rafmacalaba/datause-encoder-data huggingface dataset:
Almost 50% of our observations are firms listed on the Japanese exchange, reflecting the absolute size of their economy relative to the other countries. Excluding Japan, however, we have about the same number of firms listed for each country, about 300 firms on average, except for the Philippines (68 firms). As shown in Table 1, in absolute terms and as a percentage of total firms, the largest number of bankruptcies occurred in Thailand, Korea, and Malaysia; the smallest number of bankruptcies occurred in the Philippines, Singapore and Taiwan. As reported in our summary statistics, middleincome countries in the region (Indonesia, Korea, Malaysia, Philippines, and Thailand), which typify less developed financial markets and business law, account for 80% of reported bankruptcies.

We use the methodology developed by La Porta et al. (1999) and extended by Claessens et al. (1999) to study the ultimate ownership structure of East Asian corporations. To distinguish between cash-flow and control rights, we identify various forms of intermediate ownership structures, such as cross-holdings, pyramiding, and deviations from one-share-one-vote rules. For example, suppose that a family owns 11% of the stock of Firm A, which in turn owns 21% of the stock of Firm B. In this case, we contend that the family holds 11% of the control rights of Firm B, which is calculated as the weakest link in the chain of voting rights. In contrast, however, we contend that the family holds only 2% of the cash flow rights of Firm B, which is calculated as the product of the two ownership stakes along the chain.

> reorganization and establishing independent bankruptcy courts. In Indonesia, for example, there were only 11 bankruptcy filings between 1978 and August 1998, and 24 filings in the last three months of 1998. Similarly, in the Philippines, there were only 35 bankruptcy filings between 1982 and 1996, and 52 bankruptcy filings in 1997 and 1998. In contrast, the large increase in the number of bankruptcy filings in 1998 in Hong Kong, Japan, Korea, Singapore, and Taiwan, was caused by the increase in overall financial distress and not because of improvements to the bankruptcy system.

8
# **Tables**

Table 1: Summary Statistics and Balance of Covariates

|Fu|ll Sample<br>(1)|Relationship<br>Offer<br>(2)|Regulator<br>Offer<br>(3)|Friendly<br>Call<br>(4)|Collection<br>Call<br>(5)|p-_value_<br>(6)|
|---|---|---|---|---|---|---|
|_Panel A: Borrower characteristics_|||||||
|Reported income, Rs ’000|39.73|38.49|38.69|40.17|39.51|0.86|
||[58.03]|(1.57)|(2.17)|(1.47)|(1.30)||
|Female|0.10|0.11|0.10|0.11|0.11|0.96|
||[0.30]|(0.01)|(0.01)|(0.01)|(0.01)||
|Age|33.17|33.86|33.61|33.02|33.15|0.07|
||[8.70]|(0.30)|(0.27)|(0.22)|(0.21)||
|No credit history|0.67|0.69|0.67|0.68|0.67|0.45|
||[0.47]|(0.01)|(0.01)|(0.01)|(0.01)||
|Credit score|730.98|731.25|727.08|729.93|731.11|0.42|
||[51.38]|(2.31)|(2.09)|(2.08)|(1.67)||
|_Panel B: Loan characteristics_|||||||
|Principal, Rs ’000|11.81|12.23|12.08|11.88|11.89|0.35|
||[6.58]|(0.19)|(0.17)|(0.14)|(0.11)||
|Balance, Rs ’000|7.05|7.44|7.26|7.39|7.43|0.72|
||[5.63]|(0.14)|(0.14)|(0.12)|(0.10)||
|Monthly payment, Rs ’000|1.45|1.50|1.46|1.46|1.47|0.48|
||[0.75]|(0.02)|(0.02)|(0.02)|(0.01)||
|Loan duration (months)|7.80|7.76|7.77|7.77|7.79|0.81|
||[0.91]|(0.02)|(0.02)|(0.02)|(0.02)||
|Months remaining|5.08|5.19|5.15|5.32|5.31|0.08|
||[2.37]|(0.06)|(0.06)|(0.06)|(0.05)||
|Day called|8.12|7.99|8.39|8.45|8.54|0.70|
||[4.40]|(0.37)|(0.36)|(0.38)|(0.34)||
|_Panel C: Borrower credit history_|||||||
|<br>Prior loans|0.38|0.37|0.42|0.34|0.35|0.06|
||[1.03]|(0.02)|(0.03)|(0.02)|(0.02)||
|Prior defaults|0.00|0.00|0.00|0.00|0.00|0.15|
||[0.03]|(0.00)|(0.00)|(0.00)|(0.00)||
|Prior forbearance|0.17|0.19|0.20|0.17|0.17|0.09|
||[0.37]|(0.01)|(0.01)|(0.01)|(0.01)||
|Observations|9,622|1,901|1,829|2,451|3,441||

