rafmacalaba/datause-encoder-data huggingface dataset:
reach into countries with which it is not contiguous. Except for one I in table 1 when it was active in combat in Chad, its activities are coded with an F, but this information does not identify where Libya was intervening. Burkina Faso’s involvement in the Mano River complex is another example of an important player that has thus far avoided internal violence. Except for 1987, when it experienced a coup that seems to be the source of the X, Burkina Faso does not appear in table 1. According to the coding rules, it is at peace as much as any country in the sample. In addition to African countries, other countries that play this role are outside of Africa, including Cuba, France, the United States, and countries acting under the aegis of the United Nations, which have sent ground troops to participate in African conflicts.

Most of the countries that participate in these complexes are involved in reciprocal interactions, however, in which one country supports antagonists in a neighboring country and the neighbor responds in kind. Such pairs include Sudan and Uganda, Angola and CongoKinshasa, and Liberia and Côte d’Ivoire, among many others. Yet, the PRIO/Uppsala data record few if any of the international activities of Burkina Faso, Liberia, Libya, Sierra Leone, Côte d’Ivoire, or Somalia, among others. This lack of information means that econometric studies based on these data cannot incorporate these intercountry relationships even if they use specifications based on neighborhood effects, as in the spatial econometric literature (see below). Côte d’Ivoire has been a long-time member of the Mano River complex, influencing the dynamics of the complex well before it experienced associated violence and, in particular, providing a launching place for Taylor’s invasion of Liberia in 1989. From the coup attempt of September 19, 2002, until at least 2011, it has experienced a violent contest over the monopoly of force within its own borders. This contest is tied to the dynamics of the Mano River war complex (International Crisis Group 2003, 2004, 2007; Banégas and Marshall-Fratani 2007, 23
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WPS3433 Measuring the Risk on Housing Investment in the Informal Sector: Theory and Evidence from Pune, India<sup>_∗_</sup> Mudit Kapoor, David le Blanc The World Bank Washington, DC 20433, USA 7th October 2004

# **1 Introduction**

In this paper, we provide an economic framework to analyze investment in informal housing in developing countries. Squatting is a major form of housing tenure in urban centers of developing countries.<sup>1</sup> With rapid growth of cities in the developing world, informal housing has not only subsisted, but has indeed thrived.<sup>2</sup> It is estimated that in Pune, India, 40 percent of the population live in informal settlements.<sup>3</sup> It has also become increasingly clear that slum dwelling units do not convey the entire picture of ”informal housing”. In countries where land markets are very distorted, slums (settlements consisting of shacks, without basic services and utilities) are only the most visible part of informal housing, the tip of the iceberg. Informal housing also consists of dwelling units with construction (concrete, bricks, etc.) similar to those in the formal sector. Moreover, some

> _∗_ The research and data collection have been co-funded by a World Bank research program grant on “Urbanization and Quality of Life” and the UK DFID’s Urban Knowledge Generation and Toolkits program. We are grateful to Somik Lall for providing us access to the data used here. _The findings, interpretations and conclusions expressed in this paper are those of the authors and do not necessarily represent the view of the World Bank, its Executive Directors, or countries they represent._

> 1See Friedman, Jimenez and Mayo (1988) for an overview.

> 2For example, in Morocco, it is estimated that the informal housing stock has grown at an annual rate of 4 percent between 1992 and 2002, faster than the formal stock. In that country, informal housing now accounts for 10 to 15 percent of the total housing stock, in spite of large programs of upgrading and resettlement in the past 20 years by successive Governments. In other countries, the informal to formal ratio in the housing stock is even higher.

> 3One important issue for cross-country comparisons is the definition of ”slums” and ”informal housing”. The empirical section will clarify the definition of ”informal housing” used in our analysis.

