rafmacalaba/datause-encoder-data huggingface dataset:
**Temporary employment and part-time work were relatively common in Kosovo.** Most employees in Kosovo had temporary contracts (74.8 percent in 2018) and only 4.5 percent had a part-time job. Firms were concentrated in the commerce and services sectors. The share of public sector employment constituted 41.6 percent and the share of private sector employment was 58.4 percent.

# Recent trends in wages

## **There is a public-sector wage premium, and real wages in the public sector increased faster than in**

**the private sector; gender, age and education wage disparities were also common.** In 2018, the average net monthly wage in Kosovo was approximately 393 euros, constituting 457.3 euros in the public sector and just 344 euros in the private sector. Nevertheless, because the average wage may be overestimated or underestimated due to the presence of outliers, the median wage tends to be a more useful comparison. Median wages were lower than mean averages, set at 378 euros in 2018. Median wages grew faster in the public sector than in the private sector, as shown in Figure 7 Panel a and can be expected to grow even faster with approval of the new wage law for public sector employees in early 2019.<sup>17</sup> When using median wages, there is still a sizable public-sector premium, especially among workers with higher educational attainment (Figure 7 Panel b). Wages were considerably lower for young people; in 2018, the average earnings for those younger than 35 was 351 euros (median 330 euros). For those older than 35, it was 414.6 euros (median 404 euros).  In 2018, the employed population with post-secondary education was earning on average 121.5 euros more than those with secondary education and 172.2 euros more that those with primary education. Only about 3.5 percent of workers in 2018 earned a wage around the minimum wage. (The decomposition of minimum wage workers can be found in Section III.)<sup>18</sup>

**Figure 7. Real median net wages (2015 EUR)**

**a. By sector (private and public)**

<!-- Start of picture text --> 500 392 409 426 432<br>400 342 343 350<br>264 271 282 291 289 302 313<br>300<br>200<br>100<br>0<br>2012 2013 2014 2015 2016 2017 2018<br>Private Public<br>2015 Euros<br><!-- End of picture text -->

> 17 Average and median wage are estimated using Kosovo LFS and wages are deflated using CPI with 2015 as a base year. Series for CPI are obtained from Kosovo Agency of Statistics estimates (http://ask.rks-gov.net/en/kosovo-agency-of- <u>statistics/add-news/harmonized-index-of-consumer-prices-hicp-april-2018</u> ). 18 The proportion of minimum wage workers is computed using the 2018 LFS as the proportion of workers with nonmissing wage who reported having a wage between 0 and 200 euros.

14
Table 4. Loan Summary Statistics by Loan Categories

||Vendor|Working<br>Capital|Overdraft|G&A|Discountable|
|---|---|---|---|---|---|
|Obs|2040|3933|1867|1107|41755|
|Maturity (in mo|nths)|||||
|Mean|2.2|5.3|3.3|25.5|1.9|
|Std Dev|1.3|6.2|1.6|15.5|1.2|
|Min|0.1|0.0|0.0|0.8|0.0|
|Max|12.2|48.8|17.6|146.2|36.8|
|Bank real intere|st rate|||||
|Mean|1.8|17.4|43.4|14.0|18.4|
|Std Dev|12.0|18.8|32.6|8.1|9.2|
|Min|-14.7|-14.7|-14.7|-14.1|-14.6|
|Max|94.2|124.9|179.4|51.7|128.2|
|Number of colla<br>|terals associated<br>|with the loan<br>||||
|Mean|0.02|0.39|0.75|0.73|0.17|
|Std Dev|0.16|0.57|0.55|0.52|0.40|
|Min|0.00|0.00|0.00|0.00|0.00|
|Max|2.00|4.00|3.00|4.00|4.00|
|Outstanding de|bt of a firm to one|bank||||
|Mean|5,619,981|4,051,038|981,927|15,100,000|1,351,568|
|Std Dev|16,800,000|10,500,000|4,203,347|21,300,000|2,967,162|
|Min|10,109|10,036|11,615|6,359|10,163|
|Max|108,000,000|128,000,000|71,000,000|104,000,000|61,700,000|
|Loan Value (re|ais)|||||
|Mean|45,697|186,573|166,879|84,844|31,504|
|Std Dev|210,464|1,213,142|1,344,528|264,060|137,288|
|Min|5,001|5,000|5,011|-|5,000|
|Max|3,881,290|68,200,000|23,200,000|5,953,071|5,616,909|

