rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 6072

# **Abstract**

Using micro-level data on mutual funds from different financial centers investing in equity and bonds, this paper analyzes how investors and managers behave and transmit shocks across countries. The paper shows that the volatility of mutual fund investments is quantitatively driven by investors through injections of capital into, or redemptions out of, each fund, and by managers changing the country weights and cash in their portfolios. Both investors and managers respond to returns and crises, and substantially adjust their investments accordingly. These mechanisms generated large capital reallocations during the global financial crisis. Their behavior tends to be pro-cyclical, reducing their exposure to countries experiencing crises and increasing it when conditions improve. Managers actively change country weights over time, although there is significant short-run “pass-through,” meaning that price changes affect country weights. Consequently, capital flows from mutual funds do not seem to stabilize markets and instead expose countries to foreign shocks.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at craddatz@worldbank.org and sschmukler@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
While equation (1) highlights the correlation between access to finance and job growth it does not provide a definite causal relationship. Clearly, employment growth could be driving firms’ access to finance (i.e., financial institutions might be more likely to lend to growing firms and similarly growing firms are likely to demand more financing to sustain their operations) or there could be other variables that drive both employment growth and access to finance. In other words, both reverse causality and omitted variable bias are potential concerns in identifying the causal effect of finance on employment growth.

To address these concerns, we proceed as follows. First, to partially alleviate the problem of endogeneity arising from omitted variable bias at the country level, in lieu of separate country and year fixed effects, we introduce survey (i.e. country-year specific) fixed effects. The coefficient α in equation (1) now measures the within survey impact of firm financing on job growth and by design controls for all time-varying country level variables relating to, for example, policy, institutions, regulatory quality and aggregate growth. We drop country level controls, inflation and GDP growth in this specification.

Second, to identify a causal link from access to finance to employment growth, instead of directly analyzing the impact of finance on employment growth, we examine what happens to employment growth for firms in countries that undertake financial infrastructure reforms that are intended to increase the supply of credit. In particular, we look at firms in countries that introduced credit bureaus relative to firms that are in countries that do not introduce such a reform. There is an extensive literature that studies the link between reforms in the information sharing systems and increased access to finance and finds a strong positive link. The empirical analyses has been both at the country level (Jappelli and Pagano (2002); Detragiache, Gupta, and Tressel (2005), Djankov, McLiesh, and Shleifer (2007)), and more contextually relevant in our 14
Mortality Reductions from Measles and Tetanus Immunization: A Review of the Evidence* by Michael A. Koenig The Population Council and International Centre for Diarrhoeal Disease Research, Bangladesh

# Table of Contents

|Measles Immunization|3|
|---|---|
|Mortality Impact of Immunization<br>|3|
|Replacement Mortality|7|
|<br>Convergence in Survival Over Time<br>Impact Among High-Risk Children|9<br>10|
|Potential Selection Bias|13|
|Age at Immunization|16|
|Tetanus Immunization|18|
|Reductions in Neonatal Deaths Due to Tetanus|18|
|Reductions in Neonatal Mortality|21|
|<br>Duration of Immunity|23|
|<br>Number of Doses|24|
|Potential Selection Bias|29|
|Mortality at Later Ages|30|
|Estimates of Potential Mortality Reductions from Measles<br>and Tetanus Immunization|30|
|References|33|

* Paper prepared for the Johns Hopkins Institute for International Programs/World Bank Seminar on Child Health Priorities in the 1990s, Baltimore, Maryland, June 20-22, 1991.
average treatment effects, ignoring minor differences in sampling individuals in differently sized households and differently sized villages within clusters.

# **Service Provision**

Did the CDD program deliver and maintain local public goods, and improve the local population’s access to, and the quality of, services provided? We find no evidence that Tuungane had a positive impact on average levels of service provision in the health and education sector. The magnitude of 0.05 standard deviation (SD) is small and not significant.

