rafmacalaba/datause-encoder-data huggingface dataset:
2 KENYA AND CHINA’S TRADE RELATIONSHIP that local producers are suffering from cheap Chinese goods. Some even argue that imports are hurting Kenya’s prospects of industrialization.

<!-- Start of picture text --> 20000<br>China, P.R.: Mainland<br>World<br>10000<br>0<br>2012 2013 2014<br>Year<br>Imports (USD millions)<br><!-- End of picture text --> Figure 2: China takes a sizeable share of total Kenyan imports (2012-2014) Source: IMF World Economic Outlook Database 2014 <!-- Start of picture text --> 3000 3000<br>China, P.R.: Mainland China, P.R.: Mainland<br>2000 2000<br>India India<br>Japan Japan<br>1000 South Africa 1000 South Africa<br>United Arab Emirates United Arab Emirates<br>0<br>2004 2006 2008 2010 2012<br>Year Country<br>(a) China and India are a major source of imports (b) China and India were top two sources of imports (2013)<br>(2004-2013)<br>Imports (USD Millions) Imports (USD Millions)<br><!-- End of picture text --> Figure 3: China and India are major sources of imports (2004-2013) Figure 3a shows trends in Kenya’s top import partners between 2004 and 2013. With strong growth since 2004, China and India have become major sources of imports to Kenya. China ranks second to India in number of exports to Kenya, and its low production costs and better positioning in global value chains may help it become the top source of imports for Kenya (figure 3b). Chinese imports grew at an annual rate of 33 percent, and Indian imports also grew quickly at 30 percent per year.

Kenya likely imports even more from China and India because many imports from the UAE are re-exported manufactured products such as phones, computer monitors, or jewelry originally from China or India. It is is difficult to find the exact amount of re-exports from China, but the UAE re-exported US $384.5 million worth of goods to Kenya in 2014, a large fraction of those 5
**Table 1: Distribution of binary**

# **variable for satisfaction**

|_Binary_<br>_variable_|_Health_<br>_services_<br>_Education_<br>_services_|
|---|---|
|_S = 1_|58.1%<br>50.2%|
|_S = 0_|41.9%<br>49.8%|
|_Total_|100<br>100|
|_Note_: S=1<br>code=(2,<br>_Source_: G|if code=1; S=0 if<br>3,4,5)<br>DS-2 (2006)|

37
- Sturgeon, T., J. Van Biesebroeck and G. Gereffi (2008), "Value chains, networks and clusters: reframing the global automotive industry," _Journal of Economic Geography_ , 8(3), pp. 297-321.

- Sturgeon, T.J., and G. Gereffi (2009), “Measuring success in the global economy: international trade, industrial upgrading, and business function outsourcing in global value chains”, _Transnational Corporations_ , 18(2).

- Timmer, M.P. (ed., 2012), _The World Input-Output Database (WIOD): Contents, Sources and Methods_ , WIOD working paper nr. 10, available at www.wiod.org.

- Trefler, D. and S. C. Zhu (2010), “The Structure of Factor Content Predictions”, _Journal of International Economics_ , Volume 82(2), pp. 195-207.

Winkler, D. and W. Milberg (2012), “Bias in the ‘Proportionality Assumption‘ Used in the Measurement of Offshoring”, _World Economics_ , Vol. 13, Issue 4, 2012, pp. 39-60.

- Yi, K-M (2003), "Can Vertical Specialization Explain the Growth of World Trade?" _Journal of Political Economy_ , 111(1), pp. 52-102.

38
exactions/impact fees, a strong municipal accounting standard to ensure that exactions are used for specific purposes, and the ability to curb corruption during the collection of these fees from developers.

Just as it was describe in the case of betterment levies, strong institutions are important for the implementation of impact fees. In this particular case, strong municipal accounting standards are important to ensure that exactions are used for specific purposes.

