rafmacalaba/datause-encoder-data huggingface dataset:
23

**Table 4**

|**EULER**EQUATION AND THE EFFECT OF FINANCIAL REFORM<br> <br> <br>|
|---|
|Coemcient<br>(A)<br>(B)|
|(Xl<br>0.190<br>0.164|
|(2.42)<br>(2.9|
|cx2<br>0.041<br>0.032|
|__________<br>__<br>_<br>(3.78)<br>(3.52)<br>|
|O3Na<br>0.051<br>0.013|
|._________________<br>(2.41)<br>(1.51)<br> <br>|
|a3Ob<br>0.036<br>0.014<br>|
|______<br>________<br>X_____<br>(2.49)<br>(2.54)<br> <br>|
|oa4Oa<br>-1.884<br>|
|-(1.49)<br> <br>|
|z40b<br>-1.862<br>|
|___________________<br>~-(1.70)<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>|
|WI<br>25.02<br>18.741<br>|
|6<br>4<br>|
|W2<br>12.87<br>6.547|
|4<br>2|
|Ml<br>-8.59<br>-9.035|
|Sargan/Hansen<br>15.981<br>21.995<br> <br>|
|l________________ _l17<br>19<br>See footnotes to Table 1. In the definition of instruments we allow for<br>the pre and post liberalization<br>split for the debt to capital ratio.|

Although we do not present the econometric estimates for reasons of space, we have obtained very similar results when firms are divided according to their age. Also in this case, the financial constraints facing young firms were not affected significantly by financial reform.

One of the drawbacks of the Euler Equation approach is that it imposes a tight structure on the data and is sensitive to extraneous model mispecification. For this reason, it is useful to supplement the results obtained from Euler equations with those derived from estimating a more loosely specified investment equation of a generalized accelerator type, with the addition of variables that measure the availability of internal funds, like cash flow. The idea is that, once we control for variables that capture investment opportunities (the change and the lagged level of output in the equation below) cash flow should matter more if a firm is financially constrained. 21 The

> **21** See Harris, Schiantarelli and Siregar (1992) for evidence from unrestricted investment equations estimated on a panel of Indonesian firms that financial reform has been beneficial for smaller firms in
per capita household income) accounted for 3.1 per cent of total income while the top decile accounted for 43 percent. The ratio between the income of the top and bottom decile was 45 times (de Ferranti et al., 2004 for all figures). An important part of the reason for high levels of poverty in Mexico is the high level of inequality. On average, East Asian countries are much more equal, and so have lower levels of poverty for their mean income level. Malaysia has levels of incomes inequality significantly above the East Asian average, and, as shown in Table 1.1, is only somewhat more equal than Mexico, and less equal than Costa Rica and Uruguay.

National and urban income inequality still high in Mexico despite some recent improvements, particularly in the 2000-02 period as the following table shows.

_Table 1.1 Income inequality in Mexico in an international perspective_

||_Gini_<br>_coefficient_|_Share of top 10_<br>_per cent in total_<br>_income_|_Share of bottom_<br>_20 per cent in_<br>_total income_|_Ratio of incomes_<br>_of 10_<sup>_th_</sup>_to 1_<sup>_st_</sup><br>_decile_|
|---|---|---|---|---|
|Brazil (2001)|59.0|47.2%|2.6%|54.4|
|Guatemala (2000)|58.3|46.8|2.4|63.3|
|Colombia (1999)|57.6|46.5|2.7|57.8|
|Chile (2000)|57.1|47.0|3.4|40.6|
|Mexico (2000)|54.6|43.1|3.1|45.0|
|Argentina (2000)|52.2|38.9|3.1|39.1|
|Jamaica (1999)|52.0|40.1|3.4|36.5|
|Dominican Rep. (1997)|49.7|38.6|4.0|28.4|
|Costa Rica (2000)|46.5|34.8|4.2|25.1|
|Uruguay (2000)|44.6|33.5|4.8|18.9|
|Malaysia (1997)|49.2|38.4|4.4|22.6|
|United States (1997)|40.8|30.5|5.2|16.9|
|Italy (1998)|36.0|27.4|6.0|14.4|

_Source:_ Based on de Ferranti et al., 2004 _Table 1.2 Inequality Measures_ _<u>for Mexican Households, 2000 – 2004.</u>_

||_2000_|_2002 _|_2004 _|
|---|---|---|---|
|_Urban_||||
|Gini|0.484|0.462|0.476|
|Theil|0.472|0.408|0.489|
|_Rural_||||
|Gini|0.532|0.496|0.468|
|Theil|0.624|0.546|0.458|
|_Total_||||
|Gini|0.536|0.508|0.503|
|Theil|0.576|0.502|0.539|

Source: Own estimates based on ENIGH.

