rafmacalaba/datause-encoder-data huggingface dataset:
To empirically estimate this theoretical model, we exploit information on maternal mental health during the first round and data on cognitive outcomes for our sample of 1,095 children for whom we have PPVT Z-scores from the second and third rounds of data collection. A naïve estimation of the effects of exposure to lagged maternal stress on cognitive development will regress a measure of maternal stress in 2002 on the PPVT Z-scores in 2006/7 and 2009/10, using the following specification: P 0 1M<sup>+ αα</sup> 2<sup>CC</sup> 3<sup>MM</sup> 4<sup>MM</sup> (2) ii,tt ii,tt−1 ii,tt<sup>+ αα</sup> ii,tt<sup>+ αα</sup> ii,tt<sup>+ εε</sup> iitt PPTT = αα + αα where P represents the PPVT Z-scores for child _i_ in period _t_ (i.e., 2006/7 or 2009/10). M<sup>captures the value of any of the three maternal mental health indexes we estimated using</sup> ii,tt PPTT data from 2002. ii,tt−1 , , and are vectors of child, mother, and household/community observable and time-varying characteristics that can lead to differences in cognitive ability across ii,tt ii,tt ii,tt CC MM MM children and influence their parents’ investments in them. These vectors include all the variables presented in Table 1, all of which have been documented to affect child cognition (for a review, see Shonkoff and Phillips, 2000). represents a random, idiosyncratic error term. Under the assumption of complete exogeneity of iitt M 1<sup>,</sup> εε measures performance in the PPVT at each period _t_ for children whose mothers were depressed ii,tt−1<sup>, the parameter of interest, αα�</sup> in 2002 **.** The fact that the specification uses measures of maternal depression and child’s vocabulary taken at different points in time addresses to a large extent the possibility of reverse causality. However, the probability that there are unobserved factors –such as pollution, access to services, or changes that have affected households between rounds—that influence maternal mental health and children’s outcomes cannot be entirely ruled out. Consequently, we use an instrumental variable (IV) approach to address the possibility of omitted variable bias.

In addition, the IV estimation helps to remedy the problem of measurement error in the main explanatory variable, which could be a relevant factor in the context of this paper. In particular, our main explanatory variable captures symptoms of mental health issues that affected mothers 30 days prior to the survey in 2002. We used those symptoms and estimated indexes of ∗ mental health, which constitute proxies of the unobserved, latent variable M . Thus, estimations of equation (2) that incorporate the proxy for maternal depression can produce ii,tt−1 inconsistent estimators of 1 and lead to attenuation bias of these coefficients if M error term are negatively correlated (Greene 2005; Berger and Spiess 2009). ii,tt−1<sup>and the</sup> αα ii,tt εε 10
27 participation increases, spending becomes more populist (small private transfers to win votes from the poorest) rather than focused on the delivery of broad public goods.

The risks of greater political participation for public spending policies examined in the previous section may extend to other kinds of public policies for infrastructure provision, such as the management of public utilities and state owned enterprises, and the facilitation of private investments in infrastructure. Public utilities may serve both rent-extraction and populism, providing subsidized services to win votes, at the expense of investments needed for reliable functioning of the utility. Partnership with private investors may be hampered by non-transparent land lease policies, and lack of credible commitment by the government to allow levy of user charges to recover returns from investment.

# **Populist pressure on public policy—opportunity for reform?**

Yet, greater political participation, particularly in recent years, may also provide opportunities to shift the equilibrium of electoral competition away from populist distribution of private transfers towards greater voter coordination on broad public goods. Large scale political mobilization and empowerment of poor and traditionally disadvantaged social groups over the 1990s documented through voter surveys may yet become an opportunity for reform.

According to a national survey of voting behavior conducted in 1996, 75 percent of the sample respondents were not guided by anyone in their voting decision, and of the 25 percent who sought advice only 7 percent sought it from caste and community leaders (Oldenburg, 1999). Based on detailed interviews of village residents in a sample of north Indian villages, Krishna (2003) concludes that political parties can no longer rely on big landlords and caste leaders to mobilize the votes. Studies of electoral performance show that state and national elections are characterized by volatility, that is, large party-vote swings between elections; party
Foreign Direct Investment, Other Capital Flows and Current Account Deficits: What Causes What?

MAXWELL J. FRY, STIJN A. CLAESSENS, PETER BURRIDGE, AND MARIE-CHRISTINE BLANCHET* _University of Birmingham, World Bank, University of Birmingham, University of Birmingham_ JEL Classifications: F21, F32, 05. Key words: foreign direct investment, current account deficits.