Notes: The table reports summary statistics for contacted borrowers and tests of covariate balance. Panel A reports summary statistics on borrower characteristics from the lender’s administrative data and credit reports, panel B reports summary statistics on loan characteristics observed in the lender’s administrative data, and panel C reports summary statistics on borrowers’ credit history based on credit bureau data. Column (1) shows summary statistics for the entire sample with standard deviations in brackets, columns (2) to (5) shows means for each variable across treatment conditions, with standard errors in parentheses. column (6) reports the _p_ -value of equality of means across the four main treatment conditions. Because the sampling proportions were changed after the fourth month of the experiment, all balance tests control for an indicator equal to one for all months after this date. Column (1) may differ from weighted averages of columns (2) to (5) for this same reason.

35
indicator for the extent of regulatory burden on the private firms obtained from the Doing Business project (World Bank); Business Freedom index obtained from Heritage Foundation’s Economic Freedom in the World that aims to capture (lack of) government interference with the private sector. Note that higher values of the Corruption, DTP and Business Freedom index imply a better business environment (less corruption, less burdensome regulations for the private firms, and more freedom from government interference).

# 4.2 _Base regression results_

Our baseline regression results are provided in Table 6. These results focus on the linear relationship (without any interaction terms) between labor productivity and informal competition. Results with the interaction terms included are discussed in the next section.

Regression results in Table 6 show a large and statistically significant (at the 1 percent level) negative relationship between labor productivity and informal competition. This holds with or without the various controls and for all the specifications shown in Table 6. Excluding controls, the estimated coefficient value of the informal competition variable equals -0.767 (column 1). The estimated value implies that the labor productivity of firms experiencing the highest level of informal competition (equal to 1) is lower by 54 percent than the labor productivity of firms that face the lowest level of informal competition (equal to 0). Controlling for country fixed effect raises the estimated coefficient value of informal competition variable from -0.767 above to -0.178 (column 2). The sharp drop in absolute terms suggests that a large part of the negative labor productivity and informal competition relationship comes from across country rather than within country effects. Controlling for the various firm characteristics increases the estimated coefficient value of informal competition (in absolute terms) from -0.178 above to -0.268 (column 5). The 18
Tatyana Bogomolova, Gregorio Impavido, Montserrat Pallares-Miralles

# **ACKNOWLEDGMENTS**

Bank staff who contributed to this simulation analysis of Public Service Pension Fund (PSPF) included Gregorio Impavido (LCSFF and project coordinator), Tatyana Bogomolova and Montserrat Pallares-Miralles (both HDNSP). The authors of the paper benefited from a first mission to Kampala in December 2004 where they held meetings with officials from the Ministry of Public Service (MPS), the Ministry of Finance, Planning and Economic Development (MOFPED), the Ministry of Defense (MOD), the Uganda Insurance Commission (UIC), the Capital Market Authority (CMA), and the Kampala City Council (KCC). During this first mission, data for the projections were collected and a one-week PROST training was delivered to MPS, MOFPED, and KCC officials at the Makerere University. The authors had the opportunity to visit Kampala again, in April 2005, when they met also with staff at the Uganda Bureau of Statistics (UBOS) to complete the data collection, revise the baseline, discuss preliminary findings with government officials and agree on alternative options for reforming PSPF.

