rafmacalaba/datause-encoder-data huggingface dataset:
10 capital, fell in this category. The second, cooperative property, was re2ognized but not well-developed or widely u.sed in Yugoslavia as in some of its socialist neighbors.3 The third, private property, was restricted to personal ownership34 of real property (with a general maximum of one mediumsized house or 2-3 apartments per person, not including vacation homen), small businesses (primarily individual service providers such as lawyers or craftsmen), and small private farms (with a maximum size of 10 hectares, increased to 30 hectares in 19883'). Unlike other socialist economies (except Poland), most farming in Slovenia was done on a small scale, and 85 percent of all land was privately owned.

After World War II, a special 1946 decree prolonged the validity of any prewar legislation not clearly in opposition to socialist principles until it could be replaced by new legislation. The private civil law adapted from the Austrian civil code remained essentially unchanged, although much of it fell into disuse. It continued to apply only in the small area carved out for private propertv and private transactions. It took over 30 years for the country to adopt new legislation in the two main areas of private civil law-property and obligations. In the property area, the Law on Foundations of Property Relations was adopted in 1980."6 Even this law, however, retained many of the principles of the Austrian predecessor, and incorporated relatively few principles unique to socialism and worker self-management. Amendments in 19903 removed these few socialist principles from the law, returning it more or less to its original foundations from the Austrian empire.

# Recent Slovene Initiatives

The Law on the Foundations of Property Relations remains valid in Slovenia, as do numerous special laws in the area of property rights that were adol,ted in Yugoslavia in recent years and not ex-licitly abrogated by Slovene laws. At some point the general _law_ will be replaced by specific Slovene legislation. Already specific Slovene laws have been adopted in the areas of Yugoslav Official Gazette, No 53, (1976), with most recent amendment, Yugoslav Official Gazette, 40 (1989)).

33 Yugoslav Constitution, supra note 7, Articles 62, **_65._**

- 34 Yugoslav Constitution, supra note 7, Article 78.

- 35 Amendment 23 to Yugoslav Constitution, supra note 7.

36 Zakon o temeljih lastninskopravnih razmerij (Law on Foundations of Property Relations], Yugoslav Official Gazette, No 6 (1980).

Yugoslav Official Gazette, No 36 (1990), page 617.
24 principle suggests that those resources be applied instead where the linkage is stronger to begin with.

As development progresses, the ability of SMEs to play a significant role in development depends on a wider set of market and non-market linkages -- to education and training from both domestic and foreign universities, to foreign firms considered as technology sources, to government and/or private research organizations. These linkages are appropriate concerns for firms that already have a basic understanding of the functioning and requirements of the global marketplace; it is not clear that there is an effective role here for Bank programs directed specifically at SMEs.

## **Empirical Observations on the Role of SMEs -- From Data Relating**

# **Mostly to the U.S. and Other Advanced Countries**

Job Creation. In developing and advanced countries alike, policy attention to small and medium enterprises has long been advocated on the ground that SMEs are a particularly effective vehicle for expanding employment. There was not, to my knowledge, any time when this assessment even appeared to be firmly grounded in careful empirical research. Rather, it seems to have been derived by extrapolation from a handful of facts; careful attention to the problems of interpretation that intervene between those facts and the policy conclusion was long deferred. Recently the pendulum has swung; the careful attention that was lacking has now been given, and the argument
Table <u>3: Legal Gender Disparities and Years of Education of Women Relative to Men</u>

