rafmacalaba/datause-encoder-data huggingface dataset:
Source: Ministry of Public Works

**Table 2.4: Accidents on Chilean highways**

|**Year**|**Highway accidents (per 100,000 people)**|
|---|---|
|1985|8.7|
|1990|11.9|
|1994|18.3|
|1995|21.8|

Source: Ministry of Public Works With respect to airports, the situation was even more dramatic. Economic growth sharply increased demand for air transport. Passenger traffic, national and international alike, has grown at rates of up to 20% a year (see Table 2.5). In 1995, the total number of passengers that used airports was 3.5 times greater than 10 years earlier. This situation has created a high degree of saturation in the use of airport infrastructure, requiring urgent expansion in the capacity of most of the country's terminals.

**Table 2.5: Air transport traffic, 1991-96**

|**Year**|**Domestic passengers**<br>**(thousands)**|**International**<br>**passengers**<br>**(thousands)**|**Freight**<br>**(tons)**|
|---|---|---|---|
|1991|1,856|1,260|126,205|
|1992|2,428|1,382|134,371|
|1993|2,990|1,609|144,363|
|1994|3,569|2,000|180,704|
|1995|4,024|2,342|188,305|
|1996|4,750|2,610|204,000|

Source: Direcci6n General de AeronAutica Civil One estimate has put the economic cost of inadequate infrastructure-including waste water treatment-at more than US$1.5 billion annually (see Table 2.6).3 In addition, Chile has the strategic ambition-within the framework offered by Mercosur-of becoming the gateway for Latin American trade with Southeast Asia, Australia and New Zealand. However, this would require an additional endowment of more than 900 million dollars in infrastructure.

In 1995, MOP estimated that total infrastructure investment needs until the year 2000 would reach more than US$11 billion (Table 2.7). As a rule of thumb, the international experience indicates that infrastructure investment should reach between 3.5% and 5.5% of GDP. In Chile, 3.5% of GDP is equivalent to around US$1.8 billion. This last figure can be taken as a lower bound on the annual

> 3 The estimates presented in Table 2.6 were determined by the Chilean Chamber of Commerce, which had obvious vested interests in overstating the existing deficit. Therefore, these figures must be taken with a grain of salt. However, most analysts would agree with the general observation that there were significant costs related to the growing infrastructure deficit at the time.

4
effect than big city effect is apparent in the test statistics reported at the foot of Table 3, especially for the poverty gap index and in the indirect and total effects.

These differences between big city effects and smaller town effects are not an artefact of less brightly lit fringe areas of big cities being counted as part of the smaller, more dimly lit, towns. If a fixed mask approach is used that excludes areas that ultimately become part of the big city from being ever counted as part of small towns, the results are largely the same (columns (3) and (4)). The elasticities have slightly smaller magnitude than under the variable mask approach in columns (1) and (2) but the dominance of small town over big city effects is just as apparent. Another sensitivity analysis is to use the sum of lights approach, by multiplying lit area by the average DN within that area and the results in columns (5) to (8) confirm significantly larger secondary town effects. The results also hold if the distinction between smaller towns and rural areas is based on a 30% luminosity threshold rather than the 20% threshold used in Table 3 (Appendix Table 2 has these sensitivity analyses).

It might be argued that the “town effect” we identify may be in large part an indirect “city effect,” i.e., that city growth drives town growth, which then helps drive rural poverty reduction. Table 4 takes this argument seriously by providing analogous results to Table 3 but dropping the variables for towns and only reporting results for H since the results have similar patterns to those for PG. (For balance, the table also gives the results when we drop the city lights variables.) We find a total effect of the area of city lights of -0.715. Combined with the results of Table 3, this suggests that 87% of the total big city effect is via induced town growth, and 13% is direct. However, it should also be noted that the R<sup>2</sup> has fallen from 0.631 to 0.546 in comparing columns 1 of Tables 3 and 4. This suggests there is a sizeable share of the variance that may be a direct effect of town growth, independently of city growth. Moreover, the tests of the nesting restrictions needed to derive the ‘big city only’ or ‘small town only’ models show that the models that drop the variables for smaller towns are not consistent with the data, whereas one would not reject the restrictions needed for dropping the big city variables.

