rafmacalaba/datause-encoder-data huggingface dataset:
significant ongoing maintenance requirements. With PFI, the public sector defines what is required to meet public needs and remains the client throughout the life of the contract. The public sector also ensures, by contract, delivery of the outputs it sets. The private sector takes on the responsibility for providing a public service, including maintaining and enhancing or constructing the necessary infrastructure.

While significant, the role of the PFI should not be overestimated. It will account for just 11% of total investment in 2003-04 (approximately 4.5 billion pounds) and from this amount, the transport sector will get 1.3 billion pounds. The largest transport projects financed through the PFI are the modernization of the London Underground and the M6 Toll Road.

Other European countries are also increasingly relying on PFIs for transport projects. Germany is relying on private investment (construction, operation and maintenance) to extend the federal motorways. A new law (April 2002) provides the legal basis for implementing a distance related toll for the use of motorways by heavy vehicles, which will be the finance source for the investment done by the private sector. Ireland also created a PFI and will also rely on it to fund some toll roads.

Australia, after selling assets and privatizing through long term leases (for instance airports) took the lead in setting administrative rules to ensure access to essential facilities, estimating productivity gains and setting benchmarking mechanisms to compare the performance of all operators<sup>9</sup> .

In developing and transition economies, concessions are the most common form of private sector participation in transport. Table 3 illustrates that concession contracts accounted for 56% of all transport projects between 1990 and 2001. It also shows that while Latin America and East Asia are the most active in promoting concessions, greenfield projects have been quite successful in East Asia over the last eleven years or so. The Middle East has been the least effective (or maybe the least interested) at building a partnership with the private sector in transport, although there was some activity in private ports. South Asia and Africa come next—including the successful bi-national railway concession between Abidjan and Ouagadougou. Part of the problem is that in these regions most types of risks levels, not only political and regulatory, but also commercial, are high. The ability to pay for transport services in many countries of this region is very modest, implying that tariffs for most passenger services, for instance, have to be quite low. This, in turn, implies rather long-run commitments in order to recover investments.

> 9. Studies specific to airports, ports and railways can be obtained from the Australian Competition and Consumer Commission (www.accc.gov.au)

11
days generated per job card at the block-level. In 2009/10, the number of person-days generated was 11.15 on average with a standard deviation of 5.58. Fourth, I explore the self-selection of households into the program by comparing the changes in the risk index of households who were registered with the NREGS by 2007 with the rest of the sample.

At the district level, the NREGS should have been introduced in the poorest districts first. This could potentially bias the estimates downwards because poorer districts are less likely to have extension services and marketing structures in place that would enable households to seize the opportunity to plant more profitable cash crops. However, in most states - and in Andhra Pradesh in particular - the prioritization of the poorest districts was not systematically implemented. In this sample, the general economic characteristics of treatment and control districts do not differ greatly.<sup>26</sup> The treatment intensity at the block level should also be exogenous to potential outcomes. Estimates could be biased if funds allocated to blocks responded to rainfall shocks and if these rainfall shocks also affected a household’s input allocation decision. However, the amount of funds to be sanctioned per block is defined between December and March for the following financial year (April to March). Since I am using lagged values of disbursed funds, these amounts are fixed 14 to 18 months before household’s decide on their input allocation.<sup>27</sup> Lastly, I explore differences in crop choices across households who registered with the NREGS or not. Here, the possibility that unobserved shocks or other time-varying variables affect both the decision to register and a household’s crop choice cannot be ruled out. I employ matching techniques to reduce selection bias, but this is admittedly not sufficient to rule out non-random assignment.

The parallel trends assumption could be violated due to differences in crop productivity which cause the share of certain crops in total input allocation to increase independently of the NREGS. Given the small number of districts in the sample, this could significantly bias the results. District-wise time trends in the risk index of crop production are displayed in Figure 1. One of the treatment districts (Mahaboobnagar) displays

> 26See appendix B and Table E.3 for more information.

> 27It is also fixed between 6 and 8 months before the start of the monsoon, which could affect next years input allocation through time-lags in the effect of shocks. For more information on the time line, see appendix B.

