rafmacalaba/datause-encoder-data huggingface dataset:
|**Tab**<br>(gro|**le16Thestructureoflaborcosts**<br>sswage=100)<br>||**2000**|**2002**|
|---|---|---|---|---|
||Laborcostcomponents<br>|**1995**<br>|<br>||
|**1**|Employee grosswage|100|100|100|
|2|Employercontributions(payroll taxes)<sup>a)</sup><br>|21.78<br>|16.98<br>|17.92<br>|
|2.1|healthinsurance<br>|7<br>|7<br>|7<br>|
|2.2|professionaldisease<br>|0<br>|0<br>|0.47<br>|
|2.3|pensioninsurance<br>|12.75<br>|8.75<br>|8.75<br>|
|2.4|unemploymentinsurance<br>|0.85<br>|0.85<br>|1.7<br>|
|2.5|watercontribution<br>|0.8<br>|0<br>|0<br>|
|2.6|chamberofcommerce contribution|0.38|0.38|0<br>|
|3|Employee taxesandcontributions<sup>a)</sup><br>|37.00<br>|31.69<br>|30.55<br>|
|3.1<br>|healthinsurance<br>|7.0<br>|9.0<br>|9.0<br>|
|3.2<br>|pensioninsurance<br>|12.75<br>|10.75<br>|10.75<br>|
|3.3|child allowancecontribution<br>|2.2<br>|0<br>|0<br>|
|3.4|unemploymentinsurance<br>|0.85<br>|0.85<br>|0<br>|
|3.5|personalincometax(effective)<br>|14.2<br>|11.1<br>|10.8<br>|
|4|Net(take-home)wage(1) - (3)<br>|63.00<br>|68.31<br>|69.45<br>|
|5|Laborcosts(1)+(2)<br>|121.78<br>|116.98<br>|117.92<br>|
|6|Taxwedgeb)[(5)-(4)]/(5)*_100_|48.27|41.60|41.10|

a) as a percentage of employee gross wage b) as a percentage of labor cost Source: Ministry of Finance; Bank<sup>staff calculations.</sup> All in all, _unit_ labor costs in Croatia are high,<sup>higherthaninCroatia'smain</sup> competitors.<sup>26</sup> This has an important negative<sup>effect on labordemand.First,thereis</sup> a direct effect whereby higher labor costs<sup>implylower labor demand.Second,thereis</sup> an indirect effect whereby higher unit labor<sup>costsimplies lowerprofit marginwhich</sup> discourages investment by lowering resources<sup>availableforinvestmentandexpected</sup> returns on investment. Lower investment,<sup>in turntranslates into less jobs creation and</sup> lower employment.

Why are labor costs high in Croatia? Given<sup>that the taxwedgeis moderate,it</sup> is workers net pay that is the primary reason<sup>of highunitlaborcosts.Onepossible</sup> factor behind relatively high wages is wage<sup>pressureexertedbyinsiders, thatis</sup> workers with secure jobs who therefore<sup>haveastrongbargainingposition.</sup> In addition, the industry level bargaining,<sup>whichprevailsinCroatia, especiallyinlarge</sup>

> 26 Unit labor costs in Croatia is even higher if one<sup>uses the hourlywage rate insteadof average</sup> wage per worker. This is due to the relatively<sup>shortworkinghours in Croatia (effectively37.5hours</sup> per week), which is less than in most other countries<sup>in theregion,where theworking hoursare 40 per</sup> week.

-49-
have undergone backward revisions over time, representing some improvement in quality.

The next section presents the main results of our analysis. A large, additional set of analyses based on variations on the specifications presented in the next section yield basically the same results. These results, summarized in some 36 different tables are available on request from the authors.

# **5. Main Results and Discussion**

# A. Baseline Results and Some Simple Extensions

Table 2 presents the main results from the baseline regression analysis. In this and in other Tables in the main text, our choice of variable has in part been determined by the resulting total number of observations. For example, because fiscal balances data from the IMF-WEO yield the most number of observations, we use this data source rather than the WB data or EBRD data. The results however are invariant to data source, as is clear from the Appendix Tables. In addition, where data on a particular variable can be drawn from several sources, data from these sources are typically closely correlated, with correlation coefficients of 0.92 or higher. In the case of inflation, for example, data from the IMF-WEO and from WB have a correlation coefficient of 0.92. Data on total government expenditure are much less similar, though the correlation coefficients are still relatively high, over 0.70.

