rafmacalaba/datause-encoder-data huggingface dataset:
Our last country-level control is intended as a general robustness check for the overall quality of governance. The control is the level of corruption as measured by “Freedom from Corruption” ( _Corruption_ ) sub-index of the Heritage Foundation’s Economic Freedom index. Note that higher values of _Corruption_ variable imply less corruption.

Moving to firm characteristics, available evidence, mostly for the developed countries, suggests segregation of men and women by sectors and occupations. For example, the U.S. Bureau of Labor Statistics reports that in 2009, of the 45 million women who worked full time in wage and salary jobs, 17 million were employed in education and health services, and 5 million in wholesale and retail trade; financial activities and professional and business services each employed about 4 million women.<sup>6</sup> We filter out all such factors from potentially affecting our main results by controlling for 12 industry dummies or industry fixed effects (see Table 1 for details).

Presence of women in positions of power like firm ownership may signal greater preference among women for labor market over household activity and also greater empowerment of women. Both these factors are likely to be positively correlated with women’s employment and non-discrimination. Further, women owners may discriminate less against other women seeking jobs thereby contributing to women’s employment although the evidence on this is somewhat mixed (Nelson and Bridges 1999; Penner and Toro-Tulla 2010). We guard against the implied spurious correlation here by controlling for a dummy variable equal to 1 if the firm has one or more women owner and 0 otherwise ( _Women owner_ ).

Our next control is firm-size as measured by the (log of) total number of permanent fulltime employees ( _Employment_ ). The variable serves as a proxy for a number of firmcharacteristics such as access to finance, industry, regulatory pressure faced, etc., which could

> 6 This information is available online at: http://www.bls.gov/opub/ted/2011/ted_20110216.htm.

12
<!-- Start of picture text --> Figure 6<br>Disentangling the demand elasticity<br>(a) As a function of B ′ (b) As a function of spreads<br>0.0 0.0<br>-0.1 -0.1<br>-0.2 -0.2<br>-0.3 -0.3<br>Reduced-form Elasticity Reduced-form Elasticity<br>Structural Elasticity Structural Elasticity<br>Changes in Expected Repayment Changes in Expected Repayment<br>-0.4 -0.4<br>0.52 0.54 0.56 0.58 0.60 0.62 0.64 3.5 4.0 4.5 5.0 5.5 6.0<br>B' Spreads, pp<br>Elasticity Elasticity<br><!-- End of picture text --> Note: The figure shows the reduced-form inverse demand elasticity _η_ ˆ (black lines) and the structural one _η_ (blue lines). The vertical differences between the two lines (represented by the red lines) capture the endogenous changes in bonds’ expected repayment, _α_ . Panel (a) shows the results as a function of _B_<sup>_′_</sup> , while Panel (b) shows the results as a function of annualized bond spreads.

# **5.2 Decomposing the Reduced-form Demand Elasticity**

We formally disentangle the different channels through which changes in _T_ affect bond prices. As shown in Equation (19), index rebalancing affects bond prices through two mechanisms: (i) the (inverse) structural demand elasticity of active investors, _η_ , and (ii) changes in expected repayment, _α_ . Using the calibrated model, we can isolate the effects driven by changes in expected repayment to properly identify the structural demand elasticity.

Figure 6 decomposes the channels outlined in Equation (19). The black line shows the reduced-form (inverse) demand elasticity _η_ ˆ, while the blue line depicts the model-implied structural elasticity, _η_ . The vertical differences between these two curves (red lines) indicate the portion of the reduced-form elasticity attributable to endogenous changes in the repayment ˆ function, _α_ . We find that the magnitude of _η_ is always higher than that of _η_ . This is consistent with our empirical analysis based on CDS spreads (described in Section 3). The difference can be substantial, particularly for larger values of _B_<sup>_′_</sup> and for higher bond spreads.

The first column of Table 6 shows the unconditional average for both the reduced-form ˆ elasticity, _η_ , and the structural elasticity, _η_ . On average, the structural elasticity accounts for less than two-thirds of the reduced-form elasticity. The rest is explained by endogenous changes in the expected repayment, _α_ . Despite the fundamentals ( _y, B_<sup>_′_</sup> ) being fixed and the ∆ _T_<sup>_′_</sup> shocks being exogenous, the reduced-form elasticity significantly differs from the structural one.

