rafmacalaba/datause-encoder-data huggingface dataset:
equivalence scales to account for differences in household demographic composition (Ravallion and Lokshin, 2002)<sup>13</sup> .

Following Ravallion and Lokshin (2002), we construct quantiles of objective welfare that match the raw distribution of responses across the categories of the income ladder question for each country, collapsing the categories 8-10 into one category because very few respondents rank their household’s welfare in rungs 9 or 10.<sup>14</sup> The matrix in Table 1 provides further illustration of the weak association between objective and subjective rank<sup>15</sup> . Cramer’s V statistic is only 0.10 and of the 1732 individuals who place their household on the bottom rank of the subjective welfare ladder only 231 are also on the bottom ladder based on objective welfare.<sup>16</sup>

**Table 1: Objective and subjective welfare rank, full sample**

||||||Subjec<br>|tive welf<br>|are rank<br>|<br>|||
|---|---|---|---|---|---|---|---|---|---|---|
|||1|2|3|4|5|6|7|8|Total|
||1|231|289|392|332|329|91|44|24|1,732|
||2|247|381|579|615|718|204|104|53|2,901|
|rank|3|385|650|1,241|1,298|1,548|580|304|129|6,135|
|fare|4|358|605|1,304|1,622|2,129|873|410|193|7,494|
|wel|5|326|621|1,604|2,127|3,192|1,451|768|309|10,398|
|ctive|6|108|210|639|898|1,393|767|450|215|4,680|
|Obje|7|51|95|271|420|736|484|337|182|2,576|
||8|26|50|105|182|353|230|159|142|1,247|
||Total|1,732|2,901|6,135|7,494|10,398|4,680|2,576|1,247|37,163|

_Source: LiTS II, 2010._ One hypothesis suggested by Ravallion and Lokshin (2002) is that responses to the economic welfare ladder question can be a function not only of household’s income, but also, of factors

> 13 In their paper, Ravallion and Lokshin (2002) find that the large differences between subjective and objective measures of welfare in Russia can only partly be explained by the differences in the weights ascribed to household demographic composition (in constructing equivalence scales) or cost-of-living differences. In this paper we are not able to test for this hypothesis, as the LiTS survey does not include local-level prices.

> 14 Here the objective rank is based on per capita household expenditures, and the table aggregates the observations for the entire sample.

> 15 Regressions show that only 10 percent or less of the variation in the welfare ladder is explained by household expenditures in any of the countries in the sample. The asset index exhibits a somewhat higher (albeit still low) correlation with the economic ladder variable: with the exception of Albania and Bulgaria it explains less than 20 percent of the variation in the welfare.

> 16 Note that unlike in Ravallion and Lokshin (2002) we have a less precise measure of objective welfare, based on a short consumption module, and we cannot account for special differences in prices or poverty lines. Nonetheless, the matrix in Table 1 is based solely on an ordinal ranking of welfare, and there is evidence that the LiTS expenditure measure credibly accounts for the variation in living conditions in Europe and Central Asia (Zaidi et al., 2009 and Appendix 1 to this paper).

8
Sources: Latinobarómetro Corporation (2021); Cuesta, Madrigal and Pecorari (2022) As illustrated in Figure 2, and in accordance with global trends, trust in national governments has declined since 2009 for most LAC countries. There are a few notable exceptions, such as El Salvador, where confidence climbed by nearly 60 percentage points (from 12.9 to 72.4 percent), and the Dominican Republic, where trust increased by nearly 25 percentage points (from 28.2 to 52.3 percent) between 2017 and 2020. While confidence levels fell in the majority of LAC countries, they rose in a few exceptions and to different extents. On average, however, the trend for trust in national governments has been declining since 2009, with levels in 2020 almost half of those in 2009.