The results in Table 2 confirm the broad findings from the previous literature, namely, initial conditions, macroeconomic stabilization, and liberalization and structural reform all matter for growth and are in the expected direction. The second measure of structural reform, price liberalization, is however, not significant. The explanatory power of the baseline model is broadly comparable with earlier finding, with R-squares of 0.50 to 0.59. The main results are robust to estimation methods, representing an improvement over the previous literature which relied heavily on simple OLS regressions (e.g., Sachs 1996; Fischer and Sahay 2000). One important new result is the robustness of fiscal balance as a positive determinant of growth. It may be recalled from Section 2 that the existing literature has yielded a mixed record on the importance of fiscal consolidation.

14
- 6 -

# **II. EMPIRICAL MODELS AND DATA**

The new RAI is defined by the proportion of the rural population who live within 2 km of the nearest road in good condition. Road conditions are assessed in different ways, depending on the available data in the countries. In general, the assessment is conducted based on the surface type and roughness of each road. From an engineering point of view, the RAI only takes into account paved roads in good and fair condition and unpaved roads in good condition (see World Bank (2016) for further details).

Urban areas are excluded using the Global Rural-Urban Mapping Project (GRUMP) data set. The population distribution data come from WorldPop, which is available at a resolution of 100 meters and regularly updated whenever new data (such as national census data) become available.

World Bank (2016) shows that the new RAI estimates are somewhat different from the original estimates in 2006, mainly because of methodological differences. Among the sample countries where results are available, rural access varies significantly, from 17 percent in Zambia to 56 percent in Kenya and 87 percent in Bangladesh (table 1). An advantage of the new RAI method is that the index is available not only at the national level, but also at the subnational level. The latter is the primary data source for the following empirical analysis (map 2).

**<u>Table 1. Summary of the New RAI Estimates</u>**

|Country|Population<br>(million)|Land area<br>(1,000 km<sup>2</sup>)|Population<br>density|<br>Road data<br>length(km)|Original<br>RAI, 2006|New<br>RAI|Year|
|---|---|---|---|---|---|---|---|
|Bangladesh|159.1|130.2|1,222|<br>250,688|37|86.7|<br>2015|
|Nepal|28.2|143.4|197|<br>77,819|17|54.2|<br>2015|
|Ethiopia|97.0|1000.0|97|<br>85,880|32|21.6|<br>2015|
|Kenya|44.9|569.1|79|<br>160,886|44|56.0|<br>2009|
|Mozambique|27.2|786.4|35|<br>29,614|27|20.4|<br>2010|
|Tanzania|51.8|885.8|59|<br>94,039|38|24.6|2008/2014|
|Uganda|37.8|199.8|189|<br>140,910|27|53.1|<br>2015|
|Zambia|15.7|743.4|21|<br>51,070|64|17.0|2011|

_Source:_ World Bank 2016.
Thus, the context is determined by a wide array of policies and public investments, and the overall efficiency, equity, and transparency of their design and execution. See box 2.6.

# **Box 2.6 Growth and the Investment Climate**

According to the 2003 World Development Report: “Eliminating poverty requires growth and investment. Efforts to generate a strong investment climate – including sound macroeconomic fundamentals, good governance, and basic infrastructure – need to be strengthened at the national level. ... The importance of a sound investment climate as a core component of sustainable development is becoming widely recognized, as exemplified by the prominent position of governance on national agendas and by the progress over the past decade in confronting corruption through the concerted efforts of governments, civil society, and the private sector to change the rules of the game (World Bank 2002c, p.17). (emphasis by author) The risks to which rural households and their assets are exposed are also part of the context. This includes climatic risks such as drought, flood and hurricanes), price risks (for outputs and inputs), commodity price fluctuations, lack of markets, human health risks, plant and livestock diseases and pest infestations, and risks associated with conflicts and crime that affect personal security (de Ferranti and others 2000; IADB 2000; Keipi and Tyson 2002; Kiesel 2001).<sup>16</sup> The presence of risk often invokes a cost of risk management; this cost can include lower income due to risk avoidance behavior (opportunity costs) and risk-reducing activities (actual costs), and costs associated with coping activities. Risk also induces fluctuations in consumption and can, by itself, lower household well-being (for example, household concerns about food insecurity).