'This paper was written for the International Economics Department of the World Bank under the World Bank's Research Support Budget RPO-678-15. The views expressed in this paper do not necessarily reflect those of the World Bank, its Board of Directors, its management, or any of its member countries. EViews, version 1.0, and _MicroTSP,_ version 7.0, were used for all the regressions presented in this paper.
_Figure 5: Distribution of monthly per capita expenditure, nominal and spatially adjusted_ <!-- Start of picture text --> Spatially Adjusted<br>Nominal<br>0 200000 400000 600000<br>Per capita Montly HH Expenditure<br>Density<br><!-- End of picture text --> _Notes:_ The vertical line indicates the monthly value of the $2.15 poverty line (2017 PPP) _Source_ : World Bank staff calculations, based on HNAP summer 2022 Demographic and WASH survey As shown in Figure 5, the spatially adjusted and unadjusted (nominal) welfare metrics display a very similar distribution, although the spatially adjusted one is relatively more concentrated. Table 7 below reports national level poverty estimates at LIC $2.15 (2017 PPPP) as well as LMIC $3.65 (2017 PPP) international poverty lines based on the two welfare metrics.

_Table 7: National poverty estimates based on nominal and spatially adjusted welfare_

||Nominal|SpatiallyAdjusted|
|---|---|---|
|$2.15 2017 PPP|26.92|25.62|
||(0.008)|(0.008)|
|$3.65 2017 PPP|69.34|70.41|
||(0.011)|(0.001)|

_Note:_ Standard errors in parenthesis _Source_ : World Bank staff calculations, based on HNAP summer 2022 Demographic and WASH survey As shown in Figure 6, the distribution by governorate and the welfare quintiles constructed on each of the two welfare metrics are substantially different, with clear implications in terms of poverty profile (Figure 7).

_Figure 6: Distribution by_ _<u>governorate and quintiles of spatially adjusted and nominal monthly per capita expenditure</u>_ <!-- Start of picture text --> spatially adj<br>nominal<br>spatially adj<br>nominal<br>spatially adj<br>nominal<br>spatially adj<br>nominal<br>spatially adj<br>nominal<br>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%<br>Aleppo Rural Damascus Idleb Damascus Homs Hama Lattakia<br>Al-Hasakeh Dar'a Tartous Deir-ez-Zor Ar-Raqqa As-Sweida Quneitra<br>Q5<br>Q4<br>Q3<br>Q2<br>Q1<br><!-- End of picture text --> _Source_ : World Bank staff calculations, based on HNAP May 2022 Demographic and WASH survey
Second, recognition and formal appreciation of NPCs is also needed, for example through government financial incentives which often target only doctors. Third, an expansion in the training capacity of NPCs with appropriate training equipment is needed. Of particular relevance is the expansion of training capacity to rural areas that could help fixing the NPCs in those areas and thus close the gap in health personnel needed to deliver services in poorly served regions. However, this expansion is dependent on the existence of qualified tutors, who are mainly located in urban areas.

<!-- Start of picture text --> Ratio of Inhabitants per Health Worker<br>Zambezia<br>Nampula<br>Cabo Delgado<br>Gaza<br>Niassa<br>Tete<br>Manica<br>Inhambane<br>Sofala<br>Maputo Province<br>Maputo City<br>0 20000 40000 60000 80000 100000 120000 140000<br>Doctors Middle-Level Health Workers Basic Level Health Workers<br><!-- End of picture text -->

# **3.6 Education Services**

One may question whether professional skills in social services such as education are a public good which is inadequately valued by the private sector in Mozambique. On the face of it, a large proportion of students are training to be secondary school teachers out of the total number of students enrolled, the total number of new students admitted, and the total numbers of graduates from universities in Mozambique in 2006, as shown in Table 8. However, it should be noted that many of the students training to be secondary school teachers choose that path because they are not judged ‘good’ enough to study for other professional degrees. In fact, education specialists in Mozambique claim that only the worst secondary school graduates pursue training to be secondary school teachers.