|||||**O**|**LS**||||
|---|---|---|---|---|---|---|---|---|
||||**Years of ed**|**ucation, wom**|**en over men**|**ages 25 plus**|||
||**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|**coef/se**|
|Legal disparities: Overall measure|-0.004**||||||||
||(0.002)||||||||
|Legal disparities: Accessing<br>Institutions||-0.012***|||||||
|||(0.004)|||||||
|Legal disparities: Using Property|||-0.015||||||
||||(0.013)||||||
|Legal disparities: Getting a Job||||-0.002<br>(0.003)|||||
|Legal disparities: Going to Court|||||-0.054*||||
||||||(0.030)||||
|Legal disparities: Providing Incentives<br>to Work||||||-0.036**|||
|||||||(0.018)|||
|Legal disparities: Building Credit|||||||-0.001<br>(0.011)||
|Legal disparities: Violence Protection||||||||-0.015**|
|||||||||(0.008)|
|Log of GDP per capita (constant 2005<br>US$)|0.073***|0.075***|0.069***|0.070***|0.070***|0.070***|0.070***|0.073***|
||(0.016)|(0.016)|(0.016)|(0.016)|(0.016)|(0.016)|(0.016)|(0.016)|
|Rule of law|-0.027|-0.018|-0.013|-0.015|-0.014|-0.015|-0.011|-0.020|
||(0.019)|(0.019)|(0.018)|(0.019)|(0.018)|(0.018)|(0.018)|(0.018)|
|region==East Asia & Pacific|0.028|0.034|0.024|0.012|0.009|0.033|0.013|-0.002|
||(0.035)|(0.034)|(0.036)|(0.034)|(0.034)|(0.036)|(0.040)|(0.036)|
|region==Europe & Central Asia|0.049**|0.059***|0.052***|0.052**|0.052***|0.051**|0.056***|0.042*|
||(0.021)|(0.017)|(0.019)|(0.022)|(0.019)|(0.020)|(0.022)|(0.023)|
|region==Latin America & Caribbean|0.115***|0.116***|0.113***|0.114***|0.110***|0.108***|0.116***|0.096***|
||(0.029)|(0.025)|(0.027)|(0.028)|(0.027)|(0.028)|(0.035)|(0.030)|
|region==Middle East & North Africa|-0.094*|-0.085|-0.121**|-0.163***|-0.136***|-0.163***|-0.168***|-0.159***|
||(0.054)|(0.053)|(0.061)|(0.048)|(0.047)|(0.047)|(0.052)|(0.047)|
|region==South Asia|-0.215**|-0.211**|-0.216**|-0.246***|-0.225***|-0.255***|-0.245***|-0.258***|
||(0.085)|(0.084)|(0.092)|(0.089)|(0.085)|(0.089)|(0.091)|(0.089)|
|region==Sub-Saharan Africa|-0.090*|-0.079|-0.097*|-0.113**|-0.111**|-0.111**|-0.110**|-0.113**|
||(0.050)|(0.050)|(0.051)|(0.050)|(0.049)|(0.049)|(0.054)|(0.050)|
|Constant|0.323**|0.252*|0.298**|0.304**|0.289**|0.291**|0.287*|0.293**|
||(0.145)|(0.148)|(0.147)|(0.149)|(0.146)|(0.144)|(0.147)|(0.145)|
|Number of observations|153|153|153|153|153|153|153|153|
|Adjusted R2|0.674|0.679|0.665|0.658|0.671|0.665|0.658|0.668|

note: *** p<0.01, ** p<0.05, * p<0.1, Huber/White standard errors reported Omitted region: North America 27
Labor Law was very good, good, so-so, bad, or very bad. They were also asked the same question retrospectively for the period before September 2008 (before the global financial crisis) to enable assessment of whether concerns about the crisis weakened implementation of the new Law. Responses are summarized separately for local residents and migrants in Table 1. While very few respondents felt that implementation of the law was “bad” or “very bad” (3% of local residents and 6% of migrants), about 40% expressed ambivalence (“so-so”). The majority of respondents (56% for both groups) felt that implementation of the new Law was “good” or “very good”. Although slightly more migrants are dissatisfied compared to local residents, the difference is small. Also, there is no evidence from the retrospective assessments that enforcement was better before the financial crisis; if anything slightly fewer people report good enforcement prior to September 2008.

