rafmacalaba/datause-encoder-data huggingface dataset:
Many municipalities experienced sharp increases in crime, some of which started out as relatively peaceful locations to displaying very high homicide rates, even by international standards, while others have maintained constant levels of violence. Figure 2 displays the evolution of homicide rates for some selected Mexican states. For example, while Puebla, Queretaro and Quintana Roo show a relatively low and constant homicide rate per 100,000 inhabitants, states like Sinaloa, Guerrero, and Sonora present a sharp increase in their violence rates, especially as of 2007.

**Figure 2. Homicide Rate Evolution. Selected States**

<!-- Start of picture text --> 1990 1995 2000 2005 2010<br>Year<br>Queretaro Sinaloa<br>Puebla Guerrero<br>Quintana Roo Sonora<br>80<br>60<br>40<br>20<br>0<br><!-- End of picture text --> Source: INEGI (2014).

The recent spike in violence in Mexico has drawn attention both in the policy and academic arenas. It has been argued that the increase in violence was triggered as a response to aggressive government policies (Guerrero, 2011; Guerrero, 2010; Merino 2011), although no consensus has been reached. Dell (2014) examines the direct and spillover effects of Mexican policies towards the drug trade. She exploits variation from close mayoral elections to identify the counties that experienced a more intense anti-drug policy. Her analysis suggests that the violence reflects rival traffickers’ attempts to wrest control of territories after policies have weakened incumbent criminals.

However, there does not exist a consensus regarding whether the increase in violence has indeed been triggered by government security policies. In fact, there seems to exist some evidence 7
# **Figure 2 Share of Intellectual property in the capital stock and capital formation in investment**

Intellectual property as % of machinery, transport equipment and intellectual property <!-- Start of picture text --> 7<br>6<br>5<br>4<br>3<br>2<br>1<br>0<br>1964 1969 1974 1979 1984 1989 1994 1999 2004 2009 2014<br>Capital Investment<br><!-- End of picture text --> _Source_ : Penn World Tables version 9.0

# _Assumptions for depreciation rates_

The depreciation rate from the BEA for equipment has been modified to allow for both the share of software investment and research and development in China. It is set as a weighted average of the software depreciation rate used in the Penn World Tables, the depreciation rate for R&D estimated by Li and Hall (2017) and the BEA depreciation rate for equipment in the private sector and government sectors. The weights are the shares of software and R&D in the stock of equipment and software as estimated in the Penn World Tables plus an estimate of the stock of R&D capital (see below). To obtain the overall depreciation for each sector, the modified depreciation rate for equipment is weighted with the BEA rate for structures. The weights reflect the average shares for each type of asset for investment in each sector in urban areas in 2010 (Table 6).

**Table 6 Investment share by asset in urban areas**

||Business|Government|Infrastructure|
|---|---|---|---|
|Equipment|0.279|0.110|0.110|
|Structures|0.721|0.890|0.890|

_Source:_ CEIC In 2016, the resulting aggregate depreciation rate for the business sector is estimated at 7.8%, while the rates in the government and infrastructure sectors are estimated at 2.7% and 3.5%, respectively. The impact of including a higher deprecation rate for software and R&D outlays is minimal at around 0.3 percentage point. For the non-residential sector, combining business, government and infrastructure, these assumptions generate a depreciation rate that averaged 4.9% over the decade to 2016. Such a rate is very similar to that observed in countries that are members of the OECD: almost 60% of that group of countries had non-residential depreciation rates that were between 5.2% and 4.8% in the 10 years ending in 2016 (Figure 3).

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<!-- Start of picture text --> work only both school & work<br> none<br>.5<br>.4<br>.3<br>.2<br>.1<br>12 13 14 15 16 17 18<br>age<br> all<br> work only both school & work<br> none<br>.5<br>.4<br>.3<br>.2<br>.1<br>12 13 14 15 16 17 18<br>age<br> boys<br> work only both school & work<br> none<br>.5<br>.4<br>.3<br>.2<br>.1<br>12 13 14 15 16 17 18<br>age<br> girls<br>%<br>%<br>%<br><!-- End of picture text --> Figure 2 - Distribution of children according to school attendance and current work status, by age and gender.