In panel (b) of **Figure 2** , we break up the results by sector and the seven dimensions that make up service provision: infrastructure quality, capacity, availability of material and equipment, staff quality, administration quality, facility-community interactions and the costs and use of health facilities. We find that eight years after program onset, the infrastructure quality of hospitals and school buildings is significantly higher in treatment areas. The quality of hospital buildings is 0.16SD higher in Tuungane areas, which is largely driven by higher quality floors, higher quality walls, and a better rating of facility quality by villagers in the hospital’s catchment area (see Appendix G). We find similar results in the education sector. School buildings in treatment villages score 0.19SD higher than those in control villages, largely driven by higher quality floors and roofs, the presence of windows and the villagers’ rating. We also find that health facilities in Tuungane areas are significantly better stocked (0.22SD).<sup>11</sup> We do not find evidence that schools in Tuungane areas are better stocked. We also find no evidence that the Tuungane program improved the capacity, staff quality, administration quality, facility-community interactions and the costs and use of health facilities.

# **Health**

The health situation in Congo is dire. For example, in control communities, 15% of respondents mention that, within the household, a child younger than five years old passed away during the previous year (a number similarly found in the DHS 2013). Panel (a) of **Figure 2** shows that there is no evidence that Tuungane improved health outcomes. The program had no impact on under-

> 11 That is, enumerators calculated the number of antibiotics, anti-malaria and anti-inflammatory tablets present in the hospital’s stockroom.

8
- Hopenhayn, H. (2016): “Firm Size and Development,” _Econom ́ıa Journal_ , 17, 27–49.

- Hopenhayn, H. A. (1992): “Entry, Exit, and Firm Dynamics in Long Run Equilibrium,” _Econometrica_ , 60, 1127–1150.

- Hsieh, C.-T. and P. J. Klenow (2009): “Misallocation and manufacturing TFP in China and India,” _The Quarterly journal of economics_ , 124, 1403–1448.

- Hsieh, C.-T. and B. A. Olken (2014): “The Missing “Missing Middle”,” _Journal of Economic Perspectives_ , 28, 89–108.

- IBGE (2019): “Pesquisa Nacional por Amostra de Domic ́ılios Cont ́ınua,” .

- INDEC (2019): “Encuesta Permanente de Hogares, EPH,” Instituto Nacional de Estad ́ıstica y Censos, Rep ́ublica Argentina.

- INE Paraguay (2019): “Encuesta Permanente de Hogares Continua, EPHC,” Instituto Nacional de Estad ́ıstica, Paraguay.

- INE Uruguay (2019): “Encuesta Continua de Hogares,” Instituto Nacional de Estad ́ıstica, Uruguay.

- INEC (2019): “Encuesta Nacional de Hogares, ENAHO,” Instituto Nacional de Estad ́ıstica y Censos, Costa Rica.

- INEGI (2018): “Encuesta Nacional de Ingresos y Gastos de los Hogares,” Instituto Nacional de Estad ́ıstica y Geograf ́ıa, M ́exico.

- INEI (2019): “Encuesta Nacional de Hogares, ENAHO,” Instituto Nacional de Estad ́ıstica e Inform ́atica, Per ́u.

- Karabarbounis, L. and B. Neiman (2013): “The Global Decline of the Labor Share,” _The Quarterly Journal of Economics_ , 129, 61–103.

- Korea Labor Institute (2019): “Korean Labor & Income Panel Study, KLIPS,” Korea Labor Institute.

- Ministerio de Desarrollo Social y Familia (2017): “Encuesta de Caracterizaci ́on Socioecon ́omica Nacional, CASEN,” Ministerio de Desarrollo Social y Familia, Chile.

- National Statistical Office (2018-2019): “Periodic Labour Force Survey,” Ministry of Statistics and Programme Implementation, National Statistical Office, Government of India.

- Official Statistics of Japan (2019): “Labour Force Survey,” Ministry of Internal Affairs and Communication.

