rafmacalaba/datause-encoder-data huggingface dataset:
Kocherlakota (2001), who focuses on the interaction between limited enforcement and inequality to explain the failure to adopt high-TFP technologies, and Bental and Demougin (2006), who explain productivity differences by endogenous differences in incentive schemes along the development path.

Alternatively, Banerjee and Duflo (2005) stress that there are huge differences in rates of return within countries, so that many firms in developing countries are in fact not taking advantage of available technologies and investment opportunities. They point to “nonaggregative” reasons why this might be the case: government failures (inadequate regulations, excessive interventions), credit constraints and failing insurance markets, intergenerational constraints, among others.<sup>7</sup> If this is the case, adding to the infrastructure capital stock may fail to significantly boost productivity as long as other types of bottlenecks remain.

Although not always grounded in the same theoretical arguments, polar views are also found in the large empirical literature that developed in the last 15 or 20 years to try to assess the real impact of infrastructure on output, growth and productivity. As we will see when reviewing these contributions in Section 3, authors have come up with everything from hugely positive output elasticities, as in the seminal work of Aschauer (1989) that launched a flurry of subsequent research, to zero or even negative elasticities. Of course, results are not always strictly comparable due to differences in the samples and time periods under study (covering alternatively time-series data for a single country, worldwide cross country samples or single country state-level panels) and econometric techniques used. We will discuss below to what extent these disparities may account for the variations in results found in the literature.

On top of an ever growing literature, several surveys have by now extensively reviewed the literature on the effects of infrastructure on output and growth, comparing and contrasting the different methodologies available and the different results obtained. From Gramlich (1994), who focused mostly on the US aspect of the debate to Sturm, Kuper

> 7 Note that the first point of the list overlaps with Prescott’s argument above.

6
Table 7b: Impacts of PROSPERA on Type of School All 6<sup>th</sup> Graders in Medium, High and Very Highly Marginalized Communities 2008 School Fixed Effects

||Telesec|General|Technical|
|---|---|---|---|
|Variables|(1)|(2)|(3)|
|PROSPERA beneficiary<br>2008|0.068***|0.006*|-0.072***|
||(0.004)|(0.004)|(0.005)|
|Female beneficiary|0.014**|-0.007|-0.009|
||(0.006)|(0.005)|(0.007)|
|Female|-0.008|0|0.009|
||(0.005)|(0.004)|(0.006)|
|Constant|0.442***|0.258***|0.295***|
||(0.026)|(0.022)|(0.031)|
|Observations|68,871|68,871|68,871|
|R-squared|0.561|0.531|0.454|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis.

Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, and calculator.

Table 7c: Impacts of PROSPERA on Type of Secondary School: Sample: 6<sup>th</sup> graders in 2008 in Medium, High and Very Highly Marginalized Communities Nearest Neighbor Matching Models

|Variables|7th grade<br>Telesecondary|Enrollment<br>7th grade General<br>school|7th grade Technical<br>school|
|---|---|---|---|
||Propensity score matching|with exact matching on age an|d state|
|Girls|0.1263***|-0.0209|-0.0912***|
||(0.0164)|(0.0149)|(0.0188)|
|Observations|6,654|6,654|6,654|
|Boys|0.104***|-0.014|-0.0920***|
||(0.0212)|(0.0133)|(0.022)|
|Observations|4,853|4,853|4,853|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

Why is the increase in secondary school enrollment concentrated in tele-secondary schools? One obvious explanation is that these are the schools which are available in the poor rural areas that are the focus in this paper, and it is not feasible to reasonably attend other schools (due to distance).

22
This is despite the fact that Bangladesh is a small country in terms of land area and there are no administrative restrictions to migration, which would tend to equalize earnings across space. In the light of these findings, an important question our paper examines is whether the effect of geographic location on household welfare has changed in the 15 or so years since the early-1990s, and if so, in which direction.

In order to address the questions above, we apply a model specification that uses as a starting point the regression model used by Ravallion and Wodon. To provide a context for our analysis and illustrate the nature of economic and social transformation that occurred in Bangladesh during 2000-2005, we also discuss a range of poverty correlates and how they have evolved during this period. The paper is structured as follows. A profile of poverty, including a descriptive analysis of the correlation between consumption poverty, household characteristics and geographical location, are presented in Section II. Section III uses a multivariate regression framework to identify the relationships between household and geographic characteristics and poverty. Section IV examines the trends in the correlates of poverty over time, to analyze how changes in these characteristics and the returns to these characteristics may have contributed to poverty reduction. Section V concludes the paper, summarizing the main findings and identifying areas for further analysis.

