rafmacalaba/datause-encoder-data huggingface dataset:
others’ involvement (van der Gaag et al., 2019). In terms of behaviors, columns (6)-(10) show that displacement reduces women’s ability to decide on the money they earn. In terms of the distribution of household chores, there are no significant differences between IDP and non-IDP women, except when including the full set of controls. In this specification, women tend to experience a slight redistribution of unpaid domestic work following displacement. Reference networks are strongly correlated with less traditional behaviors around domestic chores but do not influence women’s decision-making power over money.

Overall, these estimates indicate that gender norms that limit women’s economic opportunities become more rigid with displacement. This finding is consistent with studies of Syrian refugees in Jordan (Culcasi, 2019), IDP widows in Nepal, Chechen refugees in the Czech Republic, and IDP women in Darfur (De La Puente, 2011; Ramnarain, 2016; Szczepanikova, 2005), which reveal that women can work for pay outside the home, but there is an expectation that they will be the main caregivers in the household. In the case of Colombia, Calderon et al. (2011) show that IDP women work more hours than non-IDP women in rural areas, but greater engagement in the labor market does not translate into improved bargaining power. Indeed, gender norms about paid and unpaid work are intertwined, but they can also move in different directions. According to Harper et al. (2020), this is the case when norms stretch to encompass women doing paid work, without any corresponding shifts in male responsibilities.

19
having worse peers drawn into (and staying in) school or capable students having new incentives to remain in low-quality schools. A quasi-experimental analysis of a government school uniform distribution program in India found heterogeneous impacts, with positive effects for girls and historically-disadvantaged castes but negative effects for wealthier students (Adukia 2014). This further supports the idea that free uniforms may improve outcomes for the most disadvantaged students.

These results reinforce the importance of collecting long-term follow-up data. This is particularly important given that the average impact evaluation of an instructional intervention gathers its follow-up data after 13 months of treatment and less than a month after treatment has concluded (McEwan 2015): In other words, very few evaluations explore the long-term impacts, but this is crucial to understanding the true return on educational interventions.

Future systematic reviews should take care when interpreting short-term results, taking into account these results and others which demonstrate that long-term impacts may vary – sometimes dramatically – from initial effects. Gathering long-term data is costly, but without it, the trajectory of impacts resulting from the wide range of interventions currently being implemented remains a mystery. Most education interventions have the ultimate objective of improved long-term well-being – such as more learning, more completed schooling, or better labor market outcomes – and short-term educational improvements are merely a hopeful proxy. As more education studies gather long-term data, an essential understanding of the relationship between short-term and long-term gains may illuminate which investments truly change children’s life trajectories.

