rafmacalaba/datause-encoder-data huggingface dataset:
# **Figure 9: Latent bond factors and observed global factors**

Each panel compares the largest two extracted latent factors _퐹푡_ from (5) with observed global factors. All variables shown are standardized to facilitate interpretation.

# **(a) First latent factors**

<!-- Start of picture text --> Federal funds rate (r=49.2%) TED spread (r=-7.5%) VIX index (r=-60.1%)<br>Latent factor Latent factor Latent factor<br>MSCI World Index (r=89.2%)<br>Latent factor<br>EMBI (r=87.1%)<br>GBI-EMU (r=98.2%) EMBI-div (r=88.2%)<br>GBI-EM (r=22.1%) EMBI+ (r=86.6%) Debt/GDP (r=82.1%) Reserves/GDP (r=65.2%)<br>Latent factor Latent factor Latent factor Latent factor<br>(b) Second latent factor<br>TED spread (r=-18.0%) VIX index (r=-32.0%)<br>Latent factor Latent factor<br>Federal funds rate (r=-13.2%) MSCI World Index (r=2.1%)<br>Latent factor Latent factor<br>EMBI (r=31.5%)<br>GBI-EMU (r=-0.1%) EMBI-div (r=27.3%)<br>GBI-EM (r=74.6%) EMBI+ (r=33.6%) Debt/GDP (r=43.2%) Reserves/GDP (r=46.4%)<br>Latent factor Latent factor Latent factor Latent factor<br>20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019<br>20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019 20092010201120122013201420152016201720182019<br><!-- End of picture text --> to incorporate such data. However, remote-sensing data requires considerable geospatial preprocessing, domain-specific expertise as well as significant computational resources.

# **5.3 Lack of regional focus**

At the outset of this study, we selected the largest possible set of countries to identify general relationships between natural capital and government yields. This meant, however, that countryor region-specific characteristics had to be abstracted from for the sake of generality. There is little doubt that narrowing the scope to a region, e.g. Latin America and Caribbean or Southeast Asia, would allow us to account for relevant variables such as local crop types, resource dependencies or the effect of the El Niño–Southern Oscillation. Comparison of such regional studies with this extended set of countries would produce interesting insights and lead to relevant takeaways for local governments and policy makers.

25
<!-- Start of picture text --> Election Turnout Share of Votes for Left<br>Year Year<br>Share of Votes for Center Share of Votes for Right<br>Year Year<br>Electoral competition among candidates<br>Year<br>2002 2006 2010 2014 2018 2022 2002 2006 2010 2014 2018 2022<br>2002 2006 2010 2014 2018 2022 2002 2006 2010 2014 2018 2022<br>2002 2006 2010 2014 2018 2022<br>.04 .04<br>.02 .02<br>0 0<br> (95% CI) (95% CI)<br>β β<br>-.02 -.02<br>-.04 -.04<br>.04 .04<br>.02<br>.02<br>0<br>0<br> (95% CI) (95% CI)<br>β -.02 β<br>-.02<br>-.04<br>-.04<br>.04<br>.02<br>0<br> (95% CI)<br>β<br>-.02<br>-.04<br><!-- End of picture text --> Figure D.4. Parallel Trends for the Common Support – Presidential Elections Discrete Treatment Variable _Notes:_ The figure illustrates the results of an event study estimation for the municipalities in the common support of the predicted pscores. We interacted the discrete variable PEP, which takes the value of one in the municipalities where there are PEP holders, with mayor electoral years excluding the 2015 election, the last one before 2018, when the PEP program was implemented. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period and listed in Table C.2. Standard errors were clustered at the municipality level and bars represent 72
# 3.1 Most firms export few products

Only a minority of Mexican firms export, and those exporting tend to concentrate on few products. As illustrated in the top panel of Table 3, only about a quarter of firms were engaged in exporting in 1994. In the aftermath of the Peso crisis and the introduction of NAFTA, the percentage of exporting firms increased to one-third in 1997, but by 2003 it returned to the initial level. What’s more interesting, however, is that 80% of exporting firms sell abroad only one or two products, and only about 10% of firms send five or more products to foreign markets (see the bottom panel of Table 3). This contrast with the bipolar distribution of exporters in developed countries. For instance, according to Bernard, Redding, and Schott (2006b), while 58% of American exporters rely on one or two export products, over a quarter of them send five or more products abroad (see Table 5). It appears that in Mexico during the period under study the importance of firms exporting one or two products declined somewhat relative to those exporting a larger number of products.

