rafmacalaba/datause-encoder-data huggingface dataset:
Republic, El Salvador, Hungary, Kazakhstan, Lithuania, Mexico, Peru, Slovakia, Poland, and Uruguay.

The data we assemble contain the detailed portfolios of the universe of Chilean pension funds in all types of securities and asset classes at a monthly frequency for ten years, 1996 to 2005. We also compile the monthly returns of each instrument included in these portfolios. The dataset contains 3,869,290 observations, with information on the holdings and returns of 24,322 different securities for up to 57 pension funds. We then compute different estimates of herding, which are associated with funds buying/selling the same assets simultaneously.

This new dataset allows us to shed light on a series of questions regarding different aspects of pension fund investment strategies and overall behavior. In particular, do pension funds herd, buying/selling the same assets simultaneously? Is their herding pattern different across asset classes with varying liquidity and information? Does their herding behavior vary at different levels of the pension fund organization structure? What does their behavior tell about the incentives that managers face to compete with each other? Does herding change with purchases in primary and secondary markets? Does it differ on the buying and selling sides? Is trading activity associated with variations in returns? Understanding herding is important because this behavior can contribute to market volatility. Moreover, it means that managers are not generating independent assessments in the markets and might not be providing different services to pensioners.

In addressing these questions, we investigate at least three important aspects of herding discussed in the literature. First, traders might copy other traders in the process of extracting private information (Shiller and Pound, 1989; Sharfstein and Stein, 1990; Banerjee, 1992; and Bikhchandani et al., 1992). Since some assets are more obscure than others, the degree of 4
aggregation point intercept, are unlikely to provide representative samples and tend to overestimate the migrant population. However, they also show that reweighing the intercept point estimates to account for visits by the same individual to multiple aggregation points may generate estimates rather close to the census-based method.

III. MIGRATION AND POLICY: DATA REQUIREMENTS AND LIMITATIONS With an understanding of the issues involved in measuring the migration process, we now turn to the specific policy questions that are often pursued with regard to migration. Our focus here is on examining the data requirements and identifying the data limitations analysts are likely to face when analyzing these issues.

# **Welfare Impact: Poverty and Income Distribution**

The interactions of migration, poverty and income distribution are of primary interest to researchers and policy makers, and have been extensively studied in the past (e.g. Lipton, 1980; Stark et al., 1986; de Haan, 1999). Income differences are among the key determinants of migration between countries and, conversely, migration flows tend to change income levels and distributions in both origin and destination economies. Yet because migration is difficult to identify statistically, few studies have been able to convincingly demonstrate a causal relationship between migration and either poverty or inequality. There are several fundamental challenges.

25
Besides, loans from PACS were perceived as less likely to be repaid even if a good harvest followed a drought than were either loans from commercials banks or MFIs.

Finally, respondents answered a hypothetical question that asked to provide a ranking of lenders they would pay first if they had an outstanding loan from different lenders but did not have enough money to meet all repayment obligations. As it turns out, PACS loans always appeared last in the repayment priority list, behind all other loan types. The first in the list was always a loan from an MFI. Thus, the frequent announcement of relief measures appears to undermine the culture of repayment.

# **5. Conclusion**

If an expanded role for government has to be the solution, care should be taken in ensuring the effects of government interventions have the intended consequences. There is strong evidence pointing to the crucial importance of government institutions regarding the functioning of the financial sector in general and access to credit in particular. However, instead of replacing private agents in the market, governments can make a more valuable contribution in securing an adequate environment for private credit institutions to flourish and to improve institutional mechanisms in ways that further the availability of credit to poor individuals. The range of policies that would contribute to this goal goes beyond the extent of this article: building these institutions involves decision-making with long time-horizons, and the guidance provided by existing research is only partial and incomplete. We have, however, some good indications on where the right path might lie. Providing good institutions, such as efficient, speedy and fair courts is an area where only the government can play an active role. Also, governments can provide much needed registries of credit information, liens and property ownership; and make sure legislation is reliable and property rights and responsibilities of companies and financial institutions are properly defined. Reforms in these areas may be difficult to accomplish in the short term, but there are many other dimensions where progress comes at lower costs: Djankov, McLiesh and Shleifer (2007) use a panel of aggregate data for 129 countries over 25 years to confirm that creditor rights and the existence of credit registries (public or private) exhibit a strong correlation with higher ratios of private credit to GDP. Interestingly, the information infrastructure (for instance, credit registries) matter relatively more (compared to creditor rights) in poorer countries than in rich countries.