The team would like to acknowledge the cooperation and support received from the managers and the staff of the institutions and agencies aforementioned, as well as the comments provided by Yee Mun Sin (HDNSP) and Rodney Lester (OPD) during the internal review process. The team is particularly grateful to Mr. Stephen Kunsa and his team in MPS for the extensive comments received during the various stages of preparation of this paper.

- 2 -
regions of the world. However, it should be noted that the use of DDT comes with significant unintended costs (externalities) to human health and the environment. The analysis reported in this paper indicates that the monetary value of health externalities of DDT alone can exceed $28 billion with widespread use of DDT in all malaria endemicity areas<sup>20</sup> . Hence some of the potential gains from lowering malaria incidence may be offset by the unintended costs of DDT use.

These estimates call for a country by country analysis that weighs the costs and benefits of DDT use. Such a comparison suggests that most countries in Sub-Saharan Africa facing high incidence of malaria may witness net benefits in malaria control from the use of DDT.

The analysis also shows that if the use of DDT is restricted to areas with the highest malaria endemicity (PfPR > 40%) then the health externalities caused by DDT are relatively small ($5 billion), while still helping in a major reduction in malaria incidence. The gains from the control of malaria in such a scenario can then greatly exceed the externalities caused by DDT in most countries. So, there may be a stronger case for using DDT in malaria vector control in areas with the highest malaria endemicity (PfPR > 40%).

# **References:**

American Cancer Society. 2009. _Cancer Facts & Figures 2009_ . Atlanta, GA.

Aneck-Hahn NH, Schulenburg GW, Bornman MS, Farias P & De Jager C (2007) Impaired semen quality associated with environmental DDT exposure in young men living in a malaria area in the Limpopo Province, South Africa. J Androl, **28** : 423–434.

ATSDR (2002) Toxicological profile for DDT, DDE and DDD. Atlanta, GA, United States Department of Health and Human Services, Public Health Service, Agency for Toxic Substances and Disease Registry.

Austin H, Keil JE, & Cole P (1989) A prospective follow-up study of cancer mortality in relation to serum DDT. Am J Public Health, **79** (1): 43–46.

> 20 A separate estimate shows that use of DDT in all malaria endemicity areas may cause a trade externality of $2.85 B and environmental externality of $2.88 B from loss of biodiversity. If DDT use is restricted to only the high malaria endemicity areas it would cause a trade externality of $0.60 B with little loss of biodiversity.

16
# **Table 6: Incidence of undernutrition for countries with data on male BMI**

||Under|weight|Male he<br>|ad is underwei<br>|ght<br>|Male head<br>|is not under<br>|weight<br>|
|---|---|---|---|---|---|---|---|---|
||Men|Women|Underweight<br>women|Stunted<br>children|Wasted<br>children|Underweight<br>women|Stunted<br>children|Wasted<br>children|
|Ethiopia|0.371|0.266|0.301|0.392|0.122|0.249|0.355|0.066|
||||(0.300)|(0.291)|(0.408)|(0.700)|(0.709)|(0.592)|
|Ghana|0.104|0.061|0.171|0.183|0.083|0.050|0.126|0.056|
||||(0.146)|(0.069)|(0.070)|(0.854)|(0.931)|(0.930)|
|Lesotho|0.188|0.062|0.084|0.406|0.041|0.043|0.240|0.029|
||||(0.154)|(0.185)|(0.156)|(0.846)|(0.815)|(0.844)|
|Namibia|0.232|0.137|0.270|0.183|0.070|0.097|0.169|0.067|
||||(0.289)|(0.142)|(0.137)|(0.711)|(0.858)|(0.863)|
|Rwanda|0.158|0.073|0.116|0.375|0.053|0.057|0.360|0.016|
||||(0.166)|(0.092)|(0.246)|(0.834)|(0.908)|(0.754)|
|Senegal|0.275|0.216|0.278|0.184|0.088|0.219|0.181|0.062|
||||(0.184)|(0.145)|(0.190)|(0.816)|(0.855)|(0.810)|
|Sierra|||||||||
|Leone|0.155|0.091|0.138|0.247|0.102|0.083|0.279|0.062|
||||(0.118)|(0.060)|(0.106)|(0.882)|(0.940)|(0.894)|
|Mean|0.240|0.159|0.259|0.358|0.108|0.141|0.296|0.053|
||||(0.256)|(0.196)|(0.291)|(0.744)|(0.804)|(0.709)|