Table 3: Most firms export few products

|Years|1994|1995|1996|1997|1998<br>% of|1999<br> frms|2000|2001|2002|2003|
|---|---|---|---|---|---|---|---|---|---|---|
|do not export|75.96|71.53|68.8|67.38|68.23|70.56|71.52|73.5|74.73|74.92|
|export|24.04|28.47|31.2|32.62|31.77|29.44|28.48|26.5|25.27|25.08|
|No. of export products|||||||||||
|1|62.23|60.48|59.52|58.65|57.26|57.10|57.09|57.74|58.37|57.14|
|2|17.55|18.30|17.53|18.30|18.07|17.36|18.61|17.17|16.78|17.98|
|3|7.95|7.87|8.78|9.17|9.47|9.51|9.13|9.47|9.14|9.97|
|4|4.12|4.50|5.00|5.03|4.69|5.81|4.74|5.17|5.26|4.90|
|5|2.58|2.53|2.69|2.61|2.86|2.85|3.65|2.83|3.17|3.15|
|6|1.91|2.46|2.15|1.56|2.27|1.87|1.51|1.89|1.90|1.63|
|7|1.29|1.33|1.15|1.04|1.01|1.39|1.51|2.11|1.54|1.99|
|8|0.79|0.49|0.83|1.16|1.45|1.56|1.26|0.83|1.03|0.68|
|9|0.42|0.60|0.67|0.58|0.91|0.51|0.74|0.72|0.95|1.04|
|≥10|1.21|1.40|1.76|1.90|2.05|2.00|1.83|2.08|1.82|1.59|
|Total|100|100|100|100|100|100|100|100|100|100|

Notes: Includes single- and multi-plant firms

# 3.2 In contrast to the US, most Mexican exports come from singleproduct exporters

The majority of Mexican exports come from firms that export a small number of products. In Table 4, we group firms based on the number of products they sell abroad 8
**<u>Table 4 Applied MFN rates before and after WTO accession by HS 2 digit product lines</u>**