Table 1. Political trust and citizen participation in the Latin America and Caribbean (LAC) region<sup>11</sup>

|||<br>Trust|||<br>Citizen partic|<br>ipation|
|---|---|---|---|---|---|---|
||Confidence in the<br>National<br>government|Confidence in<br>National<br>congress/parliament|Confidence<br>in the<br>judiciary|Signing a<br>petition|Taking part in<br>authorized<br>demonstrations|Working on a problem<br>that affects you or your<br>community|
|**Total LAC**|27.3%|20.5%|25.7%|19.9%|14.4%|25.7%|
|**Argentina**|23.4%|18.6%|16.7%|47.8%|18.2%|31.2%|
|**Bolivia**|25.3%|28.3%|20.3%|10.3%|12.1%|27.9%|
|**Brazil**|27.1%|23.9%|37.0%|34.8%|14.2%|24.6%|
|**Chile**|17.9%|13.1%|15.9%|25.3%|31.0%|27.9%|
|**Colombia**|26.0%|15.2%|24.5%|24.2%|16.7%|31.0%|
|**Costa Rica**|14.4%|19.0%|40.5%|22.9%|13.6%|23.5%|
|**Dominican Rep.**|52.3%|34.5%|36.9%|12.7%|18.5%|31.4%|
|**Ecuador**|10.0%|13.0%|18.1%|11.1%|9.5%|24.3%|
|**El Salvador**|72.4%|12.2%|31.2%|14.7%|5.7%|22.7%|
|**Guatemala**|22.4%|20.7%|21.6%|9.6%|7.7%|16.9%|
|**Honduras**|11.3%|12.7%|17.0%|9.5%|9.5%|23.8%|
|**Mexico**|27.9%|23.0%|24.4%|16.4%|9.7%|17.9%|
|**Nicaragua**|38.0%|30.4%|33.2%|14.6%|11.7%|16.7%|
|**Panama**|23.3%|15.8%|24.1%|13.2%|9.3%|26.4%|
|**Paraguay**|13.3%|10.3%|13.3%|21.0%|15.8%|27.5%|
|**Peru**|16.6%|7.4%|16.7%|9.5%|10.4%|27.2%|
|**Uruguay**|57.4%|54.4%|57.9%|34.9%|22.8%|29.9%|
|**Venezuela**|19.4%|19.7%|18.3%|20.0%|18.8%|27.8%|

Source: Latinobarómetro Corporation (2021) When looking at citizen participation variables, the picture becomes more mixed. Signing a petition is relatively rare in the region, with the regional average standing at 20 percent (see Table 1 above). One outlier is Argentina, where nearly half of individuals say they have ever signed a petition. In Brazil and Uruguay, this percentage likewise approaches 35 percent. Over the last two decades, the proportion of people who have ever signed a petition has stayed below 20 percent, having risen only modestly since 2006.

Figure 2. Confidence in government over time in LAC

> 11 See table A1 in Annex A for trust in police and electoral bodies.

11
Among unbanked individuals cashing checks, 21 percent mainly use banks, 68 percent use CCOs and 5 percent use grocery stores. Undoubtedly, one of the explanations for the heavy reliance on CCOs is that many banks will not cash paychecks for non-depositors. In addition, an individual cashing his or her check at a CCO can buy money orders, stamps, envelopes, and pay utility bills at that same location.

# **Table 5 Means of Paying Bills**

||**Percentage**<br>**among the**<br>**banked**|**Percentage**<br>**among the**<br>**unbanked**|
|---|---|---|
|**Most common method of paying bills**|||
|Check|63.3|0.0|
|Money order|12.6|28.6|
|Cash|10.4|36.4|
|Billpayment service|1.9|6.5|
|Automated paymentfroma bankaccount|0.8|0.0|
|**Location for purchase of most money orders**|||
|Checkcashing outlet|38.4|60.1|
|Post office|34.7|19.8|
|Supermarket|12.7|8.7|
|Bank|8.4|2.6|
|**Location for accessing bill payment service**|||
|CCO|83.0|84.9|
|Bank|3.0|0.0|
|Other|14.0|15.1|

As indicated in Table 6, another payment that many lower-income urban households make is to transfer funds to family members living outside the U.S. In the OCC survey, 20 percent of the respondents indicated that they had made at least one such remittance over the past year. Of those who did so, 45 percent wired the funds and 24 percent mailed a money order. No other method accounted for over 5 percent of typical means of transferring the money.

