rafmacalaba/data-use-mentions-extended huggingface dataset:
It is noted that, through differencing, the individual specific characteristics _ηi_ and the other timeinvariant explanatory variables (e.g., gender, ethnicity) are eliminated. Since education levels do not change much for adults and everybody is two years older in 2004, human capital variables such as education and potential experience are not included.<sup>37</sup> **_Location_** _i_ dummies are also discarded since the households who moved out between 2002 and 2004 were not usually interviewed in 2004. If the individual did not change the ownership category of his or her employment, the ownership term also drops out. Thus, the coefficients **_α_** are identified only from those who “switched” between different ownership categories.

# **4.2. Data and Summary Statistics**

Out of the individuals whose ages range 15-59 in the VHLSS 2004, 10,526 persons are matched as having appeared in the VHLSS in both 2002 and 2004.<sup>38</sup> Table 7 demonstrates how these individuals changed their employment/ownership status, showing the numbers of individuals for each cell by the status in 2002 (by row) and 2004 (by column). Of 10,526 panel individuals, about three quarters stayed in the same employment/ownership status, and the remaining quarter either changed employment status (unemployed<sup>39</sup> /employed) or the ownership category in which they work as the first job. Out of 109 individuals who reported working for foreign firms in 2004 (column 1 in Table 7), 36 individuals also worked for the foreign sector in 2002. Among 73 individuals who newly entered into foreign sector employment in 2004, 17 persons were students

> 37 The differential impacts of personal characteristics on wage growth depending on ownership may be estimated through the interaction terms between ownership and personal characteristics (Lemieux _et al_ ., 2009). However, since the coefficients of interaction terms between ownership and education turn out to be
362)|(0.360)|(0.359)|
|Population (logs)||-100.948***|-94.482***|
|||(36.755)|(36.360)|
|Urbanization (%)||-1.582**|-1.486**|
|||(0.745)|(0.736)|
|WBL Index||17.887|17.764|
|||(11.050)|(10.931)|
|Women can work||1.877|1.469|
|the same night hours||||
|as men Y:1 N:0||||
|||(3.140)|(3.172)|
|Women can work in||-27.171***|-25.654***|
|the same industries||||
|as men Y:1 N:0||||
|||(3.744)|(3.879)|
|Ratio of Female to||-11.870**|-11.328*|
|Male Population||||
|||(5.789)|(5.785)|
|Fertility Rate||-46.903***|-46.026***|
|||(7.940)|(7.955)|
|Starting a Business||0.035|0.035|
|(DTF)||||
|||(0.081)|(0.081)|
|Inflation (%)||0.640**|0.677**|
|||(0.284)|(0.282)|
|Year fixed effects<br>Yes
a large number of other specifications. First, the estimates are based on household observations. They are clustered and therefore not assumed to have independently-distributed errors at the village level. However, since bed net distribution could be focused on villages, and radio access varies by village, the village could be the more appropriate unit of observation.

Table 6 reports the results of estimating equation (1) at the village level. The survey includes information on village level characteristics that might affect the availability of bed nets, in addition to radio access: the distance of the village to the nearest urban center or to the nearest bus or train stop; the number of new potable water sources in the village and whether the village has a paved road; the number of functional private schools; whether the village has access to a literacy center of health/maternity center; the level of education of the village chief. All of these are controlled for. The village population comes from census data. Unfortunately, the census has no income data at the village level. We therefore average village responses to the income and housing quality questions and control for these averages. Finally, in a linguistically diverse country, linguistic heterogeneity could affect access to bed nets. We control for two variables, again based on household responses to the survey: whether the main village language and the main commune language are the same; and the probability that any two households in the village sample speak the same language.

The estimates in Table 6 reinforce the results presented in Table 4: radio access increases both the willingness of households to pay for bed nets and the diversion of potentially free bed nets into the paid market. As before, access to community radio has no significant effect on the total number of bed nets and a negative, insignificant effect on the number of free bed nets reported by households. In contrast, again as in the earlier estimates, radio access leads households to acquire a significantly larger number of paid government bed nets.

