rafmacalaba/datause-encoder-data huggingface dataset:
This makes it hard to interpret the existing evidence on the effects of demand side policies such as subsidies and CHW. For instance, one RCT in Ghana and a difference-in-difference analysis from South Africa document positive effects of user fee removal on some health outcomes for children (Tanaka, 2014; Powell-Jackson et al., 2014).<sup>1</sup> On the other hand, randomized trials of health insurance subsidies in Nicaragua, Mexico, and, most recently, India have resulted in few health or utilization effects, with often low uptake and retention (King et al., 2009; Thornton et al., 2010; Malani et al., 2021). As Malani et al. note in addition, even very large health insurance trials may not be powered to detect mortality reductions. Given this mixed evidence and the challenge of analyzing effects on rare events, information on _utilization conditional on the child’s health status_ is central to interpreting the demand response to policy interventions. If utilization is low, such data can for example disentangle whether there is no significant unfilled need for care or whether an intervention failed to curb underuse, perhaps due to unobserved supply-side effects. If utilization is high, it can help understand what share constitutes overuse vs. appropriate use, and whether complementary interventions such as CHW visits can help moderate any misallocation.

In this study, we measure effect of subsidies and CHW visits on healthcare demand conditional on illness, using novel health data collected at home. In partnership with the NGO Mali Health, we conducted a randomized control trial of a health care subsidy, which reduced primary care cost per formal care visit for children under five years by 72% on average, and a community healthworker (CHW) program, for which CHWs visit families biweekly and teach them basic health knowledge. The interventions were cross-randomized at the compound (dwelling) level, with a final sample of 1768 children from 642 compounds. Comprehensive quality controls at Mali Health’s partner clinics and the small sample mitigate general equilibrium effects and allow us to focus on the demand response to the interventions.

Nine months after intervention start, we collected nine weeks of daily data on children’s health and health care. We recorded all health consultations along with 14 symptoms that

> 1There are surprisingly few studies on the health effects of removing user fees, and review articles note the mixed quality of the evidence (Ridde and Morestin, 2012; Dzakpasu et al., 2013).

3
(Henry, 2000a; Henry, 2000b; Alfaro et al., 2004; Bekaert et al., 2005; Kose et al., 2010). These papers show that equity inflows are associated with a boom in aggregate investment and higher economic growth of the recipient countries. However, we know relatively little about the channels through which equity inflows affect real economic activity.<sup>7</sup> Our paper adds to this literature by studying for the first time the effects of capital inflows using issuance-level data. We show that supply-side changes in capital inflows allow firms to raise new financing and expand investment, which might be behind the patterns documented in this literature. Moreover, our paper shows that the effects are not uniform across types of firms. Capital inflows seem to reduce the cost of equity finance for large firms more than for other firms, which likely reflects the fact that differences in firm size is a proxy for differences in global investors’ knowledge and interest in firms.

The literature has found it challenging to disentangle supply and demand influences when gauging the effects of capital inflows on financial and real economic activity in a multicountry setting. Examining the case of one country, using detailed firm-level data, Baskaya et al. (2017a) and Baskaya et al. (2017b) isolate supply-side influences on capital inflows. In this paper, we propose a novel set of instruments in a multi-country context to distinguish between supply-side and demand-side effects on capital inflows, and we find that the supply side is important.

Our paper also contributes to another literature that asks why firms issue equity and bonds geared toward foreign investors. Part of this literature has studied firms’ issuance activity in international markets, characterizing which firms issue abroad and why. Foreign markets can offer benefits compared to domestic ones in terms of access to better financing

> 7 Mitton (2006), Gupta and Yuan (2009), Levchenko et al. (2009), and Igan et al. (2016) use industry- and firmlevel data to study the effects of liberalizing equity markets on industry growth and firms’ operating performance.

7
Brynjolfsson, Erik, and Lorin M. Hitt. 2000. “Beyond Computation: Information Technology, Organizational Transformation and Business Performance.” _Journal of Economic Perspectives_ 14 (4): 23–48.

Brynjolfsson, Erik, and Lorin M. Hitt. 2003. “Computing Productivity: Firm-Level Evidence.” _The Review of Economics and Statistics_ 85 (4): 793–808.

