rafmacalaba/datause-encoder-data huggingface dataset:
-20-

................

Table Impact of Environment on Adjustment Policy <!-- Start of picture text --> MON GOV RERA<br>Intercept# 38.20 19.10 21.40<br>YASHR 0.041 -0.150** 0.844**<br>TOTB -0.332* -0.382** -1.554**<br>RR 0.329** 0.058** 0.155<br>LTDPC 8.871** -0.544 -4.407<br>STDPC 8.896** 0.927 15.433*<br>D7 -3.070** 2.318<br>D8 -3.030** 0.399<br>D9 -2.695** 1.648<br>DO -2.266** 0.718<br>Dl -2.027** 3.988**<br>D2 -1.788** 4.339**<br>D3 -1.477** 4.006**<br>D4 -0.738 4.055**<br>D5 -0.422 4.306**<br>Summary statistics:<br>R2 0.91 0.97 0.82<br>F value 76.52** 232.18** 31.88**<br>...................................... ..............................................................................<br><!-- End of picture text -->

- The intercept was not estimated, but was chosen to center the fixedeffect terms around a median of zero;.

- ** - Significant at 95 percent level of confidence.

- - Significant at 90 percent level of confidence.
# **Business Regulations and Poverty**<sup>*****</sup>

Simeon Djankov<sup>a</sup> Dorina Georgieva<sup>b</sup> Rita Ramalho<sup>c</sup>

**JEL Classification Codes:** H12, H62

**Keywords:** property rights, poverty, new business formation

> a Simeon Djankov. The London School of Economics. The World Bank. Email: <u>S.djankov@lse.ac.uk.</u>

> b Dorina Georgieva. The World Bank. Email: dgeorgieva@worldbank.org.

c Rita Ramalho. The World Bank. Email: rramalho@ifc.org.

> *Support for this research was provided by the Atlas Network (grant no. 319254/2017) to Dr Djankov. We thank the editor Costas Meghir, Sir Charles Bean, Olivier Blanchard, Jerome Haegeli, Aart Kraay, Adam Posen, Ricardo Reis, Matt Warner, Justin Wolfers and seminar participants at the London School of Economics and the Peterson Institute for International Economics for valuable comments. The findings expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank or its member countries. The paper was originally published in Economics Letters 165 (2018) 82-87.

1
values in our sample) halves the negative effect of volatility on school enrollment (from - 10% to -5%), and dramatically reverts the impact of extreme output events.

# _c. Poverty_

The evidence that income shares tend to move in a regressive way as volatility increases does not, per se, imply that low income households are worse off in _absolute_ terms in volatile economies. More precisely: Is the evidence reported above the reflection of a situation in which the rich gains at the expense of the poor in a zero (or negative) sum game, or is it the case that rich people can profit from a volatile but growing environment, at no cost in terms of the income of the lower quintiles? In other words, do the effects of income distribution reported above translate into a deterioration of the poverty indicators?

**Table 10** provides a positive answer to that question. The table shows estimates of a regression of per capita income growth for the bottom quintile to examine the absolute effect of aggregate fluctuations on income of the poor. The tests, which are similar to those used above for income distribution, reveal a systematically negative effect of alternative measures of volatility on poor households. Again as before, public expenditure appears to play a mitigating role, a result robust to alternative versions of the volatility variable.

A consistent story arises when we use a measure of the poverty gap and a poverty headcount index,<sup>14</sup> where the negative effect of volatility is alleviated by public expenditure. Once again, extreme adverse output events place an additional burden on poverty, beyond the effect of cyclical fluctuations, confirming the importance of non-linear effects. The effects, as **Table 9** shows, are economically important, particularly in the face of extreme output declines, when public spending may sharply what would otherwise be a dramatic increase in poverty). Overall, these results complete a remarkably consistent picture of the undesired welfare effects of aggregate volatility, and the benign influence of safety nets.

> 14 Poverty gap ratio is the mean distance separating the population from the poverty line of PPP-adjusted $1 per day (with the non-poor being given a distance of zero), expressed as a percentage of the poverty line. The poverty headcount index is the share of the population with income below the poverty line. All these formulas are calculated based on data on individual income. If household-level data are used, the formulas are adjusted by the household size times sampling expansion factor for every household i.