18
Policy Research Working Paper 4949

# **Abstract**

This paper examines the extent to which social networks among indigenous peoples have a significant effect on a variety of human capital investment and economic activities, such as school attendance and work among teenage boys and girls, and migration, welfare participation, employment status, occupation and sector of employment among adult males and females. The analysis uses data from the 10 percent population sample of the 2000 Population and Housing Census of Mexico and an empirical strategy that allows taking into account the role of municipality and language group fixed effects. The authors confirm empirically that social network effects play an important role in the economic decisions of indigenous people, especially in rural areas. The analysis also provides evidence that better access to basic services, such as water and electricity, increases the size and strength of network effects in rural areas.

This paper—a product of the Poverty Reduction Group, Poverty Reduction and Economic Management Network—is part of a larger effort in the network to analyze poverty and monitor and evaluate the effectiveness of poverty reduction programs. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at eskoufias@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
country. The devaluation of the yuan and its attachment to the dollar have improved over the years the country's competitiveness and strengthened its attractiveness to foreign investors.

Huyen (2015) conducted a survey of several foreign companies established in provinces in Vietnam to identify the determinants of their FDI decisions. The results show the importance of the quality of infrastructure, access to finance and the availability of raw materials in the process of setting up foreign companies in Vietnam. Bartels et al, (2014) highlighted the importance given by multinationals to the quality of living in the host regions, the existence of a favorable environment for innovation and the emergence of industrial hubs allowing investors to benefit from positive externalities. This finding is however nuanced by certain research which found that FDI attractiveness is a dynamic process that is evolving over time with certain categories of projects requiring specific criteria that may not be necessary in the case of other projects. Thus, certain territories may be considered attractive for a specific sector or production activities and not attractive for others within the same location (Hatem, 2004).

# **3. FDI inflows to Tunisia: Where do we stand now?**

The FDI inflows to Tunisia have experienced a decline since the revolution in January 2011. In recent years, a major part of those inflows was mainly channeled by investors already established in the country. In the recent 2020 edition of the “Doing of Business” index of the World Bank, Tunisia has ranked 78th compared to the 88th position in the 2018 edition. The country was able to gain 10 places in two years. However this is still short of the highest ranking the country ever reached in 2012 when it made it to the 46th position (World Bank, 2020).

Table 1 presents a summary of the FDI data to the country as released by the Agency for the Promotion of Foreign Investments (FIPA, 2019, 2017, 2016, 2015, 2014). Foreign inflows have substantially declined since 2008. The trend has slightly reversed from 2017 but still about 14% below its level in 2008. It is worth noting that the surge in FDI (excluding energy parts) in 2012 was mainly explained by the privatization and transfer to international investors of the state’s shares in a domestic telephone operator.

**Table 1** . _FDI inflows in millions of Tunisian dinars (TND) (2008-2018)_

|**_Years_**|**2008**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2017**|**2018**|
|---|---|---|---|---|---|---|---|---|---|---|
|**_FDI_**|3127|2165|1616|2504|1813|1806|1967|1901|2131|2742|
|**_FDI_**(_excl.energy_)|1124|848|553|1618|737|913|995|1097|1318|1832|

_Source : FIPA (2019, 2017, 2016, 2015, 2014)_
earnings or profits from agricultural and business activities were unchanged (revenues were up but so were costs). The findings indicate a relaxation of liquidity constraints, especially in the large transfer arm, but the windfall income was used primarily for consumption, assets, and upgrading to metal roofs, rather than investments causing changes in labor market outcomes.<sup>12</sup> In 2015, the Overseas Development Institute (ODI) issued a report of a high-level panel on humanitarian cash transfers (ODI 2015). While the report calls for increased use of cash transfers in humanitarian assistance settings, it is largely silent on the effects of cash transfers on labor market outcomes. This is natural as increased labor market participation or change in occupational choice is not a primary goal of humanitarian assistance programs, which aim to prevent adverse outcomes for vulnerable households by ensuring adequate coverage of basic needs. Furthermore, relief operations, especially in cases of emergency, do not lend themselves to well-identified impact evaluations. However, it should be noted that refugee status is increasingly more permanent, or at least long-term, rather than temporary and, hence, assistance programs do aim to increase work opportunities and earnings for this population.