Brynjolfsson, Erik, Xiang Hui, and Meng Liu. 2019. “Does Machine Translation Affect International Trade? Evidence from a Large Digital Platform.” _Management Science_ 65 (12): 5449–60.

Brynjolfsson, Erik, Danielle Li, and Lindsey R. Raymond. 2023. “Generative AI at Work.” NBER Working Paper 31161, National Bureau of Economic Research, Cambridge, MA.

Brynjolfsson, Erik, and Andrew McAfee. 2014. _The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies_ . New York: W. W. Norton and Company.

Brynjolfsson, Erik, and Adam Saunders. 2010. _Wired for Innovation: How Information Technology Is Reshaping the Economy_ . MIT Press: Cambridge, MA.

Brynjolfsson, Erik, and Shinkyu Yang. 1996. “Information Technology and Productivity: A Review of the Literature.” _Advances in Computers_ 43 (January): 179–214.

Brynjolfsson, Erik, and Michael D. Smith. 2000. Frictionless Commerce? A Comparison of Internet and Conventional Retailers, _Management Science_ 46 (4): 563–85.

Brynjolfsson, Erik, Lorin M. Hitt, and Heekyung Hellen Kim. 2011. “Strength in Numbers: How Does Data-Driven Decision Making Affect Firm Performance?” Available at SSRN: https://ssrn.com/abstract=1819486.

Brynjolfsson, Erik, Tom Mitchell, and Daniel Rock. 2018. “What Can Machines Learn, and What Does It Mean for Occupations and the Economy?” _AEA Papers and Proceedings_ 108: 43–47.

Brynjolfsson, Erik, Daniel Rock, and Chad Syverson, 2021. “The Productivity J-Curve: How Intangibles Complement General Purpose Technologies.” _American Economic Journal: Macroeconomics_ 13 (1): 33–72.

Calderon, Cesar, and Catalina Cantu. 2021. “The Impact of Digital Infrastructure on African Development.” Policy Research Working Paper 9853, World Bank, Washington, DC.

Carbonero, Francesco, Ekkehard Ernst, and Enzo Weber. 2018. “Robots Worldwide: The Input of Automation on Employment and Trade.” IAB Discussion Paper 7/2020, Institute for Employment Research, Federal Employment Agency of Germany.

Callen, Michael, Saad Gulzar, Ali Hasanain, Muhammad Yasir Khan, and Arman Rezaee. 2020. “Data and Policy Decisions: Experimental Evidence from Pakistan” _Journal of Development Economics_ 146 (September): 102523.

27
poverty slightly increased after a slow drop in 2001; again, the size of the middle class increased. Overall, these figures suggest that the evolution of the absolute middle class has followed the same pattern than the official poverty incidence (see figure 3), which is both counter intuitive and contradictory to the definition of middle class presented here. As we argue, the lack of a conceptual framework to support a definition of thresholds results in descriptive statistics rather than meaningful analysis.

**Figure 3: People living on $2-13 a day; on $2-10 a day; and incidence of poverty in Peru**<sup>**/a**</sup>

_Percentage of households_ _Source_ : Author‘s calculations based on data from ENAHO cross-sectional databases. /a The left axis draws the size of the middle class according to B&D and Ravallion, while the incidence of poverty is drawn in the right axis.

Atkinson and Brandolini (2011) have a consistent view in the sense that they suggest that income distribution and income measures alone can no longer represent accurate definitions of the middle class. They argue for the inclusion of wealth holdings and position in the labor market into income definitions of class. The latter has been the longest tradition in sociological work.

# **4. The data**

The vulnerability-to-poverty approach to defining the middle class is constructed in three stages. The first stage identifies actual characteristics that are associated with movement in or out of poverty. Then, the second and third stages construct probabilities of falling into poverty, and an income level associated to those, respectively. Based on such models, the methodology results in an estimate of the value of the lower income threshold, in PPP terms. In these stages we exploit longitudinal data for Chile (2001-2006), Mexico (2002-2005), and Peru (2002-2006).