||**Simple**|**average**|**Weighted**|**average**|**Import**|**Import**||
|---|---|---|---|---|---|---|---|
|**Product code**|**2011**<br>**tariff, %**|**2020**<br>**tariff, %**|**2011**<br>**tariff, %**|**2020**<br>**tariff, %**|**share**<br>**2011, %**|**share**<br>**2020, %**|**Industry**|
|01 Live animals|5.4|3.6|27.0|2.1|0.2|0.2|17|
|02 Meat|45.7|43.6|27.7|17.5|3.4|0.8|14|
|03 Fish|10.5|6.1|10.1|3.9|0.3|1.2|14|
|04 Dairy, eggs,honey|21.1|14.9|19.4|14.9|0.8|0.7|14|
|05 Prod.of animal origin|7.6|7.3|5.2|5.1|0.1|0.1|14|
|06 Live plants|11.9|4.9|14.8|5.2|0.5|0.5|17|
|07 Edible vegetables|15.1|11.4|15.1|11.5|1.5|1.2|17|
|08 Edible fruits and nuts|7.8|6.1|7.9|4.3|2.3|2.7|14, 17|
|09 Coffee, tea, and spices|6.0|5.0|4.1|2.8|0.7|0.6|14|
|<br>10 Cereals|15.5|8.4|15.2|7.3|0.1|0.2|14|
|11 Prod.mill.indust|11.1|9.7|13.0|8.6|0.1|0.1|14|
|12 Oil seed, oleagi fruits|4.5|4.2|3.3|1.3|0.3|0.6|17|
|13 Lac; gums, resins|5.0|5.0|5.0|5.0|0.1|0.1|14|
|14 Vegetable materials|13.0|10.2|15.0|9.6|0.0|0.0|17|
|15 Fats & oils|11.0|9.0|7.2|5.4|0.5|0.4|14|
|16 Prep of meat, fish etc|20.4|17.8|21.4|14.9|0.2|0.2|14|
|17 Sugars and confectionery|8.7|15.9|18.8|24.5|0.2|0.9|14|
|18 Cocoa and preparations|13.0|5.3|8.2|3.4|0.6|0.5|14|
|19 Prep.of cereal, flour|15.0|11.6|11.7|9.1|0.4|0.3|14|
|20 Prep. of vegetable, fruit, nuts|13.6|9.5|11.1|9.0|0.7|0.6|14|
|21 Miscellaneous edible preps|17.9|10.9|14.3|8.7|0.7|0.9|14|
|22 Beverages, spirits and vinegar|18.6|12.5|18.4|9.7|0.9|1.0|14|
|23 Residues & waste from food|7.7|5.5|7.2|4.4|0.7|0.6|14|
|24 Tobacco and tobacco substitutes|12.6|8.1|15.0|5.2|0.3|0.8|14|
|25 Salt; sulphur; earth & stone|5.0|4.7|5.9|5.0|0.3|0.2|12|
|26 Ores, slag and ash|3.9|3.9|4.2|4.1|0.1|0.1|8|
|27 Mineral fuels, oils & product|4.7|4.6|2.7|4.2|0.3|0.7|3|
|<br>28 Inorganic chemicals|5.1|4.8|2.2|2.0|1.0|0.9|9|
|<br>29 Organic chemicals|4.8|4.6|4.9|4.4|0.8|1.2|8|
|30 Pharmaceutical products|8.1|4.1|6.6|3.2|1.1|6.5|8|
|31 Fertilisers|9.7|6.5|10.0|6.4|0.0|0.0|8|
|32 Tanning/dyeing extracts|4.3|4.0|5.2|4.4|1.1|1.0|8|
|33 Essential oils & resinoids|8.9|5.5|12.7|6.0|1.9|1.8|14|
|34 Soap, washing preps|12.5|6.1|13.9|5.7|0.5|0.7|14|
|<br>35 Albuminoidal subs; glues|6.3|4.9|8.1|4.4|0.3|0.3|14|
|36 Explosives; pyrotechnic prod.|17.5|6.3|18.8|6.4|0.0|0.0|8|
|<br>37 Photographic goods|6.5|5.7|6.3|5.3|0.1|0.1|8|
|38 Misc. chemical products|6.0|4.8|5.1|4.9|1.2|1.3|9|
|<br>39 Plastics and articles thereof|10.2|5.9|10.0|5.7|3.7|4.1|8|
|40 Rubber and articles thereof|13.2|6.3|7.5|5.2|0.9|0.8|8|
|41 Raw hides and skins|3.3|3.4|4.1|4.1|0.0|0.0|13|
|42 Articles of leather|20.4|12.7|20.5|12.9|0.5|0.5|13|
|43 Furskins and articles thereof|9.7|5.0|11.8|8.8|0.2|0.1|13|
|44 Wood and articles of wood|15.0|8.5|16.1|7.7|0.3|0.4|11|
|45 Cork and articles of cork|5.0|5.0|5.0|5.0|0.0|0.0|11|
|46 Manufactures of straw|15.0|8.0|15.0|7.3|0.0|0.0|11|

27
Spence, M. (1973): Job Market Signaling. _Quarterly Journal of Economics_ , 87:355-374.

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Van der Meer, P. (2002): Overschooling and unemployment. Downloaded at: som.eldoc.ub.rug.nl/FILES/reports/themeD/2002/02D60/02D60.pdf Verdugo, R. and N. Verdugo (1989): The impact of surplus schooling on earnings. _Journal of Human Resources_ , 24(4):629-643.

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34
'equitisation' of (i.e., entry of new firms<sup>into) the economyas opposedto the result of a</sup> rapid index growth.<sup>21 7</sup> He suggests that<sup>the latter would be the explanationfor thegrowth</sup> of capitalization in the U.S., which is related<sup>to thestock market boom of the latter half of</sup> the 1990s.