Source: Own calculations based on World Bank, Fortaleza Survey 2003. Note: There is one other category—attending school only—which is not plotted.

Figure 2 highlights that, after the age of 16, there is an increasing share of teenagers who, though not having a job, drop out of school. By the age of 18, almost half of the teenagers is neither in school nor at work. These figures cast some doubt on the relevance of child labor as the driving force pulling teenagers out of school in urban Brazil.

9
WORLD, GEOGRAPHIC REGIONS, **AND** INCOME GROUPS 31 Surmry Projection for 25-Year Periods EUROPE AND FORMER SOVIET REPUBLICS

|AGEGROUP|1985|2000|2025|2050|2075|2100|2125|<br>2150|
|---|---|---|---|---|---|---|---|---|
|TOTAL M+F|769742|825469|893937|922094|930123|941153|94956|8<br>953780|
|MALES|||||||||
|0-4|29556|28228|28333|28107|28420|28420|28419|28418|
|5-9|29272|28871|28343|28390|28402|28399|28398|28397|
|10-14|29468|29696|28422|28388|28397|28393|28393|28393|
|15-19|30421|29298|28359|28370|28367|28371|28376|28378|
|20-24|29959|28931|28039|28253|28294|28317|28327|28332|
|25-29|30970|28940|27776|28056|28194|28236|28253|28260|
|30-34|29695|29747|28306|27981|28104|28154|28174|28184|
|35-39|27508|29222|28941|27940|28003|28063|28092|28107|
|40-44|21466|29985|28351|27m79|<br>27860|27929|27978|28003|
|45-49|21921|28373|27746|27238|27593|27725|27807|27843|
|50-54|22195|25703|27317|26655|27127|27393|27514|27567|
|55-59|19844|19377|27316|<br>26592|26587|26907|27076|27151|
|60-64|17251|18649|25654|26284|25849|26210|26447|26557|
|65-69|9965|17154|24294|24441|24615|25168|25491|25658|
|70-74|9379|13345|20317|22021|22623|23534|24025|24274|
|75+|12159|15463|28969|44454|48502|52073|<br>55053|56581|
|TOTAL|371030|400983|436483|451198|456939|463292|467822|470102|
|FEMALES|||||||||
|0-4|28366|26859|26924|26970|26975|26971|26969|26967|
|5-9|27981|27515|26957|26968|26966|26958|26956|26954|
|1G-14|28190|28359|27053|26976|26965|26956|26954|26953|
|15-19|28910|28240|27034|26984|26952|26950|<br>26950|26951|
|20-24|28980|27860|26809|26935|26929|26940|26942|26944|
|25-29|30216|28012|26677|26841|26911|26928|26932|26933|
|30-34|29232|28702|27315|<br>26857|26898|26914|26917|26919|
|35-39|27315|<br>28742|28091|26909|26875|26889|26896|26901|
|40-44|21550|29854|27886|<br>26832|26841|26845|26865|26876|
|45-49|22709|28697|27390|26528|26734|26778|26820|26837|
|50-54|23664|26543|27344|26264|26544|26688|26750|26774|
|55-59|22528|20624|27708|26685|26412|26565|26648|26682|
|60-64|22844|21207|27284|<br>27131|26252|26385|26498|26550|
|65-69|15192|21226|27561|26478|<br>25867|26119|26269|26356|
|70-74|15362|18835|25280|25286|25072|25634|25897|26039|
|75.|25672|<br>33212|50138|70253|73992|<br>77341|80482|82041|
|TOTAL|398712|424486|457454|470896|473184|477861|481745|483678|
|BIRTH RATE||14.4|13.0|12.2|12.0|11.9|11.7|11.7|
|DEATH RATE||9.8|9.8|11.0|11.6|11.4|11.4|11.5|
|**NET**MIGRAT|ION<br>RATE|.0|-.0|.0|.0|.0|.0|.0|
|GROWTH RAT|E|.47|.32|.12|.03|.05|.04|.02|
|TOTAL FERT|ILITY|2.002|2.013|2.067|2.065|2.064|2.062|2.062|
|etO)**-** BOT|H SEXES|74.43|78.48|81.96|83.86|84.93|85.73|86.05|
|IMR**-** BOTH|SEXES|15.0|8.0|3.6|2.6|2.3|2.1|2.0|
2 contractionary. Section 3 develops a simple model to evaluate the impact of a devaluation on aggregate output. Section 4 analyzes the results of model simulations and discusses policy implications. Section 5 concludes the paper.