To summarize, our key finding is that the lights data indicate that the effects of urban economic growth on rural poverty are almost entirely attributable to towns rather than cities.

# _5.3 Distributional Effects of City and Town Expansion_

Do the effects we have seen on rural poverty measures entail significant systematic changes in the extent of inequality within rural areas? A simple test for such effects is to 18
**Table 1: Survey of Public Investment and Spending Multipliers**

|**Country &**<br>**Region**|**World Bank**<br>**Income**<br>**Group (2020)**|**Author(s)**|**Study**<br>**Period**|**Methodology,**<br>**including**<br>**identification**<br>**strategy**|**Multiplier range**|**Type**|
|---|---|---|---|---|---|---|
|Peru|Upper Middle<br>Income|Central<br>Reserve<br>Bank of<br>Peru (2012)|1992-2012|VAR (output<br>elasticities of<br>expenditure to filter<br>out automatic<br>stabilizers)|0.24; 0.92 (short-<br>term)<br>0.49; 1.42(medium-<br>term)|Current; Capital<br>spending|
|Peru|Upper Middle<br>Income|Rossini_et_<br>_al._(2012)|2005-2011|SVAR (a<br>multivariate, linear<br>representation of a<br>vector of observables<br>on its own lags)|0.78; 1.36 (short-<br>term) 0.52; 2.63<br>(medium-term)|Current; Capital<br>spending|
|Australia|High Income|Hunt_et al._<br>(2009)||DSGE model<br>(simulated impacts<br>on the output)|1.22<br>1.12|Public Investment<br>Public Consumption|
|Czech<br>Republic|High Income|Ambrisko<br>_et al._<br>(2015)|1996 - 2011|DSGE model<br>(simulated impacts<br>on the output)|0.5 (in the first year)<br>0.6 (in the first year)|Government<br>investment<br>Government<br>Consumption|
|Germany|High Income|Veld<br>(2016)||European<br>Commission’s macro<br>Quest model<br>simulations<br>(simulations under<br>different scenarios)|0.6|Public Investment|
|Italy|High Income|Acconcia_et_<br>_al._(2014)|1990 - 1999|OLS with IV to<br>address endogeneity|1.5|Public spending|
|Japan|High Income|Bruckner<br>and<br>Tuladhar<br>(2014)|1990 - 2010|Panel VAR at<br>prefecture level|0.26|Government Spending|
|Japan|High Income|Kanazawa<br>(2018)|1980 - 2014|Local projection – IV<br>method (using the<br>extracted measure of<br>the excess return<br>shocks as an<br>instrument variable)|1.64 (after one year)|Public Investment|
|Japan|High Income|Kuttner and<br>Posen<br>(2002)|1990 - 1999|VAR (output<br>elasticities of<br>expenditure to filter<br>out automatic<br>stabilizers)|2 (cumulative four-<br>year multiplier)|Public Spending|

5
The State Statistical Committee (SSC) of Azerbaijan has carried out a regular household budget survey since independence in 1991. However, the survey design which it inherited from the Soviet period did not allow it to supply reliable data on living standards. Since 1999 the SSC, in collaboration with a group of international technical experts, has been working on improving the HBS design in order to produce nationally representative data (Marnie, Lapins and Zaidi 2001). In designing the new survey, the team has attempted to rectify the faults of the old HBS, while limiting the additional human and financial resources required, since the survey has to be sustainable for the future. The 2001 Household Budget Survey was the first year of a newly designed survey. During the first year the SSC received technical assistance both from the World Bank and the Asian Development Bank (ADB) to further improve the methodology (SPPRED 2004). As a result of this further collaboration the way in which information on consumption expenditure was collected changed, and as a result the values of consumption obtained from the 2001 and 2002 surveys are not comparable. The 2002 Household Budget Survey is now considered as the baseline for future poverty monitoring (SPPRED 2004). We therefore use the 2002 HBS for our imputation in the census<sup>3</sup> .