Table 5.15

|**StockMarketLiquidit**<br>|**y***<br>|
|---|---|
|**erae**|**vrae**|
|**g**<br>**(1990-95)**<br> <br>|**g**<br>**(1995-00)**<br>|
|Indonesia<br>3.7<br> <br>|3.7<br>|
|HongKong<br>4.1<br> <br>|5.2<br>|
|NYSE<br>4.1<br> <br>|5.8<br>|
|Malaysia<br>3.8<br> <br>|4.1<br>|
|Korea<br>0.6<br>|1.5<br>|
|China<br>n.a.<br> <br>|6.7<br>|
|Taiwan<br>25.1<br> <br>|23.8<br>|
|Thailand<br>8.4<br>|4.9<br>|
|Singapore<br>n.a.|3.1|
|<br>*monthlymarkettumoverasaper<br>stockrelativetomarketcapitalizati<br>Source:Wilson(2002:Table2.4,2|centage<sup>of</sup><br>on<br>6)|

Stock market liquidity (shown in Table<sup>5.15)has increasedin several countriesof</sup> the region in the latter half of the 1990s<sup>but in most casesremainslower than that in the</sup> U.S. Taiwan, which has a high level of<sup>tuxnover,2'8is anotable exception.</sup> Freeman and Bartels (2000) assert that stock<sup>markets are of primaryimportance to</sup> Southeast Asia's post-crisis recovery, which<sup>they viewas primarily,although not</sup> exclusively, involving corporate sector<sup>debt restructuring,particularlyfor Indonesiaand</sup> Thailand. With bank lending still constrained<sup>anddevelopment of a regional bond</sup> market a long-term possibility, they see<sup>theequity marketsof theregion as importantfor</sup> obtaining necessary capital, some of which<sup>must comefromabroad.</sup> In a review of the five Southeast Asian<sup>equity markets,Freeman(2000) identifies</sup> several weaknesses in the region's<sup>equity marketsincluding low capitalizationand</sup> trading volume, as well as sectoral bias.<sup>This bias refers to the overrepresentationin the</sup> equity markets of banks and finance<sup>companies(with their high levels of non-perfomiing</sup> loans) and property development companies<sup>(withtheir high leverageand vacant</sup> buildings). On the other hand, high<sup>tech companies (electronics,computer-related,</sup> telecommunications, media, etc.) are underrepresented<sup>-even though theseare of more</sup> interest to institutional investors.<sup>219</sup> Singapore<sup>is an exceptionwith only19.6 percent</sup> (April 2000) of total market capitalization<sup>in the financesectorcompared to 42 percent</sup> for Manila and 34 percent for Jakarta.<sup>220There arealso problems with the stocks</sup> underlying the markets as the well-documented<sup>needfor improved corporategovemance</sup> attests.

83
.

# Executive Summary

The World Bank's technical assistance work with new environmental protection institutions (EPI's) stresses costeffective regulation, with implementation of market-based pollution control instruments wherever this is feasible. At present, however, few EPI's can do the requisite benefit-cost analysis because they lack data on industrial emissions and abatement costs. For the foreseeable future, appropriate estimation methods will therefore have to be employed as complements to direct measures of environmental parameters at the firm level. We are developing the Industrial Pollution Projection System (IPPS) as a comprehensive response to this need. Estimation of IPPS parameters is also giving us a much clearer and more detailed view of the sources of industrial pollution. In this paper, we report on our findings to date.

