rafmacalaba/datause-encoder-data huggingface dataset:
9 place. However, the massive efforts now beginnin8 to return previously nationalized real property to former owners is likely to create great upheav%l in property markets and uncertaiuty in real property rights for some time tc come.

# The Histolical Legacy

A large part of Yugoslavia--including Slovenia, Croatia, Bosnia and Herzegovina--was part of the Austro-Hungarian empire before the creation of Yugoslavia in 118. After 1918 Austrian law continued to heavily influence lawmaking in the new country, and the Austrian General Citizens Code ("AllgemeineG Buergeliches Gesetzbuch") of 1811 (as later amended)28 became the basis for property end contract relations among private natural psrscas and legal entities, whether in private or business activity,29 It was translated and essentially adopted as general law except in a few fields (such as bankruptcy) where specific Yugoslav statutes held precedence.

During the socialist period after World War II until 1990, three forms of property w're legally recognized in Yugoslavia. The first, "social" property, 30 was owned in principle by all the people31 and was managed under the uniquely-Yugoslav variant or socialism, the system of worker selfmanagement.2 Most of the economy, including 90 percent of all fixed however, did significant damage to land records. Many transfers of social property and most transfers of private apartments were not registered. Prewar private owners of land and buildings often remain on the books, thus providing a basis for implementing the denationalization law discussed below.

26 Allgemeines Buergerliches Gesetzbuch (General Civil Code), Oesterreische I.G.S. No 946 (1811).

29 The Austrian Code was very broad, covering not only propercy and contract principles, but also family law, inheritance, and (through later amendment) bankruptcy, taxation, and collateral.

# **'0** Yugoslav Constitution, supra note 7.

**31** Social property was in theory everyone's property but was in fact no one's property, in that no private individual could transfer rights to the property. In practice "usufruct" (or use) rights were allocated to firms at low cost. This administrative allocation resulted in arbitrary and unequal distribution in access to social capital among workers.

32 Most of the principles and rules regarding forms of property and the system of worker self-management of social property were contained in the first law on self-management adopted in 1950 (Temeljni zakon o gospodarjenju z dr.avnimi gospodarskimi podjetji in viljimi gospodarskimi zdrutenji po delovnih kolektivih (Basic Law on Management with State economic Enterprises and Higher Economic Associations by Working Collectives], Yugoslav Official Gazette, No 43 (1950)), the constitutions of 1953, 1963, and 1974 (supra notes 4-7), and the Law on Associated Labor of 1976 (Zakon o zdruienem delu,
16 <!-- Start of picture text --> U / 42 x<br>C<br>Time<br>. 11 l1111111 II<br>Figure 9: Constrained Savings<br>with Export Growth<br><!-- End of picture text --> <!-- Start of picture text --> '-4~~~~~~~~~~~~~~~~~~~~~~~~~~4<br>Time<br>114 1~1 I I I I II I IIIII<br>Figure Constrained Savings<br>with GDP Growth<br><!-- End of picture text --> <!-- Start of picture text --> C~~~~~~~~~~~~~C<br><!-- End of picture text -->
the Humanities and Social Sciences (Area I) category who would receive a 30% credit, could receive a 40% loan when she chooses an Economics and administration (Area II) subject, and a 50% loan when she chooses an Engineering and Natural Sciences (Area III) discipline. If a student is credit constrained, she might opt for a study field for which larger credits are granted (for a given level of tuition fees). In this sub-section we investigate the hypothesis that access to loans has an effect on career choice. To that end, define the following dummy variables: DAREA_I=1 when the student chooses a study in Area I and 0 otherwise. DAREA_II=1 when the student chooses a study in Area II and 0 otherwise. DAREA_III=1 when the student chooses a study in Area III and 0 otherwise.

The proposed econometric model is a PROBIT model of the type where _j_ is an index denoting the student’s career choice.