As a result, many studies of humanitarian assistance report impacts on food security and nutrition, but not on labor market outcomes. For example, randomized experiments comparing food assistance by the World Food Programme (WFP) with cash transfers (or vouchers) for Colombian refugees in Ecuador and food insecure households in Niger do not report any impacts on labor supply, earnings, or occupational choice (Hoddinott, Sandström, and Upton 2014; Hidrobo et al. 2014). The International Rescue Committee (2014) examines the effects of a winter cash assistance program targeted to Syrian refugees in Lebanon. Eligible refugees in need of assistance were selected for the program if they lived at or more than 500 meters above sea level, which provides the identification strategy for the study. The transfers, which amounted to about $575 during November 2013 to March 2014, reduced the share of households that reported any of their children working during the past month from 10% in the comparison group to 4% among the beneficiaries. The program also reduced adult labor supply during the past four weeks by 13% from 3.1 days in the comparison group to 2.7 days in the treated group. The evidence here is consistent with the standard economic model of labor supply, where beneficiaries may prefer leisure to costly (and perhaps dangerous or risky) work.

> 12 A longer-term evaluation of the same program finds an increased level of assets among transfer recipients, but no other impacts (Haushofer and Shapiro 2018).

11
related to purchase of energy by fuel type and by economic activity. Aggregate information from SIEN is provided between 2009 and 2018, whereas firm-level information from the DGII is available between 2007 and 2021. Our analysis refers to the combined dataset relative to the period 2009–2018. Summary statistics at the activity level are reported in Appendix B.1.

**Estimation of CO2 emissions.** Form 606 provides the link between DGII firm data and SIEN emissions data, which enables us to estimate energy consumption and CO2 emissions by firm. Form 606 is a monthly report submitted by companies to DGII that provides information on the purchase of goods and services that include a tax receipt number (NCF). To quantify the energy consumption and CO2 emissions by firm, we leverage on the information on purchases of different fuels for energy generation as well as electrical energy purchases. The energy activities reported in Form 606 are displayed in Appendix B.2.

**Emissions from Electricity Consumption.** A firm’s expenditure in electricity is equated to a firm’s expenditure reported in Form 606 in sectors 4011 (electric energy generation), 4012 (transportation of electric energy), and 4013 (distribution and sale of electric energy). Using a firm’s location, it is possible to assign a price per KWh to each firm based on the average price reported by the Dominican Republic’s electricity distribution companies: EDESUR, EDENORTE, and EDEESTE (EDEs). Each of the EDEs serves a specific region in the country, and there is variation in the average electricity price across regions. Dividing the firm’s electricity expenditure by its location’s average electric price, we estimate the firm’s energy consumption in kilowatt-hour (kWh). To estimate CO2 firms’ emissions from electricity consumption, it is necessary to estimate a conversion factor from kWh consumed to kilogram (kg) of CO2 emissions. BNE provides information on the total Gigawatt hour (GWh) of electricity produced, with the SIEN representing nearly 93.3 percent of the 21.7 thousand GWh of electricity generated, isolated systems accounting for 5 percent, and auto producers for the remaining 1.7 percent. Dividing the total electricity generated in GWh by the total emissions of CO2 in kg, we obtain the conversion factor needed to transform firms’ electricity consumption into firms’ emissions of CO2.

