rafmacalaba/datause-encoder-data huggingface dataset:
The pseudo R-squared of the logit regression is 0.22, which suggests plausible explanatory power. The PS of each observation is estimated based on the regression. Figure A.1, in the Appendix, plots densities of the estimated PS in the treatment and control groups as well as the cut-point of the PS values above which observations are trimmed. To illustrate the effects of trimming and reweighting, Table A.1 displays simple differences of the explanatory variables between the treatment and control groups in the untrimmed sample and the PS weighted and trimmed samples. Although simple differences between the groups are large and statistically significant in the untrimmed sample, trimming and matching based on the propensity score eliminates all significant differences.

In Table 5.2, we report the estimation results on ATET of TEEP. We examine changes in overall and mathematics NAT scores from grade 4 in SY 2002/03 to grade 6 in SY 2004/05.<sup>18</sup> Panel 1 shows the simple DD results for the overall test and mathematics test scores. The effects on both scores are small in magnitude and insignificant statistically.

Panels 2 and 3 show the results using DD and PS matching (weighted regression) and DD and PS matching (kernel), respectively. The two methods give close results, which suggests that TEEP has significant impacts on both overall and mathematics scores. The magnitude is about 4 overall and 5 for mathematics.<sup>19</sup> In other words, TEEP attributes to an increase of

> 18 Mathematics is the only common subject that was tested by all schools in the two grades. Overall score is the summation of scores of all the subjects being tested. 19 Interestingly, mathematics scores increased more than the overall scores, though the initial level of the average mathematics score was higher than the overall score.

17
higher costs are more than offset by higher net interest margins and higher not-interest income (section 4), resulting ultimately in higher profitability. Indeed, the higher wages paid by listed banks could simply reflect a more skilled labor force, required to develop more sophisticated and profitable lines of business. The higher profit ratios suggest that this strategy has paid off. Finally, we note that listed banks have lower ratios of loss loan provisions to loans, although the coefficient is not significant.

All in all, these results imply that listing generates higher costs but that these costs are more than compensated by higher revenues and profits. As noted before, the stricter governance and disclosure requirements imposed on listed banks could be driving these results. This interpretation is consistent with a recent survey of bank governance in MENA (OECD (2009)), which concludes that corporate governance of non-listed banks is generally poor, particularly those that are family controlled – these banks tend to engage heavily in connected lending and perform poorly as a result. This would suggest that MENA regulators should encourage or even mandate listing, as some countries already do. However, there is also a possibility that our results could be affected by selection bias: better managed and more successful banks may be precisely those banks that decide to list.

# _Bank structures and performance_

Finally, we complement the analysis of ownership and performance by focusing briefly on the impact of size and balance sheet structures on performance. We note that larger banks tend to be more profitable, as indicated by higher returns on assets and equity, although only the latter variable is statistically significant, and none of the profit indicators is significant in the weighted regressions (Appendix C). Larger banks tend to have lower ratios of interest income to assets and lower net interest margins, results that are consistent with their larger securities portfolios, although most of the relevant coefficients are not statistically significant. Most importantly, larger banks have lower cost ratios, despite paying higher wages on average. This result is essentially due to a lower ratio of employees to assets, reflecting scale economies. These results are all statistically significant and help explain their higher profitability.

Banks which generate higher non-interest income tend to have lower net interest margins (as they focus on particular markets) and higher cost ratios but are still able to drive higher 19
**Table 8: Augmented Solow model with infrastructure variables and the effect of the level of inequality: low, medium or high inequality**