In the case of Chile, we use the panel version of the _Socioeconomic Characterization Survey_ (CASEN Panel) carried out by the Ministry of Planning (MIDEPLAN). The first wave was undertaken in 1996 using a random sub-sample of 5,210 households from the cross-sectional CASEN covering four regions of the country, which represent 60 percent of the national

- 8 -
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# References and bibliography

This country report draws upon a wide range of papers, databases, models, and maps that were created as part of the Africa Infrastructure Country Diagnostic. All of these can be downloaded from the project website: http://www.infrastructureafrica.org. For papers go to the document page (http://www.infrastructureafrica.org/aicd/documents), for databases to the data page (http://www.infrastructureafrica.org/aicd/tools/data), for models go to the models page (http://www.infrastructureafrica.org/aicd/tools/models), and for maps to the map page (http://www.infrastructureafrica.org/aicd/tools/maps ). The references for the papers that were used to compile this country report are provided in the table below.

## General

Africa’s Infrastructure: A Time for Transformation (AICD Web site), http://www.infrastructureafrica.org.

Banerjee, Sudeshna, Quentin Wodon, Amadou Diallo, Taras Pushak, Helal Uddin, Clarence Tsimpo, and Vivien Foster. 2008. ―Access, Affordability, and Alternatives: Modern Infrastructure Services in Africa.‖ AICD Background Paper 2, Africa Region, World Bank, Washington, DC.

- Estache, A., and M. Vagliasindi. 2007. ―Infrastructure for Accelerated Growth for Ghana: Needs and Challenges.‖ Background Paper, Country Economic Memorandum, World Bank, Washington, DC.

- Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2009. _Africa’s Infrastructure: A Time for Transformation._ Paris and Washington, DC: Agence Française de Développement and World Bank.

- Keener, S., and S. G. Banerjee. 2005. ―Ghana: Poverty and Social Impact Analysis of Electricity Tariffs.‖ ESMAP Technical Paper 088, World Bank, Washington, DC.

- Lall, S., J. Sandefur, and H. G. Wang. 2008. ―Can Industrial Development Bridge Ghana’s Spatial Divide?‖ Mimeo, World Bank, Washington, DC.

## Growth

Calderón, César. 2009. ―Infrastructure and Growth in Africa.‖ Policy Research Working Paper 4914, World Bank, Washington, DC.

- Escribano, Alvaro, J. Luis Guasch, and Jorge Pena. 2010. ―Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa.‖ Policy Research Working Paper 5191, World Bank, Washington, DC.

- Yepes, Tito, Justin Pierce, and Vivien Foster. 2009. ―Making Sense of Africa’s Infrastructure Endowment: A Benchmarking Approach.‖ Policy Research Working Paper 4912, World Bank, Washington, DC.

26
yields an unbalanced pseudo-panel at our unit of analysis: country years. Notably, the pseudo-panel imbalance is not systematically related to country trade policy or labor market outcomes and is instead a function of pre-determined survey schedules. As surveys are nationally representative, we use expansion weights in our calculation of country-level aggregates.

Several of our aggregated variables warrant more detailed explanation. We calculate employment rates for children under 14 and children 14–17 as the share of the surveyed age group reporting employment. We separately calculate under 14 and 14–17 employment for several interrelated reasons. First, we are concerned that survey respondents might misreport employment by young children due to social, religious, cultural, or even legal prohibitions, since 14 is frequently the legislated minimum age for employment and hence misreporting or mismeasurement of employment rates for 14–17 year olds is far less likely.<sup>9</sup> Second, our theoretical model explicitly predicts different responses in younger child labor markets and older child labor markets. By leveraging our rich survey microdata we are able to separately calculate child employment by age ranges and test our theoretical hypotheses. Finally, 14 years is commonly the age that differentiates primary and secondary schooling. Our differentiated child employment rates will roughly correspond to primaryand secondary-school-aged children.

For each country at each date, we calculate the relative income of households with children as the median household per capita income weighted by the share of the household members that are children, divided by the median household per capita income for the whole sample. We compute two related versions of this measure. In the first, we consider households with _any_ children (i.e. under 18 years of age) and weight household observations by the share of the household members under 18. In the second measure we only consider households that have children in both of our age ranges (younger than 14 and 14–17) to better capture intra-household child labor substitution effects predicted by our theoretical model. Both measures will approximate income inequality impacts on households that contain children accounting for how exposed a household is to potential child labor bans.<sup>10</sup>

> 9For example, the United States Fair Labor Standards Act establishes the minimum age for non-agricultural employment at 14.

