rafmacalaba/datause-encoder-data huggingface dataset:
Table 10: Local spillovers on savings

||(1)<br>Has Saved<br>(last 2 yrs)|(2)<br>Participate<br>in a VSLA<br>(currently)|(3)<br>Participate<br>in a ROSCA<br>(currently)|(4)<br>Savings<br>stock<br>(Total)|(5)<br>Savings<br>stock :<br>VSLA|(6)<br>Savings<br>stock :<br>ROSCA|
|---|---|---|---|---|---|---|
|Panel A. Pooled Estimates|||||||
|Treatment (ITT)|0.06**|0.08**|-0.04|-10986.98|4770.73***|-7858.09|
||(0.02)|(0.03)|(0.03)|(14747.08)|(1502.82)|(10043.35)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|81.8%|14.4%|28.1%|99043.92|3539.34|42099.05|
|Observations|1,102|1,102|1,102|1,101|1,101|1,102|
|Panel B. Treatment Arm Estimates|||||||
|VSLA (T1) (ITT)|0.06*|0.10**|-0.02|-14206.90|4553.32**|-11517.95|
||(0.03)|(0.05)|(0.04)|(16351.49)|(2134.31)|(11145.30)|
|Cash Grant with repayment (T2) (ITT)|0.05|0.04|-0.04|-22848.34|4737.46**|-18850.16**|
||(0.03)|(0.04)|(0.03)|(15239.54)|(1914.75)|(9355.21)|
|Cash Grant (T3) (ITT)|0.08***|0.12**|-0.09**|25366.19|5298.25*|27268.92|
||(0.03)|(0.05)|(0.04)|(28454.78)|(2823.74)|(25458.08)|
|PDS Lasso selected controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Department X (Urban/Rural)|Yes|Yes|Yes|Yes|Yes|Yes|
|Mean in Control|81.8%|14.4%|28.1%|99043.92|3539.34|42099.05|
|p-value T1=T2|0.71|0.19|0.62|0.51|0.94|0.44|
|p-value T2=T3|0.28|0.14|0.24|0.08|0.85|0.06|
|p-value T1=T3|0.45|0.80|0.11|0.16|0.82|0.14|
|p-value T1=T2=T3|0.53|0.23|0.25|0.20|0.97|0.14|
|Observations|1,102|1,102|1,102|1,101|1,101|1,102|

Robust standard errors clustered at locality level. Savings stock amounts are in CFA franc and winsorized at 99%. The specification includes control variables chosen using post double selection (PDS) Lasso based on the set in Table A6. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01
reported gaining more experience and improving their skills as a result of the increased number of clients. On the other hand, 30 percent of the SPs complained of overwork from caring for two or three times more clients than they had previously cared for (Brody et al. 2015).

With respect to job satisfaction, no significant difference was seen in the mean job satisfaction score reported by SPs from voucher and nonvoucher facilities, suggesting that the staff at voucher facilities were not significantly overworked (mean score of 23.1 and 21.6, respectively; Brody, Irige, and Bellows 2015). However, among higher and midlevel SPs (proprietors, administrators, and departmental managers), job satisfaction was significantly higher at voucher facilities compared with nonvoucher facilities (Brody, Irige, and Bellows 2015). Moreover, in voucher facilities, job satisfaction was significantly higher among higher-level and midlevel SPs than among lower-level SPs such as nurses, nursing assistants, and laboratory assistants (Brody, Irige, and Bellows 2015).

Despite the many positive outcomes of the RHVP and the improvement in general—as expected—the sample size limited the ability to identify significant differences in changes in the gap between poor and nonpoor women making four or more ANC visits, delivering at a private facility, or receiving PNC among women in villages with a voucher client compared with villages with no voucher client. Further, no significant differences were seen between voucher and nonvoucher facilities with respect to certain clinical aspects of quality of care, perhaps because SPs lacked regular training or incentive to update skills. In addition, a number of challenges were identified by the IVEA. As of the end of 2010, the IVEA found challenges existed regarding regular training of SPs, quality of services in some facilities, compliance with the frequency and format of reporting by facilities, and administration of the audit trails generated by the claimsprocessing database management system (IVEA 2010). However, the VMA had addressed some of these challenges by the beginning of 2012, including improving the verification and claims processes and ensuring that contracted health facilities meet performance standards (IVEA 2012). In addition, given that there was no random assignment of sites to the voucher program, differences between voucher and nonvoucher sites could be due to unobserved differences in characteristics between the sites that could bias the estimated effect of the program.