1
Table 3: Multinomial and logistic estimation of an ad being male- or female-specified compared to being <u>gender-unspecified and female-specified compared to being male-specified</u>

||Model A -Men (base=<br>unspecified)|gender-|Model A- Women<br>gender-unspeci|(base=<br>fied)|Model B - Women (ba|se= men)|
|---|---|---|---|---|---|---|
||Coef.|RRR|Coef.|RRR|Coef.|OR|
|Occupation category (base|=other)||||||
|Professional|0|1.00|-0.18***|0.84|-0.14***|0.87|
|Teaching|-1.55***|0.21|0.76***|2.13|2.37***|10.70|
|BPO|-1.49***|0.22|0.21***|1.24|1.68***|5.36|
|Service|0.54***|1.71|0.75***|2.12|0.26***|1.30|
|Clerical|-0.32***|0.73|1.12***|3.06|1.45***|4.26|
|Elementary|1.45***|4.25|0.60***|1.83|-0.944***|0.39|
|Machine-related|1.57***|4.80|-2.15***|0.12|-3.89***|0.02|
|Sales|0.97***|2.63|-0.24***|0.79|-1.16***|0.31|
|Type of employer (base =|staffing company)||||||
|HR enterprise|0.20***|1.22|0.30***|1.35|0.06**|1.06|
|Household|-0.43***|0.65|1.25***|3.47|1.95***|7.00|
|Unknown|-0.11***|0.90|0.09***|1.10|0.18***|1.20|
|SME|0.26***|1.30|0.37***|1.44|-0.01|0.99|
|Job characteristics|||||||
|No. of vacancies|-0.01***|0.99|-0.03***|0.97|-0.02***|0.98|
|Shift: Part-time|0.16***|1.17|0.43***|1.54|0.32***|1.38|
|Shift: Nights|0.61***|1.85|-0.84***|0.43|-1.62***|0.20|
|log (Real offered salary)|-0.07***|0.94|-0.71***|0.49|-0.78***|0.46|
|Experience|0.19***|1.21|0.23***|1.26|0.02***|1.02|
|Period Dummy|||||||
|Year 2012|-0.25***|0.78|0.33***|1.39|0.43***|1.54|
|Year 2013|-0.32***|0.73|0.63***|1.88|0.83***|2.30|
|Year 2014|-0.47***|0.63|0.63***|1.87|1.02***|2.77|
|Year2015|-0.67***|0.51|0.41***|1.50|0.87***|2.38|
|Year2016|-0.33***|0.72|0.77***|2.15|0.80***|2.23|
|Year 2017|-0.81***|0.45|0.04*|1.05|0.91***|2.49|
|City Fixed Effect||YES|||YES||
|Constant|-0.70***|0.50|3.52***|2.78|5.67***|289.78|
|Observations||830,929|||301,382||
|Pseudo R2||0.166|||0.311||

*** p<0.01, ** p<0.05, * p<0.1

There exists high variation of gender-bias, favoring men for certain occupations and women for others, which ultimately contributes to occupational gender segregation in the labor market. Based on our results, women are favored for clerical jobs; a gender-specified ad for a clerical job is more than 4 times as likely to target women than men compared to the base category. On the other hand, men are preferred in sales, and elementary occupations. We have already seen that professional, teaching and BPO jobs are less likely to be gender-targeted, however, among the gender-specified ads in those categories, teaching and BPO jobs have high female bias and professional jobs in general are slightly biased towards men (Figure 4). The statistical significance of the period dummy indicates that demand for female workers is increasing over time.

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and were effective tools in encouraging safe sex while providing sex workers with access to free condoms, they can be potentially used to identify the compensating differential for condom use. We can posit the existence of an equation determining whether a sex worker always uses condoms:

# C=C(X, _S) (2)_

where C is a binary variable denoting condom use, X is the vector of sex worker characteristics, and S is a dummy indicating if the sex worker has seen the flip chart. We will estimate (2) and (1) simultaneously using instrumental variables to get unbiased and consistent estimates of the impact of condom use on income. In particular, it helps deal with an important type of unobserved heterogeneity in this market - the sex worker's physical attractiveness which will bias the estimate of the impact of condom use on income if condom use is not instrumented. Physical attractiveness is perhaps the most important attribute in determining earnings from sex work and is also very difficult to measure. Since it is not measured, estimates of the impact of condom use on sex worker wages can be seriously biased because sex workers who are relatively attractive and consequently have a high demand for their services may also be more willing to "purchase" safe sex by using condoms. However, their earnings would remain high relative to the rest of the population. This would cause condom use to show a positive correlation with the average rate per act, which is a problem similar to the bias caused by unobserved productivity on estimates of compensating differentials in other labor markets (Hwang, Reed and Hubbard, 1992). While any such unobserved heterogeneity is a potential problem in OLS estimates of prices, so long as the excluded variable - contact with a peer educator - is not correlated with the source of unobserved heterogeneity, condom use can be instrumented to correct for the bias.