Weaknesses in secondary education are a key supply-side problem for the quality of professionals and professional services in Mozambique. World Bank (2007) provides a detailed set of policy recommendations to address the weaknesses in secondary education ranging from improvements in school leadership and management to the provision of 25
3

# **I.** COMMODITY MARKET INVESTMENTS AND THE GSCI

The growing interest in commodities as traded assets prompted Goldman Sachs to develop the Goldman Sachs Commodity Index (GSCI) in 1991. The GSCI incorporates the notion that the returns on commodity assets must include an opport nity cost component. The return on this index replicates the return from a fully collateralized portfolio of commodity futures<sup>2</sup> . The GSCI is constructed in a manner similar to a capitalization-weighted stock market index except that the commodity futures are weighted according to the dollar value of the conunodities world production. In 1992, for instance, the GSCI dollar weights were as follows: 48% energy futures, 23% livestock futures, 21% agricultural futures and 8% metals futures. Since 1982, the weights have generally shifted away from agricultural and livestock futures and towards energy futures.

The tc tal returns from the GSCI are made up of three components - the return from T-bills (the opportunity cost component), the spot return (changes in the commodities prices) and the "roll yield". Table 1 shows the breakdown of total returns by each of these three components over the 1970-1992 period. The roll yield essentially represents the loss or gain from trading futures contracts. In general, the most active futures contract (usually the first nearby contract) is held until a month before its expiration when it is "rolled over" by selling it and purchasing the second nearby contract. The profit or loss from this represents the roll yield. If a commodity is in backwardation, the roll yield is positive because the futures price increases towards the spot price as the maturity date approaches. Over the 1970-1992 period, since commodity futures 2The return on the GSCI is calculated on the assumption that the investor posted collateral consisting of US T-bills equal to the value of the futures prices.
show that the aggregate indices of infrastructure quantity and quality exhibit a strong negative relationship with income inequality —as measured by the Gini coefficient of income distribution.<sup>20</sup> Although further analysis of the infrastructure-equity nexus is beyond the scope of this paper, we note that Calderon and Serven (2010) find that infrastructure development —as proxied by higher availability and quality of infrastructure services— leads to lower income inequality after controlling for standard determinants of inequality as well as reverse causality.

# **3.2 Assessing the contribution of infrastructure to growth**

We empirically assess the contribution of infrastructure to growth by estimating a simple equation that relates growth per capita to a set of standard controls, augmented by measures of the quantity and quality of infrastructure. For this purpose, we construct a large macroeconomic panel data set spanning the period 1960-2005 and comprising a total of 136 countries. Table A-1 lists the variables and data sources. As before, we limit our coverage to countries with total population over one million. Also, to remove cyclical factors and focus on longer-term effects, we work with non-overlapping 5-year averages. Data is not available for all countries in all time periods, and hence the panel is unbalanced. To keep a meaningful time-series dimension, we restrict our regression sample to countries with at least three consecutive 5-year observations.

To deal with unobserved heterogeneity, as well as likely endogeneity of infrastructure and other growth determinants in a dynamic panel data context, we use the GMM-IV system estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998). After controlling for period-specific effects, we account for unobserved country-specific effects by differencing and instrumenting. Endogeneity and reverse causality are addressed by using lagged levels and differences of the growth determinants (i.e. internal instruments). In addition, we also use external instruments to control for the potential endogeneity of the infrastructure indices. Following Calderón and Servén (2004,

> network (0.22). In addition, growth is positively associated with the quality of telecommunications (0.23), quality of electricity supply (0.14) and road quality (0.23).

> 20 This likely reflects the fact that increased quantity and quality of infrastructure typically come along with broader access to infrastructure services. Calderon and Servén (2010) show that access to infrastructure is negatively associated to income inequality. More specifically, the percentage of population with access to telecommunication, power and transportation services is negatively correlated with the Gini coefficient of income distribution.

17
treatments randomized within clinic, described and analyzed in detail in Lopez et al. (2022). In both, patients were eligible to receive a free course of antimalarials as part of simple – malaria treatment. This was operationalized via vouchers which could with provider pre– scription and certification be redeemed for free antimalarials at the clinic pharmacy. The voucher reduced the cost of treatment, leaving both the revenue to the clinic and the cost of other types of treatment to the patient fixed. We provide a summary of the main results in Appendix C.

Trained “intervention officers” oversaw the voucher distribution and showed the patient information videos to patients. Intervention officers did not perform any survey activities and were stationed in a separate part of the clinic from the data collection staff. Patients did not need to participate in any survey or other study activities to be eligible to receive the voucher or information intervention.

# **3.3 Data Collection**

Study field activities were conducted in November and December 2016, covering the end of the rainy season and, therefore, the period of highest malaria risk. Our primary analysis uses data from the following sources.

