rafmacalaba/datause-encoder-data huggingface dataset:
summarize, while the results will naturally reflect a period in time, the snap-shot reflects economic stress in all three countries and recession, albeit not the worst of the crisis, in one country (Argentina).

With this qualification, Table 3 presents our results and compares them with those associated to the Basle II requirements and with S&P historical evidence of default rates. Simulation results provide a measure of the size of expected losses and unexpected losses and of the values that capital and loan loss reserves need to achieve to protect banks from insolvency in 95%, 99% or 99.9% of negative occurrences – or 95%, 99% and 99.9% of the distribution. For the three countries considered, expected losses proved to be in an interval between 1% and 5% (column 2). Assuming provisions to cover this amount, the amount of capital necessary to provide protection for UL up to 99% of the distribution (99% of negative occurrences) appears to be in the range of 15% for both Argentina and Mexico. The capital that would be required to cover 99.9% of the distribution is significantly higher at 21% for Argentina and 31 % for Mexico.

We can compare these results with the level of capital that would be generated using the estimated probability of default and the formula proposed by the Basel Committee for the Foundation IRB approach.<sup>14</sup> The Basel formula applied to the simulated PDs generates capital requirements equal to 9.7% for Mexico, 14.1 for Brazil and 14.9% for Argentina (column 6), which appear considerably lower than those generated by our simulations at the 99.9% level of confidence.

> 14 We have used the formula for assessing the capital requirement for the corporate portfolio as described in Basel Committee’s Consultative Paper 3 (Basel Committee, 2003) and revised in the Basel Committee’s note of January 2004 (Basel Committee, 2004).

10
New information is presented on the estimation of GNPs for individual FSU economies. It is suggested that a margin of error of +/-10% arises in apportioning income, in ruble terms, among the 15 FSU economies.

Incomplete reports for 1991-92 show that real GNP of all FSU economies declined, some by as much as 25 per cent per annum. This should be kept in mind when considering the ranking of FSU economies in Table 1, which is based on 1990.2 Moreover, national compilers will need a workshop to digest the information collected and analyzed for this study. Even in the Bank, the task is far from complete (as is apparent from the Bank's new _Stadsdcal Handbook: States of the foimer USSR)._ And the collapse of the Union in 1991 shook reporting procedures as profoundly as the rest of the administrative apparatus.

Finally, these results are compared to other studies, in Table 4 (page 20). The "relativities" given here appear to be well within the bounds of other studies. It is unlikely that mechanical extensions of this study, to 1992, will produce meaningful results. This study is therefore just a start.

2Estimas for 1991 were doveloped after this paper was prepared, by applying GDP growth rates to the 1990 figures prsenMed here for FSU economies, and then scaling all up by the U.S. inflation rate (as measured by its GNP deflator).

ii
# _Stratification by ‘wealth’_

As in previous studies, we stratify households by the Filmer-Pritchett wealth index. Where available, we use the wealth index variable in the DHS or MICS dataset. For a handful of MICS surveys, we had to construct the wealth index ourselves. In each case, when using wealth quintiles, we sorted _households_ into quintiles; individuals then acquire the quintile of their household, so that in an analysis of, say, child mortality, the ‘poorest quintile’ may account for more than 20 percent of births.

# **Results**

We ask first how well countries _as a whole_ are progressing on the MDG indicators, and display our results in such a way as to show the full variation across countries and across indicators in rates of progress. We then examine progress among the poorer wealth quintiles (we focus on the poorest two<sup>6</sup> ), both in absolute terms and relative to the rest of the population. Finally, we explore more systematically how wealth-related inequalities in the MDG indicators have changed over time, presenting results for both relative and absolute inequality. Table 5 pulls together the key summary statistics that emerge from our analysis. Figures 1-8 give much more detail, and highlight the heterogeneity across developing countries.

> 6 The poorest 40 percent is the target group in the World Bank’s new goal of “promoting shared prosperity”, see e.g. http://www.worldbank.org/content/dam/Worldbank/document/WB-goals2013.pdf, where the Bank says: “we will monitor progress in shared prosperity using the income growth of the bottom 40 percent of a nation’s population. This implies a direct focus on the income of the less well-off, as opposed to the common practice of focusing only on growth of GDP per capita and implicitly relying on the “trickle down” impact of growth on the bottom of the distribution. The measure captures the two elements central to the notion of shared prosperity: a growing economy and a fundamental concern for equity.”