The most important loan type is “working capital”, with an average amount of R$ 677 million. Its importance has a peak in 2008, of R$886 million and then wanes in 2009 (as do all other loan types, except for “G&A”). The second most important loan type is “Discountable”, with a peak in 2006 of R$331 million, which then falls sharply to a minimum in 2009 of around R$93.2 million. The overall pattern shows that all loan types but “G&A” have been declining in 2008 and 2009. Disaggregating these amounts of lending by firm size, it can be seen that most of the variation in the time trends comes from the medium size firm’s borrowing activity. For example, whereas the amounts lent to small and large firms of “Working capital” loans remains fairly stable at relatively low levels for the period analyzed, the trend for the medium firms is more volatile and reveals higher total amounts of borrowing than for the other two sizes of borrowing firms. This is consistent with the theory that the most dynamic sector of the loan market is made of medium sized firms. Smaller ones are not deemed reliable by banks, and large firms may have access to internal sources of funding.

15
be slack. If a variable is slack it is not acting as a constraint on production in the programming problem, so to be fully efficient, a farm should have no slacks. Of the farms on the efficiency frontiers for the three regions, all but one are also fully efficient, in the sense of no variables being slack. For the farms that are not on the frontier, all have one or more slack variables, so the slacks provide an indication of the inputs that are in excess supply and those that are effectively constraining production. The results for Lebowa are perhaps the most enlightening; land is the main constraint, effectively limiting output for the vast majority of the farms, while at the other extreme, fertilizer was in this sense surplus to requirements for a large number of the farms. For Venda, seeds appear to be a binding constraint for the majority of farms, while a large number had an excess of fertilizer. Land appears to be slack for many farms, but this result should be disregarded, due the problem of lack of variance of area planted in Venda. KaNgwane is far more mixed, with land being the most common constraint on production, and labor the least common.

# _Conclusions_

This section uses recent farm-level data to study the regional differences in maize production efriciency in the former homelands. Non-parametric techniques allow estimation of total productive efficiency in the absence of prices, or when this data is unreliable. Regional differences do occur, and may be due to land quality variation, but the scale inefficiencies noted in Table 4 have implications for addressing the skewed distribution of land ownership in South Africa (Van Zyl, Van Rooyen, Kirsten and Van Schalkwyk, 1994). Relatively large efficiency gains can be achieved by redistributing other land to some farmers, in order to increase farm size in the homelands.

# Economies Of Scale In Commercial **Agriculture**

As noted in earlier, the majority of the previous studies on the farm size-efficiency relationship in South Africa are flawed due to a variety of reasons, and the results are therefore not reliable. In particular, the studies generally suffer from the following shortcomings: only a minority of the studies adjust farm size for quality differences in land and other inputs; most of the studies use physical yields of specific crops or the value of agricultural output per unit of operated area, both imperfect (and at best only partial) measures of efficiency; differences in operational holding size and ownership holding size are sometimes not accounted for; and managerial inputs from the farmer and his/her family, and family labor, have not been included.'<sup>5</sup> 15 See Binswanger, _eL. al_ (1993) for a discussion of these problems, as well as appropriate measures of farm size efficiency.

23
In Table 2, we are comparing the mean size of firms that are 40+ years old with the mean size of firms that are younger than 5 years old. However, young firms could be varying in size across years due to different macroeconomic factors, entry regulations, capital market openness, etc. as well as across countries. In order to see if firms indeed grow as they age over their lifecycle, we would want to compare the size of older firms at any given point in time with their size when they were born. The Enterprise Surveys ask firms to report the number of full-time employees when the firm started operations. In Figure 5, we normalize the average size of each age cohort by the average size of that cohort at birth. We once again find an upward sloping curve, consistent with our main finding that older firms in developing countries are larger than their younger counterparts. To investigate this in a regression setting, we construct Size Ratio, which is the ratio of each firm’s current employment level to the number of employees when it started operations.