Note: The table shows the proportion of undernourished women and children in male headed households separated by the nutritional status of the household head. The figures in parentheses are the shares of those women or children who are undernourished found in each of the two groups of households according to whether the male head is underweight. Men and women are between 15 and 49 years of age. Male heads of household are also restricted to 15 and 49 years of age. Children are between 0 and 5 years of age.

43
1.48 -

-

Flgure 1.C.6 COCOA SUPPLY RESPONSE UNDER POPULATION GROWTH <!-- Start of picture text -->
1.2-<br>'.5<br>0.4<br>0.37<br>0.2<br>1967  1997  2007  2017  2027  2037<br>PERI;O MIDPOINTS<br>0  m  *+  140  o  2 0  00<br><!-- End of picture text -->
Although we do not directly observe the maturity in the markets outside those analyzed in this paper, we shed light on the short-termism in developing countries by merging the transaction-level data on debt issuances with balance sheet information for publicly listed firms for the period 2003-11. We then compare the ratio of long-term debt to total liabilities between developed and developing countries for all firms, for debt issuers (defined as firms tapping bond or syndicated loan markets at least once during the sample period), and for other debt issuers (those using neither bonds nor syndicated loans, so all the debt they hold in their balance sheets comes from other sources).<sup>21</sup> The evidence based on balance sheet data shows that debt issuers hold more long-term debt in their balance sheets than the other listed firms (Figure 8, Panel A). Moreover, the ratio of long-term debt in developing countries is lower than in developed ones when considering the whole sample of firms, but similar when considering debt issuers only. The median ratio of long-term debt over total liabilities is 15 and 19 percent for developing and developed countries, respectively. This ratio is much higher, at 30 and 31 percent, for debt issuers from developing and developed countries. For other debt issuers, this ratio drops to 12 percent and 14 percent, respectively. Furthermore, the evidence shows that there is a positive correlation between the share of listed firms using debt markets and the country level of GDP per capita (Figure 8, Panel B).<sup>22</sup> We find a similar correlation when using measures of financial market development, such as private credit or bond market capitalization to GDP.

These findings are important for reconciling our results with those in the literature and for determining the drivers of short-termism in developing countries across firms. Consistent with the existing literature are the patterns that the ratio of long-term debt is lower in developing countries

> 21 We follow the literature and define long-term debt as all firms’ liabilities with maturity longer than one year.

> 22 This finding is not driven by a better matching of the data for countries with higher levels of GDP per capita. Around 34 percent of the firms in the SDC database (which contains information for all debt issuers, including unlisted firms) are found in Orbis. Although this percentage varies by country, it is not correlated with GDP per capita or financial market development.