3
more than two years after the cessation of the cash transfer experiment in December 2009, tracking the adolescents as many of them moved on to establish their own families. These longitudinal data paint a very rich picture of the transition from adolescence into adulthood in this context. By 2012, the study stratum that had dropped out of school at baseline had effectively completed their schooling with an average of a seventh-grade education; 81% were married, 92% had been pregnant, and only about a third had done any wage work in the past three months. More than one in eight (13.5%) had been infected with HIV. The stratum of baseline schoolgirls are better-off and younger, and therefore had not proceeded as far in their transition to adulthood: in 2012, their average years of schooling was 10.4 and increasing, with only 40% ever married, 50% ever pregnant, and 5.5% HIV-positive.

In the latest follow-up survey of the study sample, which was more than two years after the cessation of cash transfers, we attempted to trace the pathways through which experimentally induced changes in human capital may translate into longer-term outcomes. Zomba is an almost exclusively agricultural economy characterized by low educational attainment and few opportunities for formal employment. As of 2009, this district was the third poorest in Malawi (in our sample, real monthly per-capita exchange rate comparable consumption in 2008 was USD 20.6). Secondary school completion rates are low – in our sample, among baseline schoolgirls, half of whom had completed primary school at baseline, only 17.0% had completed secondary school as of 2012. Although most adults 15 and over participate in some form of employment, the majority do not receive a formal income. In 2008, only 6% of the adult population received a formal income (Zomba City Assembly 2009), a number that is reflected in our data with 6% of baseline dropouts and 3% of baseline schoolgirls participating in any formal work. This context is typical for many parts of rural Africa, and, hence, is an important 9
every household in a village, checking for individuals with symptoms. Compared to villages that did not receive ProACT, the odds of symptomatic malaria were 30 times lower in the intervention villages (Linn et al. 2015). This type of policy could be applied to areas that receive travelers during the weeks when the most expected infections enter.

Finally, just as mobile phones can be used to measure aggregate movement patterns to improve targeting, they could also be used more directly for the targeting. Mobile phones are always linked to a tower if they are turned on; therefore, the provider knows as soon as an individual changes towers. An innocuous policy could be to allow users to opt into an information program that sends a targeted text message as soon as someone that has opted into the program changes location from a high malaria tower to a low malaria tower. The message could recommend and provide an incentive to get tested at the closest clinic for free. Text messaging has been used by the Ministry of Health in Senegal for providing information on diabetes, and other studies have found that SMS technology can be eective in changing behavior (Senegal Ministry of Health 2016, Aker and Ksoll 2019, Ksoll et al. 2014, Fischer et al. 2016). Mobile phones could be used for more controversial policies such as targeting mandatory quarantine. There are important ethical concerns in how new technology is used, and the severity and risk of the disease may aect the strategies that are considered.

# 5.2 Targeting Simulations

The policies in the previous section serve as examples, but I focus the simulations on how a policy should be targeted, rather than the particular policy. If there were no targeting and instead the policy were to test every single traveler coming into the 5 pre-elimination districts that are the focus here, it would mean testing 6,956,197 travelers in 2013 based on the scaled mobile phone data. If each traveler were successfully tested so that all travel cases of malaria were found, treated, and secondary infections were prevented, it would mean 663 cases treated or prevented, representing around 44% of total cases in this area. Even just taking the cost of an RDT to test each person ($0.50), without adding any other costs 28
- 2 -

# **I. Introduction**

Health care access is still an important challenge in many developing countries. In particular in remote rural areas, it is not easy to access good quality health care services (e.g., Gamm et al. 2003; Institute of Medicine 2005; Bourke et al. 2012). Even in the United States, the rural elderly living in remote areas are 13 percent less likely to visit the emergency department than their urban counterparts (Lishner et al. 2000).

