had worse development outcomes than poor children who are not exposed.

The paper contributes to the literature on arsenic and cognitive development in three ways. One is that it specifically examines the developmental effects of arsenic poisoning in younger child populations. It also demonstrates a clear dose-response relationship and compares the effects based on both WHO and Bangladesh water quality standards. Finally, it utilizes a large nationallyrepresentative sample size of around 7,500 children aged 3-5 and observes differences in poor and non-poor populations. The paper continues in four sections. Section 2 provides a background on arsenic contamination in drinking water in Bangladesh and summarizes evidence on arsenic’s subsequent effects on health and cognition to corroborate why there could be a negative effect of arsenic on early childhood development skill domains. Next, section 3 describes the data used for the analysis and the methods for building the logistic regression models, and section 4 presents the results. Finally, section 5 discusses the findings and policy implications of the presented analysis.

# 2. Background: Arsenic contamination in Bangladesh

# and its effects on health and cognition

Arsenic poisoning from drinking water in Bangladesh was ironically an unintended consequence of campaigns that aimed to increase population access to safe and clean water. During the 1970s, international organizations and NGOs encouraged Bangladeshi households to stop drinking from surface water sources such as ponds, rivers, and lakes, which are highly susceptible to fecal and 4
<!-- Start of picture text --> Figure U5: Percentage of Firms Using E-filing<br>by Number of Taxes Paid<br>0<br>1<br>Co<br>TT<br>3<br>:<br>5<br>0 20 40 60 80 100<br>Percent (%)<br>Source: IFC 2009 [Ukraine]<br><!-- End of picture text -->
accountability. Particularly significant is the number of countries that is below the minimum **conditions to implement appropriate procedures of accountability. Among them, countries with the lowest levels of informal accountability are Chile and Panama. In the case of Chile, both agencies (the National Energy Commission and the Electricity and Gas Supervision Agency) are fully accountable to the Executive, only reporting through annual reports. Panama’s performance in the informal accountability index is also explained by its accountability to the Executive and the use of just an annual report for that obligation. Furthermore, it is explained by the low levels of independence of the office in charge of internal auditing.**

# **4.3.4  Regulatory, Management, and Institutional Tools:**

**The fourth variable of analysis is the Tools Index, which encompasses the instruments available to agencies to perform their duties, including those aimed at improving not only regulatory actions but also institutional development.**

<!-- Start of picture text --> Among the different countries that compose the index, only Guatemala, Brazil, Argentina, Peru, and<br>Jamaica qualify as countries in Tier 1 of the Index. With different degrees, they meet “desirable”<br>tools and procedures to achieve a good regulatory management. On the contrary, Ecuador,<br>Honduras, and Mexico have agencies that do not meet the minimum requisites of the index.<br>Figure 21: Tools Index<br>Tools/Capacity Index<br>Source: LAC Electricity Regulatory Governance Database, The World Bank, 2007.<br>1<br>.8<br>.6<br>Index (0-1) .4<br>.2<br>0<br>GUA BRA ARG PER JAM T1 BOL T&T URU COL ESV NIC C R BAR PAN CHI R D T2 ECU HON MEX<br><!-- End of picture text -->

**It is interesting to highlight that Colombia and Chile improve their previous positions compared to other indexes such as autonomy. This fact can be explained by the emphasis given by these agencies to develop regulatory capacities and not necessarily to procedures that guarantee its independence from policy formulation authorities.**

**In order to assess agencies’ performance in this variable, we divided its indicators into two components. The first component, regulatory tools, includes instruments related to the conduct of regulatory policies. It includes mechanisms such as benchmarking, the methodology for tariff revision, and instruments that regulate consumers’ rights. The second component, institutional tools, involves instruments aimed at improving the institutional development of the agency’s management and its decisions. It includes mechanisms such as regulatory quality standards, public consultations, the structure of posts and salaries, performance-based salaries (incentives) for employees, the use of**

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level desired by shareholders. Moreover, we continue to obtain the same conclusions on shareholder protection laws, cash-flow rights, and the interaction between shareholder protection laws and cashflow rights. While not capturing all mechanisms through which supervision/regulation may positively and negatively influence bank valuations, these results confirm and extend the paper’s initial results.

The second extension of the results on bank supervision and regulation focuses on broad, national institutions. Perhaps supervision and regulation only improve the corporate governance of banks when there are good national institutions, such as low levels of government corruption, efficiently operating bureaucracies, or responsive political systems. To assess this possibility, we added an institutional indicator along with the interaction between the institutional indicator and the relevant supervisory/regulatory variable to the regressions in Table VI. As an institutional indicator, we used Kaufman, Kraay, and Zoido-Lobaton’s (1999) aggregate institutional development index, which is calculated as the average of six indicators: voice and accountability in the political system, political stability, government effectiveness, regulatory quality, rule of law, and the absence of official corruption. In no case did the bank supervision/regulation variables or the interaction term between institutional development and the bank supervision/ regulation variables enter significantly. Moreover, in no case did adding these variables alter the results on cash-flow rights, shareholder rights, or the interaction of shareholder rights and cash-flow rights.

Third, it might be suggested that supervisory power, supervisory independence, regulatory restrictions on bank activities, and capital restrictions enhance the corporate governance of banks and thereby boost bank valuations only when shareholder protection laws (SRIGHTS) are weak. We tested this by adding the interaction term SRIGHTS multiplied by the relevant supervisory/regulatory variable to each regression in Table VI. In no case do we find that the supervisory/regulatory variable 25
fraction of students from every quintile is concentrated in the middle tiers. Because of this “thick middle,” cumulative performance varies little, on average, across abilities. A similar picture holds for year 5 (panel b), although by then all the performance distributions are more concentrated in the top three tiers. Further, the performance distribution for low-ability students is more dispersed than that of high-ability students in both years.

**Figure 6: Tiers of Cumulative Classes Completed, by Ability.**

<!-- Start of picture text --> 60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>1 2 3 4 5 1 2 3 4 5<br>Ability quintile Ability quintile<br>Tier 1 Tier 2&3 Tier 4 Tier 1 Tier 2&3 Tier 4<br>Percent Percent<br><!-- End of picture text --> <!-- Start of picture text --> a. Year 1 b. Year 5<br><!-- End of picture text --> _Source_ : Calculations based on SPADIES for students from the 2006 entry cohort (first semester). _Notes_ : For students of a given ability who start year 1, panel a shows their classification into tiers of cumulative classes completed by the end of year 1. Panel b does the same for students beginning year 5.

# _Fact 6: Higher-ability students attempt a higher number of classes._

Although a student cannot fully control her performance, she can control the number of classes she attempts (registers for) in a given year. While she may not pass all the classes attempted, the number of attempts is informative of her intended effort. As Figure 8 shows, on average students attempt fewer than the 20 required classes. In addition, on average high-ability students attempt more classes than low-ability students. We will return to these facts when discussing identification of our empirical model.

**Taking stock.** The data shows that students of higher income or ability are more likely to enroll in college. Conditional on enrolling, lower ability students are much more likely to drop out, particularly in year 1. Through this channel, ability serves a strong predictor of graduation. Ability, however, is not a strong predictor of cumulative performance. Although cumulative performance varies little, on average, across abilities, it varies greatly within abilities. Further, cumulative performance is highly persistent over time—students who start on track are more likely to remain on track, less likely to fall behind, and more likely to catch up should they fall behind. As a result, cumulative performance is highly predictive of final outcomes. Nonetheless, even on-track students face the risk of falling behind or dropping out. Our model seeks to capture these data features.

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