8
levels, is significantly associated with test performance. However, measures of nutritional status account for only a small fraction of the association between wealth, parental education and TVIP scores.

An innovative feature of the data collected in Ecuador is that it includes information on parenting quality, including the Home Observation for Measurement of the Environment (HOME) scale, which has been widely used in research in the United States (Bradley 1993; Caldwell and Bradley 1984; Bradley et al. 2001). This is constructed from 11 items that are assessed by enumerators at the close of the interview, and measures punitiveness (for example, whether parents yelled at or hit children during the interview), and responsivity to children (for example, whether they responded to and encouraged children in a positive way during the interview). Each item is scored as a dichotomous variable, and the final scale ranges from 0 to 11, with higher values corresponding to less responsive and harsher behavior. Paxson and Schady (2005) show that children whose parents have lower (better) HOME scores, reflecting warmer and less punitive behavior, have significantly higher TVIP test scores. Interestingly, the parenting measures account for a substantial fraction of the associations between socioeconomic status and cognitive development that are observed in the data.

A number of broad conclusions can be drawn from the papers on Mexico, Brazil, and Ecuador. First, when comparisons are made of the performance of children in Latin America with the reference populations that were used to norm the tests, Latin American children show large developmental deficits. Second, there appear to be sharp gradients by socioeconomic status. Children from poorer households, and children with parents with lower education levels perform significantly worse than other children. Third, the gradient between socioeconomic status and child development appears to become stronger with age. This may be because the protective effect of socioeconomic status is cumulative, as Paxson and Schady (2005) speculate, which would be consistent with the model in Cunha et al. (2005). It is also possible that some of the tests that are used are better suited for older children. Note that it would not be appropriate to conclude from this evidence that it is the older ages that are critical for child development—it may well be that insults suffered earlier on only manifest themselves somewhat later. Fourth, child nutritional status and measures of parenting status are both significantly associated with performance on a number of tests of early development.

<u>Policies and programs to improve skills formation among young children in Latin America and the Caribbean: Recent analysis of the impact of programs on outcomes in early</u> childhood in Latin America and the Caribbean has focused on conditional cash transfers 15
# **6.1 Credit-led spillovers?**

Spillovers from the tradable sectors to supporting industries could occur by means of credit channels. For instance, when an underwater tradable firm is late in repaying debt, it affects bank lending to firms in supporting industries. If this were the case, supporting firms that are smaller in size should experience greater declines in employment when faced with an exogenous tradable employment shock, on the ground that that they are likely to have less access to credit.

If this is the case, it would weaken our argument for the spillovers based on input-output linkages.

|VARIABLES|1 to 4|5 to 9|10 to 19|20 to 49|50 to 99|
|---|---|---|---|---|---|
|||Aggreg|ate supportin|g services||
||[1]|[2]|[3]|[4]|[5]|
|Bartik|0.016|0.017|-0.082|0.278|0.826**|
||[0.101]|[0.171]|[0.162]|[0.312]|[0.410]|
|Controls|Yes|Yes|Yes|Yes|Yes|
|Constant|-0.479|1.907|-0.868|-1.992|-4.206|
||[0.758]|[1.390]|[1.187]|[1.742]|[3.286]|
|Observations|2,084|1,257|970|778|481|
|R-squared|0.025|0.011|0.025|0.023|0.040|

Table 6.1: Testing credit supply constraints We find, however, that this is not the case. To test this hypothesis, we use an approach similar to that introduced in Mian and Sufi (2014). We divide supporting firms based on firm size (1 to 4 workers, 5-9 workers, 10-19 workers, 20-49 workers, 50-99 workers). Subsequently, we regress the change in the number of firms (by firm size) against Bartik job losses over six regressions. If credit constraints were a problem, the coefficients of Bartik job losses would decrease as firm size increases, signifying that smaller supporting service firms (i.e., those with presumably less access to credit) would be hit harder by the tradable employment shock. Table 18
preferential tariff levels granted by Colombia to some countries pursuant to the regional agreements signed.