# <mark>4.4 TAX INCREMENT FINANCING (TIF)</mark>

Tax increment financing allows municipalities to fund current development projects with anticipated increases in revenue from property tax generated through development. This tool is designed to capture expected (new) tax revenues generated as a result of higher property values that will materialize after the development project takes place. Municipalities create TIF districts to spur development that would not occur “but-for” TIF. The increment is used to fund borrowing for specific projects or purposes, e.g. roads, affordable housing, etc.

Originating in California in the 1950s, TIF is mainly used in the United States to ensure financing where federal funds have begun to dry up. Dye and Merriman (2006) explain that the rules differ across states, but all tax increment districts must have at least one of the following characteristics: i) be blighted or underdeveloped; ii) need rehabilitation or to be converted to a new use; or iii) adhere to the “but-for” clause mentioned above. The popularity of TIFs in the US stems from it being not only an efficient financing tool but also a useful instrument for land development.

TIFs provide a cheaper alternative to finance development projects as the cost of government selling securities is in general considerably lower than what a developer would pay if he had to access the capital markets for financing; interest costs are considerably lower and the terms of repayment are more generous than what the private market would provide.

While TIF rules vary, the municipalities follow the following general methodology for TIF projects:

- Draw up a development plan, forecasting the development in the tax increment district and project costs

- Perform financial feasibility study

- Designate area as one that requires TIF based on essential characteristics

- Freeze local tax revenue at predevelopment value which still goes to the local government, any increment above that goes into TIF collection to finance general obligation municipal bond payments

- The coordinating authority is the local development authority (adapted from Wisconsin methodology, Kashian et al. 2007)

The United States uses TIF extensively, but this financing method has yet to be used in developing countries. The system itself is predicated on precise methods of measuring tax increment through appraisal methods as well as effective property tax collection hence the difficulty of applying it in developing countries where appraisal capacity is limited and data on 29
that entering apprenticeship entails large opportunity costs in terms of forgone employment opportunities. Since there are few financial instruments to smooth consumption over the long duration of an apprenticeship, these opportunity costs are likely the main constraint in access to apprenticeship that the program addresses.

We can also document changes in the population of youths entering firms as apprentices. The follow-up survey asks each apprentice in firms a set of questions about their background characteristics. Three main populations of youths can be compared: formal apprentices _f_ , traditional apprentices in treatment firm _tT_ and traditional apprentices in control firms. We use the following regression to describe heterogeneity in the apprenticeship population: (8) Traditional apprentices entering control firms constitute the excluded category. The two important coefficients in this regression are _bf_ and _btT_ . The first compares the population of formal apprentices with the population of traditional apprentices in control firms, which helps document the selection effect. The second parameter compares the population of traditional apprentices between treatment and control firms. This allows to document the characteristics of youth crowded out of apprenticeships by the entry of formal apprentices.

The top panel of Table 8 presents the results. It shows that formal apprentices are older (by 2.10 years), and more likely to be women (by 11 percentage points) than apprentices in control firms. Formal apprentices also have a higher education level: compared to traditional apprentices in control firms, formal apprentices are 48 percentage points less likely to have no education, 35 percentage points more likely to have completed primary school, and 13 percentage points more likely to have completed lower secondary school. The program therefore places youths that are more educated than the workforce traditionally hired by firms. There is no indication that the program helped insert youths with more limited networks, or more disadvantaged socio-economic backgrounds. They are also no more or less likely to know a master craftsmen, or have a master craftsman as a relative or family acquaintance. Formal apprentices do not report having been more affected by the crisis period in the country either.<sup>37</sup>

> 37The table also shows that there are very few differences in characteristics between traditional apprentices in treatment and control firms (see row labeled "Traditional" in top panel). This suggests that the crowding-

