rafmacalaba/datause-encoder-data huggingface dataset:
worker because I could get my own place and items unlike working as a domestic worker where one is always in doors with the employer”; “It was exactly like that, bad but I could manage. We all get bad treatment in the factories, but if I was a domestic worker I would be alone in receiving that bad treatment”.

What can be done? First, continue to improve working conditions in factories. In particular, Better Work’s 2013 assessment of 14 factories had several non‐compliance findings under multiple Working Conditions clusters regulated by national labor law: Occupational Safety and Health, Contracts and Human Resources, Compensation, and Working Time.<sup>23</sup> These results are consistent with our findings from fieldwork and indicate that continued attention is needed in addressing workplace conditions for textile workers. Of additional importance for female migrants is the issue of paid leave. Many have children living with them and, as the only adults living in their urban households, any health or child‐related concerns result in women losing part of their paycheck for time missed at work to address these problems.

Second, extend some of the benefits of formal employment to those in the informal sector, such as domestic workers. A handful of domestic workers suggested that some sort of organization similar to a union would benefit their professional circumstances: “I would like us to have a committee that supports domestic workers. The committee should help in problem solving between the employer and employees”. In fact, international experience from South Africa and elsewhere points to a potential model. At the international level, the government of South Africa ratified the Domestic Workers Convention, 2011 (189), thereby acknowledging the vulnerabilities of workers in this sector and ushering in new international dialogue of their unique needs.<sup>24,25</sup> At the national level, the Labor Department has sought to regulate labor conditions for domestic work, including hours and days worked, minimum wage, overtime, holidays, termination rules, and sick/maternity/family leave.<sup>26</sup> Since 2002, domestic workers have been included in safety net programs as well: if working at least 24 hours a month for an employer, the employer must register with the Unemployment Insurance Contributions Fund of the South African Revenue Service. While actual implementation is difficult to assess, the country has at least formally identified domestic workers as having specific working conditions needing formal regulations.

> 23 <mark>International Labor Organization (2013).</mark>

> 24http://www.ilo.org/dyn/normlex/en/f?p=NORMLEXPUB:12100:0::NO::P12100_ILO_CODE:C189.

> 25 http://www.ilo.org/global/standards/information‐resources‐and‐publications/news/WCMS_216613/lang‐‐en/index.htm.

> 26 http://www.labour.gov.za/DOL/find‐more‐info/all‐about‐domestic‐workers.

36
Policy Research Working Paper 6920

# **Abstract**

Improvement of energy efficiency is one of the main options to reduce energy demand and to reduce greenhouse gas emissions in Ukraine. However, largescale deployment of energy efficient technologies has been constrained by several financial, technical, information, behavioral, and institutional barriers. This study assesses these barriers through a survey of 500 industrial and commercial firms throughout Ukraine. The results from the survey were used in a cumulative multi-logit model to understand the importance of the barriers. The analysis shows that financial barriers caused by high upfront costs of energy efficient technologies, higher costs of finance, and higher opportunity costs of energy efficiency investment are key barriers to the adoption of energy efficiency measures in Ukraine. Institutional barriers particularly lack government policies, which also contributes to the slow adoption of energy efficient technologies in the country. The results suggest targeted policy and credit enhancements could help trigger adoption of energy efficient measures. The empirical analysis shows strong inter-linkages among the barriers and finds heterogeneity between industrial and commercial sectors on the realization of the barriers.

This paper is a product of the Environment and Energy Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at gtimilsina@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
# **1. Introduction**

Almost all OECD countries have temporary worker migration programs, with seasonal workers usually the largest single category, averaging over 500,000 workers a year over the 2006-2011 period (OECD, 2013). Seasonal worker programs have historically been one of the few opportunities for low-skilled workers from poor countries to benefit from the higher incomes to be earned abroad, and have been seen as a more politically palatable way of doing this than through permanent migration (Luthria, 2008).

However, recently there have been divergent policy movements across countries in the willingness to use seasonal worker programs. The United Kingdom was intending to end its Seasonal Agricultural Workers scheme in December 2013, when E.U. transitional arrangements for Bulgarians and Romanians ended; and the number of workers entering Germany under its seasonal worker program has likewise fallen as workers now fall under the E.U. free circulation regime. In contrast, Italy and the Republic of Korea have recently eased entry conditions for seasonal workers (OECD, 2013). Meanwhile, Canada’s Seasonal Agricultural Workers Program (SAWP), which began in 1966, and the U.S. H2-A temporary agricultural worker program have seen reasonably steady numbers of workers entering over the last 5 years.<sup>1</sup> A more recent policy direction has been to explicitly view these seasonal worker programs as a part of international development policy, and design such programs with this goal in mind, in addition to the traditional goal of meeting domestic agricultural needs. The subject of this paper, New Zealand’s Recognised Seasonal Employer (RSE) program, is of interest as one of the first and most prominent of these programs. The RSE was designed to meet the labor needs of New Zealand employers while also encouraging economic development in the Pacific Islands, which were designated as the main source area for workers. The policy was developed taking account of lessons from previous seasonal worker programs elsewhere and is viewed as a possible model for other countries. For example, the ILO good practices database states “The comprehensive approach of the RSE scheme towards filling labour shortages in the horticulture and viticulture industries in New Zealand and the system of checks to ensure that the migration

