rafmacalaba/datause-encoder-data huggingface dataset:
29 directly depend on 1997 perfornance . Yet as tempting as it might be to think that using lagged (as opposed to contemporaneous) values of ownership variable would eliminate the underlying endogeneity, it would merely reduce the extent of the bias primarily because of the above mentioned correlation.

Table 5 contains the distribution of enterprises by ownership type and years of privatization. Out of 618 enterprises to be used in the analysis 238 were privatized in 1996 with private share ownership averaging at 80 percent of capital. The state further reduced its shareholding in those 238 enterprises in 1997 bringing it down to 18 percent of capital on average from 20 percent. Out of 374 enterprise which continued to be fully state owned as of 1996, 54 were privatized in 1997 with state still holding 24 percent ownership on average in those enterprises. For these 54 enterprises private ownership share as of 1997 was used as a dependent variable in the first-stage regression. Subsequently a dummy variable _DUMMY97_ (which takes value of 1 if the enterprise is privatized in 1997) was introduced in the second-stage regression to account for any unobserved differences between enterprise privatized in 1996 and 199730.

# _Regression Results_

The results of estimation of Equations 3 and 4 are contained in Table 6 and 7 respectively. It is perhaps logical to start this section with a formal justification of using linear instrumental variables approach to estimate Equation 3. The OV version of Hausman (1978) test rejected the null hypothesis that the OLS and 2SLS estimates of 29 Expectations and ability to forecast play important role here. For instance if the state foresees that an enterprise could boost its output in 1997 either as a result of a relatively straightforward measure (e.g. management change) or its strengthening monopoly position on the market, it is likelier that the state will hold on to the enterprise and not privatize it. On the contrary, if potential private owners evaluate profit potential of an enterprise in 1997 as low, this is likely to keep them away from buying shares in this enterprise _ceteris paribus,_ even if it is up for privatization.

30 Note that for these 54 enterprises 1997 value of private share ownership (i.e. private ownership at privatization) was used during both stages of the analysis. The results of estimation (where enterprises privatized in 1997 were treated as fully state owned as of 1996) are not significantly different from those reported (where enterprises privatized in 1997 were treated as privatized and a dummy variable DUMMY97 was included) and can be obtained from author upon request.

27
16 facing solvency problems and a red light for the institutions that face severe and immediate problems.

**Table 5: Risk-Based Solvency Requirements**

||**Measurement of L**|**iabilities (TPs)**<sup>**1**</sup>|**Minimum Solvency**|**Solvency Buffers**|
|---|---|---|---|---|
|**Country**|**Treatment of**<br>**Longevity Risk**|**Discount Factors**|**Requirements**||
|**Netherlands**|<br> <br>Group specific<br>mortality table<br>adjusted for predicted<br>longevity<br>improvements,<br>plus buffer to address<br>uncertainty in<br>predicted values|Market yield curve<br>measured by Euro<br>swap curve|5% of Technical Provisions<br>(from EU IORP Directive)<br>Measured once per year<br>using current market values<br>Maximum period for<br>correction of deviations: 3<br>years|Maximum probability of under-<br>funding within one year<br>measured with stress test: 2.5%<br>Solvency buffers determined by<br>risk factors specific to each<br>asset class.<br>Example of risk factors include<br>yearly decline in:<br>Equity: 25-35% (depends on<br>type)<br>Currency: 20%<br>Real Estate: 15%<br>Maximum period for correction<br>of deviations: 15 years|
|**Denmark**|Fund-specific<br>mortality table<br>approved by actuary<br>and supervisor<br>Traffic light stress test<br>includes assessment<br>of the impact of a 5%<br>improvement in<br>longevity|Market yield curve<br>measured by Euro<br>swap curve|Solvency margin defined by<br>EU Life Directive:<br>4% of Technical Provision<br>plus 0.3 % of risk bearing<br>investments<br>Measured every six months<br>using current market values<br>Period of correction from<br>minimum required<br>standards:<br>One year|Traffic light system is a stress<br>test rather than part of the<br>formal solvency rule, but results<br>are taken into consideration in<br>the supervisory assessment.<br>Test defines three zones:<br>green, yellow, and red<br>Final outcome depends on<br>whether entity remains solvent<br>after test.<br>Example (yearly variations) :<br>Listed equity:<br>Red 12%, Yellow 30%<br>Interest rate (medium duration)<br>Red +-0.85%; Yellow +-1.2%|
|**Australia**|No formal liabilities<br>in DC plans|<br>No formal liabilities<br>in DC plans|No solvency requirements<br>for DC plans|No solvency requirements<br>for DC plans|
|**Mexico**|No formal liabilities<br>in DC plans|<br>No formal liabilities<br>in DC plans|No formal solvency require<br>limit designed to limit do<br>Historic VaR calculated wit<br>significance with differe<br>portfolios. Price vector pro<br>Higher risk portfolio:<br>Standardriskportfolio:|ments, but Value at Risk (VaR)<br>wnside risk for DC members<br>h rolling 550 day sample at 5%<br>nt limits imposed on the two<br>vided by 2 independent vendors<br>1% maximum daily loss<br> 0.6%maximumdailyloss|