Appendix Table 3 shows the results using the SOFES database. The main findings are as follows: The treatment effect for Bachelor-students in Humanities and Social Sciences (Area I) is negative around DS=0.08, but positive around DS=0.15 (Appendix Table 3A). The latter effects are quantitatively more important than the former, but not robust to increases in the subsample and inclusion of other controls. A more convincing positive treatment effect is found for Master-students in Area I (Appendix Table 3B). Around DS=0.08, the causal impact of the loan on the career choice of Humanities and Social Sciences (Area I) is about 15%-point. With regard to Bachelor-students in Economics and Administration (Area II) careers, mostly positive impacts are found around DS=0.08, but negative effects occur at DS=0.15. As no statistically significant treatment effects are found for Bachelor-students in Area III, this points at movements mainly between Area I and II.

Appendix Table 4 shows the results from a similar analysis using the survey data. For other factors than the treatment variable, we find (i) gender is an important determinant of career choice. Females are 9% more likely to study Humanities and Social Sciences (Area I), 12% more likely to study Economics and Administration (Area II), and 21% less likely to study Engineering (Area III); (ii) the propability of enrollment into Engineering and Natural Sciences decreases by 1% per additional year of age of the candidate, while the likelihood of enrollment into Economics and Management increases by a similar magnitude; and (iii) Having attended a private upper secondary school increases enrollment into Engineering and Natural Sciences by 5 percent. All effects are statistical significant.

28
development of schooling and vocational aspirations and self-esteem. Note that vocational training was not included in any of these six countries.

The program has changed slightly since the time of the study and varies somewhat between countries. In the past, Compassion worked in tandem with local schools (which is true of our data from Guatemala and the Philippines) but more recently has operated through local church-affiliated tutoring centers. We study the impacts from children involved in the program in India and Bolivia when small cash transfers were given to parents of participating children, but not in other countries. Other than these differences the program across countries is highly standardized.

Through the use of local enumerators, we were able to locate 93.5% of the families of these formerly sponsored children, who by the time of the survey were aged seventeen to fortythree. Our field personnel were unaffiliated with Compassion, in order to reduce bias in the responses of our subjects. We administered our survey first-hand to households of formerly sponsored children, a random sample of nonparticipating households in nineteen program villages, and a random sample of households in thirteen neighboring, nonprogram villages. The survey questionnaire was administered to family members (typically parents or adult siblings) present at the time of the survey, and data were obtained on all adult siblings in the household cohort, including the nonsponsored siblings of sponsored children. We also administered the survey to 50–75 randomly selected households with children in a similar cohort age that did not participate in the program in program villages, as well as 50–75 randomly selected households with children in a similar cohort age in nearby nonprogram villages.

Overall, our data contain information on educational and vocational outcomes, monthly labor income, consumer good ownership, and dwelling quality on 1,860 formerly sponsored children, 3,704 of their unsponsored siblings, 2,136 individuals of a similar age from nonparticipating families in villages where the Compassion program operated, and 2,444 individuals from similar, nearby villages without the Compassion program.

