rafmacalaba/datause-encoder-data huggingface dataset:
- 6 -

energy, mining, and most of the services, particularly the financial service.

It should be noted, however, that the second category of areas in which government intervention is justified leaves the subject wide open. A number of practical experiments and research are presently focussed on the exploration of the private provision of such types of services.5 Another perhaps more practical way of looking at this issue is to consider as a direct government responsibility the need to ensure the provision of the goods and services in that second category, whether through regulation of the private or by direct intervention of the public sector.

However, this scheme, now widely accepted, is biased towards private sector solutions. First, experience suggests that the issue of ownership, although important and a major determinant of economic performance, might not always be the most important. Often, the main issue is the existence of a competitive market rather than private ownership. In theory, if there is a competitive market, a public sector agency operating in that market that is actually allowed fully to behave as a competitive unit, is likely to have a level of operational performance similar to a private sector unit operating in that same market. The issue of course, is whether governments can muster the political will to give that level of freedom. Remarkably, there are not many technical studies that directly compare performance of the same activities operating in the private and public sectors and most of those available are 5 See, for example, Gabriel Roth, The Private Provision of Public Services in Developing Countries. Oxford University Press, 1987.
**Figure 4. Impacts on GDP and welfare due to the carbon tax**

<!-- Start of picture text --> (a) Percentage change in GDP from the baseline<br><!-- End of picture text --> <!-- Start of picture text --> 0.34<br>0.14<br>0.12<br>0.10 0.10<br>0.08<br>0.06<br>0.00<br>-0.03<br>-0.07 -0.08<br>-0.11<br>2a 2b 2c 2d 2e 3a 3c 3d 3e 4c 4e<br>Scenario Scenario 2 Scenario 3 Scenario 4<br>1<br><!-- End of picture text -->

# **(b) Percentage change in welfare from the baseline**

<!-- Start of picture text --> 0.28<br>0.13<br>0.09 0.08<br>0.05 0.06<br>0.04<br>-0.02<br>-0.04<br>-0.06 -0.07<br>-0.09<br>2a 2b 2c 2d 2e 3a 3c 3d 3e 4c 4e<br>Scenario Scenario 2 Scenario 3 Scenario 4<br>1<br><!-- End of picture text -->

**Table 3. Impacts on households**

(Percentage change from the baseline)

|Scenario|Disposable<br>income|Household<br>expenditure|Household<br>energy<br>consumption|Household<br>non-energy<br>consumption|Household<br>savings|
|---|---|---|---|---|---|
|1|0.18|-0.05|-14.35|0.51|5.86|
|2a|0.00|-0.11|-9.17|0.24|2.29|
|2b|-0.04|-0.14|-8.04|0.17|1.53|
|2c|-0.11|-0.17|-6.45|0.07|0.47|
|2d|0.13|-0.01|-14.47|0.56|4.71|
|2e|-0.02|0.12|-14.85|0.71|0.90|
|3a|0.32|0.16|-10.23|0.57|4.29|
|3c|0.37|0.22|-8.71|0.57|3.71|
|3d|0.26|0.09|-14.35|0.66|5.05|
|3e|0.51|0.53|-14.34|1.12|2.52|

21
**Figure 10. Real Growth in Trade of Goods and Services**

<!-- Start of picture text --> 14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>1995-99 2000-04 2005-06 2007<br>Years<br>East Asia Pacific South Asia<br>Sub-Saharan Africa Middle East and North Africa<br>OECD<br>Percent<br><!-- End of picture text --> <!-- Start of picture text --> OECD<br><!-- End of picture text --> _Source:_ World Bank's World Trade Indicators 2008.

_Note:_ Data for 1995-99, 2000-04, and 2005-06 are averages.

The geographical configurations of South Asia contain huge agglomeration potential to propel growth<sup>16</sup> . East Asia is an example of a region with a high level of intra-regional trade and intra-industry trade that enabled firms to internalize externalities arising from agglomeration. Firms exporting to the regional markets in South Asia are more constrained by the quality of connectivity and productivity enhancing infrastructure<sup>17</sup> . It is the seamless interaction of improved trade, better connectivity, and converging institutions that can accelerate growth in the lagging regions, and benefit the slower growing and smaller land locked regions and countries. In Latin America, Brazil’s growth creates export opportunities for Bolivia. In Africa, resource landlocked countries piggy-backed on the growth of Kenya. In East Asia, Thailand is an important market for Laos and Cambodia.

> 16 For example, given the large economies of scale in services industries (e.g., telecoms), incentives to invest are greater if the markets are not segmented from other the neighboring countries 17 Based on data from S. Coulibaly.