# **Chart I-1**

# **Preferential Tariffs granted by Colombia**

# **Liberalization Levels**

|**COUNTRIES**|**COLOMBIA**|
|---|---|
|ARGENTINA|Average tariff: 10.7%|
|BOLIVIA|Free trade|
|BRASIL|Average tariff: 10.6%|
|CANADA|11.7%|
|CHILE|91% of tariff items excepted from duties in 1999|
|COSTA RICA|11.7%|
|ECUADOR|Free trade|
|EL SALVADOR|11.7%|
|GUATEMALA|11.7%|
|HONDURAS|11.7%|
|MEXICO|Average tariff: 4.9%|
|NICARAGUA|11.7%|
|PERU|List of preferences|
|UNITED STATES|11.7%|
|VENEZUELA|Free trade|
|**Average MFN tariff level**|**11.7%**|

Source: Reina and Zuluaga (2001) 8
- 21 -

# **REFERENCE**

- ADB. 2006. _Standard Prequalification Document: Prequalification of Bidders User’s Guide_ . Asian Development Bank.

- Auriol, Emmanuelle. 2006. Corruption in procurement and public purchase. _International Journal of Industrial Organization_ , Vol. 24, pp. 867-885.

- Bailey, Elizabeth. 2007. Are private equity consortia anticompetitive? The economics of club bidding. _The Antitrust Source_ , Vol. 6(4), pp. 1-8.

- Benitez, Daniel, Antonio Estache. 2005. How concentrated are global infrastructure markets? Policy Research Working Paper Series No. 3513, The World Bank.

- Burguet, Roberto, Yeon-Koo Che. 2004. Competitive procurement with corruption. _Rand Journal of Economics_ , Vol. 35, pp. 50-68.

- De Silva, Dakshina, Timothy Dunne, Georgia Kosmopoulou. 2002. Sequential bidding in auctions of construction contracts. _Economics Letters_ , Vol. 76, pp. 239-244.

- De Silva, Dakshina, Timothy Dunne, Georgia Kosmopoulou. 2003. An empirical analysis of entrant and incumbent bidding in road construction auctions. _The Journal of Industrial Economics_ , Vol. 51, pp. 295-316.

- Foster, Vivien. 2005. Ten years of water service reforms in Latin America: toward an AngloFrench model. Water Supply and Sanitation Sector Board Discussion Paper Series No. 3, The World Bank.

- Estache, Antonio, Atsushi Iimi. 2008a. Procurement efficiency for infrastructure development and financial needs reassessed. Mimeograph.

- Estache, Antonio, Atsushi Iimi. 2008b. Joint bidding in infrastructure procurement. Mimeograph.

- Geene, William. 1997. _Econometric Analysis_ . Prentice Hall: New Jersey.

- Gupta, Srabana. 2002. Competition and collusion in a government procurement auction market. _Atlantic Economic Journal_ , Vol. 30, pp. 13-25.

- Hendricks, Kenneth, Robert Porter. 1992. Joint bidding in Federal OCS auctions. _The American Economic Review_ , Vol. 82, pp. 506-511.

- Hoffman, Elizabeth, James Marsden, Reze Saidi. 1991. Are joint bidding and competitive common value auction market compatible? Some evidence from offshore oil auctions. _Journal of Environmental Economics and Management_ , Vol. 20, pp. 99-112.

- Iimi, Atsushi. 2004. (Anti-)Competitive effect of joint bidding: evidence from ODA procurement auctions. _Journal of The Japanese and International Economies_ , Vol. 18, pp. 416-439.

- Iimi, Atsushi. 2006. Auction reforms for effective official development assistance. _Review of Industrial Organization_ , Vol. 28, pp. 109-128.
In terms of economic relevance, the results suggest that per additional day of employment generated in the block, each household would increase the risk index by 0.24 percentage points (col. 6). One standard deviation increase in the number of person-days generated per job card (6.9) would increase a household’s risk index by 1.66 percentage points and raise net income from agricultural production, _ceteris paribus_ , by about INR 640 (or US$ 13.9). This is particularly interesting from a cost-benefit perspective, since these net income gains per household are higher than the wage cost (evaluated at the sample average of observed NREGS wages) of creating 6.9 days of employment under the NREGS, e.g. INR 467 (US$ 10). Of course, wage costs make up for only a part of overall program costs and not all of the NREGS participants own their own land, but nevertheless the magnitude of this effect is striking.

# **5.4 Robustness checks**

This section presents a number of robustness checks. The first robustness check is intended to rule out the possibility that the observed effects is not due to the NREGS. The second set of robustness checks is intended to rule out potential alternative mechanisms through which the NREGS could affect crop choices.