16
**Table 8: Conditional Expectation Correction of TFP Equation : CLogit  selection equation Dependent variable Log TFP (adjusted for skills)**

|||O|LS|||Random|Effects (GL|S)|
|---|---|---|---|---|---|---|---|---|
||All||High FDI<br>States|Low FDI<br>States|A|ll|High FDI<br>States|<br>Low FDI<br>States|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Adjustment for market|||||||||
|power|1.841||2.004|1.692|1.520||1.501|1.634|
||(49.61)**||(33.55)**|(33.85)**|(51.49)**||(29.40)**|(43.09)**|
|Foreign market access|-0.121|-0.156|-0.132|-0.155|-0.114|-0.143|-0.133|-0.142|
||(9.99)**|(9.89)**|(6.14)**|(8.51)**|(6.11)**|(5.92)**|(4.06)**|(4.80)**|
|Domestic market access|2.925|3.199|2.820|1.536|3.164|3.504|3.160|1.606|
||(7.20)**|(6.01)**|(3.15)**|(3.49)**|(4.79)**|(4.09)**|(2.23)*|(2.11)*|
|Own industry local size|-0.209|0.006|0.022|-0.519|-0.147|0.027|0.040|-0.354|
||(1.19)|(0.03)|(0.06)|(2.08)*|(0.53)|(0.07)|(0.07)|(0.82)|
|Constrained by labor regulation (%)|-0.030|-0.033|-0.009|-0.019|-0.039|-0.043|-0.022|-0.028|
||(2.60)**|(2.16)*|(0.30)|(1.20)|(2.08)*|(1.78)|(0.51)|(1.02)|
|Output lost to power outage (%)|-0.037|0.124|-0.030|0.088|-0.029|0.100|-0.002|0.075|
||(1.07)|(2.77)**|(0.45)|(1.63)|(0.57)|(1.50)|(0.02)|(0.90)|
|Business age|0.159|0.186|0.134|0.130|0.161|0.200|0.131|0.155|
||(10.12)**|(9.06)**|(5.07)**|(6.43)**|(6.90)**|(6.58)**|(3.41)**|(4.93)**|
|High FDI state|-0.482|-0.672|||-0.478|-0.653|||
||(9.59)**|(10.23)**|||(6.25)**|(6.59)**|||
|Low FDI state|-0.533|-0.671|||-0.561|-0.697|||
||(10.30)**|(9.90)**|||(6.99)**|(6.70)**|||
|Selectivity term(1,2)|-1.241|-1.389|-0.037||-0.245|-0.184|-0.007||
||(1.17)|(1.00)|(0.54)||(0.50)|(0.28)|(0.20)||
|Selectivity term(1,3)|0.972|1.142|0.102||0.134|0.103|0.011||
||(1.30)|(1.17)|(1.47)||(0.39)|(0.22)|(0.33)||
|Selectivity term (3,1)|-0.748|-0.737||0.111|-0.260|-0.164||0.027|
||(0.85)|(0.64)||(1.20)|(0.64)|(0.30)||(0.72)|
|Selectivity term (3,2)|1.072|1.158||-0.126|0.266|0.178||-0.057|
||(0.99)|(0.81)||(1.75)|(0.53)|(0.26)||(1.91)|
|Constant|-23.909|-17.558|-25.590|-12.852|-24.612|-20.259|-25.676|-13.967|
||(9.17)**|(5.14)**|(4.54)**|(4.40)**|(5.87)**|(3.72)**|(2.90)**|(2.82)**|
|Industry dummies?|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Yer dummies?|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|3440|3440|1322|1525|3440|3440|1322|1525|
|Establishments|||||1427|1427|555|604|
|R-squared|0.60|0.32|0.62|0.64|0.6|0.31|0.6|0.62|
|Rho|||||0.89|0.88|0.87|0.92|