What about firm managers? In the PBMFS conducted in 2009, managers were asked “How strictly have labor regulations been enforced?” Respondents were asked to answer this question for different points in time, with the possible responses being _very strict_ , _strict_ , and _not strict_ . Results are presented in Table 2. The vast majority of respondents found enforcement to be very strict (24.6%) or strict (71.3%) during the most recent period (January to June, 2009), with only 4.0% reporting “not strict”. Managers reported that strictness has increased steadily over time; in 2007 21.6% of respondents answered “very strict” and 7.3% answered “not strict”. Thus, just as for the worker surveys, there is no evidence of less strict enforcement in late 2008 or early 2009 when China was hit hard by the global financial crisis. Results reported in the bottom panel of Table 2 also reveal that larger firms report stricter enforcement than smaller firms.<sup>3</sup>

> 3Multivariate analysis of the determinants of the strictness of firm enforcement, using the same firm survey data, confirms that larger firms face stricter enforcement even after controlling for other variables, and also that enforcement is stricter for state-owned enterprises (compared to private and especially foreign enterprises), for firms in Sichuan, Shaanxi, and Jiangsu (in declining order, relative to Zhejiang), and in cities with higher GDP per capita, lower 2007 economic growth rates, lower wages, smaller industrial sectors, and smaller populations (Park, Giles, and Du, 2012).

8
<!-- Start of picture text --> 100%<br>$ 80% =Oils<br>= =DGL Veg<br>E 60% = Vegetables<br>° = Fruits<br>£ 40% = Dairy<br>g =Protein foods<br>oC 2%<br>= Starchy staples<br>19% 20% 21%<br>0%<br>Sri Lanka Pakistan Afghanistan<br><!-- End of picture text -->
# **III. Determinants of Internal Migration**<sup>**10**</sup>

The literature has long recognized that migrants tend to move in response to differences in economic opportunities. For instance, the human capital model of Sjaastad (1962) hypothesizes that the migration decision is influenced by the present value of the difference in income streams between alternative locations. According to this model, the higher rate of migration among the young can be understood as a logical outcome of investment decision-making since the young face a longer life investment horizon. Extending the wage-differential approach, Todaro (1969) formulates a rural-urban migration model that incorporates the probability of obtaining a modern sector job as an additional determinant of migration. Furthermore, the model of Harris and Todaro (1970) demonstrates that, under certain circumstances, urban job creation may actually increase local unemployment by inducing more migrants to move to urban areas.

There exists a large body of literature which investigates the determinants of migration empirically. One popular approach is to use the macro gravity model of migration, inspired by Newton’s law of gravity.<sup>11</sup> Several studies have applied the macro gravity model (or a variation thereof) to Vietnam (Dang _et al._ , 1997; Nguyen-Hoang and McPeak, 2010; Phan and Coxhead, 2010). Studying the 1989 census data, Dang _et al._ (1997) find that while government intervention played a key role in moving people from sending provinces/cities to targeted resettlement areas, people still moved into the urban areas which gained population, on balance, from migration. Their study also finds that, even during the initial phase of the _doi moi_ policy, women tended to go to areas with higher relative levels of industrial development.

Using the 1989 and 1999 _Censuses of Population and Housing_ data, Phan and Coxhead (2010) estimate a standard macro migration equation to examine the determinants of inter-provincial

> 10 This section is not intended to be exhaustive. See, for instance, Lucas (1997) for an excellent review.

> 11 The model specifies migration as a positive function of the attractive “mass” (measured by income or by population) of origin and destination areas and a negative function of distance between them.
Page 42

# Promising Approaches **in** Agricultural Extension

101. In delivering extension services to women, several key points could be considered as a start:

- a) It is probably more sensible to adjust the existing system to reach both female and male farmers than to establish a separate extension system for women farmers.

- b) Diagnsis: Women farmers are not homogeneous. What do the main groups of women farmers focus on? What extension advice is offered? Does extension cover only "main crops" or cash crops?