# 2 Channels through which a Devaluation can be Contractionary

# 2.1. Introduction

To focus on the effects of a devaluation, Edwards (1989) uses pooled data from 12 developing countries to regress real GDP on a country specific time trend, a money variable (with lags), the terms of trade (with lags), a fiscal variable (with lags), and the nominal exchange rate (with lags). Edwards' findings of significant negative coefficients for the nominal exchange rate variable (without lag) support the growing literature -reviewed below- which emphasizes that devaluations can have, in the short-run, contractionary effects on output.

Krugman and Taylor (1978) showed that contrary to the view that substitution effects associated with a real depreciation are sufficiently strong to assure an expansionary effect on output and employment, there were several channels through which a devaluation could be contractionary. They show that income effects transferring real purchasing power toward economic actors with higher propensity to save can create excess savings and reductions in real output under the following circumstances: i) With an existing trade deficit, price increases of traded goods reduce real income at home; ii) Even if foreign trade is in balance, a devaluation implies profits in export and import-competing industries. If there is a decrease in real wages and if the marginal propensity to save is higher from profits than from wages, there can be a contraction; and iii) If trade taxes are an important source of government revenues, a devaluation, by redistributing income from the private sector to the government, can reduce aggregate demand if the marginal propensity to save from the government is higher than that of the private sector. On the other hand, Hanson (1983) argues, using a more general model in which substitution in consumption and production is allowed, that it is not very likely that a devaluation will reduce aggregate demand.

Thus, the absence of a general relationship between aggregate output and dev;aluation comes from the fact that the answer depends upon whether the various channels through which a devaluation can exert a contractionary effect are relevant
(although this depends in part on the choice of technology). In fact, it is often argued that an important bene fi t of deregulation is increased employment: workers forego job security for higher employment. However, employment effects can be quantitatively small.<sup>15</sup> In fact, when labor regulation became more restrictive in Brazil there were only small effects on employment, as reported by Barros and Corseuil (2001). In the presence of reduced enforcement, if all fi rms substitute formal by informal workers then there will be no change in employment. Still, there could exist large changes in fi rm performance, especially if the substitution of formal by informal workers leads to a reduction in the fi rm’s labor costs.

Net wages may increase if the fi rm is more productive, but gross labor costs will probably fall since the fi rm is paying less to social security. Net average wages may also increase if informal workers earn more than formal workers (keeping worker characteristics fi xed). This will be the case if fi rms share with informal workers the savings in social security payments they get by hiring them.<sup>16</sup> In the empirical work that follows we examine how enforcement affects the fi rm’s employment of informal workers, and how it subsequently affects total employment, wages, productivity and investment.

# 3. Data

Descriptive statistics of the main variables that we use in the analysis are reported in appendix table A1. The source of fi rm level data is the Investment Climate survey collected by the World Bank. The survey is representative of a set of manufacturing sectors that together account for 75% of the manufacturing value added and employment in 2002 (details of the survey are given in the data appendix). In the collection of this dataset the World Bank worked with a private survey fi rm. Given the detail and the sensitive nature of some

> 15For example, if fi rms change their technology then the demand for labor schedule changes and employment can increase or decrease, depending on whether the new technology is more or less labor intensive. Furthermore, assume that the wage of informal workers plus the potential penalty cost from hiring them is approximately equal to the net wage of formal workers plus social security costs, so that total labor costs with each type of worker are the same. Then, instead of a change in employment, fi rms may just choose to substitute formal for informal workers.

> 16This is usually labeled as “ fi scal kink” (or cunha fi scal). Given that the total labor costs of formal workers greatly exceed their net wage, there is a strong incentive for fi rms to hire informally and for workers to be hired informally, if the savings from such an exercise (unpaid social security bene fi ts) can be shared between both parties.