> 1 The Canadian SAWP had 28,231 workers in 2008 and 29,025 in 2012 (http://www.hrsdc.gc.ca/eng/jobs/foreign_workers/lmo_statistics/annual2012.shtml), while the U.S. H2-A had 64,404 visas in 2008 and 65,345 in 2012 (http://travel.state.gov/visa/statistics/graphs/graphs_4399.html).

2
local leaders, etc.—together manage (or fail to manage) different types of conflict in different settings. Using a version of case-based process tracing (George and Bennett 2005, Varshney 2002), conflict pathways describe discrete stages in the evolution of conflict including conflict triggers and factors or mechanisms that sustain conflict, allow it either to escalate, stagnate, or move toward resolution. By following cases of everyday conflict, the factors that transform underlying tensions into different outcomes—violence or peace—become evident. In essence, the cases work backwards from an outcome by asking what led to what. Importantly, they were selected in a way that controls for some of the traditional weaknesses of qualitative approaches<sup>33</sup> while capturing each stage of a conflict, attempts at its resolution, and events that linked different stages. Each case study includes a summary of the case, its ”pre-history,” evolution, attempts at resolution, impacts, and aftermath.

**Figure 1 District and Sub-district Qualitative Sampling Frame**<sup>**34**</sup>

<!-- Start of picture text --> Nusa Tengarra Timor<br>East Java<br>(NTT)<br>High Capacity Low Capacity High Capacity Low Capacity<br>(Ponorogo) (Pamekasan) (Sikka) (Mangarrai)<br>KDP Non- KDP Non- KDP Non- KDP Non-<br>KDP KDP KDP KDP<br><!-- End of picture text --> Collection of the qualitative data used to construct these cases occurred in three stages and used a quantitatively oriented sampling frame in order to construct a plausible counterfactual. Propensity score matching<sup>35</sup> was used to “match” villages that received KDP with those that would have been likely to receive it but did not, further verified by qualitative interviews to

> 33 See Barron, et al (2004) for a more detailed description of the sampling methodology.

> 34 For more detailed information on how this sampling framework applied across three distinct stages of research, see Barron et al (2004).

> 35 The propensity score is a statistical measure designed to calculate the probability of a given household or village being selected for inclusion in a program (for an introduction, see Baker 2000).  The score was estimated using the PODES (1996) dataset.  (PODES, which stands for Pontesi Desa (Village Potential), is a key informant survey administered every two years by BPS that contains information on each of 60,000 plus villages in Indonesia.)  The actual propensity score was derived using explanatory variables that could serve as proxies for the economic level of the kecamatan.  Among the PODES variables used were: population, access to urban facilities such as markets, hospitals, department stores, health and education resources, main source of income, perception of poverty level, etc. These are all ‘observable’ factors, however, so to control for ‘unobservable’ factors (e.g., motivation, cohesion, leadership) we used the propensity score to select _three_ statistically comparable non-KDP (‘control’) villages and then asked our field research team to identify which of these was, in their view, the most appropriate match for the KDP (‘treatment’) village.

8
benchmark or following a benchmark unknown to us (for dead funds, this information was impossible to retrieve).<sup>15</sup> Our final database consists of an unbalanced panel, where each observation is a country-fund-time observation containing the percentage of TNAs invested in a particular country by a mutual fund, the percentage allocation of that same country at the same time for the assigned benchmark, plus fund-specific information. Because we have much more matched data on equity funds than bond funds we rely more heavily on the former than the latter. However, despite the much lower data availability, the results on bond funds are broadly consistent with those on equity funds.

We also classify funds according to their degree of activism, that is, the extent to which their country allocations deviate on average from those of their respective benchmarks. Following Cremers and Petajisto (2009) but using country weights instead of security weights, we classify funds as “explicit indexing,” “closet indexing,” “mildly active,” and “truly active” funds. Explicit indexing funds are either ETFs or passive funds. Closet indexing funds do not declare to be passive but behave similarly to explicit indexing funds. Mildly and truly active funds are those that deviate importantly from their self-declared benchmarks. Specifically, for each fund we first compute its active share each month and then take the average over time as a time-invariant measure of a fund’s deviation from its benchmark allocations. This measure gives the average percentage of a fund’s portfolio that deviates from its benchmark.<sup>16</sup> Because mutual funds in our sample have only long positions, this measure ranges from 0 to 100 percent. We then define closet indexing funds as those that on average have an active share within two standard deviations of the active share of explicit indexing funds. Funds not belonging to the explicit indexing or closet indexing groups are classified into mildly active (truly active) if they are in the lower part (upper) of the distribution of the active share measure (using the median active share).<sup>17</sup> Our database of mutual funds, before matching with the benchmarks, contains 2,837 equity funds and 838 bond funds with three geographical investment scopes: global, global emerging, and regional funds (Table 1). Equity funds are domiciled around the entire world but most of the funds are located in Canada, France, Ireland,