# **<<A>>** Conclusion

One of the major findings of this study is that the voucher program accelerated a shift from home to facility deliveries among voucher clients, as compared with nonvoucher clients, and was 33
Waddington et al. (2009) identify eight studies on sanitation infrastructure. With regard to water infrastructure supply, the picture is not much better: Esrey et al. (1991), Fewtrell et al. (2005), and Waddington et al. (2009) include 22, six and eight articles in their analysis of water infrastructure interventions, respectively.

Esrey et al. (1991) find a 17% reduction in diarrhea induced by improved water supply and a 22% reduction induced by improved sanitation infrastructure. Fewtrell et al. (2005) show a reduction in illness of 25% for water and 32% for sanitation infrastructure. The results are, however, insignificant for water interventions if only diarrhea is considered as the dependent variable. Waddington et al. (2009) report no significant impact on diarrhea morbidity for water supply and a 37 % relative reduction in diarrhea incidence for sanitation infrastructure (but with low precision due to the small number of relevant studies). All 3 meta-studies suggest that the impact of sanitation infrastructure is larger than the health impact of improved water supply. In addition, none of the three studies finds any evidence for complementarities between water and sanitation interventions: the impact of single interventions appears to be similar to the impact of the same interventions in combined programs.

The studies underlying these three meta-studies were mostly based on local case studies and conducted under trial conditions. The only study we could find that directly takes a broader cross-country perspective is an early study by Esrey (1996), who uses eight Demographic and Health Surveys<sup>3</sup> to identify the effects of sanitation on diarrhea. The study finds a reduction of diarrhea of 13-44% for flush toilets and a reduction of diarrhea of 8.5% for latrines. In contrast to the meta-studies discussed above, Esrey (1996) finds complementarities between water and sanitation. He shows that improved water supply has no effect on health if improved sanitation is not present and even if sanitation is present the health benefits of water are reported to be lower than the health benefits of improved sanitation. Esrey’s article – undoubtedly one of the most cited works in the filed – is, however, constrained by a rather arbitrary (small) selection of DHS surveys (8 out of the 63 surveys that were already available in 1995).

> 3 The eight surveys used in the study are Bolivia, Burundi, Ghana, Guatemala, Morocco, Sri Lanka, Togo, and Uganda.

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# XIV INTRODUCTION

Flgure 1 Annual Increments to Female Life Expectancy (In years), by Initial Level, Compared with U.N. and Previous World Bank Patterns <!-- Start of picture text --> 0.9<br>0.8 -<br>0.7<br>0.6<br>CUPRENT<br>0.5 -U<br>0.4<br>6.3-<br>0.2 -<br>0.1<br>35 40 45 50 55 60 65 70 75 80 85<br>INITIAL LIFE EXPECTANCY<br><!-- End of picture text --> Figure 2 Annual Decrements to Infant Mortality Rate (per thousand), by Initial Rate, Compared with the U.N. and Previous World Bank Pattems <!-- Start of picture text -->
-0.2-<br>-0.4-<br>-0.6<br>-0.8<br>w . u  ~  ~ ~ ~ ~ ~~~~PV<br>-1.4<br>-1.6<br>-1.68<br>-2.0<br>-2.2<br>-2.4-<br>-2.6-<br>-2.6<br>-3.0 -.  ,,.,..  -....,,,,,<br>160  150  140  130  120  110  100  90  80  70  60  50  40  30  20  10<br>INITIAL INFANT MORTALITY PATE<br><!-- End of picture text -->
Unconditional debt relief, which accounted for approximately 81% of claims, was processed immediately so that virtually all claims had been settled by the end of June 2008. The deadline for settling claims under the partial debt relief scheme for loans with collateral of more than two hectares of land was extended several times because of slow take-up –first to December 2009, and subsequently to December 2010. To ensure that we accurately capture the total amount of debt relief granted in a district, our analysis relies on data collected in December 2011, when the program was closed and all claims had been settled.

The Adwdrs program received significant media attention and was the center of an intense political debate. Proponents of the program heralded the bailout as a cure for endemic problems of debt overhang and poor investment incentives in the rural sector. Responses to the program from the financial sector were more wary and warned, in particular, about the potentially detrimental effects of the bailout for credit discipline and future access to credit among borrowers benefiting from the bailout. _The Economist_ , for example, noted “Some fear that the government’s largesse will do lasting damage to a culture of prudent borrowing, productive investment and prompt repayment.”<sup>5</sup>

# **3 Data and Descriptive Statistics**

We examine the impact of India’s bailout for rural households using data at the district and bank-district level. Our main dataset is a panel covering 489 (of 593 total) districts of India from 2001 to 2012.<sup>6</sup> Our primary unit of analysis is an Indian census district, an administrative unit roughly comparable to a U.S. county. In the base year 2001, India had 593 districts with an average population of 1,731,897 inhabitants. In that year, the dis-

> 5 _“Waiving, not drowning: India writes off farm loans. Has it also written off the rural credit culture?”_ The Economist. June 3, 2008.