The magnitudes of the documented biases critically depend on the persistence of the 28
slightly higher than other countries.<sup>7</sup> In the last two columns, we examine the possibility of conditional convergence between agglomerations in the Northern and the Southern regions as a group. The coefficient of the Southern dummy is significantly positive when the same speed of convergence is assumed (column (3)), potentially indicating a higher steady-state growth rate of the Southern region cities. This is consistent with the finding of Andrade et al. (2004). However, overall, we cannot reject the hypothesis of identical speed of convergence and steady state growth rates between the two regions (column (4)).

Figure 7 and Figure 8 confirm these findings. Figure 7 shows a negative (linear) relationship between the annual income growth rate between 1970 and 2000 and the log of per capita income level in 1970. Figure 8 shows the pattern between 1991 and 2000. The fitted lines of the Northern and the Southern regions seem to have a similar slope but different intercepts.

**Table 5:** β **Convergence of Per Capita Income**<sup>**1)**</sup>

|ln(income in 1970)|**(1)**<br>Basic<br>equation<br>-0.015|**(2)**<br>Basic equation<br>+ 5 regional<br>dummies<sup>2)</sup><br>-0.021|**(3)**<br>Basic equation<br>+ 2 regional<br>dummies<sup>2)</sup><br>-0.022|**(4)**<br>**(Chow test)**<br>Basic equation<br>+ 2 regional<br>dummies<sup>2)</sup><br>-0.018|
|---|---|---|---|---|
||(10.21)|(12.63)|(13.62)|(5.43)|
|ln(income in 1970)* Dummy(south)||||-0.005|
|||||(1.20)|
|Constant|0.104|0.136|0.126|0.111|
||(14.66)|(16.72)|(18.32)|(7.77)|
|Dummy(south)|||0.011|0.031|
||||(6.75)|(1.84)|
|Number of observations<br>|123|123|123|123|
|Adj. R<sup>2</sup>|0.46|0.60|0.60|0.61|
|Speed of convergence(%)|2.03|3.44|3.47||

- 1) Dependent variable = (1/30)*ln[income(2000)/income(1970)]

2) Five regional dummies correspond to the North, the Northeast, the Southeast, the South and the West-Central regions, with the West-Central as a base. Two regional dummies are for the northern

> 7 Most published studies have investigated convergence across administrative regions rather than urban areas. With regional dummies added, the convergence speed across U.S. states for 9 subperiods between 1880 and 1990 was 1.9 percent, Japanese prefectures for 7 subperiods between 1930 and 1990 was 2.3 percent, and European regions for 4 subperiods between 1950 and 1990 was 1.9 percent (Barro and Sala-iMartin (1995)).

16
Czubala, Witold, Ben Shepherd and John Wilson. 2009. “Help or Hidrance? The Impact of Harmonized Standards on African Exports”, Journal of African Economies doi:10.1093/jae/ejp003 Eaton, Jonathan and Samuel Kortum. 2002. “Technology, Geography, and Trade”, Econometrica, 70(5): 174179.

Hanson, David. 2005, ”CE Marking, Product Standards and World Trade”, Edward Elgar, Northampton,USA.

Helpman, Elhanan, Marc Melitz and Yona Rubinstein. 2008, “Estimating Trade Flows: Trading Partners and Trading Volumes”, The Quarterly Journal of Economics, Vol. 123, No. 2, pp 441-487, May.

Jarmin, Ron S. and Javier Miranda. 2002, “The Longitudinal Business Database”, Working Papers, U.S. Census Bureau, Center for Economic Studies Moenius, Johannes. 2000. “The BISTAN Data Retrieval Reference”, mimeo. University of California at San Diego.

Moenius, Johannes, 2004, “Information Versus Product Adaptation: The Role of Standards in Trade”, International Business and Markets Research Center Working Paper, Northwestern University.

Moenius, Johannes, 2006a, “The Good, the Bad, and the Ambiguous: Standards and Trade in Agricultural Products”, Paper presented at the IATRC Summer Symposium, Bonn, May 2006, http://www.agp.uni-bonn.de/iatrc/iatrc _program/Session_ %203 _/Moenius.pdf_ Moenius, Johannes, 2006b, ”Do National Standards Hinder or Promote Trade in Electrical Products?” IEC Centenary Challenge 2006, http://www.iec.ch/academia/papers/moenius.pdf Pierce, Justin R. and Peter Schott. 2009. “A Concordance Between Ten-Digit U.S. Harmonized System Codes and SIC/NAICS Product Classes and Industries”, NBER Working Paper No. 15548. Portugal-Perez, Alberto, Jos ́e-Daniel Reyes and John S. Wilson. 2010. “Beyond the Information Technology Agreement: Harmonization of Standards and Trade in Electronics.”, The World Economy, Vol. 33, Issue 12, pp. 1870-1897, December.