33
# **Table 3: Household Composition and Child Labor**

_limited to children 6-15 in rural households in panel communities 1993 and 1998 data are combined for this table_

||Active=1||Active=0||
|---|---|---|---|---|
||-1|-2|-3|-4|
|Sample Size|5062||4491||
||mean|s.e.|mean|s.e.|
|Household Size|6.103|0.080|6.187|0.101|
|# Persons 0-5|0.605|0.035|0.614|0.033|
|# Persons 6-15|2.570|0.035|2.545|0.040|
|# Persons 16-24|0.750|0.029|0.762|0.040|
|# Persons 25-64|1.993|0.011|2.066|0.019|
|# Persons 65+|0.187|0.012|0.204|0.013|
|# Siblings|3.018|0.073|2.968|0.081|
|# Younger Sibs|1.632|0.053|1.017|0.037|
|# Younger Brothers|0.840|0.033|0.521|0.023|

Active is defined as participating in market work or participating in household production for at least 7 hours in the last week. All means weighted to reflect sample design. Standard errors corrected for psu clustering.
fire in Godhra on February 27, 2002. While several theories circulate on who set the train on fire, the dominant one is that a Muslim mob set the train on fire due to an altercation between a Muslim tea seller and the Hindu activists and possibly the attempted molestation of the tea seller’s daughter (Khanna, 2008; Simpson, 2017). Several of the 58 people killed on the train were women and children.

Following this incident, a three-day-long violence was perpetrated by Hindu mobs against the minority Muslim population. The riots were considered one of the most severe forms of Hindu-Muslim violence in post-independent India in terms of both geographical spread and brutalities. While previous Hindu-Muslim violence in India had been an urban phenomenon, in 2002, it spread to rural locations as well (Jaffrelot, 2003). According to official figures, the riots killed 790 Muslims and 254 Hindus. However, unofficial statistics suggest that close to 2000 Muslims were killed over the next three months, and many others were injured (Amnesty International, 2005). More than 200,000 Muslims were displaced because of the looting and burning of their homes and businesses (Srivastava, 2011). There were further outbreaks of statewide violence over the next year.

Women were largely victimized the Gujarat riots of 2002. Several women were raped and killed. Survivors reported sexual violence such as rapes, gang rapes, forced nudity, torching, mutilation of the stomach and sexual organs, and insertion of objects into bodies (Hameed et al., 2002). According to Jha and Farasat (2016), Muslim women were not the only victims of sexual violence in the Gujarat carnage of 2002; Hindus were also targeted in some cases. Many of these cases were not reported to the police because of the fear of further attacks, stigma, and ostracism.

During episodes of communal violence, women are often targeted to create fear and also because they are viewed as transmitters of the culture of a particular community. Exposure to such physical and sexual brutalities can have deep physical and emotional effects on the victims, their families, their communities, and their marital decisions. The fear of sexual violence and stigmatization can lead to early marriages of women. Due to the communal conflict in Gujarat in 2002, we expect a relatively larger effect on Muslim women’s marital age since they were the primary victims of sexual violence. In this paper, we study the effects of the Gujarat riots of 2002 on women’s marital outcomes.

# **4 Data**

This study uses data from the fourth round of the National Family Health Surveys (NFHS-IV), based on the Demographic and Health Surveys (DHS), conducted in India during 2014-15. These are nationwide repeated cross-sectional surveys that are representative at 6
6

# SECTION 1. Housing Privatization In The Former Soviet Union: Factors And Social Effects

# 1.1. A Comparative View of the Scope of Housing Privatization

A review of the scope of housing privatization in several republics of the former Soviet Union including Russia will help set the context for the more detailed analysis of developments in Russia and will provide a basis for comparison.

In 1990-1996, the structure of the housing stock in the post-Soviet states experienced radical changes through the process of privatization. Privatization of housing started earlier and was more widespread in **_Lithuania_** than in any other country of the former Soviet Union. Table 1.1 gives the structure of the Lithuanian housing stock by form of ownership in 1991 and 1994: Table 1.1. Lithuania, housing stock by form of ownership (% of total).

|Type of Ownership|1991|1994|
|---|---|---|
|Private|39|87|
|State and municipal|52|8|
|(C22erative|9|6|

Source: **Ziliaskene,** 1995.

Privatization took place very fast; at present most apartments are private property. In contrast to 1990, most Lithuanian dwellings are privately owned (Vine et al., 1996).