<u>To a large extent, the context is shaped by factors</u> _<u>external</u>_ <u>(or outside the control) of households. Domestic and international policies, institutions and markets, and forces of</u> nature shape the context. On the other hand, households can invest in assets, and allocate their assets and select livelihood strategies in a manner that reduces risks associated with the prevailing context. Furthermore, important links between policies and risks exist, because policies and investments can either increase risk and exposure to risk, or help households better manage risk and vulnerability to poverty (Siegel and Alwang 1999; Anderson 2001; Varangis and others 2002; Siegel and others 2003). In addition, social protection and safety nets can help households manage risk (Lustig 2001; World Bank 2001; Devereux 2001). The general lack of risk management instruments for the rural poor in Central America constrains their ability to protect their assets and to generate higher returns from their portfolio of assets (see box 2.7).

> 16 See, for example, a recent study of Nicaragua’s agricultural sector (World Bank, 2002d) in which an entire chapter was devoted to agricultural risks and risk management strategies.

11
was primarily due to concerns about side effects from the COVID-19 vaccine. Respondents stated that health experts were the most likely to be able to increase their willingness to get vaccinated.

# <u>5.1.1 Awareness of COVID-19 (based solely on the phone survey)</u>

Awareness of the existence of COVID-19 in general in PNG is very high, however there is a much more limited understanding of the number of cases and COVID-19 related deaths. A prior representative phone survey in PNG showed more than 95 percent of respondents were aware of the existence of COVID-19 (World Bank 2020). In the phone survey we focus on in this paper, around 9 out of 10 respondents were aware of specific actions the government has taken to address COVID-19 in regard to the health and/or education sectors. However, when asked to estimate COVID-19 cases numbers and deaths across PNG or within their province, almost 80 percent of respondents stated they were unsure. The remaining respondents that provided an estimate were remarkably accurate. On average, respondents provided very similar estimates of the perceived number of COVID-19 cases in their province as what was actually the case in terms of reported figures (see Figure 4).

**Figure 4 – Reported and perceived number of COVID-19 cases by province**

<!-- Start of picture text --> 7000<br>6000<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>Actual Perceived<br><!-- End of picture text --> Note: This chart is based on answers provided by the 20 percent of respondents that did not select unsure to question 22 in Appendix B. AROB - Autonomous Region of Bougainville, NCD – National Capital District.

# <u>5.1.2 Awareness of COVID-19 vaccines (based on both the phone survey and online experiment)</u>

A significant minority of respondents in both surveys had not heard of a vaccine for COVID-19 being available in PNG. Respondents were asked “Do you know if a vaccine for COVID-19 is available in this country?” and 27.3 percent of phone survey respondents and 18.0 percent of participants in the online survey experiment (in the control group) answered no. As expected, having knowledge of a vaccine for COVID-19 was more common among those with higher levels of education in both the phone survey and 15
sizes that the impact of disasters does not necessarily increase the prevalence of IPV, even if it is assumed that negative economic shocks lead to increased IPV.<sup>5</sup> Specifically, husbands have the potential to engage in IPV if consumption falls below a critical threshold. However, turbulent disasters have the potential to decrease consumption and to change intrahousehold roles and gender norms, which can in turn also change the critical consumption threshold and further affect the prevalence of IPV (e.g., Panda and Agarwal 2005; Heise 2011; Doss 2013; etc.). Whether a disaster increases or decreases the prevalence of IPV in this setting depends on the change in the consumption threshold relative to the change in consumption.

To more fully investigate the relationship between disasters and IPV, we analyze the impact of a devastating earthquake and a series of aftershocks that precipitated a humanitarian and displacement crisis in Papua New Guinea (e.g., Government of PNG et al. 2018; IOM 2018; etc.). The disaster occurred between the third and fourth phase of a nationally representative survey that collected extensive information on IPV. The initial phases of the survey focused on urban areas and then progressively surveyed more rural and remote regions, and comparing the IPV changes after the third phase in the provinces most strongly affected by the disaster to other regions allows us to estimate the immediate impacts. Several robustness checks illustrate that the changes in IPV following the disaster are likely not being driven by substantial differences between urban and more remote populations across provinces or in regional differences in survey implementation.