However, using the 2002 HBS creates a series of problems for the practical implementation of this methodology due to the fact that between the year of census data collection (1999) and the 2002 HBS, Azerbaijan has experienced a sharp drop in fertility levels with the Total Fertility Rate (TFR) decreasing from around 2 children in 1999 to 1.8 children per women in 2001 (UNICEF 2003). For this reason we have had to modify our measure of

> 3 The World Bank is currently undertaking a Programmatic Poverty Assessment in Azerbaijan. The Azerbaijan Programmatic Poverty Assessment is a three-year program comprising technical assistance, capacity building and analytical work to be carried out jointly with Azeri counterparts. The technical assistance includes consultations on modifying the methodology for the consumption aggregate estimates and poverty line calculations. Potential changes include adjustments for differences in food prices, the usage cost (rental value) of durable goods, and exclusion of health expenditure. Such adjustments are unlikely to be evenly distributed across deciles suggesting that they would lead to a different welfare ranking. No formal agreement on revising the consumption aggregate had been reached when undertaking the poverty mapping study, which therefore relies on the welfare aggregate used by the SPPRED (2004). This consumption includes nominal expenditures on food, durables, health, and rent for housing.

5
# **3.2 Mobile Telephony**

Mobile broadband is the dominant channel through which people access the internet in many developing countries. In a global cross-country study, Edquist et al (2018) analyze the impact of mobile broadband diffusion on GDP between 2002 and 2014 by using a difference-in-differences model and show that a 10 percent increase in mobile broadband adoption boosts economic growth by creating a 0.6 to 2.8 percent increase in GDP. Similarly, Williams et al (2012) show that a 10 percent substitution from 2G to 3G increases GDP per capita growth by 0.15 percentage point in a cross-country study including Brazil, Canada, China, France, Germany, India, Italy, Japan, the Republic of Korea, Mexico, the Russian Federation, South Africa, the United Kingdom, and the United States. Haftu (2019) also analyzes the impact of mobile phones and the internet on per capita income in 40 Sub-Saharan African countries for the period of 2006 – 2015. A 10 percent increase in mobile phone penetration results in a 1.2 percent change in GDP per capita. Internet penetration, on the other hand, appears not to contribute to per capita GDP, potentially due to low technology penetration, low skills, lack of sufficient local content, and the relatively immature state of the technology in the continent.

Beyond macroeconomic effects, the widespread growth of mobile phone coverage in many developing countries has the potential to contribute to human development outcomes through the improvement of education outcomes. Aker et al (2012) look at the impacts of an adult education program in 113 villages in Niger. The goal of the program is to teach students how to use mobile phones, which enables them to practice their literacy skills outside class by sending and receiving text messages, making phone calls, and using mobile money applications. Adults in villages in which an education program was offered were tested immediately after the program and received test scores in writing and mathematics that were 0.19 – 0.26 standard deviations higher than those that did not participate. Moreover, standardized math test scores remained higher seven months after the end of the program. These results suggest that simple information technology can be harnessed to improve educational outcomes and build skills even among remote rural populations.

Mobile phone technology also contributes to labor market outcomes. Klonner and Nolen (2010) study the labor market effects of the rollout of mobile phone coverage from 1995 to 2000 in rural South Africa. The findings show that localities that received network coverage experienced a 15percentage point increase in employment, primarily among women no longer burdened with large childcare responsibilities. The authors also find a significant shift in occupational patterns in rural areas; with agricultural employment decreasing substantially, especially among males.

Mobile connections not only accelerate economic growth but also improve inequality in the income distribution. Using panel data of 177 countries for the period 1990 – 2019 and GMM11
Based on the registered pre-analysis plan,<sup>7</sup> we estimate the following ANCOVA specification to test the reduced-form, intent-to-treat impact of Jeevika on each group of outcomes: where ! is the outcome of interest for household 8 in village 9 in panchayat : in year ;, "#$7 -../012$ is random assignment of the panchayat to early (2012) rollout of the intervention, 4"#$ is a vector of pre-specified baseline controls used in the primary specification, 5 represents the $ vector of stratification dummies, and 6 is a random individual-level error (notation constant "#$ across specifications for simplicity). In addition, to test for heterogeneous treatment effects on households that were landless at baseline, we estimate specification (2):<sup>8</sup> where <' represents the treatment effect on land holding households, <% is the difference in outcome ! between landed and landless households in control areas at follow-up, <' + <3 is the treatment effect on landless households, and <% + <3 is the difference in the outcome between the landed and landless at follow-up in the presence of the intervention.

