rafmacalaba/datause-encoder-data huggingface dataset:
sector’s minimum wage was raised, but only to a compromise of BDT 5,300 (USD 68) a month for low-skilled workers. This represents an increase by 77% compared to the previous minimum wage.<sup>3</sup> Figure 3 provides a histogram of the monthly earnings of workers in the RMG sector in 2013 quarter 1 (before the Rana Plaza collapse) and 2015 (after the minimum increase had been in effect for over a year) using the Bangladesh Labor Force Survey data described in section 3.1. Note that while our data on earnings includes all take home pay (inclusive of payments like bonuses and overtime pay) the minimum wage applies only to base pay, so that more workers earn below the new minimum wage than would be expected if they reported base pay separately. Overtime pay is likely to be important: it is legally required for every extra daily hour beyond 8 hours for each of the six working days (Menzel and Woodruff, 2021), and 62% of workers in 2013 quarter 1 worked strictly more than 48 hours per week. Indeed, an estimated 20% of workers’ take-home pay is overtime (Reuters, 2018). Even so, 1.3% of workers earned total take-home pay below the new minimum wage in 2013, while none did by 2015.

# **3 Empirical strategy**

## **3.1 Data and outcome variables**

We use the Labor Force Surveys (LFS) conducted in 2003, 2005, 2010, 2013, 2015, and 2016 by the Bangladesh Bureau of Statistics. The Bangladesh LFS was designed to be representative at – – the national level for both rural and urban areas as well as for each of Bangladesh’s eight divisions. All working-age (age 15 to 64) individuals from the selected households are interviewed for detailed labor market outcomes including labor market status, type of employment, monthly wages for those that are paid employees, hours worked, detailed occupation and sector of activity at the four-digit level, as well as working conditions. The sample sizes are large, with over 100,000 individuals in working age interviewed in years prior to 2015, and above 300,000 in 2015 and 2016. The 2003-2013 surveys took place over the course of a year, while the 2015 and 2016 surveys 3In December 2018, after the period studied by this paper, the minimum wage was further raised to BDT 8,000.

9
Communist Party Ideology database developed by Horowitz and Browne (2005). Ideologies of governments are here understood in terms of the positions of ruling individuals, groups, or parties on the most salient policy issues. Following the tradition of much applied work in comparative politics, we use a two-dimensional policy space, where one dimension captures left-right differences in economic ideology-cum-policy, and the other dimension captures differences in “national identity” policies. In economic policy, the main issue that faced the post-communist world in its first dozen years was whether, and how, to make the transition from planning or market socialism to capitalism. In debates over national identity, the main policy issues concerned the status and treatment of internal ethnic minorities and of ethnic groups in neighboring countries. To code leaders and parties, we use criteria described in Appendix A and Horowitz, Hoff and Milanovic (forthcoming) to distinguish four intervals along each of the two ideology-cum-policy dimensions; see Table 2. The number in each cell represents total country/years that ideologically different parties or coalitions were in power over the period 1989-2006. For example, center-left parties were in power for 188 country/years or almost 39 percent of country/time.

TABLE 2. IDEOLOGICAL CLASSIFICATION SCHEME FOR PARTIES CONTESTING ELECTIONS IN POSTCOMMUNIST COUNTRIES, AND NUMBER OF COUNTRY/YEARS RULED BY DIFFERENT REGIMES

|_Economic ideology_<br>_National identity policy_|_Far Left_|_Center-Left_|_Center-Right_|_Far Right_|
|---|---|---|---|---|
|Extreme Nationalist|0|0|0|0|
|Moderate Nationalist|66|188|229|0|
|Moderate Autonomist|3<sup>*</sup>|0|0|0|
|Secessionist|0|0|0|0|

Note. Total number of country/years is 18 years times 27 countries = 486. *Moldova 2001, 2002, and 2003.

19
# **2. SERVICES POLICY DATA**

We describe first, in Section 2.1, the scope of the database. A much more detailed account of the Database is provided in a companion paper, ―Guide to the Services Trade Restrictions Database‖ (Borchert, Gootiiz and Mattoo, 2012, referred to hereafter as ―Database Guide‖) and in supplementary material available at http://iresearch.worldbank.org/servicestrade. Section 2.2 then briefly discusses how this database relates to other databases covering services policies and procedures.