22
**Table 2. Incidence of Smoking Cessation by Decade and Birth Cohort**

|||||B|irth cohort||||
|---|---|---|---|---|---|---|---|---|
|Decade|All|1910-20|1920-30|1930-40|1940-50|1950-60|1960-70|1970-80|
|1940-2000|1.076***|1.034***|1.05***|1.085***|1.127***|1.162***|1.163***|1.156***|
|Z-stat|[52.99]|[12.33]|[19.62]|[27.19]|[34.47]|[31.04]|[15.33]|[3.46]|
|N|162,993|18,158|28,825|29,394|37,674|39,282|18,115|2,290|
|Failures|63,741|11,774|16,175|13,548|13,213|8,887|2,437|112|
|Time at risk|3,174,642|621,820|862,255|690,859|617,511|400,659|112,211|5,558|
|1940-1950|1.036***|1.039***|1.003||||||
|Z-stat|[4.58]|[3.79]|[0.15]||||||
|N|32,942|18,158|12,555||||||
|Failures|1,426|878|219||||||
|Time at risk|216,140|146,815|36,655||||||
|1950-1960|1.06***|1.053***|1.062***|1.094***|||||
|Z-stat|[12.14]|[7.01]|[8.16]|[4.81]|||||
|N|58,107|17,280|28,606|13,047|||||
|Failures|4,362|1,743|2,033|3,49|||||
|Time at risk|448,876|166,460|239,013|3,9155|||||
|1960-1970|1.078***|1.057***|1.067***|1.121***|1.176***||||
|Z-stat|[24.49]|[10.18]|12.82|[18.84]|[10.92]||||
|N|82,989|<br>15,537|26,573|29,045|15,755||||
|Failures|1,1335|<br>3,042|4,127|3,384|726||||
|Time at risk|651,869|14,4175|250,288|237,646|45,401||||
|1970-1980|1.081***|1.027***|1.053***|1.092***|1.147***|1.184***|||
|Z-stat|[31.24]|[5.44]|[11.36]|[16.75]|[26.18]|[13.98]|||
|N|108,763|12,495|22,446|25,661|36,948|18,913|||
|Failures|18,954|3,561|4,780|4,547|5,640|1,280|||
|Time at risk|834,046|108,368|202,809|235,695|289,536|53,829|||
|1980-1990|1.08***|0.999|1.033***|1.063***|1.118***|1.167***|1.191***||
|Z-stat|[29.54]|[-0.05]|[6.65]|[11.56]|[20.34]|[26.02]|[10.37]||
|N|107296|7228|15001|18242|27595|36412|12005||
|Failures|20698|2234|4100|4087|5291|5711|783||
|Time at risk|740,146|48,770|110,048|140,578|217,681|251,814|34,669||
|1990-2000|1.073***|1.015|1.02*|1.026**|1.057***|1.126***|1.15***|1.156***|
|Z-stat|[14.23]|[0.81]|[1.92]|[2.50]|[5.56]|[10.61]|[11.56]|[3.46]|
|N|<br>54779|<br>1740|4945|7324|11981|17317|<br>14548|2290|
|Failures|6966|316|916|1181|1556|1896|1654|112|
|Time at risk|283,565|7,232|23,442|37,785|64,893|95,016|77,542|5,558|

***, **, * denote statistical significance at the 1%, 5% and 10% confidence levels, respectively.

_Note_ : Analysis from smoking histories from the 1978, 1979, 1980, 1983, 1985, 1987, 1988, 1990, 1991, 1992, 1994, 1995, 1997, 1998, 1999 and 2000 National Health Interview Surveys for individuals age 25 and above at the time of the survey. Smoking histories are reconstructed using the age at smoking initiation and cessation. See also note 5. Proportional hazard model (Cox model) stratified by gender, year of birth, race, age at interview and survey year. Based on individuals who were smoking at age 25. Failure is smoking cessation. Robust z-statistics in square brackets.

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# **3.2. Transport Costs**

Transport costs also matter to trade. Each kilometer a good travels requires fuel, labor, and capital expense. Does distance to markets matter as much today as it did decades ago? Discussion on this matter continues and empirical work has addressed this question. For example, Hummels (1999) estimates the elasticity of shipping and costs with respect to distance, and charts its evolution over time for air and ocean shipping over the period 1974-98. He finds that the difference between costs associated with shipping comparable ocean/shipped commodities over a long (9000 km) route and a short (1000 km) route decreased by 27 percentage points from 1974 to 1998. The effect of distance on costs appears to decline over time. Over the years, technological improvements, such as the introduction of containerization in maritime transport in the 1950s, appear to have contributed to the reduction in transport costs.