21
**Table 4.C: Returns to Education Estimates for China**

|**Mincerian Re**|**turns t**<br>|**o Years of**<br>|**Schooling**<br>||
|---|---|---|---|---|
||**URB**|**AN (U)**|**RURAL (R)**||
||**Men**|**Women**|**Men**<br>**Women**|**Year**|
|Jamison, D., & Van der Gaag, J. (1987)|4.5|5.5||1985|
|Xie, Y., & Hannum, E. (1996)|2.2|4.4||1988/1989|
|Johnson, E. N., & Chow, G. C. (1997)|2.78|4.46|2.95<br>4.82|1988/1989|
|Liu, Z. (1998)|2.39|3.31||1988/1989|
|Meng, X. (1998)|||1.1<br>2.2|1986/1987|
|Maurer‐Fazio, M. (1999)|3.74|4.94||1991/1992|
|Li, H. (2003)|4.3|6.9||1995/196|
|Bishop, J.., Luo, F., Wang, F. (2005)|3.56|4.43||1995/1996|
|Zhang, J., Zhao, Y., Park, A., Song, X.<br>(2005)|2.9|5.2||1988|
|Zhang, J., Zhao, Y., Park, A., Song, X.<br>(2005)|8.4|13.2||2001|
|Hauser, S. M., & Xie, Y. (2005)|3.6|7.4||1995/1996|
|Ren, W., & Miller, P. W. (2012)|||3.81<br>7.18|2006|
|Chen, Q., Xu, J., Zhao, J., & Zhang, B.<br>(2017)|||6.8|2004|
|Xiao, S., & Asadullah, M. N(2018)|7.1|9||2010|
|**SECONDARY(**|**Estimat**|**es for All**|**China(R+U))**||
|||**Men**|**Women**||
|Bargain et al. (2009)||3|15|1987/88|
|Bargain et al.(2009)||11|25|2002/04|
|**HIGHER SECONDAR**|**Y(Esti**|**mates are f**|**or All China(R+U))**||
|||**Men**|**Women**||
|Bargain et al. (2009)||9|26|1987/88|
|Bargain et al.(2009)||26|51|2002/04|
||**COLL**|**EGE**|||
||**R**|**ural**|**All China**<br>**(R+U)**||
||**Men**|**Women**|**Men**<br>**Women**||
|Gustafsson, B and Li, S (2000)|8.9|10.2||1988/1989|
|Gustafsson, B and Li, S (2000)|15.5|20.8||1995/1996|
|Bargain et al. (2009)|||24<br>42|1987/88|
|Bargain et al. (2009)|||54<br>84|2002/04|
|Wang, L. (2012)|21.1|27||1995/1996|
|Wang, L.(2012)|48.2|56.6||2002/2003|

Note: Full citations are provided in the online appendix.
concentrated. This growing inequality over space in the poverty rate also featured in the rise over time in the coefficients of variation for the poverty statistics in Table 1.

The description of poverty patterns over time and space is completed by Figure 3, which shows the proportion of baseline poverty eliminated by 2011/12. The spatial patterns are especially clear when considering the poverty gap index in the right panel of Figure 3. The southern states of Kerala, Karnataka, TN, and AP, and all of Maharashtra except the Coastal region (where the poverty gap index increased) eliminated from 60-95 percent of the baseline poverty gap index in the rural sector and similar progress is seen in much of northwest India. However, in Assam, Bihar, Chhattisgarh, Jharkhand, Orissa, and parts of Madhya Pradesh, a much smaller proportion of rural poverty was eliminated. Thus, the rate of reduction in poverty is also spatially auto-correlated, with Moran statistics of 0.30 for the proportion of the rural poverty gap eliminated and 0.15 for the proportion of the rural headcount poverty that was eliminated. In other words, rural poverty started out as spatially concentrated, the poverty reduction process has also been spatially uneven, and so rural poverty at the end-line for our data is even more spatially concentrated than it was at the start.

# _3.2 Night Lights Data_

If India were China it would be fairly easy to examine how growth of different types of cities affects rural areas. China has annual GDP data for prefectures, a sub-provincial unit a little more disaggregated than NSS regions are with respect to India’s states, and also for each city and county. One could compare how rural poverty is affected by growth in big cities versus growth in smaller ones, such as county-level cities.<sup>11</sup> However, the available sub-national economic statistics are much more limited for India. For example, annual data on Gross District Domestic Product are reported by the Planning Commission, but exclude some big states like Gujarat, and for many states the data end in 2005/06 and so miss the recent period of rapid poverty reduction. The Population Census for 1991 and 2011 roughly lines up with the 50<sup>th</sup> and 66<sup>th</sup> or 68<sup>th</sup> round NSS data but not with the 61<sup>st</sup> round, and does not inform about economic growth. The lack of agglomeration-level boundaries, like the Metropolitan Statistical Areas (MSA) in other countries, also means that India’s census data correspond to administrative rather than to economic definitions of cities.