## **2.1 Data description**

The Services Trade Restrictions Database contains information on policies that affect international trade in services – defined, as is now customary, to include the supply of a service through cross-border delivery, by establishing a commercial presence, or by the presence of a natural person.<sup>1</sup> The perspective is one of a foreign supplier who wishes to provide services to consumers in a particular country, and we focus mainly on policy measures that discriminate against foreign services or service providers. Domestic regulations that affect domestic and foreign agents alike are covered only in the instances where they clearly have major impact on trade.<sup>2</sup> The database encompasses information from a total of 103 countries, of which 79 are developing and 24 OECD countries, broadly representing all regions and income groups in the world.<sup>3</sup> First-hand information for developing countries was collected by administering a

> 1  We do not cover ―consumption abroad,‖ a mode of delivery that is particularly important in services like tourism, education and health – sectors not covered by the database.

> 2  The greatest challenge is posed by domestic regulations that are in principle origin-neutral but in fact impose a bigger burden on foreign service providers.  We make an effort to deal with the key regulations in professional services, such as qualification and licensing requirements, but much more could be done to improve the coverage of such measures in areas like financial services.

> 3  The regional affiliation of countries in this paper (and the Database) follows the official World Bank country classification, which groups all non-high income countries into world regions.  For ease of exposition there are two exceptions to this rule: Trinidad and Tobago is presented as part of the Latin America and Caribbean (LAC) region, and Bahrain, Kuwait, Oman, Qatar and Saudi Arabia are referred to as Gulf Cooperation Council (GCC) countries.  All six are high-income countries in official World Bank terms.

5
Capitalizing on the harmonized data used in this study, we also estimate pooled regressions to examine the overall association between cognitive skills (as measured by reading proficiency scores and complexity of computer use on the job) and earnings across all of the eight countries together.<sup>24</sup> We estimate a country-level fixed-effects model (allowing for differences in average relationships between skills and earnings across countries) with robust standard errors.<sup>25</sup> Our results are presented in Table 9. Column 1 of the table presents results for reading proficiency scores with the usual controls; in column 2 we add schooling to the model with reading proficiency scores; and in columns 3 and 4 we estimate the relationship between the computer complexity dummy variables and earnings before and after controlling for completed years of education. Finally, in column 5 we include all cognitive skills in the model with schooling. As reported above, we find that the estimated effect of cognitive skills is lower in magnitude but continues to be positive and significant after controlling for education. In the full model, the returns to education are 4 percentage points, and hourly earnings increase by a similar magnitude for every standard deviation increase in reading proficiency scores. For computer skills, we find that as complexity of computer use on the job increases, and after controlling for schooling and reading proficiency, hourly earnings increase by 28 to 51 percentage points.

**Table 9. Pooled regression estimates for cognitive skills with country fixed effects, workers age 25 to 64**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Gender|-0.302***|-0.268***|-0.294***|-0.262***|-0.260***|
||(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|
|Potential experience|-0.000|0.007|0.008|0.013**|0.013***|
||(0.01)|(0.01)|(0.01)|(0.00)|(0.01)|
|Experience squared|-0.000|-0.000|-0.000**|-0.000**|-0.000***|
||(0.00)|(0.00)|(0.00)|(0.00)|(0.00)|
|Self-employed|-0.209***|-0.157**|-0.140*|-0.103|-0.100|
||(0.07)|(0.07)|(0.06)|(0.07)|(0.07)|
|High-skilled white collar|0.461***|0.283***|0.234***|0.103|0.096|
||(0.09)|(0.08)|(0.06)|(0.06)|(0.06)|
|Low-skilled white collar|-0.034|-0.087|-0.115*|-0.152**|-0.155**|
||(0.07)|(0.06)|(0.06)|(0.06)|(0.06)|
|Schooling||0.054***||0.047***|0.043***|

> 24 Noncognitive skills measures were not included in the pooled regression.

> 25 An additional advantage of fixed effects models is that they can provide some control against bias due to omitted variables, provided that these variables and their effects are time-invariant. Our models do not include relevant labor market indicators that could potentially influence hourly earnings, and the fixed effects framework could serve as a potential safeguard from bias due to this omission.