As explained above the data for this analysis are from a random sample of 612 sex workers in Sonagachi from a survey conducted in 1993. The sex workers were surveyed by a team social workers under the supervision of the All India Institute of Hygiene and Public Health (AIIH&PH). AIIH&PH had a presence in Sonagachi for more than a year at the time of the survey and were therefore able to establish an excellent rapport with the sex workers and thus elicit accurate responses<sup>5</sup> . While the survey instrument was primarily focussed on medical evaluations, a number of socio-economic questions were also asked. It should be noted that there are two main limitations in the survey from the perspective of estimating compensating differentials. Clients were not surveyed, which prevents us from saying anything about the demand side of the market, and questions on prices were not based upon different See the AIIH&PH report (1997) for more details.

8
Figure 6: The relationship between the average rate of November fire occurrences in subforests and the socio-economic characteristics in the neighboring villages.

<!-- Start of picture text --> Number of households<br>% households heads with primary education or better<br>ln(Value of household assets)<br>−0.166**<br>% farmer households<br>% households using inorganic fertilizers<br>0.258***<br>% households using organic fertilizers<br>−0.151*<br>Distance to the main road<br>−0.178*<br>Distance from subforest to the main road<br>Distance to the village’s subforest<br>−.6 −.4 −.2 0 .2 .4<br>Number of days the forest’s average a grid cell is on fire<br>Regression includes all partitions (N=29)<br><!-- End of picture text --> _Note:_ This figure presents the coefficients from regressing average pre-treatment November forest fire occurrences in the treatment forests’ 29 subforests on the neighboring communities’ standardized socioeconomic characteristics, in the pre-intervention period. The dots represent the point estimates, and the horizontal lines represent the 90% confidence intervals. ***, **, and * indicate significance at the 1, 5, and 10 percent critical level.

trols for each of the treated subforests we not only fitted the weights on pre-intervention outcomes, forest size and precipitation data (as we did in Section 5), but also on all village characteristics as presented in Table 4. After having estimated the treatment effects for all 29 treated subforests between 2014 and 2019, we regress _αsrt_<sup>November</sup> on all village characteristics from the LSMS survey and pre-treatment average of November fire occurrences using the following random effects panel model: Here, _Y_<sup> ̄</sup> _srNov,t≤T_ 0 is the average pre-intervention level of November forest fire occurrences in subforest _s_ in region _r_ , _Hs_ denotes the set of normalized (and time-invariant) characteristics of the village matched with subforest _s_ , _νs_ is the subforest random effect, _δr_ 28
Table 23, Implied **replacement ratem** with **indexed pensions**

|**Percent**|**Indexation of**<br>**penslions**<br>to**prices**|**Indexation of**<br>**p-nmions**to**wagem**|
|---|---|---|
|United States|37.6|36.9|
|United Kingdom|66.4|52.6|
|Germany|40.0|27.6|
|Japan|28.4|18.9|
|Canada|23.1|19.4|
|Netherlands|40.5|32.2|
|Sweden|16.1|13.7|
|Switzerland|21.6|17.8|
|Denmark|33.2|25.3|

Note: This table uses the simple model developed by Vittas (1992) to calculate the implied replacement rates if the pension schemes were run as defined contribution plans and the real rates of return and real rates of earnings growth were those of the period 1967-90. The model is based on a 10I contribution rate, 40 years of active service and 20 years of retirement life.

32a
A5). One possibility we sought to examine was whether our endline survey took place too soon to capture migration effects. We conducted the endline survey from May through August 2012, which was between nine months and one year after we offered respondents passport assistance. Because the passport process was quite time-consuming, some respondents received their passports as late as three months before the endline survey, and they may not have yet had time to finish the migration process they initiated when we followed up with them.