> 11 Deng et al (2010) are an example of studying patterns of urban change in China using these spatially detailed GDP data.

9
savings. Because we measure sales volatility using all available data for each firm and we only have 9 years of data, this measure is firm-specific (i.e. it is constant over the whole period of our sample).

**Cash flows** . We include cash flows to capture the cash generating capacity of the firm. In a dynamic model of Riddick and Whited (2009) cash flow is negatively correlated with cash stock because of serially correlated productivity shocks. Thus, when firms receive a good productivity shock (meaning they have good growth opportunity) their cash flow will raise. At the same time, their investment into productive equipment will also raise to allow the firm to take advantage of the higher productivity shock. As firm uses some of its stock of cash to invest into productive assets, the financial savings may fall.

We use two measures of cash flows: gross operating margin (defined as total sales, minus costs of goods sold, over total sales) and net income to assets, which is a closer proxy to the true cash flows available to firms.<sup>11</sup> Both measures could also proxy for profitability of the firm and thus can capture positive shocks to growth opportunities.<sup>12</sup>

# **5. Cross-Country Comparison**

In this section we benchmark Egypt’s savings into physical and financial assets with other countries for which we have the data. ICA data are available for a wide range of countries. We use data for the second wave of ICA surveys, dated 2006-2008 to compare with Egypt in 2007 We compare data on listed firms in 2007 with other listed firm data obtained from Datastream (we take pre-crisis year to eliminate the impact of the financial crisis that started in 2007 in the US and other industrial countries).

Figure 3 shows cash to assets in a cross-section of countries and Figure 4 shows financial savings to assets. Egypt falls approximately in the middle of the distribution on both measures.

> 11 We use GOM as a measure for comparison with ICA data, in which this was the only cash flow measure available.

> 12 As is standard in the literature, the outliers on all continuous variables were removed at the top and bottom 1% of the distribution.

13
woman has extramarital sex for pleasure, she may not feel comfortable admitting to the sex and/or the condom use.

Reports of sexual behavior may also be inaccurate because of various other influences including recall bias, and incorrect use (of condoms specifically). Subjects may intend to report condom use or sexual encounters accurately, but they may not remember correctly or tend to remember differently from the reality based on their belief about what a good answer would be. One study showed that couples had a high level of agreement on the number of recent sexual intercourse but then men tended to over-report sexual encounters if more than a week had passed since the encounter (Lagarde et al. 1995). Though this study uses data about the last sexual intercourse, that intercourse could have happened more than a week before, possibly increasing the likelihood of recall bias. The knowledge that not using a condom use is considered risky may cause subjects to report more condom use because they are trying to appear they practice safer sex to the interviewer. This type of perceived social pressure may cause subjects to overreport condom use. However, there are many negative views of condoms (Maharaj and Cleland 2004) that may cause people to under-report their use. Incorrect use of condoms can also affect results. Someone may use a condom but use it incorrectly, thereby rendering it ineffective. One study found that 13 percent of condom users applied them after initial penetration and an additional 38 percent reported late application of condoms (de Visser and Smith 2000). Late application and other improper uses of condoms can make them less effective.

The results from this study that show that married females use condoms in extramarital intercourse less frequently than unmarried females may be subject to the 23
Policy Research Working Paper 7501

# **Abstract**

Do regions with higher working age populations grow faster? This paper examines this question using data from Russian regions and finds evidence that demographic trends influence regional growth convergence. In other words, keeping other factors constant, poorer regions grow faster than richer regions, and some of the growth convergence is explained by demographic changes: faster growth in poor regions in the past was related in part to more favorable demographic trends. This finding has important consequences for Russia. If the demographic trends in poorer regions worsen in the future, this could dampen economic convergence. Unless there are significant increases in labor productivity or additions to the labor force through migration, growth in Russian regions will moderate as the Russian population shrinks and ages in the coming decades.

This paper is a product of the Poverty and Equity Global Practice Group and the Macroeconomics and Fiscal Management Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at mmatytsin@worldbank.org, lmoorty@worldbank. org and kaspar.richter@ec.europa.eu.