There are several empirical challenges to estimating the program’s causal effects on future income and wealth. First, there may be nonrandom selection of households with eligible children into the program. Second, since a limited number of children per household were eligible for sponsorship (ranging from one in the African countries to three in the Latin American countries), intrahousehold selection of children for sponsorship may not be random. Third, there may be spillover effects from sponsored children onto their siblings or onto other children in the village, which complicates the estimation. Lastly, when estimating impacts on future labor 4
|TOTAL||1,500<br>1,000<br>2,500<br>2,500<br>1,000|
|---|---|---|
|Complex 3|100<br>50<br>200|350|
|Simple 1|200|200|
|Complex 2|300<br>100<br>50<br>100|550|
|Complex 1|100<br>50<br>200|350|
|Simple 1|50|50|
|Reward<br>in Naira|300<br>100<br>50<br>100<br>200|0|
||facility when necessary<br> rate at least every 30 minutes<br>ns every 30 minutes<br>consistency of liquor<br>d drugs administered<br>condary facility<br>ARY|rd / penalty from activities<br> min payout<br> incentive)<br> max payout<br>tivity reward)<br> max payout<br> incentive)<br> min payout<br>ivity penalty)|
||dary <br> heart<br>actio<br> and <br>terus<br>ds an<br> to se<br>ESS|rewa<br> Total <br>ation <br> Total <br> + ac<br> Total <br>ation <br> Total <br> – act|
||con<br>tal <br>ntr<br>lor <br> u<br>fui<br>fer<br>EC|ax<br>m: <br>cip<br>m: <br>on <br>m: <br>cip<br>m: <br>on|
|Action|Refer to se<br>Measure fe<br>Monitor co<br>Monitor co<br>Palpate the<br>Record all <br>Do NOT re<br>when UNN|Potential m<br>Reward ar<br>(only parti<br>Reward ar<br>(participati<br>Penalty ar<br>(only parti<br>Penalty ar<br>(participati|

62
specifically, within the set of eligible districts, blocks were selected for assignment based on two sets of criteria: (1) a population criterion that equally weighted the SC and the ST population proportions and the number of below poverty line (BPL) households from census data; (2) a set of block level infrastructural variables that measure the quality of infrastructure, public services and industrial backwardness.

We generate our matched sample by matching project and non-project blocks within 9 active project districts<sup>6</sup> on the two factors that determined assignment to treatment. Infrastructural variables included all available census data (from 2001, before the project started) that could measure disadvantage — the number of villages in the block, average distance of the village to the nearest town, total population, percentage of villages in the block which had primary and middle schools, commercial banks, cooperatives, agricultural and non-agricultural societies, medical facilities and drinking water facilities. Tis process allowed us to nearly replicate the original assignment process for PVP.

We use a two-step matching procedure, summarized in Figure 1. First, we generate propensityscore matched blocks using a standard probit model that uses the variables listed above. Within each district, a PVP block was matched to the non-PVP block with the closest propensity score. Tis ensured that the chosen non-PVP block was as likely to receive the intervention as the existing matched PVP block. Second, since the unit of analysis for this study is the village, we follow a similar process to identify specific village _panchayats_ (VP) within each matched pair of blocks. Te variables used for this village-level matching are the same as those used for the block matching. Tus, the finally selected VPs from PVP and non-PVP blocks were ex-ante equally likely to receive the program. Tis two-step sampling strategy ensures pre-treatment similarity on observable covariates of treatment across treatment and control areas.

> However, in discussion with the implementing partners, it emerged that deviations from the rule occurred when the population score did not identify the most disadvantaged blocks that the project intended to target. In particular, the population criterion seemed, at times, to be leading to the selection of more developed and therefore arguably less poor blocks. While these changes ruled out using a discontinuity design, we combined the population criterion with other information capturing the reasons for deviation — namely, village-level infrastructure — to approximate the final block selection criterion.

> 6Te sample districts were chosen to ensure representation from different geographic regions of PVP’s implementation.

11
14 In contrast with the conventional case the function Rk a r, where r is the private rental price of capital, is dependent not only on output prices but also on factor endowments. The fact that the function R(.) is symmetric implies that the term R22K, R33K, R23K correspond to the curvature of the private capital = rental price function with respect to the output prices of the two protected goods (i.e., R. [(a<sup>2</sup> r)/8p22J, etc. In the Heckscher-Ohlin "even" model r is a convex function of the protected output price. This is due to the concavity of the cost function in factor prices. This implies that the term in square brackets in (10) is necessarily positive and thus, immiserizing growth is always possible in the Heckscher-Ohlin model at a sufficiently high tariff.2v Moreover, the social return of capital is always decreasing in T for **_T_** > 0. In models where the "evenness" condition is not satisfied or in models that do not assume the long-run zero profit condition with identical firms, however, there is no reason to assume that immiserization will take place regardless of the tariff level. In general, there is no reason to impose any restrictions on the third derivatives of the revenue function and thus the social return of capital may be an increasing or decreasing function of tariffs. This is purely an empirical matter.