> 10Survey weights are for individual respondents, not households. Consequently we do not use expansion weights when computing these aggregate household income equality measures.

15
- Bensch, Gunther, J ̈org Peters, and Colin Vance (2021). _Development Effects of Electrification in Brazil–A Comment on Lipscomb et al.(2013)_ . USAEE Working Paper.

- Berg, Claudia N, Uwe Deichmann, Yishen Liu, and Harris Selod (2017). “Transport Policies and Development”. _The Journal of Development Studies_ 53.4, pp. 465–480.

- Bird, Julia and St ́ephane Straub (2020). “The Brasilia Experiment: the Heterogeneous Impact of Road Access on Spatial Development in Brazil”. _World Development_ 127, p. 104739.

- Bun, Maurice JG and Teresa D Harrison (2019). “OLS and IV Estimation of Regression Models Including Endogenous Interaction Terms”. _Econometric Reviews_ 38.7, pp. 814–827.

- Callaway, Brantly and Pedro HC Sant’Anna (2021). “Difference-in-Differences with Multiple Time Periods”. _Journal of Econometrics_ 225.2, pp. 200–230.

- Correa, Vivian Helena Capacle and Pedro Ramos (2010). “A Precariedade do Transporte Rodovi ́ario Brasileiro para o Escoamento da Produ ̧c ̃ao de Soja do Centro-Oeste: situa ̧c ̃ao e perspectivas”. _Brazilian Journal of Rural Economy and Sociology (Revista de Economia e Sociologia Rural-RESR)_ 48.2, pp. 1–26.

- Croissant, Yves and Giovanni Millo (2008). “Panel Data Econometrics in R: the plm Package”. _Journal of Statistical Software_ 27.2.

- De Chaisemartin, Cl ́ement and Xavier D’Haultfoeuille (2022a). _Difference-indifferences Estimators of Intertemporal Treatment Effects_ . Working Paper 29873. National Bureau of Economic Research.

- (2022b). _Two-way Fixed Effects and Differences-in-Differences Estimators with Several Treatments_ . Working Paper 30564. National Bureau of Economic Research.

- De Chaisemartin, Cl ́ement and Xavier d’Haultfoeuille (2020). “Two-way Fixed Effects Estimators with Heterogeneous Treatment Effects”. _American Economic Review_ 110, pp. 2964–96.

- Donaldson, Dave (2018). “Railroads of the Raj: Estimating the impact of transportation infrastructure”. _American Economic Review_ 108.4-5, pp. 899–934.

- Duranton, Gilles, Peter M Morrow, and Matthew A Turner (2014). “Roads and Trade: Evidence from the US”. _Review of Economic Studies_ 81.2, pp. 681–724.

- Faber, Benjamin (2014). “Trade Integration, Market Size, and Industrialization: Evidence from China’s National Trunk Highway System”. _Review of Economic Studies_ 81.3, pp. 1046–1070.

25
problems. The central bank role as lender of last resort coordinated with bank supervision and deposit insurance are key elements for averting bank runs. In addition, improved financial depth would also allow a better allocation of resources where and when needed, generating a beneficial impact on volatility.

**Figure 20: Estimated reduction in Crisis Volatility if determinants were brought at OECD levels (middle income East Asia)**

<!-- Start of picture text --> 13.3%<br>7.9%<br>7.2%<br>5.8%<br>3.3%<br>1.6%<br>0.4%<br>0.1%<br>Institutional Volatility of Volatility of Banking Trade Volatility of Volatility of Volatility of<br>Quality Inflation Gov. Crisis Openness TOT Foreign Capital<br>Spending Growth Flows<br><!-- End of picture text --> Average of China, Indonesia, Thailand, Malaysia and the Philippines vs. average OECD levels in 2000-05. The bars represent the estimates’ standard deviation. Source: Author’s own calculations using data from WDI, IMF and Polity IV.

26
can be correlated with treatment and outcome variables (Rosenbaum, 2002; Becker and Caliendo, 2007; DiPrete and Gangl, 2004).