||Tele<br><br>|coms<br>|Elec<br>|tricity<br>|Ro<br><br>|ads<br>|San<br><br>|itation<br>|W<br><br>|ater<br>|
|---|---|---|---|---|---|---|---|---|---|---|
||Ben<br>ch|st<br>Infra|Bench|st<br>Infra|Benc<br>h|rast<br>Inf|Ben<br>ch|t<br>Infras|Benc<br>h|rast<br>Inf|
|Education|1.38|−.52|1.82|−2.25|−|−|1.25|.50|1.01|.21|
|t-stat|3.27|−<br>2.18|1.61|−1.64|−|−|2.48|1<br>.13|1.85|.34|
|)<br>Elast(β|1.38|−3.25|.00|.00|−|−|1.25|.00|.00|.00|
|st<br>Total Inv|−.11|−.84|−<br>.22|.50|−|−|−. 27|−<br>.14|.02|−<br>.12|
|t-stat|−.23|−<br>4.15|−.25|.65|−|−|−. 42|−.30|.03|−.14|
|)<br>Elast(α|.00|−5.25|.00|.00|−|−|.00|.00|.00|.00|
|Low|||||||||||
|Infrast||1.15||2.64||−||1.32||1.51|
|t-stat||1<br>1.56||2.81||−||2.39||.73|
|γ)<br>Elast (||7.19||2.64||−||1.32||.00|
|Medium|||||||||||
|Infrast||1.15||2.64||−||1.32||1.51|
|t-stat||1<br>1.56||2.81||−||2.39||.73|
|γ)<br>Elast (||7.19||2.64||−||1.32||.00|
|High|||||||||||
|Infrast||1.05||2.16||−||1.09||1.51|
|t-stat||−<br>2.28||−<br>2.37||−||−<br>1.85||.73|
|γ)<br>Elast (<br>||6.56||2.16||−||1.09||.00|
|R <sup>2</sup>|.24|.88|.13|.92|−|−|.19|.58|.11|.35|
|Obs<br>No.|34|34|9|9|−|−|20|20|21|21|
|e<br>Differenc||YE<br>S||YE<br>S||−||YES*||NO|

_Note_ : “*” means result significant only at 10% probability level.

In the case of electricity, results do suggest that growth is lower in countries with high inequality, than in countries with medium inequality. However, such a result should be interpreted with extreme caution, since only 9 observations were available on rural and urban access to electricity; moreover, the adjusted R<sup>2</sup> in the benchmark model is very low. There is no inequality effect in the impact of water on growth. Finally, we were not able to obtain regression estimates for roads infrastructure owing to the lack of data on rural and urban access to roads (see details of how the index was constructed in the Appendix).

# _The effect of geography – Pacific Island vs. non-island countries_

The goal here was to investigate whether infrastructure provision in Pacific Island countries (PICs) was predisposed to yield systematically different results than could be expected in nonPIC settings. This exercise did not yield any significant findings. The results obtained suggest that a country’s geographic status in East Asia measured as a dummy variable did not significantly influence its growth. A part of these results may be attributed to the severe limitations in the data underlying this specification. Although there are 9 PICs in the region, we included only Fiji, Tonga and Vanuatu in the dataset constructed for this study due to severe 25
institutions). Profitability is measured as having a financial sustainability ratio above one (row 8 of Table 3). Profits are actually rather remarkable, given that the presumption had long been that meaningfully serving the poor can only be done with subsidy, a presumption consistent with mainstream economic theory (for example, Stiglitz and Weiss, 1981). Still, the profit levels are modest in a comparative sense; indeed, at the 25<sup>th</sup> percentile, the financial self-sufficiency ratio for nongovernmental microfinance organizations falls all the way to 0.78. In addition, the financial bottom line for most nongovernmental microfinance organizations is improved by the fact that they are subject only to light regulation.

We started by noting Compartamos’s outsize return on equity above 50 percent in 2004. The return compares very favorably with Citigroup’s 2004 return on equity of 16 percent, for example. Table 3 shows that the median return on equity for nongovernmental microfinance organizations is 3 percent and, for microfinance banks, 10 percent. The figures are impressive, but well below returns for either Citigroup or Compartamos in 2004. The numbers are larger, though, when we condition on profitability (columns 4, 8, and 12; here the returns to equity are 11.4 and 11.5). The data show the promise of microfinance as a financial proposition. Clearly the profit rates at the top end of microfinance institutions have started to be at levels likely to appeal to profit-maximizing investors. But those profit rates are far from the norm. The hope for the rest of the sector is that returns remain large enough to tempt social investors.

# _How important are subsidies?_

The final section of Table 3 shows how subsidies enter the funding equation. They are sizeable: Subsidy per borrower (in purchasing power parity equivalents) was $233 for the median nongovernmental microfinance lender, reaching $659 for those at the 75<sup>th</sup> percentile. (Note that the 25<sup>th</sup> , 50<sup>th</sup> , and 75<sup>th</sup> percentiles of the subsidy variable pertain to different institutions than those at the same percentiles for the profit variables.) The median bank, on the other hand, received no subsidy, and non-bank financial institutions are, as usual, in a middle range. As with the costs, the purchasing power parity adjusted data approximate the value in local currency rather than their costs to foreign donors.