**Clinic Census.** When building the sampling frame, we conducted a short survey of candidate clinics, which captured information on total reported caseloads, malaria caseloads, clinic testing, and pharmacy capabilities. This information was used to conduct the randomization.<sup>16</sup>

**Health Workers: Pre- and Post-Training Tests.** As part of the training for health workers, we administered a pre-training and post-training test to all health care providers in attendance. The test included questions on providers’ knowledge of topics covered in the basic training (e.g., recommended malaria treatments, symptoms of severe malaria). The post-training test additionally included topics covered only in the “extended training” treatment (e.g., sensitivity and specificity of RDTs).

**Patients: In-Clinic Survey.** The geography-based clinic cohorts rotated through two weeks of data collection and experimental intervention during the study period. Within each cohort, we randomly assigned each clinic to one of the 20 intervention schedules depicted in Figure 2. Intervention delivery and in-clinic survey of patients occurred on 6 days during a two-week study period. Each clinic received one control day (no within-clinic interventions), one day with patient information only, two days with voucher interventions only, and two days with both patient information and vouchers. The clinic staff was not informed of the intervention schedule in advance; instead, intervention officers communicated the day’s

> 16One clinic in the sample was not surveyed during the census and is missing information.

10
# 3.1 Data Description

The following are various variables that affect trade and have been used in gravity equations in the literature. Income and income per capita are real GDP and real GDP per capita in year t for country i. Distance is between country i and the United States in kilometers. Area of country i is measured in square kilometers. Border is a dummy variable which is 1 if the country shares a border with the US - in our case, only Mexico and Canada. WTO member is a dummy variable which is 1 if the country i was in the WTO in year t. Of the 173 countries in our sample, 142 and 152 were members in 1997 and 2001, respectively. Common language is dummy variable with value is 1 if country i has English as a commonly spoken language. Island is another dummy variable and is 1 if the country i is an island. The majority of the above data for these variables is provided by Rose [2003]<sup>8</sup> tough we added some missing observations from the World Development Indicators [2002] of the World Bank. Sample statistics are provided in Table 1a.

The trade flows, on the other hand, are from USITC, which provides extremely detailed data on various measures of bilateral trade of the United States with each trading partner under each preference program at the tariff line level<sup>9</sup> . The export value, Xijt, is the Customs Value of all exports from i to the United States in category j in year t (1997 or 2001).

Due to the lack of data, most papers in the literature use the bilateral distance to measure transport and transaction costs. However, when we use distance as a proxy for all such costs, we are explicitly assuming that these are uniform across a wide range of products and that the cost of one kilometer is the same across countries. Our proxy Transport Costs is constructed as the natural log of the ratio of CIF value (customs, insurance freight) to the customs value. It is approximately the cost of insurance and freight as a percent of the customs value of the product and has mean value of 9% in both years. This variable enables us to identify sector-specific and country-specific transport and other transactions costs, as opposed to

> 8Andrew Rose generously provides this data at http://faculty.haas.berkeley.edu/arose/RecRes.htm

> 9See www.usitc.gov.

7
14 sector shares may indicate some scope for efficiency gains, whereas below-norm shares may indicate sources of growth.

The data illustrated in these figures indicate that the value added shares of transport, trade, and banking/insurance are relatively constant for a wide range of developed countries, as measured by per capita income, while the shares of communications and and public services -- increase. Only the share of consumer services declines with rising incomes. Total services clearly increase with per capita income. Thus, future growth in total services' value added in Russia and the other former Soviet states will arise from increasing incomes as well as from closing the service gap.

Smolovment" We next compare data for Russia, the other 14 former Soviet republics, and the USSR on employment with the two alternative international data sets described in Section B: OECD and "world economies". The former is much more detailed, while the latter contains a larger sample and provides more natural comparators to some of the poorer Soviet republics. Figures 14 and 15 compare total service sector employment shares in the former Soviet states with employment shares in the "world economies" and OECD respectively. Soviet republics are identified with the appropriate two-leL' .r abbreviation while other countries are just shown as asterisks. Saviet republics are consistently below international norms in Figure 14; however, they conform to the norm in Figure 15. As with the analysis of value added shares, sub-sectoral employment shares of Soviet republics show quite clear patterns in relation to international norms.

'" Because of problems of data availability, the employment results are reported just for the initial years of each decade (i.e. 1970 and 1980). The Soviet data used for comparison arei taken from Appendix Table 3 for the same years to match.