26
7.07|5.49|−8.00|4.00|4.00|
|1990|4.98|3.15|2.17|7.51|7.07|4.99|.|7.00|7.00|
|1995|6.41|4.83|5.91|8.89|7.13|5.29|−7.00|5.00|5.00|
|2000|6.97|6.37|5.99|8.09|7.96|6.46|−7.00|4.00|5.00|
|2005|7.08|6.72|5.81|7.84|7.56|7.47|−7.00|4.00|5.00|
|2010|6.95|6.18|5.65|8.07|7.59|7.24|−7.00|4.00|5.00|
|2015|6.62|6.40|4.99|7.58|6.76|7.39|−7.00|5.00|5.00|

_Source_ : Authors’ tabulation of the Gwartney, Lawson, and Hall (2017) Economic Freedom of the World (EFW) index, Center for Systemic Peace Polity IV Index, and Freedom House Freedom in the World Report (2018). _Note_ : Table presents the EFW Index, its components, the Polity IV Index, and the Freedom House Political Rights and Civil Rights Indexes over 1975–2015 for Jordan in panel a and Kuwait in panel b.

The refugees also boosted the demand side of the economy. Figure S1.1 in the supplementary on- line appendix S1 shows that the largely Transjordanian-owned real-estate sector boomed, housing starts doubled, and construction employment
2022).

Addressing structural deficits is critical for the low-carbon transition as they constrain the effectiveness of climate policy in mobilizing private finance. Structural deficits of the Turkish economy include low credibility of policy commitments, high costs of contract enforcement combined with low business confidence in public institutions, and uneven access to bank finance (OECD 2021, G ̈urkaynak et al. 2022). Dechezleprˆetre et al. (2022) provide compelling evidence that transparency and commitment raise acceptance of climate policy. Regarding the business climate, a weak rule of law drives up equity risk premia making capital-intensive, productivity-enhancing technology less appealing than labor-intensive, low-value-added activities. Finance constraints further impede private investment in low-carbon technology and limit the power of market signals.

The present paper aims to project the paths of climate-fiscal policy interventions that allow T ̈urkiye to grow along the 2053 net-zero pathway while honoring significant feasibility constraints. The policy instruments include a carbon tax, a renewable energy subsidy, transfer payments, public infrastructure investment, a bad bank for stranded assets, and the phase-out of fossil fuel subsidies and public investment. To study the role of supporting structural policies in the low-carbon transition, we explore how the macroeconomic effects

> 1See release 055 of the GLORIA Global Environmentally-Extended Multi-Region Input-Output (MRIO) database constructed in the Global MRIO Lab (Lenzen et al. 2017, 2022).

2
rate in sample||||0.003|-0.000|-0.004||
|||||(0.002)|(0.005)|(0.004)||
|Start month of experiment|0.031**|0.025||0.005|0.021|0.046*||
||(0.013)|(0.016)||(0.011)|(0.021)|(0.024)||
|Trust in healthcare workers||-0.368|-0.421|||||
|||(0.405)|(0.340)|||||
|General perceived vaccine safety|||-0.248<br>(0.207)|||||
|General perceived vaccine||||||-0.774|1.213*|
|effectiveness||||||(1.363)|(0.729)|
|Observations|16|13|10|14|7|5|4|
|Wellcome Global Monitor<sup>a</sup>|No|Yes|Yes|No|No|Yes|Yes|
|_I_<sup>2</sup>|86.0|85.6|37.4|67.7|78.3|71.2|72.0|

_Note:_ * _p < .10, ** p < .05. a_ Because of missing data with trust in doctors, perceived vaccine safety, and perceived vaccine effectiveness, for which data from the Wellcome Global Monitor 2018 were not available for Belize, Djibouti, Jamaica, Papua New Guinea, and West Bank and Gaza, we report the results of two study-level moderator meta-regressions for experts and endemic framings: (1) without the Wellcome
of the manufacturing industries in sub-Saharan Africa and only the remaining 15% is imported.

An important pattern to point out is that the results for some countries seem highly unbelievable. For several countries, the local value added share is implausibly low, e.g. for Tanzania (-2%), Somalia (3%), and Djibouti (7%), but it is difficult to know with certainty whether this reflects reality or not. Value added in the manufacturing sector of Cape Verde represents only 11% of the output value, but we cannot rule out that the type of industries they specialize in explains this low average. In some cases, for example for Somalia and Djibouti, the very low share of value added goes together with a very high share of domestically sourced inputs. It is possible that the nature of the industrial structure and vertical integration within the country explains these outliers.