Absolute value of t statistics in parentheses

* significant at 5%; ** significant at 1%

47
in many respects those of the top performers but are clearly different from those skills characterizing survivalists – and survivalists such that the mean of this predicted probability is similar in the group of top performers and the group of constrained gazelles. This suggests that the distribution of the observable factors is the same in both categories and that on average constrained gazelles should be as likely as the actual top performers to be top performers. The survivalists are defined as all entrepreneurs for whom Pr̂(YiTop = 1) is below the threshold.6 Estimates based on equation (3) illustrate that constrained gazelles and top performers share some characteristics. Appendix A, table A.5 illustrates the determinants of inclusion among the three distinct groups of informal firms that have been identified. The results show that firms managed by men, people who created businesses because they could not find jobs as wage earners in large firms, more well educated, and older people have a higher probability of being constrained gazelles. However, these determinants also decrease the probability of being survivalists.

Table 4 and map 1 show the shares of the three groups of informal entrepreneurs by province. One may note that, whereas almost 26 percent of the constrained gazelles are located in Kinshasa, only 1 percent are based in Sud-Kivu. Katanga and Kinshasa, where firms are the least constrained by the lack of basic infrastructure and the most highly endowed in capital, are the provinces with the most top performers and constrained gazelles.

Table 5 presents the shares of top performers, constrained gazelles and survivalists within province. Within province, most of entrepreneurs are either survivalists or constrained gazelles. Among entrepreneurs in Sud-Kivu, 78 percent are survivalists. In Kinshasa, more than a half of entrepreneurs are constrained gazelles. In Equateur, 16 percent of entrepreneurs are top performers whereas in only 4 percent of entrepreneurs in Nord-Kivu are top performers.

**Table 4. Percentage Share of Firms, by Province**

||Top performers|Constrained gazelles|Survivalists|
|---|---|---|---|
|Equateur|13.68|9.47|6.90|
|Bandundu|8.88|10.14|13.31|
|Bas-Congo|3.74|4.09|5.14|
|Katanga|15.83|16.98|10.40|
|Kasaï Oriental|10.46|7.54|10.53|
|Kasaï Occidental|8.37|8.39|18.21|
|Kinshasa|25.66|25.96|13.37|
|Nord-Kivu|1.36|1.75|4.39|
|Sud-Kivu|3.78|1.03|5.30|
|Maniema|1.67|3.62|2.46|
|Province Orientale|6.57|11.03|9.97|

_Source:_ Calculations using 1 **-** 2-3 surveys. Percentages are weighted using sampling weights

> 6 As a robustness check, the paper relies on the full information maximum likelihood (FIML) model to estimate equation (3).  The FIML model handles missing values problems well by using all the information available. The results remain unchanged (appendix A, table A.4). The two groups (constrained gazelles and survivalists) are then reclassified using the predicted probabilities based on the model regressions. The groups of constrained gazelles and survivalists using Heckman (1979) two step and FIML estimations match at about 96 percent.

13
income economies in telecommunications and finance, and of varying degrees of liberalization in developing economies.

The new Database reveals that developing economies have started to reform those sectors that were first liberalized by higher income economies, with particularly striking examples in some of the most dynamic countries such as Bangladesh or Vietnam, as well as some major emerging economies such as India or China. In parallel, many high-income economies began to initiate reform in transport and professional services, where protection had been deeply entrenched in all economies. Hence, the picture emerging in 2016 is of policy convergence in telecommunications and finance driven by liberalization in hitherto more restrictive developing economies, and of policy divergence and dispersion in transport and professional services, where reform has been mainly concentrated in higher income economies.

But there is a twist. While international services markets, for instance finance and telecommunications, are increasingly free from explicit restrictions on entry and ownership, they are increasingly subject to greater regulatory scrutiny, especially in higher income economies. That raises several questions for future analysis. Do these latter measures reflect “learning-byliberalizing,” in that lessons have been learned on complementing openness with more stringent prudential regulation? Or do they reflect a shift analogous to the reversal of openness in goods trade, ostensibly on security and stability grounds, but also possibly in response to the increased competitiveness of developing economies' services firms?