The main data source for this study is the Household Income and Expenditure Survey (HIES), a household survey conducted by the Bangladesh Bureau of Statistics (BBS). The paper relies primarily on the 2005 round of HIES for poverty profile and determinant analyses; the 2000 HIES is used to make comparisons over time. The community survey of the HIES is also used to examine location-specific characteristics, 3
||TRANS-M<br>|AGHREB BARRIERS<br>||
|---|---|---|---|
|Barriers|**Possible**Corrective Measures|Progress with Current Measures|Recommended Actions at Regional<br>Level|
|Boarder Tax on Vehides|- Elimination of the entry tax on<br>vehicles crossing the Algerian border<br>and choice of other means of financing<br>the country's road system maintenance<br>fund.|||
|Contraband|- Strengthening of border controls.<br>- Liberalization of issue of import<br>licenses.<br>- Allocation of foreign exchange;<br>currency convertibility.||IM - Intermational agreements among<sup>the</sup><br>Maghreb countries on reducing the<br>subsidies granted on certain product<br>categories.<br>IM- AMU studies on where and how<sup>to</sup><br>amend existing accords (UNIM,<br>Maghreb Commission, Free Trade<br>Agreement) ratifying trans-Maghreb<br>trade, so as to simplify their application.<br>**IM/EM**- Promote more contacts<sup>through</sup><br>and circulate trade information<sup>likely to</sup><br>eliminate triangular traffic patterns<br>(Lylien PVC, exported to Germany<sup>for</sup><br>reimport into Tunisia).<br>- In regard to the increased value<sup>of this</sup><br>traffic, take into account the effects<sup>of</sup><br>the present situation in Algeria.|
|**Foreign Exchange**<br>Controls|- Macroeconomic adjustment measures<br>in consultation with international<br>partners and the IMF.<br>- Policy focus on convertibility<sup>of</sup><br>currencies.||IM - Develop banking and financial<br>institutions to cover trans-Maghreb<sup>trade.</sup><br>- Reduce the delays associated with<br>international procurement proceedings<sup>so</sup><br>as to facilitate the bringing into place<sup>of</sup><br>the corresponding financing<br>arrangements.<br>- Clarify and select types of financing<sup>for</sup><br>Algeria (export credits, or the<sup>Islamic-</sup><br>Bank-type of bridge loan).|

IM Intra-Maghreb EM = Europe/Maghreb
**Table 2**

**Regression Results of Public Transport Mobility with overall restriction (country fe)**

|**(1)**|**(2)**|**(3)**|
|---|---|---|
|<br>**PANDEMIC & LOCKDOWN EVOLUTI**<br>1035***|<br>**ON**<br>0945***|<br>0945***|
|**Covid deaths (per Million)**<br>-.<br>(0.023)|-.<br>(0.022)|-.<br>(0.022)|
|**Stringency Index**<br>-0.691***<br>(0.004)|-0.661***<br>(0.004)|-0.6621***<br>(0.004)|
|**CONTROLS**|10772***|10772***|
|**Working days**|.<br>(0.259)|.<br>(0.259)|
|**Min temperature**|0.008<br>(0.019)|0.008<br>(0.017)|
|**Avg temperature**|-0.065***<br>(0.017)|0.208***<br>(0.016)|
|**Precipitation**|-1.635***<br>(0.071)|-1.635***<br>(0.071)|
|**City density**||-0.150<br>(0.012)|
|**COUNTRY FE**<br>||<br>|
|**Country FE**<br>Yes|Yes|Yes|
|**Monthly dummy**<br>Yes|Yes|Yes|
|**Constant**<br>-3.534<br>(7.417)|-13.999<br>(7.482)|-26.528***<br>(7.509)|
|**N**<br>59,777|59,777|59,777|
|**N cities**<br>164|164|164|
|**Wald χ2**<br>151703.72***|163759.75***|163762.71***|
|**Within R**<sup>**2**</sup><br>0.7176|0.7329|0.7331|
|**Between R**<sup>**2**</sup><br>0.6690|0.6636|0.6671|
|**Overall R**<sup>**2**</sup><br>0.7085|0.7197|0.7204|

Note: *, **, *** indicate respectively level of significance of 10, 5 and 1 percent 13
thus substantial across these five countries. The gender gap is significantly higher in urban areas (ranging from 8 pp in Nigeria to 26 pp in Malawi) than in rural areas (ranging from 1 pp in Nigeria to 16 pp in Malawi). This might be caused by a higher gender gap in earnings in urban areas than rural areas, due to a larger job heterogeneity in urban areas (Nordman et al., 2016). In the case of Uganda, the gender gap is similar across rural and urban areas.