Transport connectivity is among the most important constraints in remote areas. Travel distance for rural people to visit a health facility is often much longer than that for urban dwellers (Gamm et al. 2003). This is particularly true in developing countries. In Zambia, for instance, about 75 percent of urban residents, but only 30 percent of rural residents, live within 2 kilometers of the nearest health facility (Zambia Central Statistical Office 2012). In Liberia, about 40 percent of rural residents must spend more than 2 hours to walk to the nearest health facility (Kruk et al. 2010). In Africa, about 70 percent of the total rural population do not have access to any all-weather road (Roberts et al., 2006). Transport services to visit health facilities are also not be available in rural areas even though the road network exists (e.g., Klemick et al. 2009).

Notably, however, the potential impact of better transport connectivity on people’s health care access is less obvious than we might think. There are a variety of effects on both the demand and supply sides, which make it difficult to measure the true impact of transport connectivity. For instance, rural residents may have less reliable transport connectivity to health facilities, but their household income is often lower than that in urban areas. Subscription to health insurance is also lower in rural areas (Jovanovic et al. 2003). Health insurance is an important policy measure to improve people’s health care access in developing countries (see, e.g., Spaan et al., 2012). Because of the lack of proper insurance, rural residents are less likely to use emergency medical services (Fan et al., 2011).
to commodity cycle price booms that were highly prevalent in Latin America during our sample period. The impact of SPS provisions is measured on firm exports of agricultural and agro-food HS 6-digit products (i.e., those belonging to HS Chapters 01-24) while the impact of TBT provisions is measured on firm exports of any HS 6-digit products (except HS 25-27). Our key outcome variable is firm export value at the destination-HS 6-digit product-year level. For the PPML estimation of our firm gravity equation described in Section 3, we need to expand our initial exporting country-firm-destination-HS 6-digit-year database to account for zeros. Specifically, we fill in the initial database so that each firm-destination-HS 6-digit has an observation (a row) in all of that exporting country’s sample years, with a 0 export value in a year when exports by the firm-destination-HS 6-digit do not take place.<sup>19</sup> This expanded database combined with the type of fixed effects included in our firm gravity equation allows us to exploit changes in firms’ exports over time as PTAs enter into force over the sample period to identify our coefficients of interest.<sup>20</sup> Table 2 shows summary statistics for each exporting country on the number of exporting firms, their export values, destination and product diversification patterns. The statistics are averages across the sample period and are provided for all exporting firms (Panel A) and for firms exporting agricultural and agro-food products (Panel B). The numbers of exporting firms are quite similar across the three countries. The median number of destination markets and products per firm is low, 1 or 2 at most. Firms selling agricultural and agro-food products have higher average and median export values and are more diversified than the average for all exporting firms. These patterns are not surprising given our countries’ comparative advantage in agricultural and agro-food products.

> 19A similar fill-in procedure is used in the study of SPS standards and export decisions by Fernandes et al. (2016).

> 20Appendix Table C.2 shows an example of our data fill-in procedure. We did not pursue the approach of filling in the initial database so as to make it a ‘square’ matrix where every firm in an exporting country would have an observation for every destination-HS 6-digit-year combination as is done in traditional gravity equation estimation based on more aggregate data since such database would be highly cluttered by zeros (as most firms export few HS 6-digit products to few destinations), would embed unfeasible economic choices, and would be computationally impossible to handle. Appendix Table C.3 shows the total numbers of observations in the initial database and the expanded database for each country.

11
possibility of differential levels of sunk investments in Abidjan and non-Abidjan routes. If the CIV war led to under-investment public goods in the Abidjan route, one would expect that firms and people would find it costly to relocate back to the Abidjan areas after the end of the war in 2007. The sunk investments can create “quasi rents” a la Milgrom and Roberts (1992), and slow down the process of the readjustment and reversion back to the original equilibrium. We consider two potential sources of such sunk investments: built-up density and public non-transport infrastructure provision such as electricity. As the trading and general economic activity suffered in the Abidjan route because of the CIV civil war, it is likely that government reallocated public investment from the Abidjan areas to the non-Abidjan areas. We analyze the effects of the CIV shock on night-lights density as an indicator of public investment in electricity.