The paper is organized as follows. Section 1 describes the Services Trade Policy Database and the data collection process, while Section 2 is devoted to the updated World Bank Services Trade Restrictions Index (STRI). Using the resultant indices, the following section presents current patterns of services trade policy as well as the evolution of such policy over the past decade. The final section summarizes the findings of the paper and identifies areas for future work.

4
liberalization results from the Global Trade Analysis Project (GTAP) model of global trade. They assume that households are currently maximizing utility subject to their budget constraint and this permits them to infer the change in household welfare associated with marginal price changes by multiplying these price changes by the households’ budget shares – taking into account net sales by the household as well (including sales of labor and capital services). Since this calculation is computationally simple, they do so for the entire household survey, and draw conclusions about the likely impacts of accession by individual household type and location.

One of the main findings in the work of Chen and Ravallion, as well as the subsequent work of Ravallion and Lokshin (2004) on Moroccan trade policy, is the heterogeneity of effects across different types of households with the same pre-reform level of welfare. They refer to this as “horizontal inequality.” In their Morocco paper, Ravallion and Lokshin (2004) find that, depending on the depth of cuts in cereals protection, the impact on aggregate inequality can be fully explained by the horizontal component. Specifically, they conclude that for small cuts in protection – on the order of 10% – there is an increase in inequality that is largely fueled by the benefits flowing disproportionately to the wealthy (vertical inequality). However, in the case of full liberalization in the grains markets, they find the opposite to be the case; horizontal inequality dominates. That is, the predominate impact of trade liberalization on inequality is through the differential impact on households with the same level of pre-reform income.

The potential for generating highly disaggregate policy impacts is a great advantage of the Ravallion _et al._ approach. However, like the cost-of-living approach, this abstracts from the secondorder impact that price changes might have on the households’ mix of consumption and earnings. While households are unlikely to radically change their consumption patterns in the wake of a modest change in relative prices, this is not necessarily the case with sales revenue. While a farm household might reduce its consumption of maize slightly in response to a 10% increase in price, it might shift _completely_ from producing and selling oilseeds to growing maize. In the latter case, the welfare predictions made based on a first-order approximation might be rather misleading. Consider the case where a farm household in poverty has a choice between producing two commodities: maize or wheat. Initially they specialize in wheat, but WTO accession lowers the price of wheat, boosting that of maize. So they switch all of their land to maize production. The welfare approximation based on the 18
of measurement xed eects, _πn_ . _ε_ is a random, idiosyncratic error term and all the standard errors are clustered at the village level to allow for correlation among error terms within villages. _Xj_ is a vector of time-invariant child controls dummy variables equal to one if the child is a girl and if the child is a twin and child birth order xed eects. As in Equation (7), _βc_ s are the coecients of interest and have the same meaning as in Equation (7) but identify to the impact of mother's childhood exposure to conict on her child's outcomes. The equation under the standard assumptions of dierence-in-dierences models provides estimates of the causal impact of conict on second-generation health.

# 5 Results

In this section, rst I present the impact of childhood exposure on adult stature and establish validity for the identication strategy. Second, I report impact on health and economic outcomes that are very important to women's well-being, but also provide additional explanation for the impact on second-generation health. Finally, I present the health impacts on the children of women who were exposed to the war in their childhoods.

## 5.1 Impact on Women's Stature

Table 4 presents the impact of early childhood exposure to conict on adult stature using dierence-in-dierences Equation (7). The conict intensity variable used is months of war in the village of residence. While the outcome variable, height, in columns 1 to 3 is measured in centimeters, columns 4 to 6 present height-for-age standard deviation (HAZ). The possibility of non-parallel dynamics" in the dierence-in-dierences estimation could be problematic. Because of dierence in overall trends (health, education, poverty, environmental etc.), changes in adult stature could vary systematically across villages and, in particular, there could be mean reversion. Given the data structure, I can, however, test for the identication assumption. Besides the control group (aged 16 to 21 in 1996), women in age cohort 22 to 29 in 1996 would have gained full adult height by the start of the conict, hence, the changes in adult height between these two cohorts should not dier systematically across villages. Age cohort 22 to 29 is therefore included in all the regressions as a control to validate the identication assumption.