19
|Table 1: SummaryS|tatistics||||||
|---|---|---|---|---|---|---|
||N. Obs|Mean|St. Dev|Pct 10|Pct. 50|Pct. 90|
|Panel A: Polling Station Characteristics|||||||
|Number of women registered in each polling station|21|434.0|196.7|195.0|464.0|656.0|
|Number of treated women in each polling station|21|89.0|54.2|34.0|82.0|150.0|
|Share of treated women|21|0.24|0.19|0.07|0.22|0.43|
|Turnout for women|21|0.58|0.27|0.30|0.54|0.91|
|Turnout for men|21|0.49|0.15|0.32|0.51|0.66|
|Share of PPPP among female voters|21|0.62|0.26|0.39|0.66|0.88|
|Share PPPP among male voters|21|0.63|0.25|0.33|0.67|0.93|
|Percentage of women with access to cable in the polling station|21|0.34|0.26|0.06|0.23|0.75|
|St. Dev of asset index|21|1.76|0.30|1.46|1.72|2.09|
|St. Dev of distance index|21|0.79|0.52|0.16|0.69|1.31|
|Panel B: Household Characteristics|||||||
|Household size|963|10.2|5.17|5|9|16|
|Number of women in the household<sup>1</sup>|991|2.69|1.48|1|2|5|
|Asset Index|963|0.00|1.85|-2.03|-0.49|2.66|
|Total owned land (in acres)|963|2.58|7.55|0.01|0.04|7.02|
|Average monthly expenditure (in Rs. thousands)|963|8.80|4.71|3.00|9.00|12.50|
|House quality index|963|0.00|1.38|-1.62|-0.32|1.97|
|Low Zaat Status|963|0.26|0.44|0|0|1|
|Received visit from political party staff prior to election (1=Yes)|963|0.75|0.43|0|1|1|
|Attended political rally before intervention (1=Yes)|963|0.24|0.43|0|0|1|
|Distance to polling station (Km.)<sup>1</sup>|991|0.97|0.91|0|1|2|
|Distance between households within geographical cluster (meters)|8,263|194.2|283.9|27.9|107.1|456.5|
|Distance between households within village(meters)|48,430|1,472.9|1,304.9|109.4|1,070.5|2,962.8|
|Panel C: Woman Characteristics|||||||
|Age|2,637|37.76|16.09|20|35|60|
|Woman has formal schooling (1=Yes)|2,637|0.18|0.39|0|0|1|
|Attended political rally before intervention (1=Yes)|2,637|0.80|0.40|0|1|1|
|Number of children under 5 years old|2,637|0.86|1.19|0|0|3|
|Woman has a National Identity Card (NIC or CNIC) (1=Yes)|2,637|0.70|0.46|0|1|1|
|Woman voted in last local elections (1=Yes)<sup>1</sup>|2,735|0.70|0.46|0|1|1|
|Access to radio (1=Yes)|2,637|0.48|0.50|0|0|1|
|Access to TV (1=Yes)|2,637|0.70|0.46|0|1|1|
|Access to cable (1=Yes)|2,637|0.30|0.46|0|0|1|
|Mobility Index (0 to 3)|2,637|2.17|0.42|2|2|3|
|Woman allowed to join an NGO (1=Yes)|2,637|0.73|0.44|0|1|1|
|Woman is a member of MRDO (1=Yes)<sup>1</sup>|2,735|0.11|0.31|0|0|1|
|Woman seeks advice from a religious leader or "Pir"(1=Yes)|2,637|0.64|0.48|0|1|1|

Notes:<sup>1</sup> indicates that the variable is created using only the sample from the pre-election visit. Variables are defined in Appendix Table A1.
standard financial literacy questions on interest rates and inflation asked on the survey.<sup>11</sup> About 41 percent of parents did not answer either question correctly. We classify these parents as having low baseline financial literacy. Parents who answered one or two questions correctly are classified as having high financial literacy.

Each school that provided a list of their students received two separate lists in return: one with the names of the students whose parents would watch the financial education video during the school workshop (treatment group) and one with the names of students whose parents would watch the health video (control group). Schools were provided the financial education and health videos and parent exit questionnaires. Each school was responsible for organizing and implementing the workshop at a time of their choosing and administering the questionnaire at the end of the workshop.

# **4.4. Empirical Specifications**

Based on the random assignment, the impact of student financial education is measured as the difference in average outcomes in the treatment and control groups using the following intentionto-treat OLS regression: where yi,s,f R is a measure of the financial knowledge, attitude, or behavior, of student or parent _i_ in school pair _s_ at follow-up _f_ . The variable Treatmenti,s indicates whether an individual is in a school that was randomized into treatment or not and is thus equal to one for the treatment group and equal to zero for the control group. We include school pair dummies, ds in all specifications and control for baseline values of available dependent variables, yi,s,b as per Bruhn and McKenzie (2009) and McKenzie (2012). When baseline values have missing observations, we replace these with zero and include a dummy variable indicating that the observation was missing. Standard errors are clustered at the school level. Finally, note that results from the first and second follow-up surveys are not fully comparable due to changes in class composition from one year to the next, as discussed in Section 6.3.

The analysis of the parent workshops is based on workshop attendance rather than invitation, hence the treatment variable in specification (1) takes on the value of 1 if the parent watched the

> 11 These two questions are:

> Question 1. Assume that you would like to get a loan for R$ 50,000. Bank A offers you a loan of R$ 50,000 that needs to be repaid after one year with an interest rate of 15% payable at the end of the loan. Bank B offers you a loan for the same amount, R$ 50,000, for which you will have to pay R$ 60,000 also after a period of one year. Which option do you prefer? A) Bank A, B) Bank B, C) Don’t know.