21
A negative sign on the coefficient of interest, , would indicate that the 2011 earthquake led to a diversification of imports away from Japan in those formerly high dependence products after the shock. ββ _Table 2: Diversification Herfindahl–Hirschman Index (HHI)_

|**Panel A: All**|(1)|(2)<br>Au|(3)<br>to||(4)|(5)<br>Electr|onics|(6)|
|---|---|---|---|---|---|---|---|---|
||HHI|HHI|HHI|HHI|HHI|HHI|HHI|HHI|
|(Dummy share 2004-2010 > 15)|-0.007|||-0.021|0.017***|||0.013**|
||(0.013)|||(0.019)|(0.005)|||(0.006)|
|(Dummy share 2004-2010 > 10)||0.005||0.022||0.013***||-0.000|
|||(0.013)||(0.020)||(0.005)||(0.006)|
|(Dummy share 2004-2010 > 5)|||-0.001|-0.006|||0.015***|0.012**|
||||(0.014)|(0.015)|||(0.005)|(0.005)|
|Observations|5,562|5,562|5,562|5,562|52,584|52,584|52,584|52,584|
|R-squared|0.847|0.847|0.847|0.847|0.799|0.799|0.799|0.799|
|**Panel B: Auto**||Intermed|iate auto|||Final|auto||
||HHI|HHI|HHI|HHI|HHI|HHI|HHI|HHI|
|(Dummy share 2004-2010 > 15)|0.000|||-0.019|0.003|||-0.073|
||(0.015)|||(0.022)|(0.031)|||(0.046)|
|(Dummy share 2004-2010 > 10)||0.019<br>||0.035<br>||0.014<br>||0.031<br>|
|||(0.016)||(0.022)||(0.026)||(0.043)|
|(Dummy share 2004-2010 > 5)|||-0.001|-0.012|||0.036|0.039|
||||(0.017)|(0.017)|||(0.025)|(0.033)|
|Observations|3,818|3,818|3,818|3,818|1,744|1,744|1,744|1,744|
|R-squared|0.899|0.900|0.899|0.900|0.783|0.783|0.783|0.773|
|**Panel C: Electronics**||Intermediat|e electronics|||Final ele|ctronics||
||HHI|HHI|HHI|HHI|HHI|HHI|HHI|HHI|
|(Dummy share 2004-2010 > 15)|0.025**|||0.016|0.014**|||0.013*|
||(0.010)|||(0.012)|(0.006)|||(0.007)|
|(Dummy share 2004-2010 > 10)||0.022**||0.011||0.009*||-0.006|
|||(0.009)||(0.012)||(0.005)||(0.007)|
|(Dummy share 2004-2010 > 5)|||0.012|0.003|||0.016***|0.016***|
||||(0.010)|(0.010)|||(0.005)|(0.005)|
|Observations|14,320|14,320|14,320|14,320|38,264|38,264|38,264|38,264|
|R-squared|0.791|0.791|0.790|0.791|0.804|0.804|0.804|0.804|

Note: Robust standard errors clustered at the cluster importer-product level are in parentheses. All specifications include importer-year, importer-product, and product-year fixed effects. The samples include the top 15 producers of automobile or top 15 exporters of final electronics other than Japan. *** p<0.01, ** p<0.05, * p<0.1 The results in Table 2 suggest that importers more exposed to the 2011 earthquake, those with higher shares of imports sourced from Japan in the pre-shock period, did not increase import diversification as a 13
Because advanced countries feature higher trade and financial integration (see Kose, Terrones and Prasad (2004)) than other countries, which likely affects their growth comovement, we consider separately a sub-sample of 21 advanced economies. The full list of countries is given in Table A1 in the appendix.

Real GDP is measured in constant U.S. dollars (expressed in international prices, base 2010), and annual real output growth is computed as the first difference of the log of real GDP.

The spatial weight matrix that connects cross-sectional units (countries) is an important element in the empirical implementation of the model. We measure the economic distance between each pair of countries by the magnitude of their bilateral trade, following the view that bilateral trade intensities capture economic interactions and shock spillovers across countries, so that countries that trade more are economically more connected, see e.g. Frankel and Rose (1998).