> 6Between 2001 and 2012, 47 new districts were created, typically by bifurcating existing ones. In our analysis, we aggregate all data to the level of India’s 2001 census districts.

8
analyzing this possibility is beyond the scope of the current analysis. Nonetheless, in both cases presented in Figure 9, the _new model without rescaling_ outperforms the other proposed variations of the model, indicating that the _new model without rescaling_ is very good at estimating changes along the income distribution with limited intra-sectoral information as input.

**Table 3 Share of Percentiles that Fall Inside the actual GIC Confidence Interval , by Country and Year**

|||**2017**|||**2018**|||**2019**|||**2020**||**Av**|**erag**|**e**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**1**|**2**|**3**|**1**|**2**|**3**|**1**|**2**|**3**|**1**|**2**|**3**|**1**|**2**|**3**|
|ARG|5|5|12|0|0|64|29|34|47|19|24|55|13|16|44|
|BOL|53|54|39|21|20|17|14|18|85|24|21|34|28|28|44|
|BRA|44|44|38|30|29|52|29|30|30|24|24|13|32|32|33|
|CHL|0|0|0|||||||11|18|28|6|9|14|
|COL|24|23|40|14|16|17|7|9|61|2|8|30|12|14|37|
|CRI|35|38|36|43|47|76|42|46|82|3|5|45|31|34|60|
|DOM||||74|55|82|96|100|4|8|13|57|59|56|47|
|ECU|26|39|56|67|60|83|60|59|86|5|5|53|39|41|69|
|SLV|25|20|23|15|18|36|2|1|3||||14|13|21|
|HND|16|13|31|25|33|32|56|51|15||||32|32|26|
|MEX||||23|26|20||||7|6|6|15|16|13|
|PAN|45|52|59|7|7|7|81|86|77||||44|48|47|
|PRY|65|58|71|33|38|7|18|25|53|12|12|45|32|33|44|
|PER|8|11|74|46|47|26|28|28|26|4|5|5|22|23|33|
|URY|5|5|45|4|4|72|9|11|73|5|4|88|6|6|69|
|**Average**|**27**|**28**|**40**|**29**|**29**|**42**|**36**|**38**|**49**|**10**|**12**|**38**|**26**|**27**|**40**|
|Correspon<br>: Own elabo<br>) Bolivia<br>|ds to t<br>ration<br>2019<br>|he_ol_<br>base|_d mode_<br>d on S<br>**Figu**<br>|_l_, 2 to<br>EDLA<br>**re 8**<br>|the_n_<br>C<br>**Exam**<br>|_ew mo_<br>**ples o**<br>|_del w_<br>**f Co**<br>|_ith res_<br>**untrie**<br>B)<br><br>18|_caling_ <br>**s Wit**<br> Do<br>|, and<br>**h Go**<br>minic|3 to th<br>**od Fi**<br>an Re|e_new_<br>**t**<br>publi<br>|_model_<br>c 2018<br>|_witho_<br>|_ut rescali_<br>|
|||||||||16||||||||
|||||||||12<br>14||||||||
|||||||||6<br>8<br>10||||||||
|||||||||2<br>4||||||||
|11 16 21 26|31 36|41 46|51 56 6|1 66 7|1 76 81|86 91||0<br>6|11 16|21 26|31 36 4|1 46 5|1 56 61 66|71 76|81 86 91|
|Upper|bound||Low|er bou|nd||||Up|per b|ound||Actual g|rowth||
|Actual|growth||Old|Model|||||Lo|wer b|ound||Old Mod|el||
|New - R|escali|ng|New|- No R|escali|ng|||Ne|w - R|escalin|g|New - N|o Res|caling|

Note: 1 Corresponds to the _old model_ , 2 to the _new model with rescaling_ , and 3 to the _new model without rescaling._ Source: Own elaboration based on SEDLAC <!-- Start of picture text --> A) Bolivia 2019 B) Dominican Republic 2018<br>35 18<br>16<br>30<br>14<br>25<br>12<br>20<br>10<br>15 8<br>10 6<br>4<br>5<br>2<br>0<br>0<br>-5 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 6<br>Upper bound Lower bound Upper bound<br>Actual growth Old Model Lower bound<br>New - Rescaling New - No Rescaling New - Rescaling<br><!-- End of picture text --> Source: Own estimations based on SEDLAC.