**Emissions from Fuel Consumption.** In addition to electricity consumption, many firms produce emissions through consumption of different types of fossil fuels in their production and transportation processes. Ideally, we would want to know a firm’s expenditure on each type of fuel so that we can perform a similar exercise as with electric consumption to estimate firms’ emissions from fuel consumption; however, the information provided in Form 606 is aggregated by energy sector as described in Appendix B.2. To estimate emissions from fuel consumption, we compare the International Standard Industrial Classifications (ISICs)

10
strongly participated in other activities in the preschool center. For example, caregivers interviewed reported attending meetings to learn about their children’s academic performance at the preschool (81%) and supporting the preschool through maintenance and cleaning or with in-kind contributions (i.e., providing food) (83%). Parents also report improvements in their children’s skills, such as increased communication (86%), social interactions (77%) and self-confidence (68%). As a consequence, most caregivers reported improvements in their interactions with their children. At the same time, parents reported a high level of satisfaction with the services provided in the preschool.<sup>18</sup>

# **6.5 Impact on Siblings**

The DICIPE shows important additional impacts on the younger and older siblings of the target children of our evaluation, which we report in Table A7. First, the program has no significant effects on fertility, indicating that the number of younger siblings is not different between control and treated families.<sup>19</sup> Younger siblings in the preschool age group 3 to 6 in the treatment group were 62 percentage points more likely to attend preschool—an effect that is about 85% of the estimated impact of treatment for the target children. These results are consistent with the timing of preschool construction and the ages of the target children and their siblings. Furthermore, and in accordance with existing evidence (Carneiro et al., 2023b), we do not observe statistically significant impacts on the primary school enrollment of sisters or brothers in the 6 to 12 primary school age group (columns (2) and (3) show the results, respectively).

> 18We asked caregivers to rate on a 1-5 scale how much they agreed with statements such as, “I am satisfied with the quality of education my child receives”, “As a parent I feel involved in the school,” and “As a parent I feel my opinion matter”. The mean rating for these items was 4.04, indicating that caregivers felt well supported and thought positively about school.

> 19These results are available upon request.

32
optimism among women. (There is little evidence to support changes in other behaviors, such as women working or exerting influence over household decisions.) It should be noted that a key design variable that recurs in the literature is the importance of insuring that efforts to involve women must be an exercise of a “ whole of village ” rather than one that could be seen to divide communities and undermine local authority.

**Poverty Reduction Impacts** .  A meta-analysis of CDD programs found that seven out of nine projects had statistically significant positive impacts on household consumption and living standards (Wong 2012). These included programs in Indonesia (two projects), India, Nepal, Senegal and Sierra Leone.  In these countries, the programs allocated sufficient resources and per capita allocations over several years to reduce poverty levels.

One common mistake, seen especially in smaller NGO and donor projects, is low per capita allocations which do not allow for sufficient investments to raise productivity. For example, the Democratic Republic of Congo (DRC)’s _Tuungane_ Project, which was supported by the United Kingdom government and implemented by the International Rescue Committee and CARE International, covered a relatively large geographical area, but the project only allocated USD 1 per capita. This would be insufficient to lift living standards, especially in such a fragile and complex context as the DRC (Humphreys et al. 2012). Projects with such low per capita allocations for investment are not likely to have significant welfare returns, and it is no surprise that evaluations did not find one. To have any impact on income or non-income poverty levels, it is important to ensure that there is an adequate level of investment occurring over multiple years.

**Poverty Targeting** .  Overall, most of the larger CDD programs did well on geographical poverty targeting and in selecting poor areas in which to operate. Programs that made use of poverty maps and the latest national statistical data were the most effective at geographical targeting.  For example, the impact evaluation of Indonesia ’ s PNPM Generasi program, which focused on health and education, showed that the poor accrued a greater incidence of program benefits than the nonpoor (Olken et al. 2014).  Overall, at the household level, most CDD programs invested in public goods—such as roads, bridges, schools, and health centers—that benefited the broader poor and nonpoor community. However, programs were generally found to benefit more poor than nonpoor households and individuals.  Results are mixed based on certain socioeconomic characteristics (for example, urban versus rural) and types of investments.

19
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