Reyes, Jos ́e-Daniel. 2011. “The Pro-Competitive Effect of International Harmonization of Product Standards.”, mimeo. The World Bank.

Swann, G. P. (2010), International Standards and Trade: A Review of the Empirical Literature, OECD Trade Policy Working Papers, No. 97, OECD Publishing.

Shepherd, Ben. 2006. “The EU Standards Database: Overview and User Guide.”, mimeo, available at http://go.worldbank.org/6OEYNCYSD0 Shepherd, Ben. 2007. “Product Standards, Harmonization, and Trade: Evidence from the Extensive Margin”, Policy Research Working Paper No. 4390, The World Bank.

Swann, G. P. 2010. “International Standards and Trade: A Review of the Empirical Literature”, OECD Trade Policy. Working Papers, No. 97, OECD Publishing.

20
governance problems. Transparency International’s “Global Corruption Barometer” is a promising development in this regard, but conducting nationally-representative surveys of households remains a severe challenge in many developing countries. Third, public officials surveys (sporadically conducted by the Bank in a small number of countries) should be standardized and scaled up, with a focus on assessing aspects of public sector corruption and other governance deficiencies not manifested in either state-enterprise or state-household transactions. Finally, existing efforts to collect data on laws and practices intended to prevent corruption should be scaled up, to provide more “actionable” indicators appropriate for monitoring reform commitment and progress. Promising developments in this area include the Public Integrity Index, the International Budget Project, and the PEFA indicators on public expenditure management.<sup>39</sup>

> 39 Information on these initiatives can be found, respectively, at http://www.globalintegrity.org/, <u>http://www.internationalbudget.org/, and http://www.pefa.org/.</u>

48
Table D.17. Workers by Economic Activity

||Share of Work|ers by Econo|mic Activity|Share of Agricultural Workers|
|---|---|---|---|---|
||Agriculture|Industry|Services|Growing Cereals and Fruits|
||(1)|(2)|(3)|(4)|
|Guerrilla control|0.0465***|-0.0261***|-0.0203**|0.0456***|
||(0.00985)|(0.00559)|(0.00878)|(0.00944)|
|Observations|3,636|3,636|3,636|3,636|
|Bandwidth (Km)|2.266|2.266|2.266|2.266|
|Dependent mean|0.194|0.227|0.579|0.160|

_Note:_ The table presents the results of equation 1 for the share of workers in each economic activity. The information was calculated from the Census 2007 and using ISIC v4 to classify each occupation. The unit of observation is the census tract. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The estimates use triangular kernel weights. Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, _†_ p _<_ 0.15.

Table D.18. Inequality of Income at the Canton Level

|||||_Real Per Capita Income_|||
|---|---|---|---|---|---|---|
||Logarithm|Level|Gini Index|Interquartile Range<br>(p75-p25)|Percentile Range<br>(p90-p10)|Percentile Range<br>(p90-p50)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Guerrilla control|-0.223***|-39.08**|0.0119|0.0217|1.490|0.0366|
||(0.0682)|(15.25)|(0.0163)|(0.193)|(1.256)|(0.284)|
|Observations|542|542|542|542|542|542|
|Bandwidth (Km)|3.082|3.082|3.082|3.082|3.082|3.082|
|Dependent mean|5.330|266.8|0.320|2.450|5.240|2.360|

_Note:_ The table presents the results of equation 1 for the real per capita income taken from the Household Surveys (2012 to 2018). Each Column represents a different measure of inequality using the real per capita income. Column 4 report the interquartile range, calculated as the difference of the per capita income in percentile 75 minus the per capita income in percentile 25 for each canton. Column 5 shows the percentile range of the difference between percentile 90 and 10 for each canton. Column 6 reports the percentile range of the difference between percentile 90 and 50 for each canton. The unit of observation is at the canton level. Controls not shown include a linear polynomial of the distance to the boundary of guerrilla territory, its interaction with whether the tract was under guerrilla control or not, and up to 400 fixed effects representing the closest evenly spaced break in the guerrilla-controlled boundary. The algorithm of Calonico, Cattaneo and Titiunik (2014) was used to set the bandwidth and the estimates use triangular kernel weights. Robust standard errors in parentheses.