19
results arise from differences in the starting points, SI from the 1992 data base, and P1 from the 2005 data base.

In the second pair of liberalization experiments, S2 and P2, the Multifiber Arrangement (MFA) is abolished. In this case, both the data and the policy shocks are different. The difference in the policy shocks arises because the MFA is a non-tariff barrier, the restrictiveness of which depends on the size of the quota and supply and demand conditions in the relevant markets. In the base case projection, we explicitly introduce the MFA quota growth rates over the 1992-2005 period (see below). This, combined with differential factor and income growth by region, results in significant changes in the _ad valorem_ equivalent value of the MFA distortion.

The final pair of experiments, S3 and P3, simply serve to combine the tariff and MFA simulations in order to obtain a more complete estimate of the impact of the Uruguay Round. We now turn to a discussion of the details of these experiments.

# **5. Characterization of the Uruguay Round**

Estimates of the pattern and extent of liberalization achieved as a result of the Uruguay Round are provided elsewhere (e.g.., see the papers from the recent World Bank conference on this subject). Our purpose in this section is to summarize the reductions in protection used in the model simulations presented in this paper.

_The Multifiber Arrangement:_ Within this context, the most interesting form of protection addressed by the Round is the restrictions on trade in textiles and wearing apparel under the Multifiber Arrangement (MFA). Since exporters from developing countries must either purchase a scarce export quota before making an export shipment, or pass up the opportunity to sell (or otherwise transfer) a valuable quota received from the government, the effect of these quotas is analogous to an export tax levied by the government in the country of origin. The protective effects of these bilateral quotas can therefore be summarized using export tax equivalents which 8
not accrue instantaneously. Hence some intermediate solution that will protect the poor may be advisable; this is a topic that will require further study. There is a growing sense that cash and food transfers constitute a first-best policy to protect poor and vulnerable households against rising food prices (World Bank, 2014).<sup>16</sup> World Bank (2014) provides a comprehensive review of social protection programs around the world, focusing in particular on recent experiences in less-developed countries (see also Mokomane, 2012; and Alderman and Haque, 2006). The report recommends that social protection programs be combined with efforts to strengthen the rice sector and with investments in infrastructure.<sup>17</sup> These and other good practice lessons can inform the government’s investments into its social protection programs that could help mitigate some of the negative impacts of higher rice prices. The advantage that the government has in this case is that it can decide when to open the borders to free trade, and can hence announce its plan well ahead of time, so that both the public and the private sector can prepare for the transition.

# **6. Conclusion**

Glutinous rice is the staple food in Lao PDR and as such it is important for the welfare of many Laotians. As in numerous other less-developed countries, the Lao government monitors the staple food market closely and regularly intervenes to ensure

> 16 Access to highly disaggregated estimates of poverty (and other socio-economic indicators) such as poverty maps may help to improve the cost-efficiency of targeting the poor (see e.g. Elbers et al., 2007).

> 17 Such investments would also help lower the transactions costs involved in moving rice from surplus to deficit areas.

29
endowments across countries, oil production (∆OILij = ln( OIL<sup><u>OIL</u></sup> j<sup><u>i</u>))andcoal</sup> production (∆COALij = ln( COAL<sup><u>COAL</u></sup> j<sup><u>i</u>))sinceitisknownthatmanyactivities</sup> that generate pollution are linked with the weight-reducing activities associated with the processing of primary products. We also include two measures of skills since it has sometimes been claimed that pollution intensive activities are also skill-intensive (see Cole and Elliott (2005)). The two variables are differences in the skill ratio (i.e. (skilled+ base skills)/unskilled labor) (∆SKij = ln( SK<sup><u>SK</u></sup> j<sup><u>i</u>)),</sup> and differences in the ratio of high skilled labor (skilled/base skills), (∆HKij = ln( HK<sup><u>HK</u></sup> j<sup><u>i</u>)).Tosumup,onemayexpectthesecontrolvariablestoberelatedto</sup> the bilateral PCI, although their precise influence is ultimately an empirical matter.