# **Evidence on Built-up Density**

It is difficult to find data on indicators of sunk investments, but the recently available global settlement data provide an excellent source especially in the context of developing countries. We take advantage of the global settlement data to explore the effects of the CIV shock on built-up density as a measure of sunk investment. The estimates of the effects of the CIV shock on the proportion of area built-up density are reported in Table 3. The effect of the CIV shock on the integrated areas of the non-Abidjan route in this case is estimated as a quadruple difference, please see the coefficient of the ( _DT_<sup>0</sup><sup>_._45</sup> _× DNA × D_ 2014) in row 1 of Table 3. Column 1, row 1 of Table 3 gives us the average effect on built-up density over the whole integrated segment along the non-Abidjan route; the estimate suggests that the proportion of area built-up experienced a 2.6 percentage points increase as a result of the CIV shock compared to that in the integrated segment of the Abidjan route. This supports the idea that the CIV shock created preconditions for longer-term effects on the employment structure and the effects are likely to be persistent.

The estimates for possible heterogeneity within the integrated segment shows that the point estimates increase monotonically, but the numerical magnitudes are not substantial (see column 2, row 1 for the estimate for _DT_<sup>0</sup><sup>_._40</sup> , and column 3, row 1 for _DT_<sup>0</sup><sup>_._30</sup> ).

29
# Figure 12. Ratio of Population Aged 12-24 to Population Aged 25-59, 1950-2050, United Nations Medium Variant Projections

<!-- Start of picture text --> Figure 11a. Selected Countries with Peak in Youth Population before 2000 Figure 11b. Selected Countries with Peak in Youth Population in 2000-2010<br>Population Aged 12-24 over Population Aged 25-59, U.N. Medium Variant Projections Population Aged 12-24 over Population Aged 25-59, U.N. Medium Variant Projections<br>1 1.2<br>0.9<br>1<br>0.8<br>0.7<br>0.8<br>0.6<br>0.5 0.6<br>0.4<br>0.4<br>0.3 Thailand<br>Costa Rica<br>China<br>0.2 Mexico<br>0.2 Brazil<br>0.1 Viet Nam<br>Indonesia<br>0 0<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050<br>Year Year<br>Figure 11c. Selected Countries with Peak in Youth Population in 2010-2030 Figure 11d. Selected Countries with Peak in Youth Population after 2030<br>Population Aged 12-24 over Population Aged 25-59, U.N. Medium Variant Projections Population 12-24 over Population 25-59, U.N. Medium Variant Projections<br>1 1.2<br>0.9<br>1.0<br>0.8<br>0.7<br>0.8<br>0.6<br>0.5 0.6<br>0.4<br>Philippines 0.4 Kenya<br>0.3 Malaysia Democratic Republic of the Congo<br>Egypt Iraq<br>0.2 Peru Senegal<br>India 0.2 Pakistan<br>0.1<br>0 -<br>1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050<br>Year Year<br>Ratio Ratio<br>Ratio Ratio<br><!-- End of picture text --> 38
Table D.12. Effects of Guerrilla Territorial Control in the Elections of 2014 and 2015

|_Panel A: 2014 Presid_|_ential elections_<br>|_- Guerrillas’ Par_<br>|_ty won_<br>||
|---|---|---|---|---|
||Left Voting|Right Voting|Blank Voting|Turnout|
||Share|Share|Share|Share|
||(1)|(2)|(3)|(4)|
|Guerrilla control|-0.0350*|0.0341|0.00387***|0.0166|
||(0.0199)|(0.0221)|(0.00131)|(0.0103)|
|Observations|416|416|416|416|
|Bandwidth (Km)|2.930|2.930|2.930|2.930|
|Dependent mean|0.483|0.395|0.00700|0.565|
|_Panel B: 2015 Munic_|_ipal elections_||||
|Guerrilla control|-0.0152|-0.00723|0.00207**|0.0300|
||(0.0278)|(0.0259)|(0.000905)|(0.0219)|
|Observations|434|434|434|434|
|Bandwidth (Km)|3.239|3.239|3.239|3.239|
|Dependent mean|0.411|0.629|0.00700|0.513|

_Note:_ The table presents the results of equation 1 for our outcomes related to electoral results. The unit of observation in all Columns is the polling station. Panel A shows the results for the presidential elections of 2014 and panel B does the same for the municipal elections of 2015. The information was obtained from the Salvadoran Electoral Court. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates weight using a triangular kernel. Clustered errors at the Canton level are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

86
efficiency; and employ financial tools in a way that promotes efficiency (Bird and Slack 2004a). These tasks are often more complex in metropolitan regions than for local governments in general.

