rafmacalaba/data-use-mentions-extended huggingface dataset:
Table D.9. Main Results Restricting the Sample to Tracts without Sudden Altitude Changes with Respect to Their Neighbors

||Night Light Arcsine|Wealth Index|Years of Education|
|---|---|---|---|
||(2013)|(2007)|(2007)|
||(1)|(2)|(3)|
|Guerrilla control|-0.146***|-0.120***|-0.309**|
||(0.0240)|(0.0439)|(0.137)|
|Observations|2,572|2,561|2,562|
|Bandwidth (Km)|2.103|2.103|2.103|
|Dependent mean|3.743|0.118|6.924|

_Note:_ The table presents main results without considering segments that have a difference in altitude of more than 100 masl with respect to their neighbors. Column 1 shows the effect of whether a census tract was under guerrilla control on the arcsine of night light luminosity from NOAA. Column 2 does the same but uses as dependent variable a standardized score of household wealth. Column 3 shows as dependent variable years of education of the population older than 18 years. The unit of observation in all columns is the census tract. Information from Columns 2 and 3 was obtained from the Population Census of 2007. 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 use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0
higher share of health, on average, but this is only marginally significant. Lame-duck mayors are associated with lower share of health spending on average, a result that is consistent with the pattern observed in Table 2b of reduced health services and poorer outcomes at the village-level.

As indicated at the outset, a critical problem with the interpretation of the correlations between vote buying and access to health services is whether each is impacted independently by another set of variables such that there is little direct connection between the politics of clientelism and poor delivery of services. We argue that these concerns are attenuated by the wide array of results reported above which control for several variables—poverty, population, barangay location, household social capital, other indicators of municipal political competition— that could, potentially, be independently correlated with both vote buying and health services.

Additional results are available using household-level data to support the main results of the paper. Surveyed household respondents—who are young mothers—reported whether they received folic acid and iron supplements during pregnancy, and whether their children received iron supplements. In contrast to these supplements, whose supply can depend on actions of other politicians outside the municipality, the household survey also provides respondent-reported information on access to birthing assistance, a key health service provided by municipal health personnel (midwives and barangay health workers). Nutritional supplements are, however, also distributed by municipal health personnel, and are typically affordable within municipal budgets (as is evidenced by their showing-up as some of the village health projects financed by the municipal government).

The distribution of nutritional supplements might be compatible with or complementary to clientelist political strategies, as in the findings of Nichter (2011) in Brazil, and therefore not exhibit a negative association with vote buying.<sup>29</sup> Furthermore, household-level access to birthing assistance might also be potentially subject to clientelist targeting, with assistance provided conditional upon political support. Nichter (2011) suggests that in Brazil access to female sterilization, and generally to receiving attention from public
0.019|0.022|0.044|0.002|
|past day||(0.014)|(0.018)||(0.013)|
|Observations|871|2|,591|1,900|2,591|

_Notes._ Column 1 shows control means for females in ELA only communitues and column 4 shows control means for females not assigned to the _Goal_ treatment. Columns 2–3 and column 5 shows the difference between means in the corresponding community- or individual-level treatment arms and the corresponding control group means, controlling for randomization strata. Estimates in columns 2–3 come from a single regression, and estimates in column 5 come from another. See Table 1 for indicator definitions. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 2–3 and column 5. .01 ***, .05 **, .10 *. _Source._ Female baseline data, balanced panel.

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negative and statistically significant. This runs directly contrary to the coordination-cost hypothesis. Not surprisingly, the coefficient on the count of item-days with a purchase is positive and statistically significant. In columns 3 and 4 we repeat the analysis using percentage losses ( _L_<sup> ̃</sup> _h_ ) as the dependent variable. The signs and pattern of statistical significance are the same as for _Lh_ .

These findings do not support the hypothesis that coordination costs are responsible for the forgone bulk discounts in the SHWALITA data.

32
(figure 4). Moreover, economic activity in the formal and informal markets seems to be intertwined. According to the latest enterprise survey in 2015, around 65 percent of formal firms in Indonesia are competing with informal firms. This contrasts for example with Thailand where only 25 percent of formal firms compete with informal firms. It is also a larger share compared to EMDEs and EAP averages (respectively 54 and 47 percent) (figure 5).<sup>7</sup> Across the country, this practice is most prevalent in Bali, where almost all firms (98 percent) report competing with informal firms. Bali is a province particularly known for its dominant service sector, especially in tourism and hospitality as well as other low-value-added services.