_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
Figure 4. Has Filed a Complaint for Any Reason with the Police or Any Institution <!-- Start of picture text --> β<br>0.118<br>0.099<br>0.094<br>0.087 0.088<br>0.08<br>N = 1481 N = 1369 N = 965 N = 710 N = 516 N = 659<br>Full Sample Male Female<br>Has PEP−RAMV Doesn’t have PEP−RAMV<br>.14 .14<br>.13 .13<br>.12 .12<br>.11 .11<br>.1 .1<br>.09 .09<br>.08 .08<br>.07 .07<br>.06 .06<br>.05 .05<br><!-- End of picture text --> _Notes:_ The figure presents the propensity to file a complaint with the police or any official body. We present results for all, male, and female heads of households using the VenRePs Survey collected by Ib ́a ̃nez et al. (2024).

women. Particularly, female victims of a crime reported the event 11.8 percent of the time if they had the PEP but only 9.4 percent of the time if they did not have the PEP. The difference in reporting rates between PEP holders and nonholders is only statistically significant for women, as documented in Appendix Table C1.

# **VI IMPACTS OF THE PEP ON CRIMES COMMITTED**

To complement our main results, we also study the effects of the PEP on crimes committed by Venezuelan migrants. For this purpose, we employ data from Colombia’s Office of the Attorney General as described in the data section. We use the same 2SLS specification described in section IV to evaluate the effects of the regularization on the rate of total crimes directly committed by 30
information about buyers (over traditional financial intermediaries), those with sufficient resources use that advantage to offer more trade credit in line with the redistribution view. For example, Nilsen (1994) found that during monetary contractions (when uncertainty is greater), small, likely financially constrained firms receive more trade credit from suppliers. In contrast, where relationships between banks and firms are strong such as in Germany, suppliers’ information advantages (over banks) are not as great, and trade credit is used less (Breig 1994).

But, as noted, suppliers also must have resources to extend. They can extend their own resources to buyers, and there is evidence that profitable suppliers provide more trade credit than others in multiple contexts. For example, the influential study by Petersen and Rajan (1997) was among the first to test theories of trade credit on a large set of firms, using a dataset of small firms in the U.S. compiled by the U.S. National Survey of Small Business Finance (NSSBF) in 1988-89. They found evidence that firms with more assets and greater longevity, both indications of past profitability, and higher gross profit margins, an indication of greater cashflows, extended significantly more trade credit than others (as reflected in a higher ratio of accounts receivable to sales). In a very different context, Cull, Xu and Zhu (2009) used data from over 100,000 industrial firms (including all SOEs) in China from 1998 to 2003 to show that profitable private domestic firms were significantly more likely to extend trade credit than unprofitable ones, credit which provided a substitute for loans for these firms’ customers that were shut out of formal credit markets.

Suppliers need not fund trade credit only from their internal resources. Under the redistribution view of trade credit, firms with better access to external finance redistribute the credit they receive to less advantaged firms (Meltzer 1960; Nilsen 2002). For example, in the U.S. context Petersen and Rajan (1997) argue that firm age and size, strong correlates of trade credit extension in their sample of small U.S. firms, are also proxies for firms’ access to external finance. And their comparison with the larger U.S. firms covered in the Compustat dataset (which had better access to external finance than the NSSBF firms), shows that small firms are much less likely to extend trade credit.<sup>2</sup> In the Chinese context, Cull, Xu, and Zhu (2009) show that unprofitable SOEs were more likely to extend trade credit to their customers, many of which were already in arrears, than other SOEs and private firms. Regardless of performance, the SOEs

> 2 The average ratio of accounts receivable to sales for firms in the NSSBF sample was 7.3% compared to 18.5% for Compustat firms (Petersen and Rajan, 1997, pp.669-670).

9
Let Pd represent the price of the product in the domestic market and Pwo as the invoice price of imports inclusive of tariffs and transport costs (c.i.f). The price comparisons are usually expressed as a percentage difference between the prices expressed as follows: t = IO() [Pd - P(01/ Pw. The latter is referred to as the _ad-valoreni_ tariff equivalent or the implicit protection associated with the non-tariff barrier. The ad-valorem tariff equivalents of border protection are estimated for each year depending on data availability -- from 1982-93 in developing countries and from 1979-93 in OECD countries.

In the case of exportables, export subsidies can be used to raise the domestic producer price above the world price. They can be specified in the same way as taril'fs since an export subsidy is merely a negative export tax. Export subsidies raise the price received by domilestic producers and paid by consumers as the domestic price is increased by arbitrage until it equals the subsidy-inclusive price on sales. The _ad-valorern_ export subsidy equivalenD is expressed as follows: _IV_ = [(Pdi - P(,)/Pw I * 1().