> Question 2. Assume that the interest rate on your savings account is 1% per year and the inflation rates 2% per year. After one year without making any transactions on our saving account, would you be able to purchase more, the same amount, or less than what you could purchase today with the money in the account?

> A) More than today, B) The same as today, C) Less than today, D) Don’t know.

12
15 factors are incorporated for each pollutant, and extensive information is included about the effect on emissions of mandatory inspection and maintenance procedures and tampering.effects.

The importance of trips versus age, model year, fuel quality (volatility measured as Reid Vapor Pressure), and speed can be examined by using relationships adapted from EPA's HOBILE4 model (Shih, 1990). Short of presenting the mathematical relationships, which are difficult to extract from MOBILE4, table 2 provides a "feel" for these relationships by presenting estimated emissions changes arising from some particularly relevant scenarios. The scenarios are based on improving fuel quality (lowering RVP), substituting never for older models, substituting a zeromile versions of a model year for an aged version, increasing driving speeds, and eliminating trips by chaining or by car pooling. The focus is on hydrocarbons from autos, but information is also provided for NOx emissions from autos and HC emissions from motorcycles.

From this information, the most significant reductions in HC emissions (but not NOx emissions) are found in fleet turnover, particularly in replacing uncontrolled vehicles with those produced in the late 70's. These reductions are not necessarily the cheapest, of course. Actually, up until 1975 (with emissions standards calling for about a _60Z_ reduction in HC from uncontrolled levels for vehicles with 50,000 miles on them), the standards were met by engine modifications. These were very cheap. Afterwards, up until the 1980 standards, a fairly primitive catalyst was used, which also was fairly inexpensive. However, to meet the 1980+ standards, a three-way catalyst was needed, which has been shown to be cost-ineffective relative to other control. options available (Crandall, et al., 1986). From this, it follows that our current modeling effort should
**<u>Table 3: Effect of computerization on volume of registered transactions</u>**

|**T**|**otal Transact**|**ions**||**Sales**|||**Mortgage**|**s**|
|---|---|---|---|---|---|---|---|---|
|Computerization<br>0.058|0.017||0.021|0.013||0.133|0.127||
|[0.037]|[0.036]||[0.035]|[0.037]||[0.086]|[0.090]||
|Comp.* Urban Share|0.389|||0.076|||0.608||
||[0.083]**|||[0.092]|||[0.200]**||
|Comp.* Y1-Y3||0.047|||0.004|||0.156|
|||[0.042]|||[0.038]|||[0.097]|
|Comp.* Y4 +||0.114|||0.068|||0.225|
|||[0.076]|||[0.066]|||[0.163]|
|Comp.* Urban * Y1-Y3||0.312<br>[0.080]**|||0.071<br>[0.089]|||0.542<br>[0.203]**|
|Comp.* Urban * Y4 +||0.387|||0.146|||0.601|
|||[0.103]**|||[0.118]|||[0.238]**|
|Observations<br>2587|2587|2587|2587|2587|2587|2587|2587|2587|
|R-squared<br>0.82|0.82|0.82|0.89|0.89|0.89|0.58|0.58|0.58|

_Note:_ All regressions include year and SRO fixed effects. Robust standard errors clustered at the SRO level. * significant at 5%; ** significant at 1%.

19
# **3. Implications of public investment quality for sovereign risk and debt sustainability**

The objective of this section is to test the fiscal implications of public investment quality.

# **3.1. Conceptual framework**

One can envision several transmission channels through which public investment quality may impact public debt sustainability and sovereign risk dynamics in the context of the conventional (r−g) framework. Consider the standard debt dynamic equation: where B and Y are public debt and GDP levels, respectively; _r_ and _g_ are the real interest rate and real output growth rate; and pb is the primary balance as a share of GDP.