At the other extreme, the value added share is extremely high in some countries, e.g. for Ethiopia (90%) and Nigeria (64%). We have double checked our calculations and we are certain that outlying coefficients are the result of unusual patterns in the underlying information from the MRIO and not a mistake in the aggregation procedure. In a few cases, there seem to be clear errors in the underlying data. For example, the sum of the value added

> 15 Together with the share of output that is accounted for by value added from the sector itself, all components combined sum to one.

22
females. The proportion of unemployed who mentioned active methods of job search (contacting employers, placing ads in the press or trying to create own business) stayed at approximately 20 percent during 1997-2000. Most of these unemployed individuals contacted employers directly. Only a small portion of the unemployed were trying to start their own business, less than 1 percent of all unemployed. Slightly more than 2 percent placed ads in the press.

In 2001-2002, the LFS permitted the unemployed to indicate more than one method of job search, and this revealed a different picture of how job search is undertaken. As shown in the lower panel of Figure 20, active methods were used by more than 40 percent of the unemployed in 2001 and almost 60 percent in 2002. The SES was used by 30 percent in 2001 and by 36 percent of individuals in 2002. This includes those who were sent to job training by the SES. Attempts to start up a business were still not popular among the unemployed, at 1.5 percent of all unemployed in 2001 and just 1.1 percent in 2002.

**Women are more likely to rely on the State Employment Service** .  The Latvian LFS shows strong differences between the sexes. Appendix Table 22 provides the results of logit models estimating the impact of various factors on search strategies of the unemployed in 2001-2002. Other things equal, women were significantly less likely than men to use active methods, and significantly more likely than men to the use SES in job search.  Gender differences in the predicted probabilities of using active methods of job search were 0.08 in 2002 and 0.11 for SES, somewhat higher than in the previous year. In contrast, ethnic differences in job search strategies were not apparent in 2002, whereas in 2001 minorities were more likely to use active methods and less likely to use SES than Latvians. Most likely, this and other differences between 2002 and 2001 have to do with the fact that 2002 questionnaire offered a more detailed description of  active methods of
083|0.099|0|0.667|0.078|0.042|0.091|0|0.583|‐0.021|
|Contracts and human resources|0.136|0.133|0.133|0|0.667|0.132|0.133|0.116|0|0.667|‐0.004|
|Discrimination|0.003|0|0.010|0|0.086|0.003|0|0.014|0|0.229|0|
|Forced labor|0.001|0|0.009|0|0.111|0.000|0|0.004|0|0.111|‐0.001|
|Freedom of association|0.070|0|0.068|0|0.333|0.034|0|0.050|0|0.556|‐0.036|
|Occupational safety and health|0.190|0.163|0.116|0|0.558|0.150|0.140|0.104|0|0.581|‐0.04|
|Working time|0.135|0.091|0.122|0|0.727|0.134|0.091|0.115|0|0.545|‐0.001|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

16
Steady-state EWAPs are fairly homogeneous for men, but vary widely for women. Only 21 EMDEs have significantly above- or below-average EWAPs for men (and those are mostly in SubSaharan Africa)—less than half the number of EMDEs (59 EMDEs) with significant deviations from the same average for women’s EWAPs. More than three-quarters of the EMDEs in the Middle East and North Africa and South Asia are converging to significantly below-average EWAPs for women (see also World Bank 2024). The deviation is considerable: it averages 34 and 19 percent of the female working-age population, respectively. In contrast, two-thirds of the EMDEs in East Asia and the Pacific are converging towards above-average steady-state EWAPs for women.

The _aggregate_ steady-state EWAPs that deviate statistically significantly from the EMDE average reflect pockets of weakness in countries’ labor markets.<sup>4</sup> In Sub-Saharan Africa, these deviations in aggregate steady-state EWAPs seem to be significantly correlated with deviations in agricultural steady-state EWAPs (Figure 3). A linear regression of the steady-state EWAPs in aggregate on the steady-state EWAP in agriculture or non-agriculture illustrates the correlation (Table 3). In the steady state, for every 1 percentage point higher _agricultural_ EWAP, the _aggregate_ EWAP is also higher by 0.67 percentage point (broadly in line with the share of agricultural employment of 75 percent during 2000-19). Meanwhile, higher non-agricultural EWAPs are not associated with higher aggregate EWAPs in Sub-Saharan Africa.