15
# Do Private Inspection Programs Affect Trade Facilitation?<sup>1§</sup>

Irina Velea<sup></sup> Olivier Cadot<sup></sup> John S. Wilson<sup>±</sup> Keywords: International trade, facilitation, PSI, gravity JEL classification numbers: F13, F15

> § This paper is part of a World Bank research project on trade costs and facilitation under the Multidonor Trust Fund on Trade. The assistance of Yassine Cherkaoui in editing this draft is greatly appreciated.  Olivier Cadot and Irina Velea would like to thank Cotecna staff for useful discussions while working on a report that lead to this paper. The views expressed in this paper are the authors’ and do not necessarily reflect those of the World Bank Group, its Executive Directors, the countries they represent or affiliated institutions.

>  University of Lausanne

>  The World Bank, University of Lausanne, CEPR, CEPREMAP and CERDI.

> ± The World Bank.
**Table 4: Causal effects of Parent’s Education on Children’s Education: Klein and Vella (2009) Approach**

|||**Daughters**<br>|||**Sons**<br>||
|---|---|---|---|---|---|---|
|**Sample**||**90% Con**|**f. Int.**||**90% Co**|**nf. Int.**|
||**Estimate**|||**Estimate**|||
|||**lower**<br>**bound**|**upper**<br>**bound**||**lower**<br>**bound**|**upper**<br>**bound**|
|_1988_|||||||
|**OLS**|1.029|0.674|1.384|0.524|0.104|0.809|
|**IPW**|0.708|0.25|1.15|0.111|‐0.464|0.655|
|**Klein and Vella‐1 Estimate**|3.443|‐40.656|25.045|‐0.417|‐10.041|20.266|
|Likelihood‐ratio test of heteroskedasticity||21.17|||18.72||
|Kleibergen‐paap Wald F statistic||28.21|||40.16||
|**Klein and Vella‐2 Estimate**|‐0.998|‐13.38|10.91|0.937|‐15.65|13.407|
|Likelihood‐ratio test of heteroskedasticity||10.94|||4.03||
|Kleibergen‐paap Wald F statistic||7.43|||14.25||
|**Kleibergen‐paap Wald F stat. for the**<br>**instrument based on simple Probit**||2.76|||5.13||
|_2002_|||||||
|**OLS**|0.793|0.632|0.946|0.924|0.791|1.038|
|**IPW**|0.811|0.559|0.955|0.879|0.756|1.001|
|**Klein and Vella‐1 Estimate**|0.613|‐1.16|5.425|2.067|0.966|2.826|
|Likelihood‐ratio test of heteroskedasticity||58.84|||60.63||
|Kleibergen‐paap Wald F statistic||92.23|||72.95||
|**Klein and Vella‐2 Estimate**|1.311|‐1.089|3.491|1.583|0.083|3.341|
|Likelihood‐ratio test of heteroskedasticity||15.01|||31.73||
|Kleibergen‐paap Wald F statistic||16.79|||38.82||
|**Kleibergen‐paap Wald F stat. for the**||1.23|||4.62||
|**instrument based on simple Probit**|||||||
|NOTES|||||||

1. All regressions include county fixed effects, 90% confidence interval is boostrapped using 250 replications;

2. Exogenous controls include individual's age, age squared, mother's age and father's age, parents' ethnic minority, location dummies (topography, old revolutionary region, ethnic minority region, impoverished region, suburb of big city, and coastal region dummies); 3. IPW = normalized inverse probability weighted propensity score estimator of Hirano and Imbens (2001); 4. "Klein and Vella‐1 Estimate" uses parents characteristics, location dummies and index of county fixed effect for heteroscadesicity; "Klein and Vella‐2 Estimate" uses father age, mother's age, coastal dummy, and topography for heteroscadesicity; 5. Parents' education level is a binary measure, it is equal to 1 if at least one of parents has more than primary schooling; children's education is measured by years of schooling; 6. "Conf. Int." stands for confidence interval; 7. The computation uses STATA program written by Millimet and Tchernis (2010); 8. The samples are: 2057 (1988, daughters), 1884 (1988, sons), 2264 (2002, daughters), 3616 (2002, sons).