Public investment boosts both short-run aggregate demand, and high-quality public investment also boosts long-run aggregate supply via improvements in productivity. Large public investment multipliers for countries with greater public investment efficiency are documented by Adarov (2024), Daude (2011), Furceri and Li (2017), Gupta et al. (2014), Izquierdo et al. (2018), Leduc and Wilson (2012), and Leeper et al. (2010).

There can also be important fiscal transmission channels because scaling up of high-quality public investment may decrease sovereign risk (and the other way around for low quality public investment). Thus, better public investment quality may improve debt sustainability by boosting g and reducing r. There can also be a positive impact on the primary balance because high-quality public investment is likely to yield infrastructure that is less costly to maintain, and vice versa.<sup>19</sup>

# **3.2. Data and sample**

We use the PIQ index to gauge whether public investment quality affects the relationship between public investment and sovereign risk in EMDE countries.<sup>20</sup> We conduct this exercise by using two measures of sovereign risk: sovereign ratings and 5-year CDS spreads.<sup>21</sup> Sovereign ratings reflect an independent assessment by a credit rating agency of the — creditworthiness of a country the level of credit risk associated with investing in its debt. We focus on the three main rating agencies — Standard and Poor’s, Moody’s, and Fitch — and source the rating data from Bloomberg.

The three rating agencies that we consider do not express their rating opinions with numerical scores. Ratings are instead communicated with alpha-numeric scores that range between AAA (the highest rating) and D/SD (meaning that a country is in default) for Standard & Poor’s and Fitch and between Aaa and C for Moody’s. For our empirical analysis, we convert the ratings into a

> 19 On the maintenance costs of public capital stock, see Kalaitzidakis and Kaylvitis (2005) and Kalyvitis and Vella (2015).

> 20 Most of the countries in the PIQ dataset are EMDEs. The only countries classified as advanced economies are Latvia and Lithuania. We do not include these two countries in the analysis of sovereign risk.

> 21 For robustness, we also check the results with EMBI spreads.

15
**Figure 5. Purpose to use the internet**

<!-- Start of picture text --> News<br>Entertainment<br>Online shopping<br>Learning<br>Work related to e-commerce business<br>Information other than news<br>Work not related to e-commerce business<br>0% 10% 20% 30% 40% 50% 60% 70%<br>Percent of people using internet<br><!-- End of picture text --> More than 90 percent of people in Taobao Villages perceived their social status as equal to or higher than it was with five years ago, and they believe they will have equal or even higher social status two years in the future. Compared with other households, e-households are 8 percent more likely to perceive an improvement in social status in the past five years and expect an improvement in the coming two years (Figure 6 and Figure 7).

**Figure 6. Self-rated social status compared to 5 years ago**

**Figure 7. Self-rated social status 2 years in the future compared to current status**

<!-- Start of picture text --> 100% 100%<br>80% 32% 80% 38%<br>52%<br>62%<br>60% 60%<br>40% 61% 40% 56%<br>43%<br>20% 20% 36%<br>0% 0%<br>E-households Non-E-households E-households Non-E-households<br>Lower social status Equal social status Lower social status Equal social status<br>Higher social status Higher social status<br><!-- End of picture text --> Taobao Villages have a higher share of migrants<sup>7</sup> than other villages in China (Table 1). While a typical rural village population is 5.3 percent migrants,<sup>8</sup> the Taobao Villages in our sample are 29 percent migrants, 19.6 percent come from other provinces. The causal relationship between the inflow of migrants and formation of Taobao Villages is unclear. It may be that villages with a large inflow of migrants, typically those with a strong industrial base, are more likely to become Taobao Villages. It may also be that a village where the business environment is more conducive for e-commerce (or commerce in general) attracts more migrants, who are more likely to be entrepreneurial and move there to start their own businesses or to seize employment opportunities. What is clear is the strong positive association of migrant inflows in Taobao Villages.

> 7 Immigrants are defined as individuals who live in this village but have their hukou, which is the population registration system, registered outside the village.

> 8 The numbers are derived from the Chinese Population Census in 2010. Total rural population is taken from module 1- 1c, and numbers of migrants by origin are taken from module 1-4c.