Specifications (3) and (4) below mirror those above aside from the omission of non-stratification baseline controls, and are used to test for balance on key outcomes and demographic variables at baseline.

> 7 https://www.socialscienceregistry.org/trials/570.

> 8 Analysis of heterogeneous effects based on both baseline landholdings and caste was specified in the pre-analysis plan for this study. There is significant overlap between the landless and SC/ST populations in the sample, and impacts on SC/ST households are very similar to impacts on the landless.
|**Table 13h. Impact of WTO Acce**<br>|**ssion on F**<br>Overall<br>_change from b_|**ederal Okrug**<br>_ase year, %_|**s (calibratio**|**n to input-o**|**utput factor**|**shares)**|||
|---|---|---|---|---|---|---|---|---|
||average|Northwest|Central|South|Volga|Urals|Siberia|Far East|
|**Social Welfare Indices (ev/GDP)**|||||||||
|sigma=0|2.4|3.6|2.8|2.8|2.6|1.7|2.5|3.6|
|sigma=1|3.8|4.7|3.9|3.8|3.8|2.7|3.1|4.4|
|sigma=4|4.3|5.3|4.3|4.3|4.5|3.5|3.6|4.8|
|sigma=+inf|4.5|5.4|4.5|4.5|4.8|3.8|3.8|5.0|
|ev/C|8.1|10.3|7.2|8.1|8.9|9.4|6.5|9.8|
|**Aggregate trade**|||||||||
|Regional terms of trade (% change)|3.4|4.6|3.4|2.5|3.3|1.8|1.7|3.5|
|Regional exports (% change)|1.8|2.5|2.4|1.6|1.9|0.6|1.3|2.3|
|Real exchange rate (% change)|2.7|3.4|3.0|2.7|2.7|1.9|1.8|2.9|
|International exports (% change)|8.9|13.0|16.6|10.4|12.4|2.6|8.6|9.9|
|**Return to primary factors**(% change)|||||||||
|Unskilled labor|5.0|6.6|4.9|5.0|5.3|4.1|4.1|6.3|
|Skilled labor|5.2|7.1|5.0|5.2|5.8|4.1|4.5|6.5|
|National capital|4.3|5.0|4.5|4.2|4.3|3.5|3.3|4.5|
|Regional mobile capital|7.0|9.6|7.4|6.6|7.8|4.9|6.1|8.0|
|Energy sector resources|3.1|6.6|15.9|5.1|5.4|1.4|7.6|8.7|
|Specific capital in domestic firms|-25.7|-26.1|-27.7|-27.4|-25.8|-22.6|-22.2|-25.5|
|Specific capital in multinational firms|103.2|67.0|74.2|126.9|147.4|185.2|165.4|121.4|
|**Factor adjustments**|||||||||
|Unskilled labor (% changing sectors)|2.3|2.9|2.2|1.7|2.5|2.0|2.2|2.5|
|Skilled labor(% changingsector)<br>Source: Authors' calculations.|2.6|3.3|2.7|2.0|2.6|2.6|2.5|2.9|

55
At the student level, the fact of failing a past school year negatively affects achievement for all students. An interesting phenomenon is present with the labor variables; that is, nonindigenous students are more negatively affected in test scores by working than are indigenous children. In particular, indigenous children do not seem to be affected by working in activities inside the house, and the negative effect for them comes from working outside the house. However, the magnitude of this effect is less than half the negative impact that this variable has for non-indigenous children. Finally, teacher and school impacts are small in magnitude for the achievement of all students and for the non-indigenous-indigenous score gap.