) IPPS has been developed to exploit the fact that industrial pollution is heavily affected by the scale of industrial activity, its sectoral composition, and the process technologies . which are employed in production. Although most developing countries have little or no industrial pollution data, many of them have relatively detailed industry survey information on employment, value added or output. IPPS is designed to convert this information to the best feasible profile of the associated pollutant output for countries, regions, urban areas, or proposed new projects. It operates through sector estimates of pollution intensity, or pollution per unit of activity. |
|**Country**|**Intervention**|**Test**|**Evaluation**<br>**Method**|**Effect Size**|**EYOS_STEP**|**Reference**|
|---|---|---|---|---|---|---|
|Uganda|Northern Uganda Literacy Project (NULP)|Writing test score|DID|0.19|1.24|Kerwin and<br>Thornton (2015)|
|Brazil|Decentralised schooling system|Portuguese<br>proficiency|RCT|0.19|1.24|Leme, 2010|
|Kenya|The Primary Math and Reading (PRIMR)<br>Rural Expansion Programme|Kiswahili reading<br>fluency|DID|0.23|1.50|RTI International,<br>2015|
|Brazil|Decentralised schooling system|Portuguese<br>proficiency|RCT|0.29|1.89|Leme, 2010|
|Uganda|Northern Uganda Literacy Project (NULP)|EGRA test|RCT|0.3|1.95|Kerwin and<br>Thornton (2015)|
|India|Synthetic Phonics: kids teached to read<br>using decoding and synthetic phonics<br>skills, teacher training|Burt reading test|Multivariate<br>analysis|0.48|3.12|Dixon, Schagen<br>and Seedhouse,<br>2011|
|Cambodia|The School Readiness Programme (SRP)|Khmer language|RCT|0.5|3.25|Nonoyama‐Tarumi<br>and Bredenberg,<br>2009|
|Kenya|The Primary Math and Reading (PRIMR)<br>Rural Expansion Programme|Kiswahili reading<br>fluency|RCT|0.56|3.64|RTI International,<br>2015|
|Kenya|The Primary Math and Reading (PRIMR)<br>Rural Expansion Programme|Kiswahili basic<br>reading fluency|RCT|0.57|3.71|RTI International,<br>2015|
|South Africa|English and Operacy programme (EOP)|English test|RCT|0.6|3.90|Mouton, 1995|

38
Qualitative results are similar for regressions in Table 4 where the dependent variable is return on assets (ROA).<sup>19</sup> Again, the supervision variables are negative and significant in the OLS regression, but insignificant in the treatment effects and IV regressions. The results for the control variables are similar to those for the FSS regressions, although the inflation coefficient is no longer significant and the growth variable tends to be less significant. The ROA regressions indicate more strongly than the FSS regressions that profitability is lower for MFIs located in Sub-Saharan Africa, East Asia, and the Middle East and North Africa. The instruments are again excludable in that they are not significant when included in the simple OLS regressions. We again cannot reject the hypothesis that the errors for the selection equation are uncorrelated with those for the ROA regression based on the likelihood ratio test near the bottom of the treatment effects regressions.

# **_B. Outreach_**

In the previous sub-section we found no significant relationship between supervision and MFI profitability when we controlled for the non-random assignment of supervision via treatment effects or IV regressions. Here we test whether MFIs maintain profitability while absorbing the additional costs of supervision by curtailing outreach, specifically whether they make larger loans and fewer loans to women.

In the OLS regression in Table 5, the supervisory variable is negatively associated with average loan size measured relative to the income of the bottom quintile in each country, though the result is not significant. As in the profitability regressions, results change when we account for the non-random assignment of supervision using treatment effects or IV regressions (models 2-4). Those regressions indicate a positive link between supervision and loan size (significant in models 2 and 3).<sup>20</sup> The magnitude of the

> 19 Like the FSS variable, the income portion of the ROA variable is adjusted to reflect a variety of subsidies. Those subsidies are described in Appendix C.

> 20 MFI performance measures are from 2003 or 2004, essentially a cross-section. The stringency of supervision and regulation data is based on information from the MIX website from 2007 (and other sources, when that one proved insufficient). We acknowledge that this could produce some weakness in the results (as in models 3 and 4 in Table 5), though we find it unlikely that a large share of the MFIs in the sample were changing their supervisory status during this period. In any event, we have no way to check. The Barth, Caprio, and Levine data (used for the big banks’ supervision variable and index of official

19
Wood, L., M. Egger, L. L. Gluud, K. F. Schulz, P. Jüni, D. G. Altman, C. Gluud, R. M. Martin, A. J. G. Wood, and J. A. C. Sterne. 2008. “Empirical Evidence of Bias in Treatment Effect Estimates in Controlled Trials with Different Interventions and Outcomes: Meta-Epidemiological Study.” _British Medical Journal_ 336 (7644): 601–5.

Wooldridge, J. M. 2010. Econometric Analysis of Cross Section and Panel Data. Cambridge: MIT press.

Yusuf, S., J. Wittes, J. Probstfield, and H. A. Tyroler. 1991. “Analysis and Interpretation of Treatment Effects in Subgroups of Patients in Randomized Clinical Trials.” JAMA: The Journal of the American Medical Association 266 (1): 93–98.