Improving the level of basic service delivery is partly a question of resources but it is also a question of governance and allocation (Stren 2007). Most metropolitan areas are characterized by multiple municipalities each delivering services within their own jurisdiction. The majority of urban services (transportation, water, solid waste, and housing, for example) spill over these municipal boundaries. In transportation, for example, there is a need to coordinate services across municipal jurisdictions. For water, there is a need to determine where treatment facilities will be located. For solid waste, there is a need to determine where garbage disposal sites will be located. With respect to police protection, crime does not respect municipal boundaries so service coordination across boundaries is needed. Moreover, there is a need to coordinate policing with other policies and services. For social services, health services, and education, there is a need to determine the appropriate level of expenditures and how costs will be shared among municipalities in the region.

Local governance (which includes, for example, local and regional governments, civil society, business associations, and non-profit organizations) affects the quantity and quality of services, the efficiency with which services are provided, and whether costs are shared throughout the region in a fair and efficient way.<sup>2</sup> The governance structure of a metropolitan area also has an impact on citizen access to government (the degree to which citizens are involved in decisionmaking) and government accountability to citizens (how responsive the city is to their demands).

This paper provides some observations on the governance of metropolitan areas with some examples from case studies from around the world. It is not intended to provide a comprehensive review of each of the case studies but rather to use them to illustrate some of the observations about urban governance that appear in the literature. The case studies were chosen to reflect

> 2  In one of the few empirical studies of governance and urban performance, Kaufmann et al. (2004) construct a worldwide database of cities containing some key determinants of city performance. They find that good governance (and globalization) at both the country and city level matter for city level performance in terms of access to services and quality of delivery of infrastructure services.

7
**Table 4. Average Expenditure Share of Energy Products**

(By expenditure quintile and location in percent of total household consumption)

|||Energ<br>|yProduct<br>||
|---|---|---|---|---|
|Quintile|Central Gas|LPG|Electricity|Central Heat|
||(1)|(2)|(3)|(4)|
|**_A. All households _**<sup>**_1_**</sup>|||||
|Poorest|1.0|0.4|5.4|0.1|
|Second|1.8|0.4|4.9|0.4|
|Third|1.7|0.4|4.9|1.0|
|Fourth|2.1|0.5|4.5|1.5|
|Richest|1.9|0.4|3.6|1.8|
|_All_|1.8|0.4|4.6|1.1|
|**_B. Connected households_**<br>|**_with positive expendi_**<br>|**_tures _**<sup>**_2_**</sup><br>|||
|Poorest|6.4|11.3|6.5|...|
|Second|7.1|8.7|5.6|14.4|
|Third|6.6|7.1|5.5|16.1|
|Fourth|6.3|6.3|5.0|15.8|
|Richest|4.6|4.4|4.3|11.0|
|_All_|5.9|6.5|5.2|13.4|
|Of which:|||||
|**_B1. Large Cities_**|||||
|Poorest|5.5|...|7.8|...|
|Second|3.6|...|8.0|14.3|
|Third|3.2|...|7.9|16.1|
|Fourth|2.6|...|6.9|15.8|
|Richest|2.1|...|5.3|11.0|
|**_B2. Small towns_**|||||
|Poorest|7.3|13.2|7.9|...|
|Second|8.4|12.0|6.8|...|
|Third|8.6|10.5|6.3|...|
|Fourth|10.6|8.8|5.6|...|
|Richest|10.4|6.3|4.8|...|
|**_B3. Rural areas_**|||||
|Poorest|5.0|10.0|6.1|...|
|Second|7.9|8.0|5.0|...|
|Third|8.3|6.4|4.7|...|
|Fourth|10.2|5.8|4.2|...|
|Richest|7.6|4.2|3.3|...|

Source: HBS and Bank staff calculations.

Note: "..." indicate insufficient observations.

1Unconnected households as well as households that are connected but have missing expenditure data are assumed to have zero expenditures on energy.

2Includes only households that are connected and are reporting expenditure information. Implicitly, the calculations include households that are connected but have missing expenditure data, and these households are assumed to have expenditure levels equal to _average_ expenditures by quintile and location.

10