Decomposition results suggest that the proportion of the test score gap that is explained by family and school characteristics varies between 41 percent (Guatemala, language test) and 75 percent (Mexico, language test) of the overall gap. Furthermore, family variables contribute more than school variables to the overall explained component. Therefore, although a sizable gap remains, there is still much that can be done to reduce the gap in the future. Among promising policy interventions one could consider the following non-exhaustive list: (1) effective bilingual education instruction designed to provide indigenous children with mastery of the Spanish language before the completion of primary school; often times, however, bilingual education is poorly implemented even within schools designated as indigenous (see, for example, World Bank 2005); (2) compensatory education programs meant to equalize learning opportunities; these have proven to be very effective in closing learning gaps (see, for example, Shapiro and Moreno 2004); and (3) choice of school and increased autonomy; theoretically, school choice could lead to improved learning outcomes (see, for example, Angrist and others 2002), but there is little evidence on this for indigenous students, presumably since they are 23
Second, consider bank corruption, which is measured by asking firms whether corruption of bank officials is an obstacle to firm growth. These data are obtained from the World Bank’s World Business Environment Survey, and the details are described in greater detail in Beck, Demirgüç-Kunt, and Levine (2006). In particular, a value of one signifies that corruption is an obstacle, while a value of zero means that firms responded that corruption of bank officials is not an obstacle. The survey covers 2,259 firms across 37 countries in our sample. The firm-level regression in equation (2) of Table 1 controls for many firm level characteristics besides the national bank regulation indexes. These data allows us to test conflicting theoretical predictions regarding the impact of specific bank supervisory strategies on the extent to which corruption of bank officials impedes the efficient allocation of bank credit. The public interest view holds that a powerful supervisory agency that directly monitors and disciplines banks can enhance the corporate governance of banks, reduce corruption in bank lending, and thereby boost the efficiency with which banks intermediate society’s savings. In contrast, the private interest view argues that politicians and supervisors may induce banks to divert the flow of credit to politically connected firms, or banks may “capture” supervisors and induce them to act in the best interests of banks rather than in the best interests of society. This theory suggests that strengthening official supervisory power – in the absence of political and legal institutions that induce politicians and regulator to act in the best interests of society – may actually reduce the integrity of bank lending with adverse implications on the efficiency of credit allocation.

As shown in Table 1 equation (2), there are two key findings concerning corruption and bank regulation. First, the results contradict the public interest view, which predicts that powerful supervisory agencies will reduce market failures, with positive implications for the integrity of bank-firm relations. Rather, we observe that Official Supervisory Power never enters the Bank Corruption regressions with a positive and significant coefficient.

Second, the results are broadly consistent with the private interest view. The positive coefficient on Official Supervisory Power is consistent with concerns that governments with powerful supervisors further their own interests by inducing banks to lend to politicallyconnected firms, so that strengthening official supervision accommodates increased corruption in bank lending. Beck et al. (2006) show that sound political and legal systems reduce the pernicious effects of official supervisory power, but they never find that empowering official 11
barriers keep women from entering certain jobs and demonstrating their (relatively equal) productivity, the residual could underestimate discrimination (see Blau and Kahn, 2006).

The sorting explanation for gender pay gaps is that women work in sectors and occupations that require less human capital and on-the-job training, and therefore earn lower wages (MacPherson and Hirsch, 1995; Blau and Kahn, 2006; Blau, Ferber and Winkler, 2010). Firm-level data can help control for gender sorting as they allow one to control for labor productivity and idiosyncrasies in employers’ wage setting behavior (Neumark and Hellerstein, 2005). The few studies that use matched firm-level data sets to examine gender pay gaps have yielded mixed results; women in the United States have been shown to earn less than men relative to their productivity, while women in Israeli firms do not suffer pay discrimination (Hellerstein and Neumark, 1995, 1999; Bayard et al., 2003).

A third explanation for gender gaps in wages is that they reflect differences in human capital. To test this, measures of human capital are added to the Mincerian wage regressions (Mincer and Polachek, 1974), which consistently show women earning lower returns (Blau and Kahn, 2000). Part of the explanation is that women have received less education in the past.

Although not a driver of gender pay gaps per se, minimum wages can affect the extent of gender gaps in wages as they place a bound on how low wages can be set (Rubery, Grimshaw and Figueiredo, 2005; Dex, Sutherland and Joshi, 2000). Minimum wages are more likely to bind for women, who tend to earn lower wages. Consistent with this observation, DiNardo, Fortin and Lemieux (1996) use a semi parametric decomposition analysis and show that increasing wage inequality in the United States, especially for women, can be tied to declines in real minimum wages.

Minimum wages may also have _lighthouse effects_ impacting the wages of those for whom they are not-binding, for example by serving a coordinating role in wage negotiations or because 6
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