_Notes_ : 1/ TP = Technical Provisions Rather than impose a single potential scenario of adverse market conditions the Danish approach establishes two sets of parameters for each risk factor, which effectively imply a mild and a strong stress test. If a fund is put into theoretical insolvency by the mild test it is deemed to be in the red zone. In other words, funds in the red zone cannot
Policy ReseaRch WoRking PaPeR 4803

# **Abstract**

Brazil’s inequalities in welfare and poverty across and within regions can be accounted for by differences in household attributes and returns to those attributes. This paper uses Oaxaca-Blinder decompositions at the mean as well as at different quantiles of welfare distributions on regionally representative household survey data (2002–03 Household Budget Survey). The analysis finds that household attributes account for most of the welfare differences between urban and rural areas within regions. However, comparing the lagging Northeast region with the leading Southeast region, differences in returns to attributes account for a large part of the welfare disparities, in particular in metropolitan areas, supporting the presence of agglomeration effects in booming areas.

This paper—a product of the Poverty Reduction Group, Poverty Reduction and Economic Management Network—is part of a larger effort in the department to analyze poverty and monitor and evaluate the effectiveness of poverty reduction programs. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at eskoufias@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table D2: Gender randomization checks, before 2011

||(1)<br>Judge maj. female|(2)<br>Judge maj. female|(3)<br>Judge maj. female|
|---|---|---|---|
|Pla. maj. female|0.0222|0.0210|0.0115|
||(0.0190)|(0.0189)|(0.0154)|
|Def. maj. female|-0.00690|-0.00613|-0.0115|
||(0.0137)|(0.0133)|(0.0111)|
|DV mean|0.298|0.298|0.298|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies|No|Yes|Yes|
|Other controls|No|No|Yes|
|Observations|4717|4717|4717|

The regressions test whether female plaintiffs/defendants are more likely to be matched with female judges than male judges. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 1. Sample is restricted to the years 1976-2010. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = Plaintiffs, Def. = defendants, maj. = majority.

Table D3: Gender randomization checks, 2011 and after

||(1)<br>Judge maj. female|(2)<br>Judge maj. female|(3)<br>Judge maj. female|
|---|---|---|---|
|Pla. maj. female|0.00827|0.00850|0.00514|
||(0.00968)|(0.00951)|(0.00735)|
|Def. maj. female|0.00674|0.00617|0.00325|
||(0.00845)|(0.00832)|(0.00719)|
|DV mean|0.382|0.382|0.382|
|Court-year FE|Yes|Yes|Yes|
|Ethnicity dummies|No|Yes|Yes|
|Other controls|No|No|Yes|
|Observations|15677|15677|15677|

The regressions test whether female plaintiffs/defendants are more likely to be matched with female judges than male judges. Standard errors, in parentheses, are clustered at the judge level. All columns are based on a linear regression model. For specification details, see equation 1. Sample is restricted to the years 2011-2020. Ethnicity dummies include binary variables indicating whether a given ethnicity is the plurality, one for each ethnicity, for defendants, plaintiffs, and judges. Other controls include case type dummies, a dummy for an appeal case, and variables for the numbers of defendants, plaintiffs, and judges. To prevent a loss of observations, all categorical controls (such as case type) include a dummy that denotes if data is missing/unknown. Pla. = Plaintiffs, Def. = defendants, maj. = majority.

39
growth rate". This indicates how much faster the industry at the 75th percentile of external dependence (Machinery) would have grown compared to the industry at the 25th percentile (Beverages), if Machinery had been located in the country at the 75th percentile of the respective measure of financial development instead of the country at the 25th percentile. Since U.S data are used to calculate our measure of external dependence, the U.S. is dropped from all regressions.