Col. 1 of Table 3 presents a regression of Size Ratio on age dummies and shows that older firms are much larger compared to their size when they first started operations.<sup>26</sup> In Col.2 we add in controls for industry and country and in Cols. 3 and 4 we look at sub-samples of manufacturing and non-manufacturing industries. In all cases the age coefficients are positive and significant.

Overall, Table 2 shows that the average older firm is larger than the average firm that is less than five years old and Table 3 shows that the average older firm is larger than its size when it first started operations. Together, Tables 2 and 3 and figures 1-5 show no evidence of a declining lifecycle in our sample of firms.

> 26 The constant shows that the average firm that is less than 5 years old is 2.92 times its size when it first started operations whereas the coefficient of the 40+ age bin shows that the average 40+ year old firm is 9.64 times its size when it first started operations.

20
**<u>Table 5: Regression Results: IV Estimation (2)</u>**

||**(1)**<br>**Rem/**|**(2)**<br>**GDP**|**(1)**<br>**Log o**|**(2)**<br>**f Rem**|**(1)**<br>**Log of Re**|**(2)**<br>**m per capita**|**(1)**<br>**Rem/**|**(2)**<br>**GDP**|**(1)**<br>**Log o**|**(2)**<br>**f Rem**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Log of migrants abroad**|1.084**|1.618***|0.685***|0.815***|0.350*|0.630**|||||
||[0.500]|[0.570]|[0.237]|[0.234]|[0.213]|[0.254]|||||
|**Migrants/Population**|||||||0.205|0.297**|0.163**|0.177***|
||||||||[0.142]|[0.135]|[0.074]|[0.065]|
|**Bank deposit/GDP**|7.342*||1.770||3.111**||5.323||-0.133||
||[3.939]||[1.299]||[1.456]||[4.054]||[1.805]||
|**Bank credit/GDP**||6.325*||1.475||3.277*||2.574||-0.795|
|||[3.282]||[1.148]||[1.679]||[2.736]||[1.521]|
|**Log of GDP**|-1.362***|-1.683***|0.378**|0.300*|||-0.198|0.058|1.208***|1.254***|
||[0.521]|[0.583]|[0.184]|[0.179]|||[0.340]|[0.303]|[0.190]|[0.169]|
|**Log of GDP per capita**|-1.320***|-1.415**|-0.467**|-0.480*|-0.173|-0.478|-1.983***|-2.050***|-0.901***|-0.804***|
||[0.508]|[0.578]|[0.236]|[0.268]|[0.240]|[0.407]|[0.480]|[0.538]|[0.200]|[0.282]|
|**GDP growth**|-0.321|-0.311|-0.067|-0.064|-0.058|-0.051|-0.258|-0.203|-0.012|0.001|
||[0.286]|[0.309]|[0.082]|[0.085]|[0.121]|[0.139]|[0.267]|[0.277]|[0.089]|[0.089]|
|**Diff. in the ratio of tertiary edu.**|-0.046|-0.028|-0.007|-0.002|-0.034**|-0.031*|-0.076**|-0.067**|-0.023*|-0.020|
||[0.037]|[0.038]|[0.014]|[0.013]|[0.016]|[0.018]|[0.033]|[0.032]|[0.012]|[0.014]|
|**Constant**|31.775***|33.667***|5.256**|5.629**|-0.065|-1.307|22.738**|17.456*|-2.624|-4.403|
||[9.075]|[10.062]|[2.440]|[2.643]|[3.069]|[3.298]|[9.553]|[9.389]|[4.551]|[4.576]|
|**Observations**|85|85|85|85|85|85|85|85|85|85|
|**R2**|0.153|0.101|0.595|0.582|0.024|-0.319|0.247|0.266|0.522|0.486|
|**F-statistic (p-value)**|4.03 (0.00)|3.78 (0.00)|21.66 (0.00)|20.78 (0.00)|7.14 (0.00)|6.36 (0.00)|4.91 (0.00)|5.18 (0.00)|17.10 (0.00)|16.33 (0.00)|
|**Overidentification χ2 (p-value)**|7.25 (0.20)|7.26 (0.20)|3.87 (0.57)|3.86 (0.57)|8.05 (0.15)|6.43 (0.27)|8.88 (0.11)|9.26 (0.10)|4.10 (0.54)|3.72 (0.59)|

Note: Figures in parentheses are robust standard errors. ***significant at 1% level, **significant at 5% level, and *significant at 10% level. Both the migration and financial development variables are instrumented. The ratio of bank deposit to GDP is used in (1) and the ratio of bank credit to GDP is used in (2).