31
- 33 -

# BOX D

# Technical Assistance: Tensions Between Borrower and the Bank NEPAL FIRST HIGHWAY PROJECT (NID)

The Nepal Government's attitude towards this project's technical assistance program was ambivalent all along. The project's Government seems to have felt that the expatriate experts did not understand the aspirations of the Nepalese and, justifiably or not, it also felt that experts' skills were already available within the organization. It felt that it could use outside assistance in the area of high level general planning and not of the "nuts and bolts' of feasibility investigations or project supervision as provided under the project. As a result the experts' study and its recommendations did not receive the attention they deserved. It was not until after the experts' departure that the Department of Roads implemented some of the experts' recommendations, though the terms of reference of the experts were to help implement the recommendations accepted by the government. The impression remains that the Government was not convinced of the need for technical assistance as provided, but accepted it because it was a requirement of the project.

Given the strong influence of ID achievement on overall project performance and sustainability, the declining trend in ID performance evident from OED data is a cause for concern. The repeated references to institutional problems in recent ARIS reports echo the same concern. That deterioration in the macroeconomic environment explains only a part of this phenomenon implies that an improvement in macroeconomic conditions will not by itself solve the problem of declining ID performance. The weaknesses 'n institutional analysis and design, already noted in Chapter II, seem to have exacerbated ID implementation problems. The foregoing analysis of SID and NID projects shows that there are specific ID actions which can be taken during project design and implementation to make them more effective. There is, however, no recipe or action sequence that can be prescribed for all situations. The fact is that SID cases are not necessarily those with better initial endowments than NID cases. Both seem to face similar problems, but respond to them differently. Leadership capacity, attention to personnel issues, flexibility in planning and implementation aided by good supervision, and the harmonization of national and external inputs (TA) seem to be the key distinguishing marks of the more successful cases. The evidence offered here reaffirms much that is already known and documented about ID success. The question is why this knowledge is not being applied.
TABLE 5.2 MAXIMUM LIKELIHOOD PROBIT ESTHIATES OF **THE** STATUS PARTICIPATION EQUATION

|VariabIe|Coefficient|Std. Error|t-Stat.|2-Tail Sig.|
|---|---|---|---|---|
|CONSTANT|-3.338|1.881|-1.775|0.081|
|CACC6|-15.269|6.685|-2.284|0.026|
|NETF6|7.731|5.183|1.491|0.141|
|RER2|0.020|0.012|1.667|0.101|
|SHOCKI|-3.156|1.583|-1.994|0.051|
|DEBT6|2.652|1.525|1.<br>-8|<br>0.087|
|INV2|-8.692|4.574|-1.4v.|0.062|
|D.'|2.045|0.715|2.858|0.006|
|D2|0.266|0.672|0.396|0.693|
|D3|-2.469|1.033|-2.389|0.020|
|D6|0.725|0.637|1.138|0.259|
|D7|3.540|1.008|3.511|0.001|
|DINFLAC|-1.465|0.658|-2.223|0.030|

Ncte: Log likelihood a -24.99; pseudo R<sup>2</sup> = 0.50. Number of observations: 77. The pseudo **R**<sup>**2**</sup> measure is equal to [1-(log L,)/(iog **L]. where L,** denotes the maximum of the likelihood function when maximized with respect to all ..e parameters, and L. is the maximum when maximized with respect to the constant term oniv.
Policy Research Working Paper 4972

# **Abstract**

Macro- and micro-economic evidence suggests a positive role of remittances in preparing households against natural disasters and in coping with the loss afterwards. Analysis of cross-country macroeconomic data shows that remittances increase in the aftermath of natural disasters in countries that have a larger number of migrants abroad. Analysis of household survey data in Bangladesh shows that per capita consumption was higher in remittance-receiving households than in others after the 1998 flood. Ethiopian households that receive international remittances seem to rely more on cash reserves and less on selling household assets or livestock to cope with drought. In Burkina Faso and Ghana, international remittance-receiving households, especially those receiving remittances from high-income developed countries, tend to have housing built of concrete rather than mud and greater access to communication equipment, suggesting that they are better prepared against natural disasters.