24
# **III. The Urban Unbanked in Mexico**

This section uses a survey of households in the Metropolitan Area of Mexico City to profile the unbanked and to explain why they are unbanked. In Mexico there has been no systematic national analysis of the extent to which people are inside and outside of the banking system. In 2002, the World Bank commissioned a survey in Mexico City Metropolitan Area. The survey, known as the _Encuesta Nacional de Servicios Financieros_ (ENDSFI) was conducted by _Instituto Nacional de Estadística, Geografía, e Informática_ (INEGI) as an addendum to the _Encuesta Nacional de Empleo Urbano_ . The survey sampled 1,500 households. The surveys were conducted in face-to-face interviews with an 80 percent response rate. For people using the survey data, INEGI supplies a set of weights to convert the sample responses into responses representative of 11.4 million adults living in the Metropolitan Area of Mexico City. All of the tables in this paper are based on the ENDSFI survey and use the weighted data.

One basic weakness of the survey is that it focuses only on the largest city of the country. The population in this city differs from that in most other urban environments and differs dramatically from that in the rural areas, where almost 30 percent of the population lives. Data from Mexico City, for example, could be misleading because it has the highest average income in the country, $14,180 U.S. dollars per capita in 2003 compared to a national average of $5,450.<sup>15</sup> Undoubtedly, due to this difference in average income and other factors, a national survey would find a larger percentage of the population to be unbanked compared to the percentage found in the capital city. In terms of comparison, the Mexican survey drew on the city with highest per capita earnings, while the two cities surveyed in the U.S. were considered below average for that country.

Table 12 presents an overview of the socioeconomic characteristics of the survey population. As remarked in the introduction, income per capita in Mexico is about one fourth what it is in the United States. Indeed, the surveys show less than 45% of the U.S. households with annual income below US$15,000 compared with close to 80% in Mexico City. As noted in the table, in Mexico City 23.6 percent of the population reported that they had savings in a bank (21.3% had savings but did not borrow from a bank and 1.8% had savings and credit from a bank). As expected, this implies a much higher percentage of the population in Mexico is unbanked compared to the U.S. While noteworthy, the differences in household income across the two countries do not appear to account for the differences in the coverage of banking services.

> 15 Estimated data of  GDP by SIREM March 12, 2004

37
# **Abstract**

For the evaluation of macroeconomic policies Colombian authorities rely heavily, if not exclusively, on the operational framework known as the Financial Programming Model developed by the IMF in the 1950s. Based on this static framework, the formulation of fiscal policy in the country, just as in various Latin American countries, focuses primarily on fiscal deficit and gross debt targets. However, the type of fiscal policy advice derived from it is not useful for understanding the asset-creating nature and the intertemporal trade-offs involved in public investment decisions. This paper develops a perfect foresight, dynamic small open economy model to provide an alternative framework for fiscal analysis and policy purposes. It is shown that the two competing frameworks deliver differing paths for the expected behavior of the Colombian economy. The proposed framework is then used to study the likely consequences of using public capital spending to achieve deficit targets since, in addition to an already high public debt, in the years ahead unfunded pension obligations will put enormous pressure on the Colombian government’s solvency. The results indicate that public capital compression is costly in terms of foregone growth and very ineffective in achieving fiscal consolidation. The adoption of fiscal rules such as the _golden rule_ or the _permanent balance rule_ to shield public investment from undue budgetary pressures makes little sense in the presence of sustainability concerns. The paper shows that a transitory capital spending increase is not self-amortizing in the long run; hence an extra peso of public capital spending deteriorates the intertemporal fiscal position. A permanent increase largely pays for itself in terms of additional tax revenue but this effect is offset by a deterioration of infrastructure user charges, as long as public prices are determined competitively.

JEL: E62, H54, H55, H62 ii
Figure A3: **Share of migrants located at Libyan FMPs by length of stay** <!-- Start of picture text --> % Less than 2 weeks % Less than 3 months<br>100%<br>75%<br>50%<br>25%<br>0%<br>% Less than 6 months % More than 6 months<br>100%<br>75%<br>50%<br>25%<br>0%<br>2017 2018 2019 2020 2017 2018 2019 2020<br>Percentage of migrants in FMPs<br><!-- End of picture text --> Note: The figure shows the boxplot distribution of the share of migrants located at Libyan FMPs by length of stay during our period of analysis.