## II. The Empirical Model

The model is estimated for Chile using ainual data for the period 1974-1989. The micro policy environment in Chile 1974-89 was very stable, highlighting the role of markets and the private sector as dominant factors. The economy in general was subject to very few distortions with the exception of moderate import tariffs and a period of binding wage indexation extending from 1974 until 1983. The existence of a government enforced backward wage indexation in an environment of declining inflation contributed to maintain real wages substantially above the market clearing wage and to high rates of unemployment. Even after the elimination of official indexation in 1983, indexation practices in the

# 12/ Note that the slope of the function r in the output prices gives the sign of dRk/dc .
-35-

for-height and height-for-age in Tables 6 and 7. Since family time and resources are always scarce, the ability of parents to provide for any child may be affected by sibling competition.

The coefficients of age and its quadratic term indicate clearly that a child's growth path in these environments deviates from the norm as the child grows older and subsequently catches up. This confirms the decline in nutrition after the child is weaned particularly where proper child feeding is lacking, and where the health environment is poor. Contrary to findings elsewhere in South Asia, there seems to be no significant gender effects on nutritional status in the sample children. This result is a so indicated in the data reported in the 1985-87 National Survey (National Institute of Health 1988).

One of the community health inputs that is likely to be important is that of vaccination. Results here, however, Indicate that vaccination of the child does not have a significant effect on nutrition. Plausibly, the impact of vaccination is indirect via reduced illness although if the district dummy variables are not included in the production function the variable is significant in the equation for long-term growth. Birth at the hospital, which is strongly determined by income and proximity (as shown by the strong impact of cluster average) has a positive although slight impact on short term nutritional status. As it is more plausible for the variable to be a measure of prenatal care and therefore to explain long term nutritional status, the variable may proxy for other preventative health care.
# **TABLE A4. Job turnover. Share of unskilled workers. ICT intensity**

|||FE|FE‐2|SLS|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ICT|‐0.27|‐0.83**|‐9.00*|‐8.23*|
||(0.34)|(0.38)|(5.06)|(4.40)|
|ICT|‐0.15|‐0.65|‐7.18|‐5.71|
||(0.40)|(0.51)|(5.06)|(4.36)|
|ICT * High Productivity|‐0.28|‐0.40|‐1.58|‐3.59|
||(0.60)|(0.59)|(3.36)|(2.89)|
|ICT|‐0.092|‐0.59|‐9.48*|‐8.79**|
||(0.32)|(0.39)|(4.93)|(4.30)|
|ICT * Skills|‐0.68|‐0.88|‐0.84|‐0.62|
||(0.79)|(0.65)|(4.10)|(3.41)|
|ICT|1.22|1.22|6.24|14.0|
||(1.13)|(1.02)|(5.69)|(9.60)|
|ICT * Internet|‐1.49|‐2.06**|‐13.6***|‐21.6**|
||(1.14)|(1.04)|(5.27)|(9.59)|
|ICT|‐0.94*|‐0.70|‐17.5**|‐7.44|
||(0.51)|(0.51)|(7.64)|(7.03)|
|ICT * Computers|0.78|‐0.15|11.3**|‐0.075|
||(0.60)|(0.67)|(5.45)|(5.17)|
|Observations|3,477|3,475|3,477|3,475|

Analogous to Table 13. Regressor: average firm investment in ICT over firm sales during sample period.

44
- 13 -

existing road network. For Africa, where the existing road networks are often narrowly defined, not only quality improvement, but also network expansion is needed. The road network can be expanded by new road construction or reclassification of unclassified or informal roads into the official network. Both options would require more resources in the road sector and, possibly, institutional rearrangements, such as expansion of the road authority’s responsibility.