# **3.3 Informality at the regional level in Indonesia**

25. **Across the region, most of the provinces in the eastern part of Indonesia show a relatively higher informal economy.** Papua province has the highest informal output, comprising almost 80 percent of its economy (figure 6). This is followed by three other provinces in the eastern region (East and West Nusa Tenggara, West Sulawesi) with around 70 percent of informal output. However, with unweighted computation,<sup>8</sup> Lampung province has the highest level of informal output with the informal output capturing almost 45 percent of total economic output. Different methods of measurement of informality also resulted in a significantly different informality level. For example, an Informal Sector Survey conducted by the Statistics Agency in 2010 in two pilot provinces (Yogyakarta and Banten) used questionnaires to determine output produced by production units in the informal sector. Output informality was estimated at 37 percent and 27 percent in Yogyakarta and Banten respectively, lower than the estimation produced by the model.<sup>9</sup>

**<u>Figure 6 Output informality by regions or by provinces</u>**

<!-- Start of picture text -
sample is very small). In the average Asian country, there was basically no change, although we again observe variations around the mean.

Because this article introduces a new method for gauging trends in global food security, it is especially important to investigate the reliability of the Gallup indicator and to understand the factors that might explain these somewhat surprising results. In the analysis below, we note some of the general shortcomings of subjective indicators, which are now widely used in the contexts of general well-being (e.g., Headey et al. 2010; Kahneman and Deaton 2010; Deaton 2010; 2011), poverty (Ravallion 2012), and food security (Deitchler et al. 2011), as well as some specific problems with the Gallup indicator. We also conduct econometric tests to determine whether the observed trends in self-assessed food security are plausibly explained by changes in per capita GDP and various food price indices. As expected, we find that real economic growth improves self-assessed food security. Real GDP growth already controls for aggregate price changes. We also find some additional effects of aggregate inflation, but we find no significant additional effect of relative food price changes (i.e., changes in the food terms of trade). We also show that in many of the largest developing economies (i.e., those with the largest poor populations), nominal economic growth generally outpaced food inflation, even in 2008. Hence, it appears that strong real income growth has largely offset the adverse impacts of food inflation in many developing countries, including those with the largest poor populations.

# **<<A>>II. AN OVERVIEW OF THE GALLUP WORLD POLL FOOD INSECURITY INDICATOR**

In this section, we provide an overview of the GWP and the specific food security indicator used in this study. Our goal is limited to answering three questions. First, what is the general quality of the GWP surveys? Second, what limitations might the GWP indicator of self-assessed 5
per capita) from the macro CGE are fully transmitted to household data assuming distribution-neutrality. To calculate the number of poor, the total population in each country is adjusted using United Nations population projections. This is based on the 158 countries in the BRI study that were mapped to the poverty estimates of the 163 countries found in the PovcalNet.

> 10 The World Bank now reports international poverty lines that are more closely related with national poverty standards. These poverty lines are set at $1.90, $3.20, $5.50, and $21.70 a day (in 2011 purchasing power parity (PPP)) for low-, lower-middle, upper-middle, and high-income countries, respectively.

> 11 Detailed country specific results are available from the authors upon request.

- 10 -
# **CHAPTER 3:  USERS OF FINANCIAL SERVICES AND PROFILE OF THE UNBANKED**

1. This chapter presents the demand side perspective of financial exclusion in Bogotá.  To this end, the chapter draws on data from several sources. The introductory part is drawn from the results of a nationwide survey by the Colombian Association of Media Investigation (ACIM).  The presentation of the socio-economic characteristics of the unbanked is the result of a joint household survey by the Colombian government’s department of statistical studies, DANE, the World Bank and Universidad de los Andes.

2. ACIM carried out a household consumer survey (the General Media Survey or EGM, acronym in Spanish) through the National Consulting Center in 2000. A section of the survey centers on the possession of financial services. The survey was applied to 12,218 households in 16 of Colombia’s most important cities: Bogotá, Barranquilla, Cartagena, Medellín, Pereira, Manizales, Cali, Popayán, Ibagué, Bucaramanga, Neiva, Cúcuta, Villaviciencio, Santa Marta, Armenia and Tunja. This sample is representative for a population of 11,850,560 inhabitants and was extrapolated to the total population. The survey tabulated use of: savings and checking accounts, credit and debit cards, certificates of deposit (CDT), mortgages, personal credit, banking services by telephone, savings account with special awards, stocks and bonds.

3. Financial services, whether provided by banks, by cooperatives or by micro-finance institutions, reach a very limited percentage of Colombia’s urban population. The ACIM survey, which covered 16 Colombian cities, suggests that the percentage of unbanked increases outside of Bogotá. Figure 3.2 shows the percentages of households using different types of financial services. Considering the unbanked to be households without access to savings accounts, the ACIM survey suggests that the unbanked in urban Colombia reach close to 70%. A similar survey by the same company, in Mexico, reported findings of unbanked households at 85%.

4. The DANE
by e-commerce, since this substitution can avoid the two trips of the round-trip chains, but only one of the trips of the chained chains (Figure 1).