Figure A4: **Spatial and time placebo tests** <!-- Start of picture text --> 200<br>150<br>100<br>50<br>0<br>(a) Effect of the news in the top destination countries<br>for other FMPs<br>−0.0347−0.0317 0.0069 0.0515<br><!-- End of picture text --> <!-- Start of picture text --> 200<br>150<br>100<br>50<br>0<br>−0.0432 −0.0317 −0.0045 0.0268<br><!-- End of picture text --> (b) Effect of the news in the FMP’s top destination countries but in _another_ time Notes: The dotted line indicates the estimated coefficient for the explanatory variable in the main regression specification (see Table 1 column 2). Placebo results obtained with 10,000 replications.

37
**Table 1. Central Government Tax Expenditures, 1999/2000-2002/2003** <u>(annual, in percent of GDP, unless otherwise specified)</u>

||**Austr**|**alia**|**Cana**|**da**|**Nether**|**lands**|**U**|**S**|
|---|---|---|---|---|---|---|---|---|
||2003|2000-03<br>Average|2003|2000-03<br>Average|2002|1999-2002<br>Average|2003|2000-03<br>Average|
|Revenue (received)|23.1|23.3|15.3|15.8|45.9|46.9|16.5|18.8|
|Tax expenditures|3.8|4.1|6.6|8.0|2.0|2.0|7.0|6.8|
|Direct expenditures|22.4|22.9|14.8|14.8|47.5|46.6|19.9|19.1|
|Total expenditures<sup>a/</sup>|26.2|27.0|21.4|22.8|49.5|48.6|26.9|25.9|
|Tax expenditures/total exp. (%)|14.5|15.2|30.8|35.1|4.0|4.1|26.0|26.3|

Sources: "Tax Expenditure Statement" 2000-2004,Treasury, Australia; "Tax Expenditures" and budgetary tables, 2000-2004, Department of Finance, Canada; "Tax Expenditures" 1999-2003, Ministry of Finance, the Netherlands; and "Tax Expenditures by Function" and FY 2005 Budget, Office of Management and Budget, USA. a/ "Total expenditures" is the sum of "tax expenditures" and "direct expenditures." In Turkey (central government only) the potential tax benchmarks for PIT, CIT and VAT have been studied. A list of potential tax expenditures has been compiled that contains 186 tax expenditures relating to PIT, CIT, VAT, and special consumption tax. Most of these tax expenditures are on a large scale and are very generous. Recently, Turkey has officially estimated the cost of 15 tax expenditures provisions, which is 1.6% of GDP. However, according to various analyses and preliminary estimates from other organizations, the total cost of tax expenditures, adding the other omitted tax expenditures, would raise to at least 5% of GDP in 2003.

In China (both central and local governments), for example, tax expenditure policy has become popular since 1978, when China launched an economic transformation from a planned to a social market economy. The government has not established a tax benchmark structure, but it recognizes that most tax incentives in its system are tax expenditures. The estimated cost of tax expenditures is not available to the public, even though the central government has made estimates for about 100 tax expenditures. However, according to a reputable tax expert in China, the total number of tax expenditures is probably 1000, more or less<sup>8</sup> . The size of the tax expenditures could be well over 10% of GDP<sup>9</sup> in 2002.

# **B. The Effects of Tax Expenditures on the Budget**

Similarly to direct expenditures, tax expenditures, as spending items, affects fiscal budget balance, prioritizing resource allocation, efficiency and cost-effectiveness. Because tax expenditures (enacted as part of the tax law) constitute one component of a tax structure, the impact of tax expenditures on the simplicity of the tax administration is also discussed below.

# **1. Tax expenditures reduce tax revenue, which affects the budget balance**

> 8 Based on telephone interview with a Chinese expert in tax policy, conducted on October 19, 2005.

> 9 H.P. Brixi et al., _“Tax Expenditures -- Shedding Light On Government Spending Through the Tax System,_ ” 2004.

8
Figure 1: Trends in Local Availability of Higher Education Institutions, by Institution Type (base year=2000) <!-- Start of picture text -->
1.4<br>1.3<br>1.2<br>1.1<br>1<br>2000 2002 2004 2006<br>Year<br><!-- End of picture text -->

# Only offer Short-cycle programs Universities and University Institutes

Note: The figure displays the trend in the number of municipalities that have at least one higher education institution available in a 10 km radius around their centroid. The trends use 2000 as the base year (i.e., each series starts at 1 in 2000). Separate trends are shown for institutions specialized in shortcycle programs and for other institutions (universities and university institutions). The municipalities included are those where students in the sample attended high school. The dotted vertical line (2005) denotes the year of high school graduation for students in the sample. Source: SNIES.

30