As a first robustness check, I test whether households that registered with the NREGS change their input allocation more strongly than households who are not registered with the NREGS. To account for potential self-selection bias, I match households on their probability to register with the NREGS by using entropy balancing, a method developed by Hainmueller (2012).<sup>42</sup> Table 5 reports the effects of registering with the NREGS on the risk index of households’ crop portfolios. I find that households that already registered with the NREGS in 2007 are more likely to grow a higher share of risky crops in the follow-up period. Five different specifications are presented: Columns (1) and (2) show estimates without matching, where column (2) additionally controls for initial conditions. Column (3) excludes all households that did not register with the NREGS

> 42More details on the matching strategy can be found in appendix C. The distribution of the propensity scores is displayed in Figure D.5, and the resulting covariate balance is shown in Table E.6.

24
65 coefficients of royalty receipts, either as a ratio over total population or GDP, are consistently positive but not statistically significant. When we take the sum of royalty payments and receipts as a ratio over total population or GDP, we see that the estimate coefficient is positive and statistically significant.

# **B.3 Information and Communications Technologies Infrastructure Variables**

In this section of this Appendix, we focus on the effects of the ICT pillar on economic growth. In Tables A3 and A3a, we present the results of regressions in which various measures were used in turn to proxy for the level of ICT infrastructure.

In Regs A3.1 and A3a.1, we see that the all of the independent variables are of the correct sign and are statistically significant, with the except of the educational quality variable. In addition, we see that the control, education and innovation variables account for about 55 to 59 percent of the variation in cross-country growth rates. With the inclusion of the computers (per 1000 persons) as an additional regressor in Regs A3.2 and A3a.2, we see that in both cases the estimated coefficient is positive, which is theoretically expected and is highly statistically significant. Also note that the R-square increases by more that 6 percentage points with the addition of the computer variable.

Regs A3.3 and A3a.3 and Regs A3.4 and A3a.4 substitute the number of internet users (per 1,000 persons) and the number of internet hosts (per 10,000 persons) for the number of computers. The results are largely similar, with the estimated coefficients being positive and highly statistically significant. Similarly, the qualitative nature of the results does not change when the number of phones (per 1000 persons) is used to represent the ICT pillar. We see that the number of phones have a positive and statistically significant effect on long-term economic growth. However, when the number of televisions and the number of newspapers (both per 1,000 persons) were used to proxy for the level of ICT infrastructure, the respective estimated coefficients were still positive but is no longer statistically significant (Regs A3.6 and A3a.6 and Regs A3.8 and A3a.8). Lastly, when the number of radios was used, the estimated coefficient of the radio variable was negative, which is theoretically inconsistent, and statistically significant at the 10 percent level of significance.

Note that regressions were also estimated with the various ICT variables in the logarithmic form. While the estimated ICT coefficients in this series of regressions were generally positive and statistically significant, the estimated coefficients of the human capital / education variables generally became statistically insignificant. For this reason we will be using the non-logarithmic form of the ICT variables for our key results.
**Table 4: Elasticity of total cost with respect to fragmentation**

|||**Full sample**||
|---|---|---|---|
|Simpson index of fragmentation|0.368***||0.396***|
||(5.023)||(5.276)|
|Spatial fragmentation||0.083***|0.019**|
|||(9.268)|(2.02)|
|Land|0.054***|0.092***|0.054***|
||(4.842)|(8.245)|(4.854)|
|Land squared|-0.013***|-0.013***|-0.012***|
||(-3.346)|(-3.099)|(-3.074)|
|||4,790||
|||**Households with inherited plots only**||
|Simpson index of fragmentation|0.386***||0.409***|
||(4.702)||(4.863)|
|Spatial fragmentation||0.083***|0.021**|
|||(8.36)|(2.015)|
|Land|0.068***|0.108***|0.068***|
||(5.256)|(8.322)|(5.275)|
|Land squared|-0.012***|-0.012**|-0.011**|
||(-2.736)|(-2.565)|(-2.487)|
|||3,814||
|||**Inherited paddy fragments only**||
|Simpson index of fragmentation|0.378***||0.413***|
||(4.287)||(4.544)|
|Spatial fragmentation||0.074***|0.004|
|||(6.173)|(0.342)|
|Land|0.054***|0.113***|0.057***|
||(3.618)|(7.911)|(3.782)|
|Land squared|-0.009*|-0.008|-0.008|
||(-1.674)|(-1.536)|(-1.497)|
|||2,571||
|||**Inherited wheat fragments only**||
|Simpson index of fragmentation|0.561***||0.533***|
||(7.772)||(7.223)|
|Spatial fragmentation||0.081***|0.015**|
|||(6.772)|(2.02)|
|Land|0.084***|0.111***|0.080***|
||(5.610)|(7.589)|(5.349)|
|Land squared|-0.042***|-0.039***|-0.042***|
||(-8.095)|(-7.446)|(-8.072)|
|||2,216||

_Note:_ Asymptotic t-ratios reported in parentheses. Constant and other variables such as household characteristics, price of inputs and output and village level characteristics are included but not reported. *** p<0.01, ** p<0.05, * p<0.1.