With different types of agents and multiple impacts on mobility, there are a large number of data sources that can contribute to understanding the demand for e-commerce and its impacts on mobility. A distinction can be made between two main groups of data sources: (i) e-commerce sector data sources, which are those that can characterize indicators related to the adoption and use of e-commerce among consumers, spatial patterns of order demand and the properties of the logistics chains that support e-commerce (Buldeo Rai and Dablanc 2022); and (ii) mobility data sources, which are those that can be used to directly measure the impacts of e- commerce on personal travel demand and urban distribution of goods.

4
road. Hence, denoting by _M_<sup>_c_</sup> the monetary value the household places on having the road’s services, where _M_<sup>_c_</sup> is just the corresponding equivalent variation. A full analysis of the household’s decision problem is to be found in Bell (2009b), in which it is argued that if the reductions in unit transport costs are sufficiently small relative to the ruling prices **p** , then the interval defined by these upper and lower bounds will also be small. Illustrative calculations with commonly employed functional forms and a range of broadly plausible parameter values indicate that, when unit transport costs are halved, the – lower bound is about 3 3.5 percent lower than the EV and the upper bound is about – 6.5 7 percent higher, which point to the relevant compensated demand and supply schedules being convex. At all events, this finding suggests that a very simple formula using suitable, one-shot survey data covering villages with and without all-weather roads will yield a defensible and rather precise estimate of the benefit accruing to a sampled household as a result of reductions in the costs of transporting the goods making up ( **y** _,_ **x** ).

An alternative is to make no particular assumptions about how land and other nonlabor resources are supplied, nor about the extent of market integration, but to leave open the possibility that labor is under-employed. With the usual convention that the elements of **y** that are inputs take a negative sign, the household’s income from production is **q**<sup>_k_</sup> **y**<sup>_k_</sup> _, k_ = 1 _,_ 2. To any improvement therein must be added the benefit arising from changes in the prices of the goods consumed, viz. those in **x** , which is analogous to that appearing
Figure 15. Number of Contributors, Beneficiaries and Sustainable Livelihood Rate. Years 2018 - 2080 <!-- Start of picture text --> 6,000 12<br>5,000 10<br>4,000 8<br>3,000 6<br>2,000 4<br>1,000 2<br>0 0<br>Contributors (left axis) Beneficiaries (left axis)<br>Sustenance Rate (right axis)<br>Thousands<br>2018 2021 2024 2027 2030 2033 2036 2039 2042 2045 2048 2051 2054 2057 2060 2063 2066 2069 2072 2075 2078<br><!-- End of picture text --> Source: author elaborations based on administrative data, IESS.

Consequently, the rate of coverage on active population would present a constant trend of approximately 30% and 35% of older people (Figure 16).

Figure 16. Percentage of Population Contributing to the IESS Pension Insurance. Years 2018 - 2080 <!-- Start of picture text --> 36%<br>34%<br>32%<br>30%<br>28%<br>26%<br>24%<br>22%<br>20%<br>2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 2052 2054 2056 2058 2060 2062 2064 2066 2068 2070 2072 2074 2076 2078 2080<br><!-- End of picture text --> Source: author elaborations based on administrative data, IESS 25
such students should be limited in size to not more than about 300 students; those serving economically and socially heterogeneous or relatively advantaged students should be limited in size to about 500 students. Secondary schools serving exclusively or largely diverse and/or disadvantaged students should be limited in size to about 600 students or fewer, while those secondary schools serving economically and socially heterogeneous or relatively advantaged students should be limited in size to about 1,000 students (p.1).”

> 7 E.g. parental involvement into school matters decline with school size – Walsh (2010).

> 8 For example, Leung and Ferris (2008) use Canadian data to show that that a student attending a school with more than 2000 students is 22 percent more likely to engage in an act of serious violence.

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innovative and rapid development of many localized efforts to provide financial services to the poor outside of formal channels has generally overtaken taken policy formulation by governments. The reaction of some governments (e.g., Bosnia, Bangladesh and others) and multilateral agencies (e.g., Asian Development Bank)<sup>34</sup> is unfavorable to microfinance - comprehensive regulation is being proposed for microfinance activities and institutions through mandated standards of performance and risk-ratios. These proposals are based on untested hypothesis about the institutional and market impact of moving from an unregulated environment to one that is as tightly regulated as formal banks and financial institutions.

92. Institutions specializing in supporting and promoting the development of microenterprise development and microfinance services, such as the Consultative Group to Assist the Poorest (CGAP), US Agency for International Development, Microfinance Program at the Economics Institute in Boulder, Colorado, ACCION International, and Women's World Banking to name a few, have initiated and promoted efforts to bring about a supportive policy and institutional environment formicrofinance. This is being done by promoting and intensifying dialogue among the key participants in microfinance -- government regulatory agencies, microfinance practitioners and donors. Aside from assistance through staff training and dissemination by seminars, workshops and publications to increase the awareness and

> 31 "Tiered-banking" exists in a few countries (e.g., the Philippines, Indonesia, Peru) where specialized banks may be established at capitalization levels much lower than for regular universal banks, but with operations limited to specifically-defined geographical areas and deposit-taking limited to savings and fixed deposits.