To these variables we add the PH and FE variables mentioned above, namely the difference in the maximum lead content of gasoline (∆LEADij = ln[ LEAD<sup><u>LEAD</u></sup> j<sup><u>i</u>]),</sup> and the difference in capital-labor ratios (∆KLij = ln[ KL<sup><u>KL</u></sup> j<sup><u>i</u>]).</sup> In equation 3, the ‘deep’ determinants are given in the first two lines and are followed by the measures of interest in the third line. The regression is run for each one of the three selected pollutants for average values over the 1986-88 period.<sup>11</sup> To account for the possibility that LEAD may be endogenous (as suggested by Ederington and Minier (2003) who found evidence that environmental policy has partly served as a substitute for protection using aggregate data), we instrument LEAD by the UN’s measure of Human development (the HDI) which is a priori not correlated with the errors, while its significant correlation with LEAD is -0.47.

Recall that the purpose of the estimates is to carry out subsequent simulations that will decompose the regional PCI into its three components identified above. Aside from obvious robustness checks (see below), two objections could be raised to estimates derived from (3). The first relates to the results being entirely driven by the volume of trade rather than the composition of the latter. The second relates to the selection of the sample over which (3) should be estimated.

Regarding the first criticism, one may argue that our results are basically driven by the volume of trade and hence scale effects. We address this potential shortcoming by regressing the average (G<sup>k</sup> ij<sup>)insteadofthetotalPCI(Z</sup> ij<sup>k)and</sup> hence focusing only on the composition effect. As the trade volume dimension is absent, the traditional gravity-type control variables should in principle all disappear. However, we maintain distance as a regressor to control for the fact that dirty industries are predominantly weight-reducing activities (see e.g. Grether and de Melo (2004)) and might hence be particularly sensitive to transport costs. The alternative specification then becomes:

> 11 In the regression, we use only trade flows above a threshold value of $ 10’000.

8
To classify debt issues as domestic or international, we use the main market in which the bonds are issued and compare it to the issuing firm‘s nationality.<sup>13,14</sup> For offerings that take place in more than one market, we consider issues in each market as separate issues. In the case of subsidiaries, one could consider the nationality of the firm‘s parent company instead of its own nationality for classifying issues as foreign or domestic. For instance, a debt issue by a U.S. subsidiary of a British firm in the U.S. market could be classified as international, instead of domestic as in our classification. Which approach provides a better criterion for classifying bond issues depends on the degree of integration of financing decisions between firms and their subsidiaries, among other factors. If financial decisions are highly integrated, considering firms‘ parent nationality might provide a more accurate classification of debt issuances. But if financing decisions are relatively decentralized, considering subsidiaries‘ own nationality might be a better criterion. Actual decision-making policies probably lie somewhere in between these two extremes, with multinational firms possibly coordinating financing decisions with their subsidiaries across several markets. All the results reported in the paper are obtained classifying bond issues as foreign or domestic based on subsidiaries‘ nationality. In unreported robustness tests, we classified issues by subsidiaries based on their parents‘ nationality and obtained results similar to those reported throughout the paper.

Our main analyses focus on four non-price features of corporate bond issues. First, we analyze the size of bond issues, defined as the proceeds from the issue in U.S. dollars (at 2008

> 13 Although bond trading takes place mostly over-the-counter (OTC), most bonds are listed in exchanges due to regulatory requirements that preclude institutional investors from holding unlisted securities. SDC provides information on the market where bonds are issued, including both formal exchanges and OTC markets.

> 14 SDC classifies most Eurobonds as being listed on the Luxembourg exchange, although these securities trade mostly OTC throughout Europe. This implies that Eurobond issues by firms from Luxembourg are classified as domestic issues, even though they can trade in other European countries. However, the number of firms from Luxembourg carrying out bond issuances at home according to SDC is relatively small. We re-did all our analyses excluding these firms and obtained results similar to those reported throughout the paper.

11
In the robustness checks, I also include a measure of the supervisory and regulatory strength in the country as a control variable. In order to check whether the coefficient on T is picking up the value of information over and above that directly related to information sharing among financial intermediaries, I include in the robustness check another variable called creditreg. This index is from Jentsch 2003 and measures a number of things: (a) existence credit related information (e.g. bankruptcy data bases), (b) access to information mandated by laws or regulation, (c) centralization of information for ease of access, (d) content of databases, (e) property rights to information. I use a composite of these indices (the summation over the individual values). The main purpose of this indicator is to assess both the availability of information considered critical for lenders plus the legal and regulatory framework governing information production and access.