Second, the relationship with urbanization is relatively weak in Africa, although still statistically significant. The coefficient of _Urban_ is 0.012 in Africa and 0.083 in South Asia. This may reflect that Africa’s level of urbanization is still relatively low, although it is projected to accelerate quickly in the future.

**<u>Table 4. OLS Regression of the RAI with African and Asian Countries Differentiated</u>**

|Variable|Coef.|Std.Err.|
|---|---|---|
|_dAFR_*ln_RdDen_|0.275|(0.053) ***|
|_dAFR_*ln_Quality_|0.598|(0.049) ***|
|_dAFR_*ln_PopDen_|0.001|(0.023)|
|_dAFR_*ln_PopConc_|0.754|(0.065) ***|
|_dAFR_*ln_Urban_|0.012|(0.004) ***|
|(1-_dAFR_)*ln_RdDen_|0.120|(0.084)|
|(1-_dAFR_)*ln_Quality_|0.407|(0.040) ***|
|(1-_dAFR_)*ln_PopDen_|-0.078|(0.066)|
|(1-_dAFR_)*ln_PopConc_|4.889|(1.055) ***|
|(1-_dAFR_)*ln_Urban_|0.083|(0.026) ***|
|Constant|1.163|(0.470)**|
|Obs.|691||
|R squared|0.901||
|F statistics|427.8||
|Structuraltest (_F_stat.)|23.32|***|

_Note:_ The dependent variable is the log of _RAI_ . Robust standard errors are shown in parentheses. *, **, and *** indicate statistical significance at 10, 5, and 1 percent, respectively.
In January 2007, the benefits for planned surgeries/inpatient services were suspended due to budget constraints and later reinstated in the beginning of September 2007.

.

# **_The trend of Multi-payers under the MAP_**

Starting in September 2007, the government launched a pilot scheme shifting responsibility for provider payment to private insurers in Tbilisi and Imeriti regions. Vouchers were distributed to the approximately 200,000 people in these two regions eligible for the MAP, and the beneficiaries can select among four participating private insurance companies. Participating insurance companies have to provide the benefit package identified by the state and cannot deny membership to any people with vouchers. The HeSPA reimburses the insurance companies based on an average premium of 7.2 GEL per beneficiary per month. The plan is to change eventually from a single state purchaser to multiple payers in the health insurance market.

# **4. DATA**

# **_Data Sources_**

This paper limits its analysis to data from June to December of 2006 to avoid two confounding changes. First, the eligibility score changed from 100,000 to 70,000 starting January 2007 and second, the benefit package was reduced by excluding planned surgeries around the same time. Six months is a short timeframe, but it provides clearer evidence on the initial impact of the MAP.

The analysis in the paper uses two data sources. The first is the claim data from SUSIF, which contains information on hospital check-in and check-out dates, the amounts SUSIF paid to the providers, and disease codes for patients covered under the MAP and the UBP. The second source is the targeting database managed by SASEA, which contains household demographic information, individuals’ education levels, employment status, and welfare scores based on the proxy means test.<sup>7</sup> The two data sets share the common family ID and personal ID. A new data set, based on the common IDs, was created by merging these two data sets. After total number of utilizations and total medical expenditures for each individual were generated from the merged data set, duplicate records with the same family ID and personal ID were removed. The unit of analysis is at the individual level. The final data set contains 983,708 individuals below or at the 100,000 cut-off line.<sup>8</sup> The data set is rich and unique, as it has each utilization record, which would be unobtainable from a sample survey. However, the data set has its limitations. First, while it contains inpatient utilization by beneficiaries of the MAP, it does not have any outpatient utilization or use of pharmaceutical drugs. Second, it does not have any information on out-of-pocket spending. Third, the data does not contain pre-program utilization information.

> 7 Due to reasons of confidentiality, we only obtained a subset of the targeting database that included all the households but with only limited information. The information includes the welfare score, demographic structure of the households, each household member’s marital status, education and employment status, etc.

> 8 There are 646,954 individuals below or at 70,000.

6