> 32 In El Salvador, the thrust is to reform the law on Federacion de las Cajas de Credito and Bancos de los Trabajadores, which will permit a new type of financial institution with lower capitalization requirements, taking deposits from the public and providing loans primarily to microenterprises and small businesses.

> 33 Julia Paxton and Carlos Cuevas, A Worldwide Inventory of Microfinance Institutions, Sustainable Banking wiith the Poor (SBP) Project. Washington, DC
<!-- Start of picture text --> Percentage of Older Households with RMLs<br>Percent<br>14<br>1,2<br>1.0<br>0.8<br>0.6<br>04<br>0.2<br>0 1997 1999 2001 2003 2005 2007 2009<br>Data Source: American Housing Survey, various years<br>Note: Both public and private reverse mortgage loans are included.<br><!-- End of picture text --> <!-- Start of picture text --> Precentage of Households with HELOCs<br>Percent<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0 1997 1999 2001 2003 2005 2007 2009<br>ee<br>Data Source: American Housing Survey, various years<br><!-- End of picture text -->
# **2.1 Data**

> Our basic source of interest rate data is the International Financial Statistics of the IMF. We work with monthly data on local money market rates for the 1970s, 1980s, and

> 1990s. We choose money market interest rates because they reflect market forces better

> than deposit rates. The latter, while much more widely available, are often subject to administrative controls and in many cases display little movement over prolonged

> periods, which renders them uninformative for our purposes." When available, we choose the 90-day money market rate; otherwise, we use the 30-day rate. As international interest rate, we use the 90-day **US** T-bill rate. 12 Finally, for some experiments below, we use also the German 90-day T-bill rate.

The classification of exchange rate regimes is taken from the IMF. The Appendix lists the regime prevailing in each country over the sample period according to this source. The classification used here is based on a quarterly database from the IMF on exchange rate regimes encompassing a total of **10** categories, based on officially reported exchange arrangement for the period **1975-1996;** see the Appendix for more details. We present some empirical results (in Table 1 below) from estimating equation **(1)** using the detailed regime categories in the original source. However, to facilitate our analysis, in the rest of our experiments we condense these categories into three broader exchange rate

> In a number of cases we found that the money market interest rate data from **IFS** were identical to the deposit data. In such cases, we discarded countries/periods for which rates showed no variation or

> infrequent step-wise movements.

> 12 We also experimented with the LIBOR **US** dollar rate. The results were very similar, since the two rates are very **highly** correlated

**9**
Delay Losses (DL)|0.170***|0.069*|0.471|
||(0.054)|(0.0415)||
|Total Gains (PG+TG-DL)|5.094***<br>(1.262)|3.575***<br>(1.008)|0.408|

Notes: Of the total of 1,658 panel observations, 614 correspond to households with children up to age 10. Gains and losses are computed using the NL estimated coefficients in Table A10 and evaluated at the mean of ∆ _T_ of 6.8 months. Standard errors in parentheses are computed using the Delta Method. Following Galiani et al. (2018) the gross discount factor _R_ is the sum of the average mortality rate (by age and gender) in 2018 in the Dominican Republic reported by World Health Organization plus an interest rate of 5%. Coefficients with *, ** and *** indicate that they are statistically significant at the 10, 5 and 1%, respectively.

32
for a nationally representative sample of 8,700 households. The survey also provides detail on household members including sector of economic activity and education level. The analysis examines the first order implications of the changes in commodity prices and factor prices generated by the Mozambican CGE model for each of the 8,700 households in the sample. Specifically, changes in commodity prices are multiplied by individual household consumption shares and changes in factor prices are multiplied by the corresponding share of earnings from each factor in total household income. The factor price effect less the commodity price effect yields a money metric indicator of the first order change in utility due to the trade reforms for each household.

Importantly, in first order analysis, the net of effect of price changes for commodities that are home produced/consumed is zero as commodity price changes are exactly offset by gains or losses in factor income. This tends to blunt the impact of trade policy reform on rural households. As mentioned above, and detailed in the following section, home consumption is very important in the Mozambican context. In addition, the overwhelming predominance of informal activities implies that wage information is scarce. As a result, earnings by labor category are inferred from educational attainment data and returns to education estimated via regression analysis (Maximiano 2005). Similarly, for the large majority of households, it is practically impossible to separate overall household earnings into labor and capital components. This is less of an issue for poor households as the large majority of earnings can reasonably be assumed to be derived from labor income. In the micro-simulation model, five percent of total income is assumed to come from capital earnings for households living at less than twice the absolute poverty line.

10