17
# **Fiscal Sustainability in Burundi:**

# **Baseline Projections, Stochastic Simulations, and Policy Scenarios**

# Mizuho Kida<sup>†</sup>

**JEL Classification Codes:** C15, E61, E62, F35, H68.

**Key Words:** Fiscal Policy, Fiscal Sustainability, Public Finances, Stochastic Simulation, Fiscal Risk, Deficits, Debt, Tax, Aid, Foreign Aid, Agriculture, Coffee, Rainfall, Burundi

# **Sector Board:** EPOL

# ________________________

† Mizuho Kida is an economist in the Economic Policy and Debt Department (PRMED) of the World Bank (mkida@worldbank.org). This paper was originally prepared as a background paper for the Burundi Public Expenditure Review, under the guidance of Marco Larizza, Task Team Leader for the Public Expenditure Review. A shorter version of it was incorporated in Chapter 3 of the _2013 Republic of Burundi Public Expenditure Review ―Strengthening Fiscal Resilience to Escape the Fragility Trap and Promote Government Effectiveness_ , Report No. 126944 BI, The World Bank. The author would like to thank William Battaile, Chiara Bronchi, Naoko Kojo, Marco Larizza, Jacques Morisset, Juan Pradelli, Ralph Van Doorn, Yutaka Yoshino, Albert Zeufack, and the IMF Burundi team for their guidance, discussions, and comments. The author is responsible for all remaining errors.
# **Table 4: Probit estimates of EMPLOYED in 2000 (IFLS3, n= 859, 430 & 429), with censoring correction**

||_With censoring co_<br>|_rrection_|||||
|---|---|---|---|---|---|---|
||All||Female||Male||
||Probit|Selection|Probit|Selection|Probit|Selection|
|Independent<br>variable|<br>Coefficient:<br>_EMPLOYED_|coefficient:<br>_COLLEGE_<br>_ENTRY_|<br>Coefficient:<br>_EMPLOYED_|coefficient:<br>_COLLEGE_<br>_ENTRY_|<br>Coefficient:<br>_EMPLOYED_|coefficient:<br>_COLLEGE_<br>_ENTRY_|
|RURAL|-0.037|-0.217*|-0.002|-0.181|-0.112|-0.282|
||(0.117)|(0.119)|(0.164)|(0.161)|(0.166)|(0.179)|
|FEMALE|0.157|-0.006|||||
||(0.106)|(0.102)|||||
|AGE|0.264***|-0.028|0.225***|-0.047|0.297***|-0.014|
||(0.046)|(0.041)|(0.064)|(0.060)|(0.069)|(0.057)|
|SMK|0.085|-0.405***|0.046|-0.307**|0.067|-0.542***|
||(0.119)|(0.111)|(0.161)|(0.153)|(0.177)|(0.166)|
|MARRIED|-0.956***||-1.115***||-0.051|-0.282|
||(0.208)||(0.244)||(0.484)|(0.179)|
|EDU_HH||0.091***||0.084***||0.094***|
|||(0.015)||(0.022)||(0.021)|
|HH_INCOME||0.024***||0.014||0.034***|
|(million IDR)||(0.009)||(0.013)||(0.012)|
|EBTANAS1||0.066***||0.065***||0.065***|
|||(0.009)||(0.012)||(0.013)|
|Intercept|-5.213***|-3.101***|-4.316***|-2.643**|-5.772***|-3.398***|
||(0.959)|(0.932)|(1.315)|(1.332)|(1.449)|(1.312)|
|_Chi_<sup>_2_</sup>|43.41|43.41|25.90|25.90|19.94|19.94|
|_Rho:_||0.363||0.235||0.591|

Turning to labor market earnings and using the sub-sample of SMK and SMA graduates who do not continue on to tertiary education, for those who were employed in 2000, SMK graduates’ monthly earnings were on average 19 percent higher than those of SMA graduates.

14