Despite the contribution of technical improvements to lowering trade costs, shipping costs from African countries to major world markets can be considerably higher compared with other regions. Figures 4a and 4b show shipping costs from several cities to two of the largest European ports, Rotterdam and Algeciras, reported by Maersk, a major shipping company. To ensure comparability among figures, we collected the freight costs for a standard 40-foot container transporting textiles. Despite the distance between both European ports, freight costs from each city in the sample to Algeciras and Rotterdam are similar. Consider Santos and Dakar, the closest South American and African cities in the sample to Algeciras. Despite the fact that distance to Santos is about twice the distance to Dakar, the cost of ocean freight is lower from the Brazilian city. Moreover, the presumably low-value of exports from developing countries, especially in Africa, inflates the transport costs of a container when expressed in ad-valorem terms.

Indeed, maritime transport exhibits important economies of scale. Larger trade flows are conducive to scale economies in shipping. To further illustrate the relationship of freight costs for large versus small exporters, Hummels (2006) considers the case of Japan and Côte d’Ivoire. These countries are equidistant to the west and east coasts of the United States, respectively. Shipping costs from Côte d’Ivoire are twice as high as shipping costs from Japan. This is true even after adjusting for differences in the commodity composition of trade. In addition, Hummels and Skiba (2004) use data from importerexporter pairs to estimate that doubling trade quantities leads to a 12 percent reduction in shipping costs. Arvis et al. (2007) illustrate the tendency of shipping lines to set higher tariffs in smaller ports with less traffic, describing the case of exporters of fruits and vegetables from south Mauritania. They argue that because of maritime transport price differentials, exports are processed in the Dakar port, in Senegal, rather than in Nouakchott, despite the border crossing costs and longer distance to market for these products.

13
# **<mark>Appendix 2: Measures of outside options</mark>**

<mark>Non-fishing sector has rapidly developed during the reconstruction period when donors put a large amount of money to reconstruct private houses as well as public road and buildings. The survey contains detailed information on the scale of such infrastructure rebuilding programs (such as the total number of temporary and permanent houses which were constructed by donors and local governments). I use the data to control for the confounding impacts of other aid programs on households job choices and their income growth to identify the quality impacts if production assets (i.e., fishing boats).</mark> The survey collects detailed village-level variations in housing aid program besides fishing aid. In addition, the data contains secondary information of the number of total humanitarian relief projects (including all types of aid) and NGOs at the village level from RAN (Reconstruction Aceh Nias) dataset. Aid programs provided direct labor demand for non-fishing job opportunities from donors (as construction workers, interpreters etc), and they also generated indirect service jobs demanded through reconstruction efforts (retail, food, and hotel business etc) which were all closely related to the scale of aid works.

<mark>As shown in the below correlation matrix, three measures are significantly positively correlated (at 99%). The negative correlation between the distance to Banda Aceh and three outside option measures suggest that the aid program tended to be clustered near Banda Aceh, the capital city of Aceh.</mark>

||Number of<br>NGOs in 07|Number of aid<br>projects in 07|Total housing<br>aid in 07|Ln (distance to<br>BA)|
|---|---|---|---|---|
|Number of NGOs<br>in 07|1||||
|Number of aid<br>projects in 07|0.904***<br>(0.000)|1|||
|Total housing aid<br>in 07|0.508***<br>(0.000)|0.568***<br>(0.000)|1||
|Ln (distance to|-0.377***|-0.201***|-0.423***||
|BA)|(0.000)|(0.000)|(0.000)|1|

***p<0.01, **p<0.05, *p<0.1; p-values are in the parentheses.  Supplementary sample is used (N=529).

42
2006–08 to 2.8 years in 2015–16. Finally, the number of tax years that it would take to repay the general government gross debt in SSA increased from 2.7 in 2006–08 to 3.6 in 2015–16.