- Are these important to key groups of women, and should extension advice be adjusted to cover other crops, animals, or topics (for example, food storage, food processing, cooking, or nutrition) that especially concern women farmersi

- c) Delivery: Considering the position of various groups of women in the farily, society, and economy, how can extension advice be delivered affordably and effectively to both male and female farmers? How important is mere proximity of the information source as distinct from mode of delivery? Do women have more difficulty traveling? Under what circumstances can women talk with male extension agents, or do women farmers have to work with women extension agents? What is the potential for using women's groups? What influence does the basic education of the farmer (or her husband) have?

- d) How can feedback be assured from women farmers through the extension system to researchers who can then improve the extension advice?

- e) What will be the likely financial and administrative costs and imRact: of extension -- the immediate impact (farmers reached), the intermediate impact (the adoption of advice), and the full impact (changes in productivity and income)? How does this vary by social and economic background, education, farm size, and income level?

102. Rigorous research based on household surveys or similar evidence is scarce on these questions -- especially on question e), for extension as a whole -- so the greater scarcity of research on extension for women is not surprising. Literature searches and existing research suggest that the evidence available on extension for women comes largely from program evaluations or limited surveys of extension users (Berger and others 1984; de Beyer 1988; OED 1988a, 1988b; World Bank 1989a, 1989b; Evenson 1989 forthcoming). Additional researTh is now underway (for example, through a UNDP-financed project being executed by PHRW' in cooperation with the Africa Region), but much more operational research is needed to determine cost-effective approaches to delivering extension and their probable impact.
Bernard, A., B. Jensen, S. Redding, and P. Schott (2007). “Firms in International Trade,” Journal of Economic Perspectives, Volume 21, Number 3, Summer 2007, pp. 105–130.

Brambilla, I. D. Lederman, and G. Porto (2012). “Exports, Export Destinations and Skills,” American Economic Review, Vol. 102, No. 7, pp. 3406–3438.

Brambilla, I. D. Lederman, and G. Porto (2015). “Exporting and the Demand for Skilled Tasks,” mimeo Universidad Nacional de La Plata.

Brambilla, I. and G. Porto (2015). “High-Income Export Destinations, Quality and Wages,” Journal of International Economics, forthcoming.

Bustos, P. (2014). “The Impact of Trade Liberalization on Skill Upgrading. Evidence from Argentina,” mimeo CREI.

Caron, J., T. Fally, and J. Markusen (2014). “International Trade Puzzles: A Solution Linking Production and Preferences,” forthcoming in the Quarterly Journal of Economics.

Costinot, A. and J. Vogel (2010). “Matching and Inequality in the World Economy,” Journal of Political Economy 118(4), pp. 747–786.

Kugler, M. and E. Verhoogen (2012). “Prices, Plant Size, and Product Quality,” Review of Economic Studies, vol. 79 no. 1, pp. 307–339.

Park, A., D. Yang, X. Shi, and Y. Jiang (2010). “Exporting and Firm Performance: Chinese Exporters and the Asian Financial Crisis,” The Review of Economics and Statistics, vol. 92, No. 4, pp. 822–842.

Verhoogen, E. (2008). “Trade, Quality Upgrading, and Wage Inequality in the Mexican Manufacturing Sector,” Quarterly Journal of Economics, Vol 123, No. 2, pp. 489–530.

Yeaple, S. (2005). “A Simple Model of Firm Heterogeneity, International Trade and Wages,” Journal of International Economics, 65, pp. 1–20.

11
# **Main Drivers of Income Inequality in Central European and Baltic Countries**

## **Some Insights from Recent Household Survey Data**

### **Salman Zaidi***

> * World Bank, Washington DC. Misha Bontch-Osmolovski provided invaluable help though setting-up the EU-SILC data for analysis. This paper has benefited from the comments of numerous colleagues at the World Bank as well as in the EU8 countries covered in this paper. The opinions and views presented here are those of the author and should not be attributed to the Executive Directors of The World Bank. Correspondence: Salman Zaidi, World Bank, 1818 H Street NW, Washington DC 20433, USA. E. mail: szaidi5@worldbank.org. Phone: (202) 473 1976. Fax: (202) 522 2753.
**TABLE 1: Baseline Balance (Child-Level Variables)**