# _[Table 1 here]_

In line with studies on the increasing labor market participation of women in developing countries (Gaddis and Klasen, 2014), we find that the gender gap in off-farm employment participation has decreased over time in some countries. The annual percentage change in reducing the gender gap stands at 7.3 percent in Malawi, 11.1 percent in Nigeria, and 13.2 percent in Tanzania. We do not find a reduction over time in the gender gap in Ethiopia, and the gender gap in Uganda increased with 1.4 percent annually. In rural areas, the gender gap has been reduced in all countries except Uganda (ranging from 3.9 percent in Ethiopia to 27.8 percent in Nigeria). In urban areas, the gender gap has been reduced in Malawi (6.9 percent), Tanzania (1.1 percent) and Uganda (3.4 percent), and has increased over time in Ethiopia (1.6 percent) and Nigeria (8.3 percent).

Table 1 also presents the age-disaggregated participation rates in off-farm employment across gender and space for the five countries. Following Yeboah and Jayne (2018), we define following age categories: 1) people aged 15-24 (‘youth’), 2) people aged 25-34 (‘young adults’) – as the African Union uses the 15-35-year range to classify youth, and 3) people aged 35-64 (‘adults’). In line with the literature on youth unemployment in SSA (Fox et al., 2016), we find that participation rates in the latest survey round are lowest for adolescents (aged 15-24) and range from 20 percent in Nigeria to 45 percent in Malawi. However, we find that young adults (aged 25-34) are more likely to be off-farm employed than adults (aged 35-64) in Ethiopia (40 percent versus 35 percent), Malawi (64 percent versus 60 percent), Tanzania (60 percent versus 57 percent) and Uganda (44 percent versus 42 percent). Only in Nigeria adults are more likely to be off-farm employed (61 percent) than young adults (47 percent). This is in line with other studies that point to the importance of off-farm employment for young people in SSA (Yeboah and Jayne, 2018). However, this trend is mainly driven by higher employment rates among young adults in rural areas, as in urban areas adults are consistently more likely to be off-farm employed. The gender 10
Table 8 presents the impact of therapy on human capital outcomes at the _rapid resurvey_ .<sup>22</sup> There are some effects in the expected direction, i.e., beneficial, but these are small and do not survive corrections for multiple hypothesis testing using FDR-adjusted q-values. Nonetheless, we note that individuals in the pooled IPT-G group were more patient ( _time preferences_ : they were 5.5pp or 11%, more likely to value receiving 90,000 Ugandan shillings now versus 110,000 Ugandan shillings in one month), more likely to expect to be doing _paid work_ at age 25 (by 0.024 pp or 3%; p = 0.039, q = 0.104), and scored better on their _competencies_ tests (up 8%; p = 0.078, q = 0.104).

Table 9 turns to the effects of therapy on human capital outcomes at the 12- and the 24-month follow-up surveys. At the 12-month follow-up (Panel A), there are small effects on a number of primary and secondary outcomes. The incidence of marriage since baseline decreased by 0.025 pp or 30% (p = 0.042, q = 0.106) and there was a sizeable increase in the likelihood of being enrolled in school (0.075 pp or 23%; p = 0.021, q = 0.104). Desired fertility declined by approximately 0.2 SD (q = 0.001) and individuals in the IPT-G arm showed more patience (time preferences; q = 0.095). These modest effects of IPT-G on human capital accumulation at the 12-month follow-up are consistent with the IPT-G effects on mental health presented in Panel B of Table 6. By endline (Panel B), the impacts on marriage and desired fertility had dissipated, while the estimates for enrollment and time preferences remained similar to midline but were no longer statistically significant (all FDR-adjusted q-values _>_ = 0.143). We note that the 12-month follow-up surveys were conducted by phone due to the COVID-19 pandemic (as opposed to in person for the rest of the data collection rounds), so the difference in the impact of IPT-G on human capital between the 12- and the 24-month follow-ups could be in part due to differences in data collection methods.