89
# **Appendix Table A9: Distance Analysis**

||(1)|(2)|
|---|---|---|
||Sales Last<br>Month (win<br>5%)|Estimated Profits<br>Last Month (win<br>5%)|
|Distance to Nearest Treated Business|-1649.377|-477.693|
||(2183.64)|(526.92)|
|R-squared|0.527|0.248|
|Observations|452|445|

**Notes:** Using only control group businesses, this table presents OLS regressions of 5% winsorized sales (column 1) and 5% winsorized profits (column 2) on distance to the nearest treated business. Regressions control for a midline survey round dummy, baseline value of the outcome variable as well as stratification controls.  Statistically significant p-values are highlighted by: * (10% significance level), ** (5% significance level), and *** (1% significance level).
- 43 -

Table 10 Real Outlays in the Hithway Sector - Fondo Vial (millione of Colombia $ in 1975 prices)

||1980|1981|1982|1983|1984|1985|1986|1987|
|---|---|---|---|---|---|---|---|---|
|Total Expenditure|5267.4|6093.9|5868.6|5317.0|5214.6|5286.3|4862.6|6335.3|
|Construction,<br>Rehabilitation<br>and Maintenance<br>Expenditures|3624.4|3975.1|2853.4|2415.6|2338.3|2657.8|2403.1|3179.2|
|Memorandum Item|||||||||
|Real Gasoline Tax<br>Revenue|4239.1|4386.5|4044.4|3260.7|3197.7|3156.1|3021.7|3243.3|
|Gasoline<br>**Tax**in<br>current<br>prices)<br>(Col.**_$/gal.)_**|8.71|<br>12.50|<br>13.50|<br>16.45|<br>19.64|<br>22.06|<br>26.49|<br>32.58|
|Gasoline Tax Adjusted<br>**for**Inflation (Col.<br>$/gal.e1975<br>**prices)**|2.90|<br>3.29|<br>2.93|<br>2.97|<br>2.94|<br>2.61|<br>2.44|<br>2.41|

Source: Data from statistical appendix Tables AS **and** A9 deflated by the GDP deflator.

service (mainly on external debt) has been a recent phenomenon. From merely 0.7% of FVN's expenditures in 1980 it rose to 10.4% in 1986 and 17.40% in 1987 (see Table A8 in statistical appendix). While transfers from the central government budget have been rising, they have not been sufficient to offset the squeeze from falling gasoline revenues and rising debt service and FVN expenditures on construction and maintenance were lower than in 1980 in all years but one (1981); even a major expansion in 1987--financed mainly by external borrowing--left expenditures some 12% lower than in 1980.37

- 37/ See Table 10 where data is current prices have been converted to constant (1975) prices by use of the GDP deflator. Similar results would be obt.ined if the deflator for heavy construction activities were used instead.
program. The survey for the first wave of the dataset covered 3,247 households (hereafter baseline households) in the 2009/2010 agricultural year. The sampling was representative at the national, regional and urban/rural levels. The survey for the second wave of the dataset was conducted in the 2012/2013 agricultural year and attempted to track and resample all the baseline households as well as individuals (projected to be at least 12 years) that split-off from the baseline households between 2010 and 2013 as long as they were neither guests nor servants and are still living in mainland Malawi. Once a split-off individual was located, the new household that he/she formed or joined was also brought into the second wave. In all, a total of 4,000 households were traced back to 3,104 baseline households. An overwhelming majority, 76.80%, of the 3,104 baseline households did not split over time; 18.49% split into two households; and rest (4.70%) split into 3-6 households. Considering the 20 baseline household that died in their entirety between 2010 and 2013 and the fact that 4,000 households could be traced back to 3,104 baseline households, the dataset has an overall household attrition rate of only 3.78%.

The study dropped all non-agricultural households (580 and 845 households in the first and second waves respectively), as well as urban agricultural households (370 and 438 households in the first and second waves respectively). The urban agricultural households were dropped because farming in Malawi is predominantly rural. In order to avoid reverse causality in the maize production function, all the households for which questions about their food and non-food consumption were asked after the harvesting of agricultural products were also dropped. In the end, a panel of 1,667 households (2,472 maize plots), 771 households (1,127 maize plots) in the first wave and 896 household (1,347 maize plots) in the second wave was used for the analyses.