||**Mean (standard**<br>**deviation) for**<br>**control group**|**T2 (caregiver**<br>**training)**<br><br>**Difference in**<br>**the c**|<br>**T3 (T2 +**<br>**caregiver**<br>**incentives)**<br><br>**Means (co**<br>**ontrolgrou**|<br> <br><br>**T4 (T2 +**<br>**parenting**<br>**training)**<br><br>**mpared to**<br>**p)**|T2 = T3 <br><br>**F-test**<br>**Param**|T2 = T4 <br><br>**for Equ**<br>**eters(p-**|T3 = T4<br><br>**ality of**<br>**values)**|**Number of**<br>**Observations**|
|---|---|---|---|---|---|---|---|---|
|**Variable**|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Maintaining Attention and Accuracy During a Test|24.043|0.660|-0.151|-0.364|0521|0446|0876|2116|
|(Leiter Sustained Attention)|(16.482)|(1.215)|(1.239)|(1.319)|.|.|.|,|
|Word Comprehension / Vocabulary (Peabody Picture|24.595|0.609|0.306|0.166|0667|0526|0796|2116|
|Vocabulary Test)|(5.721)|(0.668)|(0.500)|(0.493)|.|.|.|,|
|Language Skills (Malawi Developmental Assessment|9.497|-0.306|-0.183|-0.369|0682|0837|0513|2109|
|Tool)|(3.356)|(0.292)|(0.267)|(0.276)|.|.|.|,|
|Fine Motor / Perception Skills (Malawi Developmental|10.232|-0.286|0.001|-0.284|0370|0995|0425|2116|
|Assessment Tool)|(3.260)|(0.313)|(0.281)|(0.350)|.|.|.|,|
|Male|0.437|0.024|0.026|0.008|0959|0525|0440|2120|
||(0.496)|(0.027)|(0.025)|(0.023)|.|.|.|,|
|Height-for-Age z-score|-1.667|-0.049|-0.017|0.068|0.685|0.176|0.326|2110|
||(1.069)|(0.086)|(0.087)|(0.094)||||,|
|Age (months)|48.027<br>(6.684)|-0.193<br>(0.478)|0.324<br>(0.478)|0.158<br>(0.472)|0.294|0.470|0.733|2,120|
|Chi-Squared Test for Joint Orthogonality of All<br>Variables (p-value)||0.297|0.717|0.539|0.927|0.610|0.903||

Notes: .01 - ***; .05 - **; .1 - *; Cross-sectional OLS regressions at child level using baseline data with standard errors (SEs) between parentheses. SEs are clustered at the CBCC level and observations are weighted using sampling weights. The last row shows the p-values of a series of Joint Orthogonality Tests: we first estimate a Multinomial Logit where the dependent variable is the lottery group, the explanatory variables are the variables in this table, and the base group is the control group. Then, for column (2), we test the null that the coefficients of explanatory variables that refer to T2 are jointly zero, and analogously for columns (3)-(4). For column (5), we reestimate the Multinomial Logit using T2 as a base group, and test the null that the coefficients that refer to T3 are jointly zero, and analogously for columns (6)-(7).

42
account for differences across providers in key dimensions of performance such as quality, costs, and efficiency? 3 The facility surveys designed to explore provider-household linkages have generally offered little insight into these issues. Fig. 3 illustrates the complex institutional and organizational environment that influences the performance of health care providers. This operates through the financing, support, regulation, and oversight provided by administrative levels "upstream" from the facility, through the competitive environment in which the facility is operating, and through the oversight, accountability, and influence activities by households and communities.

_Figure 3 - Provider performance_ <!-- Start of picture text --> Providers<br>Quality, costsand efficienc<br><!-- End of picture text --> Some surveys have focused explicitly on the analysis of costs and efficiency, often with a view to say something about differences between goverment and private providers (see, e.g., Bitran 1995; Wouters 1993). There are also examples of surveys that have sought to measure and analyze the impact of different management indicators on performance, in particular costs (Somanathan and others 2000). In principle, data on inputs, costs, and outputs should be provided by a well-functioning management informnation system. However, where such systems are weak or otherwise deficient, a survey approach can be valuable.