54
25 a firm, and provide flexible rules within which the investors can work out their own optimal balances between ownership o,nd authority.

Even w.th these important changes in 1988, there has been relatively little foreign investment in the past 3 years. The main reasons are clear-extreme economic instability followed by political instability. Yugoslavia's inflation soared to an estimated 2800 percent'7 in 1989 due to a lapse of fiscal and monetary control in the face of growing enterprise deficits. Dramatic attempts at stabilization at the beginning of 1990 succeeded in bringing down inflation and resurrecting some positive economic signs,"8 but they were quickly followed by the growing political crisis and eventually civil war. The ret.rning relative calm in Slovenia as its independence begins to be recognized around the world gives renewed hope that the political and economic climate for foreign investment will support the favorable legal framework introduced earlier to stimulate a renewed inflow of foreign capital.

# Bankruptcy

Although Yugoslavia, like most of the other socialist countries, had a bankruptcy law on the books throughout its socialist period,99 this law was put to little use. Bankruptcy procedures typical of those in industrial market economies were not appropriate in the socialist setting because of the absence of a clear conflict of interest among various claimants--whether shareholders, workers, or creditors. In most Central and Eastern European countries all of these claimants were arms of the state or ultimately supported by the state. For example, state-cwned banks had little incentive to collect on bad debts because state guarantees lied explicitly or implicitly behind such debts. And workers were guaranteed jobs, steady income, and related support systems whether or not their particular firms thrived. Measures in lieu of bankruptcy, including financial "rehabilitation" and _97_ Gelb and Gray, 1990.

**_98_** For more information on recent economic developments, see Rocha and Coricelli (1991), Gelb and Gray (1991), and Stiblar (1992).

_99_ The 1929 law (Dika, M.: Stebajno pravo i pravo prisilne nagodbe, Informator 2341, Zagreb, 1976, page 2) was applied in Yugoslavia until 1965, when a new lawwas adopted. {Zakon o prisilni poravnavi in stebaju [ Law on Compulsory Settlement and Bancruptcy] Official Gazette FPRY, No 15 ('965)). The latter was amended several times (Official Gazette SFRJ No 55(1969), 39 (1972), 16 (1974). It was replaced by the new law (Zakon o sanaciji in prenehanju OZD [Law on Rehabilitation and Liquidation of the OALs], Official Gazette SFRY, No 72 (1986) with most recent amendment Official Gazette SFRJ No 69 (1988)).Finally, it was replaced by the new law, discussed below.Finally it was replaced by the 1989 law discussed below.
5 shares, and intra-industry trade. So trade liberalization apparently did place domestic manufacturers in more direct competition with their foreign rivals. Also, however, it combined with macro stabilization policies to create unemployment problems that still lingered in 1979.

The middle panel of table 1 describes the plant size distribution. Here notice that output per plant remained surprisingly stable between 1967 and 1979, while the number of plants fell slightly. This lack of expansion presumably reflected the same forces that created recession and unemployment during the second half of the 1970s. Notice alsc that, while the largest plants expanded, most others contracted. So if small plants were below minimum efficient scale in 1967, they were probably more so after the liberalization.

Measures of value-added per unit input ive a crude sense for productivity levels; these can be inferred from the bottom panel of table 1. Referring to th, last column, note that value-added per plant fell more rapidly than workers per plant (however measured) between 1967 and 1979, so labor productivity dropped. Similarly, for those plants reporting complete capital stock data, value-added per unit capital fell.<sup>3</sup> Taken together, these results suggest an unequivocal fall in total factor productivity for the manufacturing sector as a whole between 1967 and 1979. Like the low output growth, this productivity dr3p probably reflected the relatively slack demand for manufactured products in 1979, coupled with a gradual

> 3/ Bear in mind, however, that measurement problems are particularly acute for capital stocks, which are reported by only a fraction of the plants and are expressed in historic cost deflated by a general capital stock price index.
29 distribution of household level utilization of education facilities detailed in Section 3. For each child attending public school, households are allocated the corresponding subsidy amount. These are then aggregated over schooling categories and household expenditure groups.