6
<!-- Start of picture text --> Figure 16: Colombia: Real 3-year rates of return of Figure 17: Argentina: Real annual rates of return of<br>pension funds, average and standard deviation, pension funds, average and standard deviation, July<br>March 2001-December 2006 (percent) 1995-March 2007 (percent)<br>25 90<br>75<br>20<br>Average 60<br>15<br>45<br>10<br>30<br>Average<br>5 15<br>Std. deviation Std. deviation<br>0<br>0<br>Jan-93 Oct-95 Jul-98 Apr-01 Jan-04 Oct-06 Jul-09<br>Dec-99 Apr-01 Sep-02 Jan-04 May-05 Oct-06 Feb-08 -15<br><!-- End of picture text --> Source: Based on data from _Superintendencia Financiera de Colombia_ .

Source: Based on data from SAFJP.

**Figure 18:** Peru: Real annual rates of return of pension funds, average and standard deviation, August 1994- December 2006 (percent)

**Figure 19:** Selected LAC countries Pension fund assets and stock market capitalization as shares of GDP (percent)

<!-- Start of picture text --> 30 120<br>111<br>Stock market capitalization<br>25 Average 100 Assets of pension funds<br>80<br>65<br>20<br>60<br>15 40 34 29 27 36<br>10 20 12 23 22 13 7 4 19 17 7 12 2 13<br>0<br>5<br>Std. deviation<br>0<br>Jan-93 Oct-95 Jul-98 Apr-01 Jan-04 Oct-06 Jul-09<br>ArgentinaBolivia ChileColombiaCosta RicaEl SalvadorMexico PeruUruguay<br><!-- End of picture text --> Source: Source Based on data from SBS.

Sources: AIOS and the World Bank Financial Development Database.

Despite intense reform efforts in LAC during the 1990s, capital markets remain underdeveloped relative to both developed and other emerging economies. Given the region’s economic and institutional fundamentals, the development of stock markets is below what it was expected to be. De la Torre et al. (2006) found a shortfall in domestic stock market activity in the LAC region, even after controlling for variables, such as per capita income, macroeconomic policies, and the legal and institutional environments. On domestic bond markets, both developed and emerging economies have shown substantial growth since the early 1990s. In LAC, most of the expansion took place in public bond markets, whereas private bond markets remain underdeveloped (Table 17).

32
penditure on a non-seasonally adjusted basis–are used. The government’s financial statements across economies may be reported at the central government or general government level. For government revenue, both general government or central government level, data are collected based on availability. When national accounts data are used for government spending, and given the identifying assumptions used in model based on Ilzetzki et al. (2013), the data is checked to make sure it is not interpolated and avoid spurious results.<sup>6</sup> Given the lack of tax only data for many economies, overall revenue including tax and nontax sources of revenue are used. To model monetary policy, this paper uses the ex-ante real interest rate defined as the three-month interest rate less one-year-ahead consumer inflation forecasts.

As a rule, when official data is not available in real terms (constant prices) it is deflated using the GDP deflator. When data is not available from official sources as seasonally adjusted, and seasonality is present, the X13-ARIMA-SEATS method to seasonally adjust the data is implemented (US Census Bureau, 2016). When data is monthly, like reported for government spending and revenue, the data is summed (or averaged in the case of interest rates) to get to a quarterly frequency.

Table 1: Data

|Variable|Defnition|Transformation|Sources|
|---|---|---|---|
|_Git_|Real public consumption expenditure, seasonally adjusted|Log level|Haver Analytics|
|_Yit_|Real GDP, seasonally adjusted<br>|Log level|Haver Analytics|
|_πit_|GDP defator, seasonally adjusted|Log level|Haver Analytics|
|_Tit_|Real general/central government revenue|Log level|Haver Analytics|
|_Rit_|Ex-ante real interest rate|Level|Haver Analytics; OECD|
|_Cit_|Real private consumption expenditure, seasonally adjusted<br>|Log Level|Haver Analytics|
|_Iit_|Real gross fxed capital formation, seasonally adjusted<br>|Log Level<br>|Haver Analytics|
|_ERit_|Real efective exchange rate, CPI-based|Log frst diference|Bruegel; Haver Analytics|
|_Sit_|Equity prices|LogLevel|Haver Analytics|

> 6The VAR model assumes that fiscal policy makers require at least one quarter to respond to output shocks.