# **3.3.2 Impact of adding cash to IPT-G**

Finally, Table 10 presents the marginal impact of providing cash transfers over and above the therapy intervention on human capital outcomes at the 12- and 24-month follow-up surveys. Consistent with the fact that the mental health effects of cash were negative (compared to the IPT-G arm), individuals in the IPT-G+ arm were less likely to be enrolled in school, had higher desired fertility, and lower expectations of being in paid work at age 25. Most marginal impact estimates are not statistically significant, but it is clear that the addition of lump-sum cash transfers to IPT-G was not an effective intervention. While we focused on the marginal effects of cash over IPT-G here, we also note that outcomes (mental health or

> 22The set of outcomes at the _rapid resurvey_ differ from those for later rounds. As it was deemed unlikely for human capital accumulation to have materialized so shortly after the therapy, data on outcomes that may be prognostic of future investments in human capital were collected in this round.

16
Perron, P. and Ng, S. (1996) “Useful Modifications to Unit Root Tests with Dependent Errors and Their Local Asymptotic Properties”. Review Of Economic Studies, v. 63, pp. 435-65.

Roy, Devesh & Arvind Subramanian, 2001. "Who Can Explain the Mauritian Miracle: Meade, Romer, Sachs, or Rodrik?," IMF Working Papers Sims, C., 1980. “Macroeconomics and Reality”. Econometrica 48, pp. 1-48.
capital restrictions, more frequent foreign exchange interventions, and greater trade exposure to the United States. Moreover, countries with more restrictive capital control policies have significantly lower degree of trade integration with the US. On the other hand, FXI tends to be a substitute for KC, while there is little statistical significance, unless one investigates the de facto capital controls. We find no strong relationship between capital flow management and the degree of dollar utilization.

Table 1: **Cross-correlation Among Country Factors**

|Variables|KC|KC<br>Infow|KC<br>Outfow|FOpen<br>Chinn-Ito|FOpen<br>(Ha)|EA|PEG|KS<br>PEG|FXI|US<br>TR|DOL|
|---|---|---|---|---|---|---|---|---|---|---|---|
|KC|1.00|||||||||||
|KC<br>Infow|0.98*|1.00||||||||||
|KC<br>Outfow|0.99*|0.93*|1.00|||||||||
|FOpen<br>Chinn-Ito|-0.93*|-0.91*|-0.92*|1.00||||||||
|FOpen<br>(Ha)|-0.67*|-0.69*|-0.64*|0.75*|1.00|||||||
|EA|-0.59*|-0.58*|-0.59*|0.58*|0.53*|1.00||||||
|PEG|0.40*|0.34*|0.44*|-0.39*|-0.16|-0.54*|1.00|||||
|KSPEG|0.42*|0.37*|0.46*|-0.37*|-0.16|-0.66*|0.93*|1.00||||
|FXI|-0.05|-0.11|0.01|0.14|0.30*|-0.36*|0.23|0.41*|1.00|||
|USTR|-0.29*|-0.28*|-0.28*|0.35*|0.35*|0.48*|-0.19|-0.18|0.01|1.00||
|DOL|0.03|0.05|0.01|-0.13|-0.39*|-0.41*|0.19|0.32*|0.07|-0.36*|1.00|

Notes: (1) **KC** is the average capital control index, a larger value indicates greater stringency. **KC Inflow (Outflow)** is the average index for restrictions on capital inflow (outflow). (2) **FOpen (Chinn-Ito)** is the Chinn-Ito index on capial account openness proposed by Chinn & Ito (2006); **FOpen (Ha)** is a de-facto financial openness measure defined as the sum of international assets and liabilities in percent of GDP (Ha et al. 2019), winsorized at 99% and standardized to 0-1. (4) **EA** is the average value of monthly classification from Ilzetzki et al. (2021), a higher value indicates greater exchange rate flexibility; **PEG** and **KSPEG** are the average of the peg indicator from Shambaugh (2004) and Klein & Shambaugh (2008) respectively, a higher value indicates greater fixity. (5) **FXI** is the standard deviation of a broad foreign exchange interventions proxy defined by Adler et al. (2021) divided by the sum of standard deviations of this proxy and log change in the exchange rates against USD. (6) **USTR** is the updated trade integration measure in Miniane & Rogers (2007). It is total imports from and exports to the U.S. divided by annual GDP denominated in USD. (7) **DOL** is the deposit dollarization measure provided by Christiano et al. (2021), and supplemented by the dollar asset share measure in Adrian & Xie (2020). (8) * indicates significance at 90%.