**_The_** _Incidence of Education Subsidies in 1987_

An estimate of the incidence of public subsidies to education across per capita expenditure deciles of individuals is presented in Table 14. Table 15 combines this information with details on education related spending disbursed directly by the household. Note that in both tables, the absolute expenditure levels are expressed in monthly Rupiahs per person, and given for the all, all urban, and all rural Indonesia distributions.0 Table 14 indicates that the aggregate subsidy to education is highest for the poorest 40 percent (with the exception of the all Indonesia tenth decile) and (generally) increasing from the first to the fourth deciles. For the remaining 6 deciles, the subsidies are relatively constant across the deciles. There is more of a tendency for the absolute amounts to increase from deciles 5 to 10 for urban Indonesia and this is reflected, though less pronounced, in the all Indonesia numbers. The aggregate subsidy represents the sum of government expenditures on each of the four education categories.

The subsidies to primary education are found to decline almost monotonically across the deciles; lower secondary subsidies follow a pattern similar to that of the aggregate subsidy and for the upper secondary and tertiary levels, the subsidy tends to increase through the fourth decile, experience a sharp decline and a steady rise thereafter. Recall that it is assumed that all students in the bottom 4 deciles attend public schools. At X Some children start school before they reach 7 years of age. The SUSENAS indicates that there were 966,809 6 year olds attending primary school in 1987. In the incidence of utilization tables of Section 3, these children do not figure since the proportions refer to the 7 to 12 age group. However, they are factored in when calculating per student subsidies to primary schooling. They are also included when the paper examines the distribution of the subsidies across groups as well as household expenditures per student in 1987, Tables 14 and 15. Finally, 6 year olds are not counted in Table 16 which compares the distribution of the share of subsidies between 1978 and 1987.
# **<u>Table 1.6. Medium-Term and Long-Term Income Mobility, RLMS 1994-2015 (percentage)</u>**

||<br>**Equivalenc**<br>|<br>**e scale**<br>|<br>**Economi**<br>|**e**|**s of size**<br>|
|---|---|---|---|---|---|
||**Unconditional**|**Conditional**|**Unconditional**||**Conditional**|
|**Panel A: 1994-2004**||||||
|Upward mobility|28.6|38.8|29.4||39.7|
|Immobility|49.3|49.3|48.4||48.4|
|Downwardmobility|22.1|37.2|22.3||37.4|
|**Panel B: 2004-2015**||||||
|Upward mobility|25.7|37.0|26.8||38.5|
|Immobility|47.2|47.2|46.1||46.1|
|Downwardmobility|27.1|41.3|27.1||41.2|
|**Panel C: 1994-2015**||||||
|Upward mobility|32.1|42.6|33.0||43.8|
|Immobility|44.6|44.6|43.4||43.4|
|Downwardmobility|23.3|40.5|23.6||41.0|

**Note** : Estimation results are based on per adult equivalent income. The OECD equivalence scale assigns a value of 1.0 to the first adult, a value of 0.7 to each additional adult (age 17 or older), and a value of 0.5 to each child (age 0-16) but does not account for the economies of size in large households. We set the economies of size equal to 0.8. The terciles are defined using the cross-sectional sample for each year. All numbers are deflated with December to December regional CPIs and weighted with population weights, where the first survey round in each period is used as the base year. Estimation sample size is 4941 panel individuals from the 5th and 13th rounds of the RLMS, 3722 and 3719 panel individuals from the 13th and 24th rounds of the RLMS and 2477and 2475 panel individuals from the 5th and 24th round of the RLMS respectively.