> 15 Having access to the benchmarks makes the matching relatively straightforward given that funds have increasingly reported their benchmarks. For instance, among the funds covered by EPFR, 28 percent of equity funds did not report a benchmark in 1996, while 5 percent did not do so in July 2012. Our matching for equity funds is rather complete because only 9 percent of equity funds in our sample do not report (or are assigned) a benchmark. For bond funds, that number is 16 percent.

> 16 More formally, it is defined as ∑| | .

> 17 The results are robust to the selection of benchmarks, where we assign the minimum active share benchmark to each fund.

8
47 Figure 1: Predominant hospital payment methods in the ECA region, 1990-2004

|Country|90<br>91|92|93|94|95|96|97|98|99|00|01|02|03|04|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Albania<br>|||||||||||||||
|Armenia||||||||**G**|**G**|**G**|**G**|**G**|**G**|**G**|
|Azerbaijan|||||||||||||||
|Belarus<br>|||||||||||**G**|**G**|**G**|**G**|
|Bosnia and Herzegovina|||||||||||||||
|Bulgaria<br>|||||||||||**G**|**G**|**G**|**G**|
|Croatia<br>|||||||||**G**<br>|**G**<br>|**G**<br>|**G**<br>|**G**<br>|**G**<br>|
|Czech Republic<br>|||||**G**|**G**|**G**|**G**|**G**|**G**|**G**<br>|**G**<br>|**G**<br>|**G**<br>|
|Estonia|||||||||||**G**|**G**|**G**|**G**|
|Georgia||||||**G**|**G**|**G**|**G**|**G**|**G**|**G**|**G**|**G**|
|Hungary<br>||||||||||||||**G**<br>|
|Kazakhstan||||||||||||||**G**|
|Kyrgyz Republic|||||||||||||||
|Latvia||||**G**|**G**|**G**|**G**|**G**|**G**|**G**|**G**|**G**|**G**|**G**|
|Lithuania||||**G**|**G**|**G**|||**G**|**G**|**G**|**G**|**G**|**G**|
|Macedonia, FYR|||||||||||||||
|Moldova|||||||||||**G**|**G**|**G**|**G**|
|Poland|||||||||**G**||||||
|Romania|||||||||**G**|**G**|**G**|**G**|**G**|**G**|
|Russian Federation|||||||||||||||
|Serbia and Montenegro|||||||||||||||
|Slovak Republic|||||||||||||||
|Slovenia||**G**|**G**|**G**|**G**|**G**|**G**|**G**|**G**|**G**|**G**|**G**|**G**|**G**|
|Tajikistan|||||||||||||||
|Turkey|||||||||||||||
|Turkmenistan<br>|||||||||||||||
|Ukraine<br>|||||||||||||||
|Uzbekistan|||||||||||||||
||Histori<br>Patien<br>Fee-fo<br>**G**<br>Global<br>Missin|cal budg<br>t-based<br>r-servic<br>budget<br>g data|et<br>payme<br>e (FFS)<br>(cap o|nt (PB<br> <br>n reimb|P)<br>urseme|nt and/|or serv|ices pr|ovided|at the h|ospital|level)|||

_Source:_ WHO _HiT_ series (http://www.euro.who.int/observatory/Hits/TopPage), World Bank staff and country reports.
using both types of data in various settings ranging from low-income countries (India and Vietnam) to a high-income country (the United States).

# **3.1. True Panel Data**

We use true panel data from a low-income country—Vietnam—and a high-income country—the United States—for illustration. Data for the former comes from three rounds of the VHLSS (Vietnam Household Living Standards Survey) in 2004, 2006, and 2008, and the latter the sample persons (i.e., those with a positive longitudinal weight) from three rounds of the PSID (Panel Study of Income Dynamics) in 2005, 2007, and 2009. The VHLSS follows a rotating panel design where half of the sample in the previous round is repeated in the succeeding round, thus resulting in a panel sample for all three survey rounds of roughly one-fourth of the original sample in 2004.<sup>19</sup> Estimation sample sizes for Vietnam are around 3,700 households between 2006 and 2008, and 1,800 households between 2004 and 2008; the corresponding figures are 5,335 households for the US for both periods.