10
WPS3337

# **Do Households Gain from Community-based Natural Resource Management? An Evaluation of Community Conservancies in Namibia**

Sushenjit Bandyopadhyay<sup>a</sup> , Michael N. Humavindu<sup>b</sup> **,** Priya Shyamsundar<sup>a</sup> , and Limin Wang<sup>a</sup> _Abstract: Community-based natural resource management is an important strategy to conserve and sustainably use biodiversity and wildlife in Namibia. This paper examines the extent to which conservancies have been successful in meeting their primary goal of improving the lives of rural households. It evaluates the benefits of community conservancies in Namibia by asking three questions: (a) Do conservancies increase household welfare? (b) Are conservancies propoor? (c) And, do participants in conservancies gain more relative to those who choose not to participate? The analysis is based on a 2002 survey covering seven conservancies and 1,192 households. The results suggest that community conservancies have a positive impact on household welfare. This impact is poverty-neutral in some regions and pro-poor in others. Further, welfare benefits from conservancies appear to be some what evenly distributed between participant and non-participant households._

# **Acknowledgements**

We are grateful to Jon Barnes for providing us with the survey data and for his comments, John Katjiua for his helpful discussion on land ownership in Kunene, and to Hanan Jacoby for advice on methodology. We thank the participants of the survey analyses workshop held in August 2003 at the Ministry of Environment of Namibia, and other World Bank colleagues who have commented on this paper. We would like to thank Kirk Hamilton, team leader, Policy and Economics Team, World Bank, who provided valuable guidance and support. We sincerely thank our donors from the Trust Fund for Environmental and Socially Sustainable Development. Funding for the survey was contributed by the Swedish government through Sida, and the UK government through DFID as well as the Namibian government.

World Bank Policy Research Working Paper 3337, June 2004 _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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ a Environment Department, World Bank. b Directorate of Environmental Affairs, Ministry of Environment and Tourism, Government of Namibia.

1
# 5 Discussion and Conclusion

This paper examines the relationship between cohort size at birth and subsequent employment outcomes for youth and adults in developing countries, utilizing a unique collection of harmonized surveys covering much of the developing world. Although standard supply and demand theory would suggest that larger cohorts uniformly worsen employment outcomes throughout workers’ careers, the empirical results are more subtle. In low-income cohorts, reduced cohort size is associated with lower employment rates, but this does not translate into higher rates of school attendance. For youth in middle-income countries, smaller cohorts are generally beneficial for employment outcomes. In particular, youth aged 15 to 19 that were born into smaller cohorts, if they choose to work, are less likely to work in agricultural employment and young women are more likely to work as wage employees. Smaller cohorts in these countries also tend to have lower unemployment rates between age 20 and 24. The magnitude of these effects can be substantial, as a 10 percent reduction in cohort size is associated with a 1 to 1.5 percentage point reduction in the unemployment rate.

Although smaller cohorts appear to benefit those youth that choose to work in middle-income countries, there is far weaker evidence of an effect for adults, in either low or middle-income countries. Adults in smaller cohorts are moderately more likely to work as wage employees, but the estimates are only statistically significant for two of the five adult age groups. Furthermore, it is difficult to discern any effects of cohort size on adults’ primary activity, sector of employment, or earnings of wage employees.

Overall, we conclude from these results that reductions in cohort size are associated with moderate improvements in employment outcomes for youth in middle-income countries. They are far from a magic bullet, though, and there is little evidence that the advantages smaller cohorts confer on working youth translate into more productive jobs throughout their career. Without efforts to improve other determinants of worker productivity, such as the quality of human capital, physical infrastructure, and protection for property rights, decreases in the quantity of workers by itself appears to have little discernible relationship with improvements in the quality of jobs for adults in developing countries.

24
# _Implications for Poverty Analysis_

The fact that both mean consumption and distributional measures vary substantially across the modules implies that there will be complex comparability problems when poverty estimates from different modes of consumption measurement are combined. For example, consider a poverty monitoring survey that switched from 14-day to 7-day recall. This switch would raise reported mean consumption (see Table 3) and lower measured inequality (see Table 7). In a setting where the poverty line is set below median consumption, these effects will work in concert to make poverty look lower than it would if the method of consumption measurement had not changed (as occurred with changes in India’s NSS). Conversely, if a switch was made from a disaggregated recall list to a collapsed list (as the Indonesian _Susenas_ does every 3<sup>rd</sup> year), it would lower the mean (implying higher measured poverty) and lower inequality (lower measured poverty) with ambiguous implications for poverty measures. Hence there are unlikely to be simple correction factors that can be used to enforce comparability on different consumption modules when attempting to measure poverty consistently over either time or space. Similarly, the literature that reacts to possible survey understatement by combining survey measures of inequality with national accounts measures of mean consumption (such as Bhalla, 2000, and Sala-i-Martin and Pinkovskiy, 2010) is likely to be misguided since survey design changes will also affect estimated inequality.