54
**Table 6:** The effects of AIA on the demand for data workers

|Regressions|(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Variable|||Worker|Wages|||
|Occupation<br>Group|Non-|Data|Other|Data|Data|Analytics|
|Y|2007-|2007-|2007-|2007-|2007-|2007-|
|ear|2013|2019|2013|2019|2013|2019|
|AIA Dummy|-0.006|-0.008|0.008|-0.011|-0.015*|-0.016**|
||(0.005)|(0.006)|(0.012)|(0.009)|(0.009)|(0.007)|
|Observations|144,136|288,347|30,812|61,863|51,415|104,859|

Note: Worker-level regressions using annual data. Worker wages are in (logged) thousands of pounds. Treated firms are identified taking the average investment values for firms in 2005/06 and calculating if this lagged investment average is above or below the AIA threshold in any given year. Regressions use a binary AIA eligibility indicator. Data occupation groups are defined following Corrado et al. (2022). Specifically, Data Analytics reflects occupations with time used for data intelligence. The remaining occupations are decomposed into Other Data, those with time used for creating data stores, databases or software; and Non-Data, those that do not spend time on data activities. The regressions are estimated using the method outlined in Callaway and Sant’Anna (2021). All regressions include year and worker fixed effects, as well as firm controls of lagged employment, a multi-plant dummy, foreign owned dummy and log age and worker controls for age, tenure, tenure squared, skilled occupation dummy, gender and the interactions of the other worker controls with gender. These are not reported for brevity. Robust standard errors in parenthesis are clustered at the firm level. ***, ** and * indicate significance at the 1%, 5% and 10% level, respectively.

AIA, compared to these same workers in non-treated firms (columns 5 and 6). In contrast, the wages of workers that perform other data tasks, related to creating databases, data stores or software (columns 3 and 4), and workers that do not perform data tasks (columns 1 and 2), were not significantly affected. Thus, the AIA policy does not seem to affect the demand for workers upstream in the data value chain, who create and store data, but rather it is the downstream processing and analysis of data that is constrained. Moreover, it appears the AIA policy did not affect labor demand in general for treated firms, but specifically reduced demand for the subset of workers performing data analytics tasks.

27
age of how many were never enrolled in school, how many dropped out of school by grade 5 and by grade 9, how many finished grade 9 with a test-score performance below 400 which signals functional illiteracy, and finally how many finished grade 9 with a test-score performance above 400. Only the last group can be viewed as having reached basic literacy in cognitive skills (cf. Pritchett (2004); Wößmann (2004)).

As the results in Figure 5.4 reveal, in 11 of the 14 countries, the share of fully literate students in recent cohorts is less than one third. In Ghana, South Africa, and Brazil, only 5%, 7%, and 8% of a cohort, respectively, reach literacy. The remaining more than 90% of the population are illiterate: because they never enrolled in school; because they dropped out of school at the primary or early secondary level; or because even after completing lower secondary education, their grasp of basic cognitive skills was so low that they have to be viewed as fundamentally illiterate. In contrast, 55% of a cohort in Armenia and 63% in Moldova can be viewed as literate at the end of lower secondary schooling.

An example of a basic test question from one of the international achievement tests can, perhaps better than anything, illustrate the scope of the problem in developing countries. One question asked to 8<sup>th</sup> graders in TIMSS 2003 was: “Alice ran a race in 49.86 seconds. Betty ran the same race in 52.30 seconds. How much longer did it take Betty to run the race than Alice? (a) 2.44 seconds (b) 2.54 seconds (c) 3.56 seconds (d) 3.76 seconds.” While 88% of 8<sup>th</sup> -grade students in Singapore, 80% in Hungary, and 74% in the United States got the correct answer (a), only 19% of students in 8<sup>th</sup> grade in Saudi Arabia, 29% in South Africa, and 32% in Ghana got the correct answer (cf. Pritchett (2004) for a similar example). Random guessing would have yielded 25% correct on average.

Ghana, and Morocco: 2003 and TIMSS 2003; Indonesia: 2002 and average of TIMSS 2003 and PISA 2003; Moldova: 2000 and average of TIMSS 1999 and TIMSS 2003; Philippines: 2003 and average of TIMSS 1999 and TIMSS 2003; South Africa: 1999 and TIMSS 1999; Thailand: 2002 and PISA 2003; Turkey: 1998 and TIMSS 1999.

56