11
UNEP has adopted a definition that attempts to incorporate aspects of job content as well as the characteristics of industry gods and services (UNEP/ILO/IOE/ITUC, 2008). It defines ‘green’ jobs as “work in agricultural, manufacturing, research and development (R&D), administrative, and service activities that contribute substantially to preserving or restoring environmental quality. Specifically, but not exclusively, this includes jobs that help to protect ecosystems and biodiversity; reduce energy, materials, and water consumption through high-efficiency strategies; de-carbonize the economy; and minimize or altogether avoid generation of all forms of waste and pollution.” This definition takes a broad industry perspective, extending beyond employment in narrowly defined environmental services. In principle, it embraces employment in producing any goods and services that have smaller adverse environmental impacts than existing close substitutes. UNEP also argues that there is a spectrum of ‘greenness’: “There are different degrees to which technologies, products, businesses, and business practices can be said to be green, ranging from reactive and remedial measures on the one hand to proactive measures on the other.” In other words, cleaning up pollution after the event is less green than stopping the pollution in the first place. That appears to beg the question as to the most efficient way of dealing with an environmental problem and would lead to the classification of many environmental services as less green than, say, employment in the packaging industry.

However, UNEP adds the rider that “green jobs need to be decent work, i.e. good jobs which offer adequate wages, safe working conditions, job security, reasonable career prospects, and worker rights. People’s livelihoods and sense of dignity are bound up tightly with their jobs. A job that is exploitative, harmful, fails to pay a living wage, and thus condemns workers to a life of poverty can hardly be hailed as green.” UNEP suggest that, at a conservative estimate, there were more than 2.3 million jobs in the renewable energy sector around the world in 2006, and more in construction, providing improved energy efficiency in buildings, in low-carbon transport and in other environmental activities. But that compares with an employed labor force globally of around 1.8 billion.

Thus the UNEP definition also extends to characteristics of the jobs themselves. However, their definition conflates different social objectives in one term. The rider is particularly problematic in developing countries where more employment may be desirable for the relief 4
lower infant mortality rates. Our econometric analysis confirms that these differences persist even when we control for socio-economic dcharacteristics, community characteristics and location. Results from samples at the level of individual children, adult females and entire villages all suggest that total infant mortality, and in particular, female infant mortality is lower among Muslims than Hindus. This is an important result for it suggests that India’s “missing women” may be most concentrated in particular caste and religious groups and may not be a general problem in the Indian population. The results of this paper also suggest that the tendency to focus merely on differences in fertility between religious groups may be simplistic. While we can not rule out the possibility that unobserved aspects of socio-economic status may be driving our results, we highlight the possibility that religion and religious customs may have a direct effect on how daughters are valued in their families. We believe that there is much scope for further research on the interations between religion, fertility, gender and mortality in India.

# **References**

1. Al Ghazzali, M. H. (1909), The Alchemy of Happiness. Translated from the Hindustani by Claud Field, London: John Murray.

2. Agnihotri, S. (2000), Sex Ratio Patterns in the Indian Population, New Delhi: Sage Publications.

3. Almond, D., Edlund, L., and Milligan, K. (2009), ‘Son Preference and the Persistence of Culture: Evidence from Asian Immigrants to Canada’, NBER Working Paper 15391.

4. Arnold, F., Kishor, S. and Roy, T.K. (2002), ’Sex-Selective Abortions in India,’ Population and Development Review, 28 (4), pp. 759-785.

5. Azim, S. (1997) Muslim Women: Emerging Identity. New Delhi: Rawat Publications.

6. Basu, A. (1997), ‘The “Politicization” of Fertility to Achieve Non-Demographic Objectives’, Population Studies, Vol. 51, pp. 5-18.

7. Bhat, M and Zavier, F (2003), ‘Fertility Decline and Gender Bias in Northern India’, Demography, 40:4, pp. 637- 657.

15
<!-- Start of picture text --> N \ A (| / fh f a j N\A A a<br>-.2<br>|<br>-.4 |<br>-.6<br>1995 1998 2001 2004 2007 2010 2013 2016<br>—— Average equity returns — Common factor<br><!-- End of picture text -->

<!-- Start of picture text --> 2<br>0 a ANY Pa (/XWN\ } PY VAY<br>IN Uy PS<br>-.2<br>-.4<br>-.6<br>-.8<br>1995 1998 2001 2004 2007 2010 2013 2016<br>—— Average equity returns — Common factor<br><!-- End of picture text -->
Table A2. 9. Foreign ownership barriers