17
political and psychological barriers which inhibit cross-border purchasing between Austria and Hungary (6), before concluding the paper.

# **CASE SELECTION AND METHODOLOGY**

Austria and Hungary were chosen as a country pair because of various reasons: health care standards and prices differ between these countries; they have a long common history; and, cross-border contacts are a common phenomenon between the two countries.

The parts on the (legal) national and European structures of receiving health care services abroad have been done through a literature survey, a legal analysis, and interviews with representatives of ministries of health and social affairs, social security institutions, public health care providers, and private health care providers. The third section is based exclusively on the information provided in the expert interviews. The fourth section presents the available public and unpublished statistics provided by Austrian and Hungarian health care institutions. The fifth section relies mainly on an analysis of public health care documents as well as explorative information provided in telephone and email inquiries. Finally, the sixth section on the barriers to cross-border health purchasing is again mainly based on analysis of the expert interviews.

# **1 THE AUSTRIAN AND HUNGARIAN HEALTH CARE SYSTEMS**

# **1.1 Austria**

# **1.1.1 Fundamental Principles**

In Austria, all employed persons, their family members, and most social assistance claimants (thus 98.7 percent of the population in 2007) are covered by statutory health insurance (see Federation of Austrian Social Security Institutions 2008b: 25). Persons who are not covered have the possibility of voluntary insurance within statutory health insurance. In 2001, 112,000 persons were voluntary insured (see Wörister and Rack 2003: 45).

In addition to the statutory health insurance, there is the option of private health insurance that offers supplementary benefits and betterments. In 1999, approximately 32 percent of the population had private health insurance (see Loy 2002: 53).<sup>4</sup> The main motives for patients to buy private health insurance are threefold. First, it covers the costs of better accommodation in hospitals. A second motive is to cover the costs of treatment by a non-contracted physician of choice. And third, it shortens the waiting times for examinations (see Hofmarcher and Rack 2006: 96).

Austria’s non-competing self-governing health insurance funds (HIFs) are classified by occupational groups (miners, self-employed persons in trade, commerce and industry, farmers, railway employees, civil servants, blue-collar workers, white-collar workers, etc.), by region, and by employer.<sup>5</sup> There are sub schemes for workers and employees,

> 4 Hofmarcher and Rack confirm that around a third of the population has private health insurance (2006: 96).

> 5 For a more detailed overview of the structure of the Austrian HIFs, see e.g. Federation of Austrian Social Security Institutions (2008a), Hofmarcher and Rack (2006), and Köttl (2008).

3
However, in dynamic models the economy grows even in the absence of a shock and the analysis should therefore be done with respect to this growth path. Also, since our model is dynamic, it takes into account not only efficiency effects, present in static models, but also accumulation effects. The latter are linked to the ratio of capital rates of return to the cost of investment goods. We pay special attention to these elements in our simulation analysis.

The results of all the simulations are described in tables 3-10: Table 3 presents macroeconomic effects, 4 to 7 the sectoral results, 8 the income consequences and 10 the poverty consequences for all five.

# **3.1   The Doha Scenario**

_Overview of shocks_ (Table 4) _._ The Doha simulation involves the removal of all export subsidies, plus cuts in domestic support and tariffs in the rest of the world. We have also assumed that the Doha scenario is implemented after the removal of all textile quotas in the context of the separate Multi-Fibre Agreement. This scenario provides special and differential treatment for least developing countries like Bangladesh which are not required to cut tariffs at all. We perform this simulation by introducing the changes in world export prices (PWE), world import prices (PM) and world demand for Bangladeshi exports (DEX) as estimated from the GTAP world model. Doha generally leads to increases in world prices for Bangladeshi imports and exports, as well as an increase in world demand for these exports.<sup>5</sup> These increases are particularly strong in the agricultural, food processing and textile/garment sectors.

_Macro Effects_ (Table 3) _._ At the aggregate level, real GDP is hardly affected in either the short or the long run.<sup>6</sup> The results indicate a small decrease in aggregate welfare measured by equivalent variations and a short-run increase in the poverty headcount, although these effects 12