8
The main conclusion of this paper is that aid does not have an impact on different measures of economic institutions once we explicitly control for country effects that are time invariant and are country specific. We conclude that estimations showing that aid either negatively or positively affects institutions omit historical determinants of institutions that are important in shaping today’s institutions. Once we account for these factors, we find no robust effect of aid on institutions. We do both cross section and dynamic panel data (GMM) analysis to ask the question whether aid has any effect on a variety of measures of economic institutions. Our results are robust to different definitions of aid (for example we distinguish between technical assistance and other forms of lending), to a number of specifications and to different datasets.

Furthermore, we find that the results in the literature that find a negative relationship between aid and democracy (a measure of political institutions) and a positive relation between aid and corruption are not robust to specification or estimation methods and vanish once we explicitly consider country fixed-effects. These results are not shown here.<sup>5</sup>

# **Data on Aid and Economic Institutions**

For the panel regressions in this paper we use indicators of economic institutions from two main datasets for the panel regressions-the ICRG and Fraser Institute datasets. As a robustness check we also use an indicator to measure institutional development more narrowly. This measure is linked to institutional development in the financial sector. We focus on a subset of indicators that represent our notion of economic institutions – those that support economic transactions but not through the political process. Indicators of democracy, checks and balances within government and political stability (or civil wars) fall in the latter category. For the ICRG dataset, available for 1984-2002 we use measures of bureaucratic quality and corruption as indicators of “economic” institutions (in contrast to government stability, internal conflicts,

> 5 But are available upon request.

10
10 growth: the value of CEEC total trade turnover in parts grew almost threefold from US $8.7 billion in 1993 to US $25.3 billion in 1997 with both exports and imports rising at a similar pace, albeit the latter from a lower level. Parts recorded the largest increase in major product categories except for fuels in imports: their share in CEEC exports rose by 50% from 6% in 1993 to 9% in 1997 and in imports by 30% from 10 % to 13%.

Since six of 10 CEECs did not exist in 1989, it is impossible to cast these developments against a longer time horizon. As far as countries for which data are available for the pretransition period are concerned, two groups of countries-Hungary and Poland, on the one hand, and Bulgaria and Romania-have each displayed similar pattern. Hungary's exports of parts increased 54% between 1989 and 1993 and Poland's by 17%t.<sup>9</sup> On the import side, the corresponding increases were 97% and 225% for Hungary and 114% and 231% for Poland. Between 1993 and 1997 these exports increased 176% and 206% respectively. Bulgaria experienced a large contraction over 1989-93 in both exports (-63%) and imports (-47%). Both recovered over 1993-97, but were still in 1997 more than 30 percent below their respective values in 1989. Romanian exports collapsed (-39%) over 1989-93 and subsequently increased 220%. Imports increased in both periods-272% and 116%.

# **3.2. Growth of** trade **in parts**

Trade in parts has been the fastest growing component in trade of CEEC as a region. But there were exceptions. Although Bulgaria's exports and imports of intermediate goods grew faster than manufactures, the share of parts in intermediates declined between 1993 and 1997 (Table 3). On the import side, in addition to Bulgaria, Latvia's and Lithuania's imports of intermediates grew faster than imports of parts over 1993-97and it was roughly the same for both Romania and Slovenia. Import growth rates of parts were higher than exports only for Hungary and Poland, and were the same for Slovenia. The meaning of it is not clear, as our comparator countries (Turkey, Malaysia and Mexico) recorded growth rates of exports higher than those of imports did. However, all CEEC as well as comparator countries run deficits in trade in parts.

The share of parts in total trade of CEEC varies quite significantly on a country-bycountry basis (see Appendix Table 2). The share in exports of manufactures ranges between 14% (Czech Republic), 12 % (Slovenia and Hungary) and 1% (Latvia). The share in CEEC imports tends to be significantly higher-it is 21% for Hungary, 15% for the Czech Republic, and 13% for Poland. Although except for Hungary, these shares are considerably lower than those for 9Calculated from data presented in Appendix Table 2.