The results in Table 4 indicate a significantly positive interaction of external dependence and overall financial development on industry growth. The coefficients on the interaction terms of all our indicators of financial sector development and external dependence are significantly positive at the five- percent level. We start with the top panel and _Finance-Activty._ The results of the OLS regressions indicate that, for a given industry with a positive external dependence ratio, a higher level of _Finance-Actiity_ results in a higher growth rate of this industry. To illustrate the significance of this result, consider the growth differential of 2.3 percent. The coefficient estimate thus predicts that Machinery would grow 2.3 percentage points faster than Beverages, if it were located in Malaysia rather than to Greece.<sup>12</sup> The results in columns 2 through 4 indicate that this result is robust to the use of other measures of financial sector development.

The results of the instrumental variable regressions confirm that the link between external dependence, financial development and industry growth is not due to simultaneity bias or reverse causality (Table 4, bottom panel). We report the regression results using TSLS and the legal origin dummies as instruments for financial sector development. The interaction terms with all

> establishments (value added in the industry divided by number of establishments) at the beginning and the end of the period.

> 12 The growth differential is calculated as follows: 1.553*0.368 (External dependence of machinery minus external dependence of beverages)*3.962(Finance-Activity in Malaysia minus Finance-Activity in Greece).

19
potential market for the PPP operators. These data were collected from the World Bank economic indicators. Definition of all the data and their sources are provided in Annex 1.

Unfortunately, detailed information on the contract types is only available for 70 of the 123 developing countries for which we have collected the data related to IRAs. Table 2 provides with the distribution of these 70 subsample countries across regions. Note that all six countries in East Asia have private participation in their water and sanitation sectors, while two countries in South Asia lack an IRA in their respective sectors.

Table 2: Distribution of sample countries across regions

|(1) East Asia|(2) Europe and<br>Central Asia|(3) Latin America<br>and the Caribbean|(4) Middle East and<br>North Africa|(5) South Asia|(6) sub‐Saharan<br>Africa|
|---|---|---|---|---|---|
|6|16|16|8|2|22|

Table 3 summarizes the basic statistics on each of these variables for the developing countries subsample we ended up working with in our assessment of the drivers of the supply of PPP in developing countries. The sample size is the same for all variables except decentralization. It shows that the sample is quite heterogeneous in some of the key control variables, such as water and sanitation and access rates, since the relatively high mean is matched by a high standard deviation. The same observation applies to the variables used to approximate the quality of governance.

Table 3: Summary statistics for the developing country sample

||Observations|Mean|Std. dev.|Min|Max|
|---|---|---|---|---|---|
|����<br>|70|0.46|0.50|0|1|
|�����������������|51|0.55|0.50|0|1|
|���|70|0.50|0.50|0|1|
|���_���|70|9586.09|6638.74|581.14|25311.91|
|�����_�������|70|87.08|14.49|49.0|100|
|����������_�������|70|67.63|28.60|10.90|100|
|����������|70|2.77|8.50|‐29.69|38.40|
|�������_�����������|70|‐0.46|0.57|‐1.40|1.30|
|���_�����|70|‐0.24|0.62|‐1.69|1.35|
|����_����|70|‐0.42|0.56|‐1.72|1.33|

Table 4 provides a first sense of the partial correlations between these country specific variables and the main variables specific to the water and sanitation sector. With respect to the core interest of this paper, the partial correlation between PPP and IRA is the most interesting one. It suggests that these two policy options are not statistically significantly related. The second clear observation is that all the key control variables, except for decentralization, are positively correlated with PPP. The existence of an IRA is only correlated with inflation and regulatory quality, and only at a 10% level of significance. In other words, basic indicators of institutional quality seem to be much more important to the chances of attracting PPP than IRA, assuming that these correlations can be used to try to anticipate the likely outcome of the econometric analysis. The non‐significance of a very basic modeling of decentralization as a dummy variable is also interesting, since it also raises questions on the effectiveness of this common political option of the sector.