# **Foreign ownership barriers**

||**Weights**<br>**by theme**<br>**(bj)**|**Question**<br>**weights (ck)**|**Coding of ans**|**wers**|||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||**_Yes_**|**_No_**|**Bulgaria**|**Romania**|**Croatia**|**Albania**|**OECD**|**EU15**|**Poland**|**Slovak Rep.**|
|**_General barriers_**|1/2||||||||||||
|There are statutory or other legal||2/3*wi(% of|||||||||||
|limits to the number or proportion of||business sectors|||||||||||
|shares that can be acquired by||in which the state|6|0|yes|no|no|no|n.a.|n.a.|yes|yes|
|foreign investors in publicly-controlled||controls at least a|||||||||||
|firms||firm)|||||||||||
|Special government rights can be||||||||Country is<br>not|||||
|exercised in the case of acquisition of||1/3|6||yes|yes|no|concerned|n.a.|n.a.|yes|yes|
|equity by foreign investors||||0||||with this<br>question|||||
|**Sector-specific barriers**|1/2||||||||||||
|Foreign ownership restrictions in||1/2|**_Yes_**<br>**Partly**|**No**|||||||||
|telecomunications|||6<br>3|0|none|none|none|none|n.a.|n.a.|none|none|
||||<50%<br><40% <35%|<30%<br><25%|||||||||
|||||||||||||49% after<br>|
|Ceiling on foreign investment in an<br>||1/2||||||||||01/05/2004 also<br>EU community|
|airline company|||0<br>1<br>3|4<br>6|0.49|0.49|0.5|none|n.a.|n.a.|0.49|<br>concecpt of the<br>ownership and<br>control could be<br>applicable|
|**Country scores (0-6)**||j|bj kckanswerjk||3.0|1.8|0.5|0.0|1.8|1.3|3.7|2.3|
|Memo item||Memo item: % of b|usiness sectors in which th<br>at least a firm|e state controls|61%|48%|77%|41%|n.a.|n.a.|96%|27%|

52
sub-national levels. The 2015 Intercensus sampled 5.8 million households, 56 percent of whom lived in urban areas. The Intercensus contains only household labor income and transfer income, and not total household income, necessitating the use of a small area estimation model to estimate municipal-level poverty rates.

CONEVAL creates these estimates using a unit-level model, with a large set of household and individual-level auxiliary variables, including demographic, labor, housing quality, and municipal characteristics at the individual, household, and municipal level. The municipal characteristics include average per capita income and other poverty estimates. The municipal estimates and detailed documentation on the methodology are available at the CONEVAL website.<sup>12</sup> The 32 Mexican states are divided into 6 groups<sup>13</sup> and a model is estimated for each, with R<sup>2</sup> values ranging from 0.52 to 0.57.

CONEVAL used the 2014 MCS-ENIGH survey in an empirical best household model to construct the small area municipal estimates. This implies that both the estimates that are evaluated below, based on geospatial data, and the CONEVAL benchmark data use the same 2014 survey. However, the R<sup>2</sup> of the unit-level model used to generate the CONEVAL estimates are roughly three times as high as the model used to generate the geospatial-based estimates, since the auxiliary data used by CONEVAL vary at the household level. Therefore, the CONEVAL estimates give far more weight to the predicted values and less weight to the survey, relative to the estimates derived from the satellite data. This reduces the mechanical correlation between the estimates and the benchmark due to the municipal random effects being conditioned on the same 2014 survey data. Furthermore, as a robustness check, we also compare the CONEVAL estimates with geospatial small-area estimates generated using the 2016 MCS-ENIGH survey, which eliminates all mechanical correlation due to the use of the same sample data.

# 3.3. CNN-Generated Auxiliary Geospatial Data

A key difference between this paper and Masaki et al. (2022) is the nature of the auxiliary data. We train convolutional neural network (CNN) models against high resolution satellite imagery

> 12 https://www.coneval.org.mx/Medicion/Paginas/Pobreza-municipal.aspx

> 13 The six state groups we use in this paper match those used to develop the official municipal poverty estimates and are listed in Table C1 in appendix C.