Zwane, A. P., J. Z., E. Van Dusen, W. Pariente, C. Null, E. Miguel, M. Kremer, et al. 2011. “Being Surveyed Can Change Later Behavior and Related Parameter Estimates.” _Proceedings of the National Academy of Sciences_ 108 (5): 1821–26.

43
<!-- Start of picture text --> Education Spending vs GDP per capita Health Spending vs GDP per capita<br>SAUBRBKWT CZESVN<br>TZAMWIBDIETHNERTJKMLIYEMZMBMDGGNBMOZERITCDBENBFAKENRWACAFUGANP LMDASENUZBKGZTGOBGDLAOSDNMNGCIVGMBCOMKHMMRTCMRGEOPA KGHAAGOVNMGINDJISLBLSOARMNICAZEINDBOLPNGHNDZWEVUTIDNLKASYRMARCHNJAMGTMPHLJORUKRGUYLBNSWZKAZCPVBLRSLVPRYPERWSMFJIVCTBLZDZALCADMADOMIRNNAMTONPANROMTUNTURBGRMKDCOLGABTHAGRDRUSBWALVABRACRIME XTTOMY SLTUURYHRVCHLMUSPOLZAFES TATGGNQSVKKNAHUNARGOMNCZEKORBHRBHS MA CARE SLETZAMWIBDIETHNERTJKMLIZMBYEMMDGGNBMOZZARERITCDBENCOGBFAKENRWACAFUGANP LMDASENUZBKGZTGOLAOBGDSDNMNGCIVGMBHTICOMKHMMRTCMRGEOPA KGHAAGOVNMGINDJISLBLSOARMNICINDAZEBOLPNGHNDZWETKMVUTIDNLKAECUSYREGYMARCHNJAMALBGTMPHLJORUKRGUYLBNSWZKAZCPVBLRSLVPRYPERWSMFJIVCTDZABLZLCADMADOMVENIRNNAMTONPANROMTUNBGRMKDCOLGABTHAGRDRUSBWALVABRACRIME XTTOLTUMY SURYHRVCHLMUSPOLZAFES TATGGNQSVKKNAHUNARGSAUOMNKORBRBBHRBHSKWT ARESGP<br>SLE NGA<br>COG<br>6 7 8 9 10 6 7 8 9 10<br>lgdp lgdp<br>ledu Linear prediction lhea Linear prediction<br>8 8<br>6 6<br>4 4<br>ledu/Linear prediction 2 lhea/Linear prediction 2<br>0 0<br><!-- End of picture text -->

**Figure 4. Public Expenditure  and GDP (both per capita and in logs)**

Previous studies that measured the efficiency of public spending recognized the positive association and suggested alternative solutions. One possibility is to split the sample by groups of countries (Gupta and Verhoeven, 2001). We follow this approach by excluding the industrialized nations from the sample, and by presenting most of the results clustered regionally (Africa (AFR), East Asia and Pacific(EAP), Latin America and Caribbean (LAC), Middle-East and North Africa (MNA) and South Asia (SAS)). A second alternative incorporates directly the per-capita GDP as a factor of production, jointly with expenditure and other inputs (Jarasuriya and Woodon 2002). The problem with this approach is that it combines variables derived from a production function approach, and hence with clear interpretation, with others (GDP per capita) that are difficult to interpret from any viewpoint. When the two types of variables are combined, their effects cannot be disentangled.

A third option consists in using as an input the orthogonal component of public expenditure to GDP<sup>5</sup> . We scored the efficiency using as input both the original expenditure variable and the orthogonalized variable. The goodness-of-fit of each model was gauged based on the frequency distribution of the inefficiency measures, as suggested by. Farrell (1957) and Varian (1990). Comparing the efficiency distributions (Figures 5) it is clear that the orthogonalized expenditure version produces distributions that are not skewed towards extreme inefficient outcomes. On this basis, the paper considered the orthogonal component of expenditure on health and education.

> 5 The orthogonalized expenditure variable is the residual of the linear regression between pubic expenditure and GDP per capita. Since residuals may take positive and negative values, the variable was right-shifted to avoid negative values to facilitate graphical presentation of the frontiers.

9