7
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**Table 2:** Predictors of LFP and Broad Employment Categories

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||Pr(LF|P = 1)|Pr(Unem|ployed= 1)|Pr(Rural N|on Farm = 1)|
|Female|-.447<sup>_∗∗∗_</sup><br>(.013)|-.447<sup>_∗∗∗_</sup><br>(.013)|.003<sup>_∗_</sup><br>(.002)|.003<sup>_∗_</sup><br>(.002)|-.082<sup>_∗∗∗_</sup><br>(.008)|-.082<sup>_∗∗∗_</sup><br>(.008)|
|Literate|-.056<sup>_∗∗∗_</sup>|-.056<sup>_∗∗∗_</sup>|-.002<sup>_∗∗_</sup>|-.002<sup>_∗∗_</sup>|.087<sup>_∗∗∗_</sup>|.087<sup>_∗∗∗_</sup>|
||(.004)|(.004)|(.001)|(.001)|(.005)|(.005)|
|Educated|-.091<sup>_∗∗∗_</sup>|-.091<sup>_∗∗∗_</sup>|.036<sup>_∗∗∗_</sup>|.036<sup>_∗∗∗_</sup>|.112<sup>_∗∗∗_</sup>|.112<sup>_∗∗∗_</sup>|
||(.005)|(.005)|(.003)|(.003)|(.005)|(.005)|
|Age|.049<sup>_∗∗∗_</sup>|.049<sup>_∗∗∗_</sup>|-.005<sup>_∗∗∗_</sup>|-.005<sup>_∗∗∗_</sup>|.009<sup>_∗∗∗_</sup>|.009<sup>_∗∗∗_</sup>|
||(.001)|(.001)|(.000)|(.000)|(.001)|(.001)|
|Sq. Age|-.001<sup>_∗∗∗_</sup>|-.001<sup>_∗∗∗_</sup>|.000<sup>_∗∗∗_</sup>|.000<sup>_∗∗∗_</sup>|-.000<sup>_∗∗∗_</sup>|-.000<sup>_∗∗∗_</sup>|
||(.000)|(.000)|(.000)|(.000)|(.000)|(.000)|
|SC/ST|.056<sup>_∗∗∗_</sup>|.056<sup>_∗∗∗_</sup>|-.000|-.000|-.045<sup>_∗∗∗_</sup>|-.045<sup>_∗∗∗_</sup>|
||(.004)|(.004)|(.001)|(.001)|(.006)|(.006)|
|Muslim|-.038<sup>_∗∗∗_</sup>|-.038<sup>_∗∗∗_</sup>|.002|.002|.090<sup>_∗∗∗_</sup>|.090<sup>_∗∗∗_</sup>|
||(.007)|(.007)|(.003)|(.003)|(.014)|(.014)|
|Marginal Landowner|-.031<sup>_∗∗∗_</sup>|-.031<sup>_∗∗∗_</sup>|.005<sup>_∗∗∗_</sup>|.005<sup>_∗∗∗_</sup>|.233<sup>_∗∗∗_</sup>|.233<sup>_∗∗∗_</sup>|
||(.005)|(.005)|(.001)|(.001)|(.007)|(.007)|
|Fraction rural||-.124<sup>_∗∗∗_</sup>||.004||.170<sup>_∗∗∗_</sup>|
|||(.048)||(.008)||(.052)|
|Fraction salaried workers||-.368<sup>_∗∗∗_</sup>||.022||.343<sup>_∗∗∗_</sup>|
|||(.054)||(.020)||(.088)|
|Fraction public employment||.048||-.012||.295<sup>_∗∗∗_</sup>|
|||(.052)||(.009)||(.066)|
|Fraction manufacturing||.042||-.005||.652<sup>_∗∗∗_</sup>|
|||(.066)||(.016)||(.075)|
|Fraction educated||.144<sup>_∗∗_</sup>||-.002||.017|
|||(.066)||(.015)||(.065)|
|Branch per capita (Log)||-.004||-.002||.035<sup>_∗∗∗_</sup>|
|||(.015)||(.003)||(.013)|
|Credit per capita (Log)||.002||.000||-.026<sup>_∗_</sup>|
|||(.013)||(.003)||(.015)|
|Observations|710787|710787|453161|453161|453161|453161|
|R<sup>2</sup>|.34|.34|.07|.07|.19|.19|
|Dep Var Mean|.68|.68|.02|.02|.23|.23|
|Dist Controls|N|Y|N|Y|No|Yes|

This table shows the impact of individual and district characteristics on labour force participation, unemployment and rural non-farm employment. The unit of observation is rural individuals aged 15-65. Columns (3)-(6) restrict the sample to rural individuals aged 15-65 who are in the labour force. Educated refers to individuals with secondary education or better. Maginal landowners are workers in households with less than 0.5 acres of land holding. Standard errors in parentheses, clustered by district.