To construct the bilateral trade weight matrix, we use information on bilateral trade taken from the IMF Direction of Trade Statistics (DOT). Specifically, for a pair of countries _i_ and _j_ , _i_ = _j_ , entry _i_ , _j_ of the trade spatial weight matrix _W_ is defined as where _Exportsij_ denotes the exports from country _i_ to country _j_ , and _Importsji_ are the imports of country _i_ from country _j_ . Once _W_ has been computed, it is rescaled dividing each of its elements by the sum of its corresponding row, so that the rows of the rescaled matrix sum to unity.<sup>7</sup> Alternatively, following Ertur and Koch (2007), we use a weight matrix _WD_ based on inverse squared distance. The elements of _WD_ are defined (before row normalization) as where _dij_ is the great-circle distance between the capital cities of countries _i_ and _j_ .<sup>8</sup> To assess the covariates of the common factors, we consider three candidate variables, namely, (i) total factor productivity; (ii) policy uncertainty, and (iii) the U.S. short-term real interest rate. Crucini, Kose, and Otrok (2011) find that total factor productivity is the leading driver of the business cycle of G-7 countries. They also find a relatively minor contribution of monetary policy. In turn, Baker and Bloom (2013) show that policy uncertainty plays an important role in driving business cycles.

Total factor productivity (TFP) is computed from the standard Solow residual using capital and labor inputs, and is drawn from the Penn World Tables version 9.0. Policy

> 7Such row standardization of the weight matrix facilitates the interpretation of the model coefficients, see Anselin (1988).

> 8The great-circle distance, the shortest distance between any two country capitals, is computed as: _dij_ = _radius× cos_<sup>_−_1</sup> [ _cos |longi − longj| coslaticostlatj_ + _sinlatisinlatj_ ] where radius is the Earth's radius, and _lat_ and _long_ are, respectively, latitude and longitude for country capitals _i_ and _j_ . The latitude and longitude coordinates for each of the country capitals in our sample were collected from the CEPII database.

8
effectively provide public goods - for instance, when on group seeks to actively obstruct the authority of the other - we also expect inter-elite competition to harm local development outcomes.

There are also theoretically based reasons to expect that relationships defined by cooperation, rather than competition, will be beneficial to the provision of local public goods. For instance, as Fearon et al. (2009) suggest, chieftaincy support of local council projects may increase the chances of programmatic success. Further, Logan’s (2008, iii) analysis of Afrobarometer data from 15 countries on the continent, finds that respondents’ evaluations of newly decentralized local authorities and hereditary chiefs move together, noting “local traditional leaders appear to draw their sustenance and legitimacy from the same well as elected officials.” This suggests that rather than control over local power and resources being a zero-sum game, cooperative relationships between chiefs and councilors may be beneficial for public goods outcomes as well as public perceptions of the quality of local government. If these types of cooperative inter-elite relationships are at play, closer ties between councilors and chiefs should be associated with greater levels of public goods.

Yet other research on the political economy of Africa suggests an opposite effect. Close inter-elite relationships may indicate that councilors are benefitting from chiefs’ clientelistic networks to buy votes and ensure future electoral support. In such instances, patronage-based redistribution may come at the expense of public goods provision (Bratton 2008; Collier & Vicente 2012; Keefer 2005; van de Walle 2003, 2012; Wantchekon 2003). In addition, if councilors draw their legitimacy indirectly from the support of chiefs rather than from their performance, the accountability of local governance and thus councilors’ incentives to invest in public goods also decline. In such scenarios, we expect that closer inter-elite relationships defined by frequent contact in the absence of disputes and a higher frequency of councilor/chief familial ties will lead to worse local development outcomes.

In sum, both theoretical and empirical work on inter-elite competition and local governance suggest countering expectations on the ways in which the nature of inter-elite relationships will affect the ability of both groups to effectively provide local public goods. We hope to contribute to this debate by providing direct evidence on different types of self-reported inter-elite relationships as we explore how power-sharing dynamics affect different dimensions of local public goods provision.

9