# **4.  How did the fiscal space fare in the post-global financial crisis period?**

Countries in the Africa region have faced a series of shocks; most notably, the plunge in oil prices, the steady decline in the prices of metals and minerals, rising borrowing costs, among others. They have also accumulated significant macroeconomic imbalances that may require fiscal consolidation measures. This section aims at testing whether the indicators of fiscal space deteriorated in the aftermath of the 2008-09 global financial crisis. More specifically, we test whether a battery of indicators that capture fiscal sustainability, balance sheet composition and external debt worsened in 2015-16 vis-à-vis 2010-13 for Sub-Saharan African countries. It first compares the evolution of fiscal space in the region as a whole vis-à-vis other benchmark regions —notably, industrial economies and non-SSA developing countries. Second, it examines the evolution of fiscal space for country groups within the region according to their extent of natural resource abundance and the resilience of their growth performance.

According to the World Bank (2016), Sub-Saharan African countries can be classified into five groups based on the resilience of their growth path. To quantify this resilience, it compares the average annual gross domestic product (GDP) growth rates of 45 countries in the region during the period 1995-2008 and the average of 2014-16. Countries with a strong GDP growth rate —that is, above the top tercile of the Sub-Saharan African distribution from 1995 to 2008 (5.4 percent)— in recent years (2014-16) and over the longer former period (1995-2008) are classified as _established_ countries. _Improved_ countries are those with a growth rate below the top tercile in 1995-2008 but with an average GDP growth rate in 2014-16 higher than that of the top tercile. Countries with an average annual growth below the bottom tercile in both periods (that is, below 3.5 percent) are classified as _falling behind_ ; those where more recent growth performance is below the bottom tercile but growth in earlier periods was above the bottom tercile are denoted as _slipping_ ; and countries with recent average annual growth between the top and bottom terciles are classified as _stuck in the middle_ . Established and improved performers are viewed as exhibiting resilience while the other countries are not.

These groupings were subsequently revisited by using growth rates for 2015–17 (see World Bank 2017b). The inclusion of the more recent period captures better the resilience of economic activity to the plunge in the prices of oil and other commodities, including metals and minerals; unfavorable external and domestic economic conditions; and the adequacy of the economic policy response. The thresholds used to classify these countries remain invariant. A more accurate calculation of central measures (say, medians and/or averages) across the different groups requires 9
5 currency, in each year. Unlike the Gini index, MLD is additively decomposable into betweengroup and within-group inequality components (Bourguignon, 1979).

Absolute poverty measures, on the other hand, summarize the extent of deprivation in a distribution with respect to a specific welfare threshold, given by the poverty line. This implies that scale matters, and so does the choice of mean (e.g. mean income from a household survey, or GDP per capita) and exchange rate when making inter-country comparisons or aggregations. It has been argued that misreporting of incomes in household surveys would justify scaling up the income distribution so that its mean equaled per capita consumption in the Private Consumption account in the National Accounts System (NAS).<sup>9</sup> But such an approach ignores the fact that the Private Consumption account includes components of institutional consumption as well as personal consumption, which could introduce a systematic overstatement of household welfare levels. Things are even worse if the scaling up is to GDP per capita itself, rather than only to per capita consumption from the NAS.

In addition, in economies with substantial subsistence agriculture and other forms of production for own consumption, it is not clear that the national accounts system provides a more accurate portrayal of real consumption than the surveys, which typically include information on consumption from own production at the household level. Finally, it is unlikely that income under-reporting or selective compliance in surveys is distribution-neutral.<sup>10</sup> If richer households under-report more than middle-income or poorer households, then the uniform re-scaling that is proposed would result in an unwarranted under-estimation of poverty. It appears likely that richer households are also less likely to participate in surveys. This has theoretically ambiguous implications for inequality, although there is evidence (for the US) that it entails a non-negligible underestimation of overall inequality (Korinek et al., 2006). In what follows we do not use National Accounts information to re-scale mean incomes or consumption from the surveys (although NAS data are used in the interpolation method of Chen and Ravallion, 2004, which is used for “lining up” household surveys with the reference years used in Tables 2 and 3).

In this paper, we report poverty measures with respect to the World Bank’s “standard” international poverty line of about $1 a day (or, more precisely, $32.74 per month, at 1993

> 9 See, for instance, Sala-i-Martin (2006).

> 10 See, for example, Banerjee and Piketty (2005) and Korinek et al. (2006).