22
**60%** of **GDP** in another ten years time. With higher real returns, the -atio of pension funds to **GDP** will be higher and may even reach **100%** of **GDP by** the year 2000. With the continuing tight limits on other investments, it is very likely that investments in corporate equities will be encouraged further **by** appropriate gradual relaxation of current limits on such investments.

Table **11**

|Siqnificanc<br>|eofPensionFund investments<br>||
|---|---|---|
|(%of|classofinstruments)||
||**1981**<br>**1985**|**1990**|
|Bankdeposits<br>|**3.0**<br>**11.9**|**19.9**|
|Centralbanksecurities|**10.9**<br>**22.9**|**38.0**|
|Treasurysecurities|**0.7**<br>46.5|**99.7**|
|Mortgagebonds|2.2<br>48.2|**56.1**|
|Corporate bonds|**1.9**<br>**7.7**|**55.6**|
|Corporate equities|**-**<br>**-**|**8.6**|
|**All**financial assets<br>|**1.7**<br>**15.8**|**19.7**|
|Bankcredits|**1.6**<br>**15.6**|40.1|
|Source:<br>Habitat**(1991)**|||

> In this respect, it is worth noting that the rule that prohibited investments in overseas assets has probably been instrumental in channeling pension fund investments into corporate securities of Chilean companies and in stimulating the development of the domestic capital markets. Although

> complete prohibition may be inadvisable as it would prevent a proper diversification of country risk, complete freedom may also be counterproductive as it would deprive the benefits of the increased

> availability of long-term funds for the domestic capital markets. The Chilean experience suggests that pension funds can be an effective force in stimulating innovation, improving efficiency and inducing desirable fiscal, legal and regulatory changes.

# **3.10** Privatization and Dispersion of Corporate Ownership

The Chilean pension funds have played a very significant part in the privatization of several state-owned companies, including Chile Metro, Chilgener, Chilquinta, Endesa, Entel, Laboratories Chile, Pilmaiquen, Schwager, Soquimich and Telefonos. The total holdings of all pension funds range from **10%** to **35%** of the equity capital of these companies. Holdings in privatized companies still represent the lion's share of pension fund equity holdings, accounting in **1990** for nearly **90%** of their total investments in corporate equities.

The low level of other equity holdings is due to the combination of strict restrictions on investments in equities of companies with concentrated ownership and the apparent reluctance of Chilean companies to suffer a dilution of control through public listing and sale of equity stakes to pension funds and other investors and especially to submit themselves to the requirements of Chapter 12 of Law **_3,500_** that governs the operations of pension funds (see above). Thus, the use of pension funds for encouraging greater dispersion of corporate ownership has not produced any significant results as yet. To some extent, this may be due to the strictness of the applied investment rules. In fact, a relaxation of these rules may prove more effective in achieving this objective.

26
Table 5: Cyclical Properties of Transition Rates among Non- Employment Sectors: Mexico and <u>Brazil</u>

|||Mexico<br>|||Brazil<br>||
|---|---|---|---|---|---|---|
|Series|St. Dev|Correlation|Elasticity|St. Dev|Correlation|Elasticity|
|O-U|0.115|-0.517|-2.207|0.109|-0.715|-3.937|
|U-O|0.074|0.536|1.407|0.043|0.381|0.813|

Notes: The table shows the standard deviation, the correlation with output and the elasticity with respect to output of the logged and HP filter de-trended transition rates across non-employmetn states (O and U) for Mexico and Brazil. Transition rates are inferred from the continuous time transition matrix for each period obtained following the procedure by Geweke et al. (1986) outlined in Section III. Computations are based on 10.000 Monte Carlo replications. The series have been smoothed using a 4 quarter moving average to remove high frequency fluctuations. O=Out of the Labour Force, U=Unemployment, E=Employment, S=Informal Self-Employed, I=Informal Salaried, and F=Formal Sector, all as proportions of working age population. Data for Mexico (left panels) is drawn from the quarterly National Urban Labor Survey (ENEU) from 1987:Q1 to 2004:Q4. Data for Brazil (right panels) is drawn from the Monthly Employment Survey (PME), quarterly averaged from 1983:Q1 to 2001:Q2.