We address this problem with the bounding approach, proposed by Rosenbaum (2002). Rosenbaum bounds provide evidence of the degree to which any results are influenced by unobserved variables. The bounding approach is useful to indicate whether inferences about treatment effects are sensitive to unobserved factors (Baker and Caliendo, 2007). Because our outcome variables are binary by nature (human capital investment coping strategies adopted: yes=1; no=0), Rosenbaum bounds are computed using the Mantel and Haenszel statistics for binary outcomes (Aakvik, 2001). Here, the null hypothesis is that income shocks have no effect on coping strategies adopted by households. The test, therefore, helps to compare households that report being affected by income shocks with households that are equally likely to be affected by income shocks, but do not report that they are ―affected‖. See Appendix for more details on this method.

We expect a positive (unobserved) selection bias, meaning that those households most likely to report being affected are also the most likely to report adoption of harmful coping strategies. This bias may lead to an overestimation of the true treatment effect and therefore, reported test-statistic should be adjusted downwards. Hence, following Aakvik (2001), we will look at Q<sup>+</sup> MH<sup>and p+</sup> mh statistics for our sensitivity analysis. We choose upper bounds of our results based on the significance levels for Γ = 1, 1.25, 1.5, 1.75 and 2. The Q<sup>+</sup> MH<sup>statistic adjusts the MH statistic</sup> downward for the case of positive (unobserved) selection.

# 5 Results

Figure 1 shows that all five countries have sufficient area of common support to reliably apply the propensity score matching technique. Table 5 presents the results from the covariate balancing exercise for robust matching based on observable household characteristics. Panel I of the table shows the percentage reduction in selection bias after balancing. Panel II shows the null hypothesis that— all covariates between the treatment and control group are jointly insignificant— is not rejected for all countries except for Bulgaria. The balancing reduces the selection bias significantly 11
<!-- Start of picture text --> Index of Economic Freedom, 1999-2009<br>80<br>o| oF<br>65<br>55 [SS eSSa<br>$= Ban ee SSA<br>45 X——*ogo<br>Ks —e ——K<br>40<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>—— BurkinaFaso —— Ghana —>— Nigeria ——~— Senegal<br>—*— Togo —*— Benin —e— MIC —*— SSA<br><!-- End of picture text -->
<!-- Start of picture text --> Chis an iN<br>|<br>r p<br>| fe pose<br>a<br>sf<br>87) ¢<br>el<br>Se }<br>oN {<br>a e<br><!-- End of picture text -->

@ Resource-rich Non resource-rich Mf North Africa and South Sudan

> Powered by Bing. © GeoNames, Microsoft, TomTom
**Table 4.8. Standards for maximum baggage delivery times at selected BAA airports (minutes)**

|Tem--I'<br>- -::- -|16-20|30-34|
|---|---|---|
|=~~~|21|~<br>~~~<br>25|
|Tez- -: .<br>-|-24-28|49-53|
|Tes'om2.8z<br>L4<br>.Mrom,<br>.|<br>11-20|22-41|
||20|35|
||20|35|
||15|33|

Source: Monopolies and Mergers Commission (1996).

Airlines may feel disappointed regarding airport services. Hence a mechanism to register their complaints also has to be in place. This aspect will have to be considered by a regulator concerned about the exploitation of monopoly power. For instance, in the case of BAA, it is the Civil Aviation Authority which is responsible for addressing complaints. This procedure applies not only to airlines but to other agents such as tour-operators or concessionaires as well. Other airports that might feel damaged by anti-competitive practices may also refer to such authorities or even to the Monopolies and Mergers Commission.

Many of the most crucial aspects of airport operations are not always the direct responsibility of the airport authority. Aircraft landings and take-off punctuality would also be determined by visual and approach air traffic services. So in order to keep with published time tables both the airport authority and the Air Traffic Control must be closely coordinated, particularly when they belong to different organizational bodies.

All the variables mentioned above, even in terms of scores or standards, constitute instances of possible regulatory quality targets. Standards might be applied either when a full divestiture has been applied or when a concession contract is intended. The convenience of intervening in order to fix quality levels should be studied by the regulator. A scrutiny mechanism and agreements with air transport carriers regarding prices and corresponding quality levels might be adequate in order to ensure good quality standards.

43