Data on world and domestic prices use were derived from several sources including the Organization lor Economic Cooperation and Development (OECD), US Depart ment of Agriculture (USDA), FAO, and the World Bank. These sources are complemented by the inf'ormation on internal and reference prices in 1986-88 reported in th1e supplemenlary lables in the country Schedules. The OECD provides PSE estimates beginning in 1979 Ior 12 OECD trading blocs and up to 22 commodities or commodity groups. The USDA has extended this coverage to 34 countries and 48 commodities or commodity groups, lor the period 1982-93." Four types of' measures **are** included in **the** OECD and USDAIERS PSE calculations, namely. market price support, direct income support, indirect income support and otlher assistance. Price support

> **"II** am grateful to Susan l1cetmaa lor providing the as yet unpublishedl dlata on ERS/USI)A updlates oni PSE estimates.

16
_Annex B. Suppkementary Tables_ _31_ Table B2. Estimnates and projections of fertility, mortality, and population growth for major regions in selected quinquennia

|_Quinquen._|_Births_|_Deaths_|_Growth_|_Total_|_Life exp_<br>_-at b_|_eetancy_<br>_irth_|_Mo_|_rtality_|
|---|---|---|---|---|---|---|---|---|
|<br>_nium and_|||_rate_|_fertility_|||_Infant_|_Under 5_|
|_reglon_|_Crude ra_|_te per 1000_|<br>_(percent)_|<br> <br>_rate_|_Males_|_Female_|<br>_s (1000 qO_|<br>_) (1000 q5)_|
|_1970-75_|||||||||
|World|31|12|1.9|4.3|60|63|83|122|
|Developed|16|10|0.7|2.2|69|76|24|28|
|<br>Developing|37|12|2.4|5.2|57|58|93|138|
|Industrial|16|10|0.7|2.1|71|78|15|17|
|Nonmarket|18|11|0.7|2.4|64|<br>73|<br>38|47|
|LAC|34|9|2.4|4.8|60|65|63|85|
|Sub-Sahara|48|20|2.8|6.6|43|47|138|<br>231|
|MENA|42|15|2.7|6.3|52|54|116|<br>169|
|Asia|35|11|2.3|4.9|59|59|84|119|
|_1985-90_|||||||||
|World|27|10|1.7|3.4|63|67|70|96|
|Developed|15|10|0.5|1.9|70|77|15|18|
|<br>Developing|31|10|2.1|<br>3.9|<br>61|<br>63|<br>78|108|
|<br>Industrial|13|9|0.5|1.7|73|79|9|10|
|Nonmarket|17|11|0.7|2.3|66|74|24|28|
|LAC|29|7|2.0|3.6|64|69|56|69|
|Sub-Sahara|46|15|3.1|6.4|50|53|111|171|
|MENA|40|10|3.0|5.6|59|61|90|121|
|Asia|27|9|1.8|3.3|63|65|68|90|
|_2000-05_|||||||||
|World|23|8|1.4|2.9|66|70|50|67|
|<br>Developed|<br>13|<br>10|<br>0.3|<br>1.9|<br>73|<br>79|<br>11|12|
|<br>Developing|25|8|1.7|3.1|65|67|55|74|
|<br>Industrial|12|10|0.3|1.8|75|81|7|7|
|Nonmarket|15|10|0.4|2.1|70|76|17|20|
|LAC|21|6|1.4|2.5|68|<br>73|37|44|
|Sub-Sahara|40|11|2.9|5.4|55|59|83|122|
|MENA|32|8|2.4|4.3'|64|65|56|73|
|Asia|21|8|1.3|2.6|67|69|45|56|
|_2015-20_|||||||||
|World|20|8|1.1|2.6|68|72|40|53|
|Developed|13|11|0.2|2.0|73|80|10|11|
|<br>Developing|<br>21|8|<br>1.3|<br>2.6|<br>67|<br>70|<br>43|58|
|<br>Industrial|12|11|0.1|2.0|75|81|<br>7|7|
|Nonmarket|14|11|0.3|2.1|71|78|14|17|
|LAC|18|7|1.1|2.2|70|75|29|34|
|Sub-Sahara|32|9|2.3|4.0|59|63|64|93|
|MENA|26|7|1.9|3.1|66|69|44|57|
|Asia|17|8|0.9|2.2|69|71|34|42|