To address this concern, in March and April 2013 we re-surveyed respondents who reported that anyone in their household was offered a job overseas between 2010 and 2012, including those who had offers they had not yet accepted. We asked them about the status of the offers they listed in the endline survey, as well as any offers that were received but not listed in the endline survey, either because they were not reported or because the offer was received after the endline survey took place.

From our set of baseline respondents, we attempted to contact 196 households, and we successfully completed 194 surveys (99%). We completed 54% with respondents and 46% with a proxy household member. (Proxy survey rates are especially high for the 2013 offer survey because if the respondent was not available at the initial visit but another household member was willing to participate, we interviewed that member rather than schedule another visit.) Using this 2013 survey of baseline respondents reporting job offers in the 2012 endline, we generate a modified indicator of overseas migration, measured nearly two years after initial passport treatment assignment. This variable modifies the previous “Migrate 38
others 2009; OECD 2012b; Poncet 2006; World Bank 2008; and World Bank 2011, 2013; _Global Development Horizons_ , a World Bank flagship report that replaced _Global Economic Prospects_ in 2011, focuses on medium- to long-term development issues). These models are used mainly to investigate the long-term consequences of policy scenarios relating to specific issues, such as public sector debt, the business climate, patterns of human development and poverty, trade reform and globalization, the international monetary system, and climate change. Another set of studies focuses on emerging megatrends and to a lesser extent on productivity and growth modeling. Only a few studies have attempted to combine growth- and productivity-based forecasts with analysis of megatrends in a meaningful way to construct a vision of the future.

Analysis of megatrends is mainly about anticipating dangers and preparing for them in a timely fashion. Conventional models of deterministic processes are not useful for doing so, because the analysis is essentially nonlinear and deals with the likelihood of unanticipated, chaotic transformative change in systems. The fact that “tipping points” cannot be predicted does not matter much because the forecaster is not required to produce a time path.<sup>12</sup> A number of recent studies offer thoughtful analysis of emerging megatrends that can be deployed in deepening work in specific areas. Scholarly studies as well as popular books that stand back in order to take a global, historical, and forward-looking perspective of the planet are to be encouraged, reviewed, and discussed by governments, business people, academics, and international organizations such as the World Bank.

Among the many books and studies that have been published in this area in recent years, we focus on a few of them that include lists of issues similar to the ones we identified more than two decades ago. These studies tackle the big picture from different angles and with varying success. In _Megachange: The World in 2050_ , edited by Daniel Franklin (2012) and his colleagues at _The Economist_ , 20 essays under four headings (people and relationships, heaven and earth, economy

> 12 Based on written comments by Paul Armington, who headed the World Bank’s International Economics Analysis and Prospects Division from 1987 to 1992.

57
<mark>Rawlings, S. and Z. Siddique. (2019) “Domestic Abuse and Child Mortality.” Forthcoming in</mark> _<mark>Oxford Bulletin of Economics and Statistics</mark>_ <mark>Rudman, L. A., and J. E. Phelan 2008. “Backlash effects for disconfirming gender stereotypes in organizations.” Research in organizational behavior 28: 61–79</mark> Schaller, J. (2013) “For richer, if not for poorer? Marriage and divorce over the business cycle.” Journal of Population Economics 26, no. 3: 1007-1033.

Schuler, S. R., S. M. Hashemi, A. P. Riley, and S. Akhter. (1996). “Credit programs, patriarchy and men's violence against women in rural Bangladesh”. _Social Science & Medicine_ , 43(12), 1729-1742.

SIGI. 2019. “Information about Variables and Data Sources for the SIGI 2019.” Social Institutions and Gender Index. <u>https://www.genderindex.org/data/</u> Stevenson, B., & Wolfers, J. (2006). “Bargaining in the shadow of the law: Divorce laws and family distress.” _The Quarterly Journal of Economics_ , 121(1), 267-288.