# HeaRth woirkePrs

The perceptions and behavior of health workers have received increasing attention in recent years. This is not surprising, given the central role that health workers play in the delivery of care. Moreover, there is increasing evidence that "micro-level" issues, such as low levels of effort and opportunistic behavior, is hampering both quality and efficiency in service delivery.<sup>4</sup> Standard principal-agent theory provides a framework for analyzing worker incentives.<sup>**5**</sup> Health workers can be seen as agents for multiple principals, including patients,

> 3Thse concept of "performance" is discussed fuirther below.

> 4 See, e.g., Ferrinho et **a]** (1998) for a discussion of absenteeism; Ensor and Witter (2001) and Killingsworth et al. (1 999) for evidence on informal payments; McPake et al. (1 999) provide a detailed and quantified assessment of the extent **and** implications of informl economic activities of health workers in Uganda.

> 5Recently, the pernciples of agency theory have motivated reforms in public sector management, which emphasize

> **performance** measurement and incentives (see, e.g., Goddard, et al. 2000; Martinez and Martineau 1998; Mills 1997). These

4
26 -

-

- Table 5: Ordinary Least Squvres<sup>Regression Results</sup> (in parenthesis Iti values t ***, ** and * denote, respectively, significance at 5%, 10% and<sup>25% levels)</sup>

|Depe|ndent Variable:|Degr<br>numb<br>perc<br>clot|ee of Produc<br>er of MFA<br>ent of the s<br>hing to the|t Speciali<br>categorie<br>upplier's e<br>EC.|zation<sup>defined by the</sup><br>s accounting<sup>for 50</sup><br>xports<sup>in textiles and</sup>|
|---|---|---|---|---|---|
|n|19|||||
|Eq. (1)|INTERCEPT<br>GNP/CAP<br>GNP|+<br>-<br>+|(3.31)***<br>(1.59)*<br>(1.49)*|R-square:<br>Adj.R-sq:<br>F Value:|0.28<br>0.20<br>3.18|
|Eq. (2)|INTERCEPT<br>GNP/CAP<br>GNP<br>EE-DUMMY|+<br>-<br>+<br>+|(1.80)**<br>(1.12)<br>(2.30)***<br>(2.16)***|R-equare:<br>Adj.R-sq:<br>F value:|0.45<br>0.34<br>4.16|
|Eq. (3)|INTERCEPT<br>GNP/CAP|+|(0.09)<br>(1.08)<br> <br>|R-square:<br>Adj.R-sq:<br>|0.47<br>0.32<br>|
||GNP<br>EE-DUMMY<br>AV. QUOTA UTIL.|+<br>+<br>+|(2.01)**<br>(2.23)***<br>(0.70)|F Value:|3.14|

Note: See the text for definition<sup>of variables and data sources.</sup> To test these hypotheses, with<sup>the index on the degree of product</sup> specialization (SPECIAL) as<sup>our dependent variable, we estimated the following</sup> equation: SPECIAL = a + a GNP/CAP + 0 GNP<sup>+e</sup> The signs of the a (-) and 0 (+) coefficients were as<sup>expected,</sup> however they were significant<sup>only at the 25 percent level, and the overall fit</sup> of the equation was poor (eq. 1<sup>in Table 5). We then introduced a dummy variable</sup> for ES (EE-DUMMY). The coefficient<sup>of the SE-DUMMY had the expected sign (+),</sup> was significant at the 5 percent<sup>level, and improved the overall explanatory</sup> power of the equation considerably<sup>(eq. 2 in Table 5). Therefore, we could not</sup> reject the hypothesis that ES<sup>is characterized by a relatively low degree of</sup> specialization. Conversely,<sup>when we introducedadummy variable for the richer</sup> East Asians, its coefficient<sup>had the expected sign(-),</sup> but it was not significant.