Our large sample of countries allows us to conduct separate analysis of AEs and EMDEs, a crucial distinction emphasized by Kalemli-Özcan (2019). Table 2 highlights the much greater volatility of EMDE exchange rates and asset returns, and also more unstable inflation and output growth compared to AEs. It also shows the much more volatile interest rates, exchange rates and prices for countries with higher KC, more floating EA regimes or fewer foreign exchange interventions.

9
less developed, where property rights are not well defined and/or protected and enforced by judicial systems.

Studies for the United States (e.g., Levy, 1983, Lease et al., 1984, DeAngelo and DeAngelo, 1985, Shleifer and Vishny, 1986, McConnell and Servaes, 1990) indeed find a positive relation between ownership concentration and corporate valuation. Other US evidence (Holdemess and Sheehan, 1988; Barclay and Holdemess, 1989) supports the argument that large shareholders better monitor management and thereby improve firm valuation. This relationship, while subject to interpretation, suggests that better monitoring and reduced agency costs are associated with more concentrated ownership.<sup>2</sup> There is, however, evidence that concentrated ownership can harm market valuation. Some studies have found, for example, an inverse U-shaped relationship between ownership by managers and a firm's valuation and its profitability (Morck, Shleifer and Vishny, 1988). The inverse-U shaped effects of managerial ownership can be explained by an incentiveentrenchment tradeoff (Stulz, 1988). Direct managerial ownership is small, however, for most East Asian corporations and empire building by unaccountable managers cannot account for the possible negative relationship between ownership concentration and valuation.

Shleifer and Vishny (1997) and La Porta et al. (1999a) focus on another problem associated with high ownership concentration, the conflict between large and small shareholders. When large shareholders effectively control corporations, their policies may result in the

> 2 Other studies (Demsetz, 1983; Demsetz and Lehn, 1985) argue that the relation is spurious. While greater ownership concentration results in stronger incentives to monitor, the expected gain from active monitoring and the costs of alternative ownership structures vary across finns. If transaction costs inhibiting investors from taking value-maximizing positions in firms are low, each **firm** would have the "right" ownership structure. Specifically, Demsetz and Lehn (1985) argue that distortions in the markets for corporate control and the managerial labor market increase the control potential of shareholders, which lead to increases in ownership concentration. They

_5_
In the 19 data sets with an answer scale, having “some difficulty” is more common than “at least a lot of difficulty”. The prevalence of at least a lot of difficulty among adults ranges from under 1% in Vanuatu to 6% in Papua New Guinea and the median stands at about 2%. The median prevalence for some difficulty is at about 9%. There is more variation in prevalence when it is measured based on functional difficulty questions other than the WGSS compared to the WGSS. The data sets with other functional difficulty questions tend to be at the lower end of the distribution (e.g. Philippines, Panama, Indonesia) and also at the maximum (Papua New Guinea). Prevalence estimates based on the WGSS range from 7% in Nigeria to 19% in Namibia.

The bottom panel of Figure 3.1 gives the prevalence of any difficulty at the household level from the lowest to the highest. The median household level prevalence is at 23%. It ranges from a low of 9% in the Philippines to a high of 55% in Papua New Guinea.

Figure 3.2: Distribution of functional difficulties across domains among adults 15+ with any difficulty (%) for datasets with the WGSS <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Seeing Hearing Moving Cognitive Selfcare Communication<br><!-- End of picture text --> For countries with two datasets under study (Rwanda (Census and LFS), South Africa (Census and GHS), Tanzania (Census and LSMS)), prevalence rates differ across datasets, perhaps due to different disability questions under use. For instance, in Rwanda, the prevalence rate is 9% with the LFS with the WGSS questionnaire while it stands at 5.5% with the Census which has other functional difficulty questions with yes/no answers.

Figure 3.2 gives the distribution of difficulties by domain for countries with the WGSS. Seeing and mobility difficulties tend to be the most common functional difficulties. Appendix 3 also has the distribution across domains broken down for some difficulty in the top panel and at least a lot of difficulty in the bottom panel.

14