In _Estonia_ privatization of apartments began in the autumn of 1993. Table 1.2 shows the distribution of Estonian housing by form of ownership.

Table 1.2. Estonia, housing stock by form of ownership (% of total).

|Type ofOwnership|1994*|1996**|
|---|---|---|
|Private|35|80|
|State|13|0|
|Municipal|42|20|
|<br>Cooperative|9|0|
|<br>Other|1|0|

**Source: LpVaa,** 1996. * = **as** of **Janary,** 1994; ** = **as** of July, 1996.

As of July 1996, the share of enterprise and municipal housing fell to 20%/, while the private (including former cooperative) stock constituted 80%; in all, 66% of the housing subject to privatization was privatized. The share of privatized apartments varies depending on the region of Estonia: in some it reaches 98%/e, while in others it is as low as 35% (Eastern Estonia, a region with a large population of ethnic Russians, many of whom are not Estonian citizens). The enterprise housing stock has all but disappeared, having either been tranderred to the municipalities or privatized by tenants.

In 1993 and 1994, selective surveys on the level of willingness to privatize were conducted. In 1993, 30% of respondents said they were not interested, while in 1994 the equivalent figure was only 12%.

According to Estonian researchers, the rental sector reform, and in particular the removal of state subsidies, promoted the process of privatization, which they generally consider to have success.

In _Latvia_ between 1990 and 1994, the share of state-owned and municipal housing decreased from 64% to 54%, while the share of private dwellings rose from 22% to 39% (Tosics and Hogedus, 1996). In _Ukraine,_ housing privatization produced the following housing structure by form of ownership in 1996:
the Bun and Kiviet LSDVC estimator. Instead, we estimate an Ordinary Least Square model with country and year fixed effects and robust standard errors.<sup>12</sup> Model 1 shows that the effect of BRD on undiversified firms’ greenfield FDI flows is negative but only statistically significant at the 10% level. A 10% increase in BRD is associated with a 1.3% decrease in FDI flows made by undiversified MNEs. In contrast, the effect of BRD on FDI of diversified firms, defined as those having subsidiaries in 10 countries or more, is close to zero and is not significant (Column 2). A Chow test shows that the coefficients in columns 1 and 2 are statistically different from one another at the 1% level, supporting hypothesis 5 that political conflict has a smaller effects on FDI made by diversified MNEs than on FDI made by undiversified MNEs. In Column 3 to 6 we also distinguish between resource-related and nonresource-related greenfield FDI flows of diversified and undiversified firms. The effect of BRD on resource-related FDI flows, shown in Columns 3 and 4, is small and statistically insignificant, irrespective of whether these are investments made by undiversified or diversified MNEs. This supports our previous finding that political conflict does not significantly affect resource-related FDI flows (see Table 1, Column 3-4).

The results for non-resource-related FDI flows, shown in Columns 5 and 6 of Table 2, show a different pattern. Nationwide conflict affects negatively the non-resource-related FDI flows of undiversified firms. This effect is significant at the 5% level, indicating that a 10% increase in BRD decreases non-resource-related FDI flows of undiversified MNEs by 1.5%. Non-resource-related FDI made by diversified MNEs are less affected by increases in the number of BRD and the coefficient is not significant. The difference between the coefficients in regressions 5 and 6 is statistically significant at the 1% level. Thus, whether firms are geographically diversified matters for the relationship between political conflict and greenfield FDI flows only in the case of non-resource-related FDI.

25
67 As a further check, the IV and OLS regressions reported in Table 63 constrain the labor input coefficients of the three national groups to be identical, but allow for different coefficients by skill group. The results suggest that the marginal<sup>product of</sup> upper level managers, taken as a group, is significantly positive.