In other EMDEs, the opposite is the case: for every 1 percentage point higher _non-agricultural_ steady-state EWAP, the _aggregate_ steady-state EWAP is 0.72 percentage point higher—almost twice as much as might be expected arithmetically, based on the share of non-agricultural employment in this group of countries (39 percent during 2000-19). In EMDEs outside SubSaharan Africa, agricultural and aggregate steady-state EWAPs are much less closely linked than in Sub-Saharan Africa and also less closely linked than non-agricultural and aggregate ratios outside Sub-Saharan Africa.

For the vast majority of EMDEs (i.e., those outside Sub-Saharan Africa
# **Figure A.1: Growth of per capita expenditure and initial expenditure level**

Panel A. Two-year growth of per capita expenditure Panel B. Four-year growth of per capita expenditure <!-- Start of picture text --> 7.5 8 8.5 9 9.5 10 7.5 8 8.5 9 9.5 10<br>Log of per capita expenditure Log of per capita expenditure<br>Provinces Fitted values Provinces Fitted values<br>Panel C. Ten-year growth of per capita expenditure Panel D. 18-year growth of per capita expenditure<br>7.5 8 8.5 9 9.5 7.5 8 8.5 9<br>Log of per capita expenditure Log of per capita expenditure<br>Provinces Fitted values diff_pcexp9 Fitted values<br>1 1<br>.5 .5<br>0 0<br>Growth over two years Growth over four years<br>-.5 -.5<br>1.8<br>1.2<br>1 1.6<br>.8 1.4<br>Growth over ten years .6 Growth over 18 years 1.2<br>.4<br>1<br><!-- End of picture text --> Note: i) Authors’ estimation using VHLSSs data 53
workers in each state between our results and the MOHA data. When the differences are negative (that is, the number of foreign workers in the remittance data set is smaller than in the MOHA data), we apply that the differences are zero. In other words, the number of foreign workers for a specific nationality in a given state from the remittance data set equals that from the MOHA data set. This is to correct that this method does not adjust for the 77 percent of the foreign worker population who use MSP channels to send money. As noted earlier, not all foreign workers have access to MSPs or they may use of alternative channels for various reasons. We add the nationality-specific differences to the MOHA data to estimate the size of the potential foreign worker population in Malaysia. This method could generate an upward bias, but cases where the differences are negative are few and the negative differences are small. Therefore, it is more likely to underestimate by not fully adjusting those ‘missing’ foreign workers (who do not use MSPs) in nationality groups where differences are positive.

# **4. Results**

**Our estimates suggest that the total foreign worker population was between 2.99 million and 3.16 million** during the period of March 2017 to February 2018, a narrower range than our earlier estimates in World Bank (2020). Firstly, we identified 2.43 million individuals who are potentially foreign workers in the remittance analysis and then adjusted the estimate on the assumption that the remittance data represent 77 percent of the foreign worker population (as explained earlier in Step 4), thus leading to 3.16

9
Ong, Hwee Teng. 2002. “Sustaining Singapore’s Productivity Growth”, _Productivity Digest July 2002._ Spring Singapore.

Phang, Sock-Yong, 2003. “Strategic Development of Airport and Rail Infrastructure: The Case of Singapore”, _Transport Policy_ 10 (2003), Pergamon.

Rao, B.V.V. and C. Lee, 1996. “Sources of Growth in the Singapore Economy and its manufacturing and Services Sectors,” _The Singapore Economic Review_ , Vol. 40, No.1, 83-115.

Stern, Scott, Michael porter and Jeffrey Furman, 2000. “The Determinants of National Innovative Capacity”, _NBER Working Paper_ 7876.

Wan, David, Chin Huat Ong and Francis Lee (2003). Determinants of firm innovation in Singapore. _Technovation_ (forthcoming).

Wong, Poh Kam, 2003. “From Using to Creating technology: The Evolution of Singapore’s National Innovation System and the Changing Role of Public Policy”, _World Bank institute Working Paper 37221_ . The World Bank.

Wong Poh Kam, Finna Wong, Lena Lee and Yuen Ping Ho, 2004 (a). “Global Entrepreneurship Monitor: GEM Singapore 2003.” NUS Entrepreneurship Center.