We attempt to quantify the implications of survey design for poverty analysis in a setting like Tanzania by presenting aggregate poverty numbers based on an international poverty line, as well as the characteristics of those households deemed poor by each module. Figure 1a presents the poverty headcount rate based on a common international poverty line of $1.25/person/day converted into 2008 Tanzanian shillings on a PPP basis.<sup>25</sup> With this absolute poverty line fixed at the same value regardless of module, we see dramatically different headcount rates depending on how consumption is measured. The benchmark personal diary records the lowest level of poverty at 47.5 percent of the population, followed by the 7-day long list (module 2) at 54.9 percent. Even though module 2 yielded mean consumption levels closest to the benchmark (Table 3 lists consumption at 3.9 percent lower and not significantly different from benchmark consumption), the fact that module 2 inequality was also higher (the estimated Gini is three points higher – Table 7) results in substantially higher poverty numbers. The 7-day subset design (module 3) reports

> 25 An alternative to the international poverty line would be to use a nationally defined poverty line (for example, the line defined by the HBS 2006/07). In either case, we avoid defining eight separate poverty lines, each on the basis of each consumption aggregate measure, since this would introduce problems as these lines would then not be comparable, as discussed in Lanjouw and Lanjouw (2001).
agency). However, the coefficient on having a Ministry regulator is significant at the 5% level and the wrong sign (ie a Ministry regulator is _positively_ associated with higher private investment and an _independent_ regulator negatively associated.) Pargal puts forward some ingenious arguments why this might be so but we do not find them convincing and they are, in any event, inconsistent with the funding result.

Pargal’s paper takes the estimation of the impact of regulation on outcomes in the power sector rather further forward but this paper still raises at least as many questions as it resolves. That seems to arise primarily because of the problem of having to rely exclusively on data drawn from the characteristics of reform legislation rather than from subsequent observed practice.

The main lesson to be drawn from the analysis seems to be that only when data which include regulatory <u>experience</u> and the amount of <u>actual</u> competition will it be seriously possible to try to disentangle the effects of regulation – and individual regulatory governance characteristics - in a regression analysis approach.

One other feature is that the availability of comparable cross-country data on _investment_ in the electricity industry in DTEs seems to be confined to data on _generation capacity_ . This seems to be the case even in the Calderon-Serven data set. This is most unfortunate given the emphasis (a) on transmission capacity for successful unbundling and (b) on the role of distribution for electricity reform in DTEs.

# **5.3 Empirical Studies of the Impact of Regulatory Agencies on Telecommunications Outcomes in DTEs**

There is a rather larger literature on the impact of regulation on telecom outcomes than on electricity outcomes. This is, not least, because there are more countries with independent regulatory agencies which have 5 years or more of existence (at least of legal existence)<sup>49</sup> . However, because this paper is primarily concerned with electricity outcomes, we will concentrate on three of the most recent and relevant papers – Fink, Mattoo and Rathindran (2003), Wallsten (2002) and Gutierrez (2003). In addition, we will concentrate on the parts of those papers concerned with estimating the effects of regulation on (a) mainline penetration rates (a measure of capacity growth) and (b) efficiency (eg mainlines per employee).

# **5.3.1 Fink, Mattoo and Rathindran (2003)**

This paper benefits from being able to use the ITU-World Bank Database on telecommunications policy. This has been supplemented by data from the Stanford – World Bank database (for Latin America) and data from the World Bank African Telecommunications Research Project. _This is a much more comprehensive data set than has so far been collected for electricity_ . In particular, the data allow for the <u>dating</u> of policy changes and hence the use of time-based dummies for different policy changes and the exploration of optimal sequencing.

> 49 See Wallsten (2002), Table 1

38