13
<!-- Start of picture text --> FCS Portfolio by Sectorboard<br>Number of Active Projects 2007-2013<br>sD SDN-W, PD<br>D<br>SDI<br>SDN-<br>SDN HE<br>SDN<br>SDN HSP<br>N-EP<br>SDN- PPS FPOIGE<br><!-- End of picture text --> <!-- Start of picture text --> NonFCS Portfolio by Sectorboard<br>Number of Active Projects 2007-2013<br>SDN-WAT PD<br>SDI<br>sD -ED<br>SDN.<br>SDN.<br>HE<br>SDN<br>-SP<br>SDN. M-EP<br>+PO<br>SDN HPS<br><!-- End of picture text --> <!-- Start of picture text --> NonlIDA Portfolio by Sectorboard<br>Number of Active Projects 2007-2013<br>SDN-WAT PD<br>SDN. “ED<br>-HE<br>SDN -SP<br>M-EP<br>SDN- MPO<br>SDN.<br>M-PS<br>SDN<br>son -ARD<br><!-- End of picture text --> <!-- Start of picture text --> FCS Portfolio by Sectorboard<br>Net Commitment of Active Projects 2007-2013 ($ m)<br>SDN-WAT a<br>LED<br>SDN, LHE<br>_sp<br>NLEP<br>SDN<br>-PO/GE<br>M-PS<br>sow SD —, HARD<br><!-- End of picture text --> <!-- Start of picture text --> NonFCS Portfolio by Sectorboard<br>Net Commitment of Active Projects 2007-2013 ($ m)<br>SDN-WAT SEPD LED<br>SDN. “HE<br>_SP<br>SDN<br>SDN. M-EP<br>SON<br>sDN4 M-PO<br>SDN: aml -PS<br><!-- End of picture text --> <!-- Start of picture text --> NonlIDA Portfolio by Sectorboard<br>Net Commitment of Active Projects 2007-2013 ($ m)<br>SDN-WAT PD<br>SDI LED<br>HE<br>SDN.<br>-SP<br>SDN<br>con<br>SDN<br>N-EP<br>SDN<br>‘sD relied<br><!-- End of picture text -->
not significantly affect the deepening, inclusion, and stability of bank credit.

For deepening and inclusion in the use of deposits, as well as output losses during crises, we find that the quality of financial sector strategy could matter. However, the positive effect of having a quality strategy (its rating) is not greater than the positive effect of a simple strategy. One explanation could be that the proposed rating does not adequately consider or weight all relevant aspects of financial strategies for financial development.

Examining one possible transmission mechanism from strategies to financial sector outcomes, we find that using strategies has significantly improved the quality of regulatory policy. This result suggests that financial sector strategies could increase the deepening, inclusion and growth stability of bank deposits—as well as reduce output losses during crises— by improving the regulatory policy for getting credit, enforcing contracts, and resolving insolvencies, among others.

The remainder of the paper is organized as follows. Section 2 provides an overview of the financial sector strategies, how they were formulated, and an assessment of their quality. Section 3 presents the data and the model for the analysis of the effect of financial strategies on financial development. Section 4 discusses the estimation results, and section 5 concludes.

# **2. What has driven financial sector strategies and how can they be evaluated?**

# **2.a. Constructing a new database to assess financial sector strategies**

The database of financial strategies includes all published financial sector strategies for which implementation began by 2012.<sup>1</sup> Our primary sources were the websites of ministries of

> 1 That is, we consider the strategies that have been in place for at least two years.

5
# 1. **Introduction**

Although it is well known from anecdotal evidence that inventories are higher in developing countries, there are almost no systematic studies that attempt to explain this phenomenon or even to quantify the difference. This study uses newly-assembled data for 52 countries in the early 1970s and 1980s to draw out some stylized facts about the pattern of inventory holdings, and more recent data in Latin American countries for the 1990s shows that the problem persist.

The motivation for this paper is the magnitude of the holdings and the potential cost to the economy. U.S. businesses typically hold inventories equal to about 15% of GDP while inventory levels in many developing countries are often twice as large and for raw materials three times as large (Table 1). If the private sector interest rate for financing inventory holdings is 15%-20%, a conservative estimate in most developing countries, then the cost to the economy of the additional inventory holdings is greater than 2% of GDP. Suppose that firms in developing countries keep high levels of inventories in response to poor infrastructure, which we find in this study to be a key determinant. Then, as an example, consider that the total transport infrastructure stock in Bangladesh is about 2% of GDP' (World Bank 1994) while this figure is about 12% in the United States.<sup>2</sup> One year's worth of savings in inventory holding costs would be enough to double Bangladesh's infrastructure stock; the infrastructure improvement could pay for itself. At the firm level, the impact of these high levels of inventories is also enormous. Given the high cost of capital in many developing countries, cutting inventory levels in half

> 1 Rough calculation based on graphs of infrastructure stock per capita and composition of infrastructure. See World Bank [1994], figures 1 and 2.

> 2 Nonmilitary nonresidential net public stock in the United States in 1991 was $2.2 trillion with $700 billion of this amount representing stocks of highways and streets. See Munnell [1992], p. 190 for U.S. infrastructure data.