Wong Poh Kam, Yuen Ping Ho, Annette Singh, 2004 (b). “Singapore as an Innovative City in East Asia”, mimeo.

World Development Report. 2004. The World Bank.

World Economic Forum (WEF), 2002. _Global Competitiveness Report_ . Geneva.

Wu, Wei Lin (2003). “Educational Upgrading through Private Educational Institutions”, Statistics Singapore Newsletter.

Wu, Wenbo and Otto A. Davis (2003). Economic Freedom and Political Freedom. Forthcoming in _Encyclopedia of Public Choice_ .

Young, Alwyn. 1992. “A Tale of Two Cities: Factor Accumulation and Technical Change in Hong Kong and Singapore”, in Olivier Jean Blanchard and Stanley Fischer, eds., _NBER Macroeconomics Annual 1992_ . Cambridge, MA: MIT Press.

Young, Alwyn. 1995. “
**Figure  3.3 Gini coefficient of household per capita income, skilled-unskilled and highschool-dropouts wage premiums over time, net of country effects. 16 LA countries, 1989-2009**

<!-- Start of picture text --> 1990 1995 2000 2005 2010<br>1990 1995 2000 2005 2010<br>1990 1995 2000 2005 2010<br>.06<br>.04<br>.02<br>0<br>Gini coefficient HPCI<br>-.02<br>-.04<br>.2<br>.1<br>0<br>-.1<br>-.2<br>Wage Premium, Skilled-Unskilled<br>-.3<br>.3<br>.2<br>.1<br>0<br>-.1<br>Wage Premium, Highschool-Dropouts<br>-.2<br><!-- End of picture text --> Source: own computations based on SEDLAC (CEDLAS and World Bank).

26
more room to reduce their consumption before reaching minimal subsistence levels. Unfortunately, since the sample only contains current households, this hypothesis cannot be investigated.

> 10. For 33 communities, the recorded coordinates fall outside of the boundaries of Uganda, and therefore these communities have been dropped. Since this is based solely on the recorded coordinates, there is no reason to believe that any systematic differences exist in these observations.

> 11. While possible for households to live an entire week without receiving any food aid or food gifts, purchasing food, or consuming their own production, this is highly unlikely. Since this appears to be a clear coding error, these households are dropped. Their inclusion does not change the analysis.

> 12. The variables for the distance to the nearest attack are an exception. These were created using all the data—rural or urban—for which correct geographical coordinates were available.

> 13. The ACLED data are drawn from a variety of sources including press accounts, books, and humanitarian worker accounts. The data are disaggregated by event type, year, participants, and geographical coordinates.

9
countries and eight in developed countries. In recent years, several investors' surveys explored the effectiveness of tax policies on FDI using alternative samples or asking more detailed questions (for example, JETRO (1995), Ernst & Young (1994) or Fortune/Deloitte & Touche (1997)). In general, these surveys have confirmed the conclusions summarized above; that if tax policy matters it is not the most influential factor in the site selection of multinationals.<sup>3</sup>

# B. Econometric Analysis

The available econometric evidence on the effect of taxation used time-series estimation of the responsiveness of FDI to annual variations in tax rates. Most econometric studies have tended to confirm the results of surveys; that investors are mostly influenced in their decision by market and political factors and that tax policy appear to have little effect on the location of FDI.

A selective sample of studies has concluded in that direction: Root and Ahmed (1978), Agodo (1978), Shah and Toye (1978), and Lim (1983). In 1978, Root and Ahmed performed an econometric study with data for 41 developing countries for the period 1966-1970. They classified countries in three categories of unattractive, moderately attractive, and highly attractive according to their average annual per capita inflow of FDI. Forty-four variables were chosen as potentially significant discriminators of the three country groups. Among the six policy related discriminators were three relating to tax levels. Of these, corporate tax rates proved to be an effective discriminator of the three defined country groups; however tax incentives laws and liberality were not found to be effective discriminators. Agodo analyzed a sample of 33 US firms having 46 manufacturing investment in 20 African countries. Tax concessions were found to be insignificant as a determinant of FDI in simple and multiple regressions.

Much of the literature of the period used highly aggregate data, evaluating the correlation between annual changes in FDI flows, and a series of factors, including In the Fortune/Deloitte & Touche's survey, taxes