# **Part 1. Private Credit, Non Performing Loans and Transparency**

# **Descriptive Statistics and Empirical Estimation**

Table A2 in the annex it shows the correlation among the variables used in the first part of the paper. A list of variables is shown in Annex 2. As might be expected, T is positively related to the measure of private credit (privo) and negatively related to the ratio of non-performing loans (npl0204). T is also positively and significantly associated with apmindex- the adjusted private monitoring index from BCL(2006).<sup>11</sup> T is positively correlated with the credit registry variable (creditregindex). It is also positively correlated with private credit and negatively so with the non-performing loans variable.

> available rather than to assess the joint effect of disclosure and related incentives for monitoring. By adjusting the BCL index, I am constructing an index that is closer to the economic transparency indicator that is the main focus of the paper.

> 11 The adjusted index deals only with access to information while the unadjusted index (pmindex), which also includes a measure of the incentive to monitor, has a lower correlation with T.

16
# **Table 9: Alternate Measure of enforcement**

|||_Change in E_|_mployment_|
|---|---|---|---|
||_(1)_|_(2)_|_(3)_<br>_(4)_|
|_Slump_|‐0.870<sup>***</sup><br>(0.238)|||
|_Surge_|0.485<br>(0.315)|||
|_Slump*Enforcement_|0.017<sup>***</sup>|0.014<sup>***</sup>|0.013<sup>**</sup><br>0.012<sup>**</sup>|
||(0.006)|(0.005)|(0.005)<br>(0.005)|
|_Surge*Enforcement_|‐0.014<sup>*</sup>|‐0.013|‐0.013<br>‐0.013|
||(0.008)|(0.009)|(0.008)<br>(0.008)|
|_Slump*LagEmp_|||‐0.002<sup>***</sup><br>‐0.002<sup>***</sup>|
||||(0.001)<br>(0.001)|
|_Surge*LagEmp_|||0.0004<br>0.001|
||||(0.001)<br>(0.001)|
|_Slump*Age_|||0.015<br>(0.009)|
|_Surge*Age_|||‐0.021<sup>*</sup><br>(0.012)|
|_Slump*log(K/L)_|||0.045<sup>**</sup><br>(0.021)|
|_Surge*log(K/L)_|||0.024<br>(0.031)|
|_Fixed effects_|Firm + Year|Firm + Sector * Year|Firm + Sector * Year Firm + Sector * Year|
|_Observations_|306,879|306,879|306,879<br>306,879|
|_R2_|0.357|0.364|0.391<br>0.392|
|_Adjusted R2_|0.062|0.071|0.111<br>0.111|
|_Residual Std. Error_|9.743 (df = 210462|) 9.699 (df = 210050)|9.487 (df = 210047) 9.485 (df = 210043)|

_Note:_<sup>*</sup> p<sup>**</sup> p<sup>***</sup> p<0.01; Errors clustered by Sector (3) and (4) include Lagged Employment, not shown for brevity.

26
<!-- Start of picture text --> Grid capacity<br>50<br>4<br>5 _--"<br>40 --"<br>. 35 Poa<br>"7<br>30 —,<br>3 --""<br>peweorrrr eee Benchmark<br>.20 =< Counterfact<br>15<br>10<br>05<br>00<br>1990 1995 2000 2005<br><!-- End of picture text --> <!-- Start of picture text --> Sectoral GVA shares<br>75<br>70<br>65<br>60<br>55 ———=Data farming<br>50 7<br>45 == Data industry<br>40<br>a - Data services<br>30 =----Coun faming<br>25<br>20 Coun industry<br>15 -----Coun services<br>-1005 | Renee<br>00<br>1990 1995 2000 2005<br><!-- End of picture text -->

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<!-- Start of picture text --> Normalized GDP per capita<br>2<br>---" ---""<br>--"<br>ae7--" ueeooTM<br>Pid <= Counterfact<br>-o* o” eee Benchmark<br>1<br>1990 1995 2000 2005<br><!-- End of picture text --> <!-- Start of picture text --> Predicted expenditure shares<br>09<br>807 —_ eee" —————eeBencBeneenc consprivpri inv<br>06 =e Benc publ inv<br>0.5 eee Bene fix cSt<br>0403. ===== CounconsCoun priv inv<br>*onSSi A woe=== SomaCounfx cot<br>en,<br>1990 1995 2000 2005<br><!-- End of picture text -->