43
matched by an increase in imports, the net impact on aggregate demand will be minimal. In this case where nQ exports are unchanged, the export boom will not succeed in raising real GDP even if there Is idle capacity.

d) the monetary consequences of the boom, which will be taken up below.

Empirically, the effect of the export price surge on real output growth is often quite small and short-lived. MacBean's (1966) seminal work explored the link between export price shocks and national output in great detail using both time series and cross-country analyses for a number of commodity exporting LDCs. He found very little evidence of a statistically significant effect. Later researchers have by and large concurred with MacBean's findings. 16/ Perusing the Tables for various countries in Appendix C, it indeed appears that, during most of the booms studied, the response of real GDP was rather small. In some cases, e.g. Jamaica during both the 1974-5 and 1979-80' bauxite price surges, GDP growth actually turned negative. It is certainly not true, in general, that commodity price booms caused a surge in real GDP. The inflation rate (measured using the GDP deflator in the Tables), on the other hand does surge in almost every country when a boom occurred. Furthermore, inflation in the post-boom periods often remained high.

The forgoing evidence on the real output and inflation effects of booms suggests that governments interested in short run stabilization policy should focus on the inflationary consequences of the boom. Cyclical fluctuations in output and employment are likely to be mild in most cases. The monetary, inflationary and exchange rate aspects of boom management are discussed in Section V below.

16/ See Krueger (1984) for a recent survey.

18
**Table 10. Advance Clearance Procedure and Imported Intermediates**

||**Log (Import**<br>**Value)**|**Import**<br>**Growth**|**Log (Nb.**<br>**Imported**<br>**Products)**|**Log (Nb.**<br>**Imported**<br>**Varieties)**|
|---|---|---|---|---|
|||**A**|**ll**||
|**Dummy for Advance Clearance**|||||
|**Procedure User**|0.428***|0.132***|0.207***|0.226***|
||(0.026)|(0.033)|(0.016)|(0.016)|
|**Year Fixed Effects**|Yes|Yes|Yes|Yes|
|**Firm Fixed Effects**|Yes|Yes|Yes|Yes|
|**Main HS 2‐Digit Fixed Effects**|Yes|Yes|Yes|Yes|
|**Observations**|22,522|14,062|22,522|22,522|
|**R‐squared**|0.913|0.278|0.896|0.908|
|||**Agribu**|**siness**||
|**Dummy for Advance Clearance**|||||
|**Procedure User**|0.593***|0.241|0.177*|0.223**|
||(0.174)|(0.224)|(0.095)|(0.098)|
|**Year Fixed Effects**|Yes|Yes|Yes|Yes|
|**Firm Fixed Effects**|Yes|Yes|Yes|Yes|
|**Main HS 2‐Digit Fixed Effects**|Yes|Yes|Yes|Yes|
|**Observations**|1,278|792|1,278|1,278|
|**R‐squared**|0.793|0.377|0.803|0.809|
|||**App**|**arel**||
|**Dummy for Advance Clearance**|||||
|**Procedure User**|0.584***|0.254**|0.310***|0.321***|
||(0.097)|(0.125)|(0.059)|(0.062)|
|**Year Fixed Effects**|Yes|Yes|Yes|Yes|
|**Firm Fixed Effects**|Yes|Yes|Yes|Yes|
|**Main HS 2‐Digit Fixed Effects**|Yes|Yes|Yes|Yes|
|**Observations**|2,345|1,485|2,345|2,345|
|**R‐squared**|0.882|0.326|0.861|0.87|

Notes: Robust standard errors in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively. Only exporter-importers are included in all the regressions.

Finally, we examine a relevant policy question that arises which is whether the reform to the advance clearance procedure that took place in Peru after 2009 helped to increase the imports of intermediate inputs. For this purpose we estimate a variant of Eq. (4) where ��������� enters by itself and interacted with a dummy variable identifying the years after the reform to the advanced clearance procedure (that is from 2010 onwards), and where this dummy variable also enters by itself (instead of year fixed effects). The corresponding results for import value (in logarithms) are shown in Table 11. The estimates suggest that imports of intermediates by exporter-importers that use the advance clearance procedure increased significantly after the reforms. This result is strongly significant when all exporter-importers are considered but also for those in the agribusiness and in apparel sectors (though in the latter case significance is lower).

28