<mark>Tauchen, H. V., A. D. Witte, and S. K. Long (1991). “Domestic violence: A nonrandom affair.”</mark> _<mark>International Economic Review</mark>_ <mark>, 491-511.</mark> <mark>Tertilt, M., and G. J. Berg. (2015). “The association between own unemployment and violence victimization among female youths.”</mark> _<mark>Journal of Economics and Statistics</mark>_ <mark>, 235(4-5), 499-516.</mark> <mark>Tur-Prats, A. (2015). “Family Types and Intimate- Partner Violence: A Historical Perspective”. Working Papers 835, Barcelona Graduate School of Economics.</mark> <mark>Tur-Prats, Ana. (2017) “Unemployment and intimate-partner violence: A gender-identity approach”. UPF working paper No. 1564.</mark> <mark>Woodhouse, J. and N. Dempsey, 2016, “Domestic Violence in England and Wales” Briefing Paper No. 6337, House of Commons Library</mark> UN Women (2015a) “Monitoring Gender Equality and the Empowerment of Women and Girls in the 2030 Agenda for Sustainable Development: Opportunities and Challenges”

- - - <u>http://www.unwomen.org/en/digital library/publications/2015/9/indicators position paper#sthash.TicCeB2P.dpuf</u>

UN Women (2015b) “A framework to underpin action to prevent violence against women”

- - <u>http://www.unwomen.org/en/digital library/publications/2015/11/prevention framework#sthash.UUhyVCWX.dpuf</u>

WHO (2014). “Global status report on violence prevention” World Health Organization Woodhouse, J., and N. Dempsey. 2016. “Domestic Violence in England and Wales.” Briefing Paper No. 6337, House of Commons Library, London, UK.

World Bank (2012) “World Development Report 2012 : Gender Equality and Development”. World Bank.

25
The reasoning is simple: If the costs of hiring and firing workers are too high, it becomes too expensive for firms create jobs. In countries where the labor market is still too heavily regulated, it is essential to increase flexibility so that the labor market can better respond to rapidly changing demand. Labor markets in most of ECA, despite having become more flexible over time, still have important rigid elements, particularly in terms of employment protection legislation (EPL), easiness to start a new business and tax regulations.

EPL is a form of labor market regulation that restricts employers’ ability to dismiss workers and can reduce flows into, but also out of unemployment. Strict EPL can slow new employment if restrictions on dismissing workers make employers wary before taking on someone new. For this reason, restrictions on dismissal could increase the likelihood of unemployment, the duration of unemployment, the attraction of using fixed-term contracts, and past a certain threshold, can even cause employers to turn to the untaxed, unregulated labor market and hire informally. Beyond affecting flows into and out of employment, EPL creates an “insider-outsider” divide. Those that have a protected job (“insiders”) are relatively guarded from losing it, while the inactive and unemployed (“outsiders”) find it more difficult to gain employment. EPL changes the distribution of jobs with important implications for first-time job seekers, youth (especially), women, the disabled, and other disadvantaged groups (World Bank, forthcoming). The higher the EPL, the more difficult the transition to new sectors could be.

Despite significant reforms, EPL remains rigid in several ECA countries (See Figure 11). In Turkey, for example, EPL is rigid for both permanent and – especially – temporary workers, contributing to maintaining resources in inefficient informal and semi-formal activities. Süral (2009) argues that simplification is needed in the regulatory environment. Otherwise such a strict EPL will continue to be featherbedding for the informal sector by pushing the firms and workers into it. This is why the OECD, in its Economic Survey of Turkey 2006, states that “the Government needs to improve conditions for job creation in the formal sector by adopting more flexible employment protection regulations, replacing severance payments with unemployment insurance.”<sup>20</sup> The cost of complying with labor regulations such as those on severance pay is high by international standards, as a result of which the employers increase working hours instead of creating employment (Süral, 2009). In FYR Macedonia, the high level of EPL in is considered mostly due to the stringency of the legislation on temporary employment and collective dismissals<sup>21</sup> , although significant reforms continue to be implemented in these areas. For high EPL countries where there remain important restrictions to hiring and firing, reducing these – while ensuring some safety net for displaced workers – could facilitate the needed structural transformation.

> 20 OECD (2006)

> 21 ILO (2008)

21