This paper—a joint product of the Global Facility for Disaster Reduction and Recovery (GFDRR) Unit, Sustainable Development Network Vice Presidency, and the Migration and Remittances Team of the Development Prospects Group, Development Economics Vice Presidency—is part of a larger effort of the GFDRR unit to disseminate the emerging findings of the forthcoming joint World Bank-UN Assessment of the Economics of Disaster Risk Reduction. Thanks to Antonio C. David for his contribution to the macroeconomic analysis when he was at the Development Prospects Group in early 2008. We are grateful to the reviewer, Dean Yang, for his advice and suggestions, and to Saroj Kumar Jha, Mirafe Marcos , S. Ramachandran, Apurva Sanghi and participants at a workshop at the World Bank for their constructive comments. Ani Rudra Silwal provided excellent research assistance. Policy Research Working Papers are also posted on the Web at http:// econ.worldbank.org. The GFDRR team leader Apurva Sanghi can be contacted at asanghi@worldbank.org. Correspondence regarding the paper should be addressed to Sanket Mohapatra at smohapatra2@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
Figure 2: Primary-to-Secondary education Transition rate, and Repetition rates, 1997-2016 <!-- Start of picture text --> Primary & Secondary education Transition and Repetition Rates<br>80<br>60<br>40<br>20<br>0<br>1995 1997 2000 20052006 2010 2012 2015 2020<br>Year<br>Primary−to−Secondary school Transition rate Primary school repetition Rate<br>Secondary school repetition rate<br><!-- End of picture text --> _Source_ : Own representation using data from UN Institute of Statistics.

As of 2013, there were about 13 higher education institutions, 8 (i.e. 62 percent) of which are public institutions. The National University of Lesotho (NUL) is the main tertiary institution in Lesotho, and remained the only local bachelor’s and master’s degree-awarding institution until 2007. The university admits about 44 percent<sup>5</sup> and 89 percent of Lesotho’s undergraduate and postgraduate students’ population, respectively (Council on Higher Education, 2013).

# **2.2 Policy background**

Lesotho instituted the FPE program in 2000 in order to meet the Millennium Development Goal (MDG) of ensuring that primary education is free and available to all (UNESCO, 2000). As mentioned earlier, Lesotho’s implementation strategy was different from that followed by other African countries. School fees were eliminated sequentially on a grade-by-grade basis starting with grade one in 2000 such that by 2006, all seven primary school grades were under FPE. The main reason for this implementation strategy was to cushion FPE’s financial impact on the public budget (Urwick, 2011).

The FPE policy is an amalgamation of several program components that address both demandand supply-side constraints to schooling. On the demand-side, the policy involves elimination of some private schooling costs such as school fees, stationery and textbooks’ costs. On the supply-

> 5This is as a percent of all those enrolled in local colleges and foreign universities, mainly in South Africa.

6
**Figure 2: Gini index of real GDP per capita of prefecture-level cities in Guangdong (2000-2013)**

<!-- Start of picture text --> 2000 2002 2004 2006 2008 2010 2012 2014<br>Year<br>All cities Guangzhou, Shenzhen and Zhuhai excluded<br>.4<br>.35<br>Gini index<br>.3<br>.25<br><!-- End of picture text --> Data source: Guangdong Bureau of Statistics, 2014.

**Figure 3: Growth Incidence Curve**

<!-- Start of picture text --> 0 25 50 75 100<br>Percentile of the cities (ranked by real per capita GDP)<br>2000-2003 2003-2009 2009-2013<br>120<br>100<br>80<br>60<br>40<br>Real per capita GDP increase (%)<br>20<br><!-- End of picture text --> Data source: Guangdong Bureau of Statistics, 2014. Note: The growth incidence curves are created with “smooth” function for readability.

5
**<u>Table 10: Impact of Internet job search on wage growth for employed job seekers</u>**