Attrition bias in the data could not be tested because there are no regression-based tests for attrition when fixed effects or MC devise models are used with a panel of only two wave – a panel 21
# **Table of Contents**

|1.<br>Int|roduction ............................................................................................................................. 1|
|---|---|
|2.<br>Ov|erall Structure of the Pension and Retirement System ....................................................... 2|
|2.1|The AFP System............................................................................................................... 2|
|2.2|The Insurance Sector ........................................................................................................ 3|
|2.3|The Structure of the Market for Retirement Products ...................................................... 6|
|2.5|The Conditions for Retirement ....................................................................................... 11|
|3.<br>Th|e Menu and Performance of Retirement Products ............................................................ 12|
|3.1|The Menu of Retirement Products ................................................................................. 12|
|3.2|The Demand for Retirement Products and Level of Annuitization ............................... 15|
|3.3|The Performance of Phased Withdrawals ...................................................................... 18|
|3.4|The Performance of Annuities and Money's Worth Ratios ........................................... 19|
|3.5|Replacement Rates ......................................................................................................... 23|
|3.6|Risk-Sharing Arrangements ........................................................................................... 24|
|3.7|Marketing Regulation ..................................................................................................... 25|
|4.<br>Re|gulation and Supervision ................................................................................................... 27|
|4.1|The Sustainability of Money's Worth Ratios ................................................................. 27|
|4.2|Prudential Regulation ..................................................................................................... 31|
|4.3|Risk Management ........................................................................................................... 34|
|5.<br>Co|ncluding Remarks ............................................................................................................. 35|
|Referen|ces ..................................................................................................................................... 39|
on corporate bond issuances provided by Dealogic for 67 developing countries over the period 1993-2017. As for loans, we transform the data into a balanced panel at the country-sector-year level.

Our main outcome variables of interest are: 1) the number of syndicated loans in the countrysector, excluding deals with MDB participation; 2) the total size of the syndicated loans (as a share of GDP), excluding both the amount lent by the MDBs and by their partners<sup>9</sup> ; 3) the number of banks per loan, excluding those partnering only in deals with MDB participation; and 4) the average loan maturity.<sup>10</sup> To explore direct mobilization effects we also look at: 5) the number of banks that participate in syndicated lending per loan; and 6) the total size of the syndicated loans (as a share of GDP), excluding the amount lent by MDBs themselves, but including the amount lent by banks co-investing in a syndicated loan with MDB participation.

**Descriptive statistics.** Table 2 reports the summary statistics for the country-sector panel data. On average there are 0.7 syndicated loans in each country-sector-year (Panel A). The average total value of loans in a country-sector-year is 0.11 percent of GDP, which is only slightly higher than the average total value of loans excluding the ones supported by MDBs. The average number of banks (excluding MDBs) involved in syndicated loans is 1.6, similar to the average number of banks without counting the ones partnering in a syndicated loan with MDBs. The average maturity of loans, without counting loans with MDB participation, is 8.7 months. Panel B reports the summary statistics for corporate bond issuances, which refer to a smaller sample than that of syndicated loans. On average, bond issuances in the country-sector pair are more numerous than syndicated loans, but their size (as a percent of GDP) is smaller.

Consistent with the idea that mobilization effects could be driven by signaling and demonstration effects as well as better information, the descriptive evidence shows that MDBs are among the first to enter a given country-sector through the syndicated loan market. If, for each countrysector pair, we define the “first” lending year as the first year since 1995 in which a country-sector receives a syndicate loan, we find that in 32 percent of these “first” years there is at least an MDB joining the first syndicated loans; by contrast, this share becomes substantially lower (23 percent) in subsequent years.<sup>11</sup>

> 9That is, we exclude the full amount of any syndicated loan the MDB is involved in, i.e., we exclude the partners’ contribution to all syndicated loans with an MDB.

> 10The dataset provides the maturity of each tranche of syndicated loans. We calculate the weighted average of the maturities of syndicated loans by weighting the maturity of each tranche by its relative size within the loan. Once we collapse the dataset at the country-sector-year level, we calculate the average of weighted maturities of syndicated loans in the country-sector-year.

> 11As our sample begins in 1993 and we do not have information of the presence of syndicated loans before that

7