**TABL** 63

**Sena (ABl** IaLxtles): OLS **and** IV **_Estim_** of **Augmnted CobbWDougs Prduton Funtion Based on Skill Groups Dpedmt Variabble** - **Log** of Conslant **Pice** Value **Added**

(Values in parcnthcscs are standard errors)

||Base<br>Value-A<br>in Domes|don<br>dded<br>tic Prices|Based<br>Value-<br>in Worl|on<br>Added<br>d Prices|
|---|---|---|---|---|
||Qm|IV|_Q_|A<br>I|
|Intercept|-0.493|-0.780|-1.199'|.1.425|
|% Senegalesc Pate|(0.489)|(0522)|(0.530)|(0.564)|
|% Senegalese State|-0.074<br>(0248)|-5.6E.4<br>(0.268)|-0.048<br>(0.258)|7.8E 3<br>(0.277)|
|neEunhi-|||||
|% French|0.176k<br>(0.097)|0.179<br>(0.100)|_0.035_<br>(0.102)|0.036<br>(0.105)|
|%Syro-Lebanese|_O.418e_<br>(0.114)|0.370<br>(0.116)|_-0516_<br>(0.118)|40.471*<br>(0.121)|
|% Other Foreign|0.109<br>(0.118)|0.116<br>(0.121)|40.061<br>(0.123)|-0.054<br>(0.124)|
|Trmde Variables:|||||
|Effective Rate of<br>Protection|8.0E-4<br>(0.007)|2.0E-3<br>(7AE-3)|-0325<br>(0.021)|40329'<br>(0.022)|
|% Exports|0.026<br>(0.159)|-0.043<br>(0.189)|-0.043<br>(0.167)|4-.122<br>(0.196)<br>(Continued)|
# **Figure 3**

<!-- Start of picture text --> Percentage of countries with a positive annual average of intervention index<br>Only intermediate Regimes<br>(variable Int.1)<br>0.90<br>0.80<br>0.70<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>1974 1980 1986 1992 1998 2004<br>% of positives (excluding obs. inside of non-direction band)<br>% of positives<br>% of positives (demeaned data)<br>%<br><!-- End of picture text --> <!-- Start of picture text --> Percentage of countries with a positive annual average of intervention index<br>Intermediate and Pegs<br>(variable int.1)<br>0.90<br>0.80<br>0.70<br>0.60<br>0.50<br>0.40<br>0.30<br>0.20<br>0.10<br>0.00<br>1974 1980 1986 1992 1998 2004<br>% of positives<br>% of positives (excluding obs. inside of non-direction band)<br>% of positives (demeaned data)<br>%<br><!-- End of picture text --> 23
# **_Civil Society_**

A full set of formally democratic institutions will not produce accountable government, without a strong civil society, which enables social groups – trade and professional associations, community groups – to function as “whistle-blowers”. Although formal institutions have more power and authority to address the incidence of corruption, the importance of informal institutions and civil society should not be underestimated. Neither internal control mechanisms nor the judiciary is immune to corruption, and more importantly they often need incentives to function properly. An independent and self-motivated press, responsive opposition groups and well-established non-governmental organizations can express themselves given that civil liberties exist to secure free access to government operations. In many empirical studies political and civil rights are found to have a positive impact on governance.<sup>8</sup>

# **From Theory to Practice: What Is Missing?**

So far we have seen the traditional approach to corruption in government. However, this approach fails to explain widespread corruption in many developing countries. Contrary to the principal-agent models, corrupt practices are not necessarily unknown to the society (‘the principal”) in most cases. Although the identities of corrupt officials as well as their activities are well known, institutions are not well developed to enforce the rule of law and to punish the disobedient. Similarly, crime-and-punishment models also perform a poor job in explaining why some officials choose illegal behavior over legal ones although they do not need additional compensation to support their living. If the marginal gain from corruption is still high enough to exceed the marginal cost of being caught, this means that probability of punishment and/or penalties are not only far from being deterrent, but also formal rules have been superseded by informal ones, which create an elite class in government immune from formal control.

> 8 See Ades and Di Tella (1997), Van Rickeghem and Weder (1997), Johnson, Kaufmann and Zoido-Lobaton (1998), Isham, Kaufmann and Prichett (1998) and Treisman (2000).

10