|||**Male**<br>|**sample**<br>|||**Femal**<br>|**e sample**<br>||
|---|---|---|---|---|---|---|---|---|
|VARIABLES|(1)<br>Wage<br>growth|(2)<br>Wage<br>growth|(3)<br>Wage<br>growth|(4)<br>Wage<br>growth|(5)<br>Wage<br>growth|(6)<br>Wage<br>growth|(7)<br>Wage<br>growth|(8)<br>Wage<br>growth|
|Internet job search|-0.197|-0.263|-0.260|-0.301|0.020|0.131|0.151|0.118|
||[0.282]|[0.281]|[0.283]|[0.286]|[0.779]|[0.774]|[0.813]|[0.819]|
|Internet access at home|0.290***|0.157*|0.171*|0.184**|0.080|-0.030|0.009|0.021|
||[0.086]|[0.093]|[0.093]|[0.092]|[0.140]|[0.143]|[0.141]|[0.140]|
|Married|0.020|0.010|0.008|0.014|0.024|-0.035|-0.065|-0.064|
||[0.057]|[0.057]|[0.057]|[0.058]|[0.119]|[0.115]|[0.117]|[0.120]|
|Age||-0.000**|-0.000*|-0.000***||0.020***|0.022***|0.022***|
|||[0.000]|[0.000]|[0.000]||[0.006]|[0.006]|[0.006]|
|Primary or preparatory education||-0.000|0.003|0.001||0.152|0.134|0.140|
|||[0.084]|[0.084]|[0.084]||[0.495]|[0.492]|[0.492]|
|Secondary education||0.011|0.015|0.025||0.320*|0.318*|0.335*|
|||[0.071]|[0.071]|[0.071]||[0.178]|[0.181]|[0.183]|
|Above secondary education||0.265***|0.272***|0.276***||0.603***|0.626***|0.633***|
|||[0.083]|[0.084]|[0.085]||[0.186]|[0.189]|[0.190]|
|Rural|||0.047|-0.077|||0.217**|0.148|
||||[0.051]|[0.377]|||[0.100]|[0.171]|
|Observations|2,494|2,493|2,493|2,493|529|527|527|527|
|R-squared|0.004|0.012|0.013|0.021|0.001|0.039|0.048|0.053|
|Region dummies|NO|NO|NO|YES|NO|NO|NO|YES|
|Dependent variable mean|0.865|0.865|0.865|0.865|0.933|0.932|0.932|0.932|

Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1 _Notes._ Each cell represents a coefficient estimate using OLS regression. The analysis is restricted to employed individuals in 2012 that were also interviewed in 2018, aged 15 to 64 years old in 2012. Employed individuals are defined according to the market definition of labor force participation. The dependent variable is the logarithm of the difference in inflation adjusted hourly wages between the years 2018 and 2012. The main independent variable of interest is a dummy variable indicator equals to one if the individual reports that finding a job is one of the purposes of Internet use. The control variables refer to the year 2012 and they include: a dummy variable for having Internet access at home, age, a dummy variable for being male, a dummy variable for being married, three dummies for the individual’s educational attainment (primary or preparatory education, secondary education either general or vocational and above secondary education either post-secondary institute or university and above, and the reference category is no educational degree either illiterate individuals or literate individuals without any diploma), a dummy variable for rural areas as well as dummies for the various geographical regions included in the last column (Greater Cairo, Alexandria, and Suez cities, Urban Lower Egypt, Urban Upper Egypt, Rural Lower Egypt, and Rural Upper Egypt). The dependent variable mean is reported in the last row.

35
**Figure 5: Service has contributed more to GDP Growth**

<!-- Start of picture text --> 6.0%<br>Contribution to annual growth rate 5.2%<br>5.0% 4.7%<br>by Sector, 1990-2012<br>4.3%<br>4.0%<br>3.2%<br>2.9% 2.8%<br>3.0%<br>2.5% 2.5%<br>1.8%<br>2.0% 1.7%<br>1.2%<br>1.0% 0.9% 0.6% 0.8% 0.7% 0.6% 0.7% 0.8% 0.8%<br>0.4%<br>0.2% 0.3% 0.3%<br>0.1%<br>0.0%<br>Low income Lower High Sub-Saharan Sub-Saharan Ethiopia India China<br>middle income: Africa - Africa - Oil<br>income OECD Non-oil Producing<br>Producing<br>Agriculture Manufacturing Services<br>Source: Authors' calculations using World Development Indicators. Ethiopia figures from national accounts. Countries<br>grouped based on World Bank definitions. "Contribution to growth" calculated as compound annual growth rate based on<br>constant GDP at PPP multiplied by period average sectoral GDP share.<br><!-- End of picture text -->

**Figure 6: Ethiopia’s labor productivity growth is higher in services**

<!-- Start of picture text --> 100 95.1<br>Ethiopian labor productivity ('000 birr per<br>90<br>worker) by major sector<br>80<br>70 1996<br>58.2<br>60 2011<br>50<br>36.7<br>40<br>29.5<br>30<br>17.8<br>20 15.1<br>12.4 13.3<br>8<br>10 5.4<br>0<br>Agriculture Manufacturing Trade Other Services Transport & Comms<br>Source: Martins (2014). Values in constant 2010/11 prices. Labor productivity calculated as output per person.<br><!-- End of picture text --> 10