These surveys provide respectively consumption and income data for the same years in 2004, 2006, and 2008 (since income data are from the last tax year in the US). In a slight abuse of notation, we hereafter refer to the specific PSID survey round by the tax year, and use income and consumption interchangeably. There is no single national poverty line for the US, so for illustration purposes we choose for the national poverty line the total household income level that provides the same poverty rates as those based on the Census Bureau’s household-varying thresholds. For example, this poverty line in 2006 is $US 13,305 yielding a poverty rate of 11 percent. The poverty line for Vietnam is constructed instead based on a basket of consumption

> 19 We construct panel data for the VHLSSs using household identification codes. Where we suspect mismatching between panel households due to incorrect identification codes, we correct these cases using a matching procedure that uses household heads’ names. Dang and Lanjouw (2013) provide more details on the panel data for Vietnam.

18
# **Table 5. Telecommunications reforms and labor market changes by socio‐demographic groups**

|||By ge|nder||By age||
|---|---|---|---|---|---|---|
||All|Women|Men|25‐34|35‐49|50‐64|
|log(employment)|0.186**|0.0800|0.276***|0.0775|0.149*|0.358***|
||(0.0743)|(0.107)|(0.0813)|(0.116)|(0.0847)|(0.0985)|
|share temporary|‐0.000847|‐0.0174|‐0.0115|0.00874|‐0.0346*|0.0144|
||(0.0166)|(0.0203)|(0.0179)|(0.0226)|(0.0188)|(0.0239)|
|share part‐time|0.0857***<br>(0.0139)|0.106***<br>(0.0189)|0.0269**<br>(0.0106)|0.0565***<br>(0.0160)|0.0833***<br>(0.0164)|0.116***<br>(0.0229)|
|share working from home|0.211***|0.166***|0.230***|0.158***|0.145***|0.236***|
||(0.0319)|(0.0283)|(0.0340)|(0.0247)|(0.0279)|(0.0376)|
|share self‐employed|‐0.0626***<br>(0.0147)|‐0.00902<br>(0.0159)|‐0.0816***<br>(0.0184)|‐0.0564***<br>(0.0182)|‐0.0913***<br>(0.0179)|‐0.0569**<br>(0.0237)|
|share with two jobs|‐0.00771<br>(0.00609)|‐0.0101<br>(0.00718)|‐0.00413<br>(0.00829)|‐0.0105<br>(0.00874)|0.00879<br>(0.00791)|‐0.0360***<br>(0.0114)|

Notes: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. Each cell shows the OLS estimate of � in equation (1). The reform variable is a dummy variable equal to one after the country introduced its first telecommunications reform, except for the “working from home” variable, where the reform variable is equal to one two years after the reform.

19
Turning to our own results, data from the Enterprise Surveys reveal notable differences between the management practices of establishments within the LAC region. According to our survey data, establishments in Colombia have the most structured management practices in place, while establishments in Bolivia have the least structured practices (Figure 2). The poor management of establishments that we see, on average, in Bolivia appears to be driven, at least in part, by a number of very badly‐managed firms, shown by the longer left tail of the distribution for Bolivia (Figure 3); this echoes the results highlighted by Bloom and Van Reenen in their 2010 paper showing that much of the variation between countries in terms of management practices is due to a tail of poorly‐managed firms present in some countries. Our results also show that certain establishment characteristics are important predictors of better management; larger establishments, as well as those that export, have higher management scores. Establishments that recruit their manager from outside the establishment (as opposed to those who promote internally), and establishments with formal workforce training programs in place, also have higher management scores. Furthermore, we confirm what was shown in the seminal Bloom et al. (2007) paper—that better management practices are associated with better outcomes. We show that more structured management practices are associated with higher labor productivity and, for a subset of larger manufacturing establishments, higher total factor productivity also. When we break down the results by each individual country, we find notably different impacts across the seven countries studied—for three of the seven countries analyzed (Argentina, Paraguay and Peru), we find no evidence of a positive relationship between management and labor productivity based on the average across all establishments. However, in Argentina and Paraguay, there does appear to be a positive link for establishments of 50 of more employees, while in Peru the data show that establishments must employ at least 100 workers before a significant relationship between management practices and labor productivity can be detected.

We make several distinct contributions to the literature on management practices and productivity. Firstly, while analyses of establishments’ management practices have been studied in many countries across the world, in only two of the countries that we study (Argentina and Colombia) can we find evidence of previous research on management practices, and so we are shedding new light on the role of management in five previously unstudied contexts. Secondly, in addition to information on management practices, our data contain detailed information on establishment characteristics, on performance metrics and on the business environment. We can exploit these rich data to more accurately estimate the relationship between management practices and establishments’ outcomes, and to test in which contexts management matters most. Finally, the surveys from which our data come are designed to be 5