44
Policy Research Working Paper 10404

# **Abstract**

There is increasing recognition that deficiencies in the public transport system impact men and women differently. While transport systems have been shown to play a significant role in women’s participation in the labor force globally, this topic has been little explored in the Middle East and North Africa. This paper examines the effect of the spatial accessibility, availability, and safety of public transportation on women’s labor market outcomes in three capital cities in the Middle East and North Africa—Amman in Jordan, Beirut in Lebanon, and Cairo in the Arab Republic of Egypt. The analysis uses three types of data collected for each city in 2022, namely, household mobility surveys, transit network data, and built environment audits. The paper investigates how the spatial accessibility of jobs in each city, the availability of public transportation close to residential locations, and the safety of public transit stops affect the labor force participation of women and their likelihood of employment. The main findings are that: (a) accessibility, availability, and safety appear to impact women’s labor force participation differentially in each city, and these impacts also vary by income level; and (b) although accessibility, availability, and safety appear to impact women’s labor force participation, they have overall little impact on women’s employment probability. The paper takes these two findings to imply that: (a) a one-size-fits-all-women solution is not appropriate when designing public transport systems; and (b) although public transport plays a critical role in improving women’s access to employment opportunities, complementary actions are needed to translate these gains into gainful employment.

This paper is a product of the Transport Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at malam5@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
fund characteristics that are driving performance. For example, fund size might erode performance because of trading costs associated with liquidity and price impact (Chen et al., 2004). Since passive funds are larger (see Table 6), the reported under-performance of passively-managed foreign funds might be a consequence of their size rather than their management style. To deal with the correlation between Active Share with other fund characteristics, we analyze the effect of past Active Share on performance in a regression framework proposed by Fama and MacBeth (1973), where we control for the effects of other fund characteristics on performance. We use the following specification where _ADJRETi,t_ is the return of fund _i_ in month _t_ adjusted by various performance benchmarks and _Xi,t−_ 1 is a set of control variables that includes fund size, turnover, and net flows. _Foreign_ is a dummy variable equal to 1 for foreign investors and zero otherwise. _Group_<sup>_a_</sup> _i,t−_ 1<sup>isadummy</sup> variable set to one if investor _i_ at time _t −_ 1 belongs to the Active Share group _Group_<sup>_a_</sup> . We take the estimates from these cross-sectional monthly regression and follow Fama and MacBeth (1973) in taking their time series means and standard deviations to form our overall estimates. An advantage of equation (4) is that we can directly compare differences in performance within an investor type (domestic or foreign) and across levels of active management. Furthermore, we can test for a given level of active management, the differences in performance by investor location.

Table 12 presents the estimation results. Among foreign funds, those that are actively managed outperform funds with passive strategies. The difference in risk-adjusted returns is both statistically and economically significant. Using the most conservative estimates, namely, those from the fourfactor model, actively-managed foreign funds outperform passive funds by 1.70 percent per year (0.14 percent per month). Although these are gross returns, i.e. they do not take into account management fees, the difference is above 0.8 percent, which is the average expense ratio that U.S. active funds charge in excess of passive funds.<sup>21</sup> We do not find any differences in risk-adjusted returns between the top and bottom groups of Active Share for domestic institutional investors.

> 21According to Thomson Reuters Lipper, the average expense ratio of actively managed equity funds in the U.S. is 1.4 percent, while the average expense ratio of passive funds is 0.6 percent.

18
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16
# Figure 15 **CHILEAN ENERGY SECTOR: IN THE FUTURE (2005)**

## **THE ENERGY MATRIX**

<!-- Start of picture text --> 9 23% i ~~~8%<br>WOOD & OTHER<br>NATURAL GAS<br>~~~~~~6%<br>[13%\<br>HIDROELECTRICITY<br>~~~40% OIL<br><!-- End of picture text --> SOURCE: NATIONAL ENERGY COMMISION 28