The data illustrate two primary empirical findings. First, there was a large decline in the acceptability of IPV in the disaster-affected regions in which the vast majority of the deaths occurred and in which virtually all of the internal displacement was concentrated (e.g., Government of PNG et al. 2018; IOM 2018; etc.). The share of men who thought it was acceptable for a husband to beat his wife declined in all five individual scenarios captured, and the total number of scenarios in which a husband is justified to beat his wife reduced by 0.64 more than in the rest of the country. The declines were large in magnitude, where the latter change represents a 19 percent reduction relative to pre-disaster levels in affected regions. However, there was little change in the acceptability of IPV among women in the same regions.

Second, there was a large difference between changes in men’s attitudes about IPV and changes in the prevalence of IPV reported by women. Specifically, while attitudes about IPV markedly improved in disaster-affected regions, there was a smaller and less precisely estimated decline in the share of women reporting to have experienced IPV in the past year in the same regions. But 5As described above, there are several settings where this assumption is empirically refuted.

3
a fifth. In Latin America, since 2000, the same strategy—complemented by expansions in social insurance—has led to extreme poverty being cut in half. In Sub‐Saharan Africa, extreme poverty rates have stayed much the same and the ranks of the extreme poor have doubled because the population has doubled. In 2011, 24 of the 25 countries with the highest extreme poverty rates were in Africa (the exception was Bangladesh).

Many East Asian countries implemented the 2.5‐point strategy to varying degrees but with considerable success. Most notably China, with economic growth rates of close to 10 percent, land reforms that improved the incentives for and the productivity of farmers, and sizeable investments in education and health, has reduced poverty below $1.90 a day from 88 percent in 1981 to 6.5 percent in 2012. Openness to trade and investments in infrastructure have led to the growth of labor‐intensive manufacturing. Vietnam’s more comprehensive land reforms made its policies even more like those of Korea, and cut extreme poverty from 49 percent in 1993 to less than 4 percent in 2012.

**Figure 3:  Extreme poverty rates since 1980, by region**

<!-- Start of picture text --> Percent of the population living on less than $1.90 a day<br>90<br>80.6<br>80<br>70<br>60.6<br>58.1 56.8 57.1<br>60<br>50.7 50.8<br>50<br>42.7<br>40.8<br>40<br>29.2<br>30<br>18.8 19.7<br>20 15.5 13.2<br>10 7.2 5.6 3.1 1.9 6.2 2.1<br>0<br>EAP SAS SSA LAC ECA<br>1981 1990 2002 2012<br><!-- End of picture text --> In South Asia, led by a growing trade in services, India’s GDP growth rates of between 6 and 7 percent have helped it reduce the extreme poverty rate from 47 percent in 1994 to less than 20 percent in 2012. This is less impressive than East Asia, in large part because of the relative neglect of agriculture and rural infrastructure. It is also because of primary health care shortfalls, and secondary enrollment ratios that barely reach 50 percent.

Even in Africa, countries that have emphasized broad‐based growth (with special attention to agriculture) and investments in human capital (especially health and education) have halved extreme poverty during these two decades. In Ethiopia, with annual growth rates of more than 10 percent since 2005, headcount poverty rates were almost halved between 1990 and 2010 (from more than 60 percent to less than 40 percent). Primary school enrollment ratios have increased from less than 25 percent to more than 80 percent, and rural access to safe water is getting close to 50 percent. Ghana has done well too. Annual GDP growth has hovered around 8 percent since 2006. Under‐five mortality rates decreased from 54 to 19 deaths per 1,000 live births between 1998 and 2014. Children today are expected to live two more years than children who 26
ultimately increase the number of workers who seek jobs in the rural and urban household sector, increasing the supply of labor where most of the poor are found.

# _a) Regulations_

In Vietnam, the minimum wage level is set at different rates for domestic and foreign enterprises. For the domestic sector -whether private or public- the monthly minimum wage is set at 144,000 VND (about $10.35) since January 1997. In the public sector, this minimum wage is used as the base on which actual salaries are calculated. All public sector wages are a multiple of the minimum wage. Thus, any increase in the minimum wage leads automatically to an increase in all public sector wages (which probably explains why its level has remained unchanged since 1997). In foreign companies and joint ventures the minimum wage is higher. Until July 1999, minimum wages were denominated in dollars. Thus the dollar cost of wages in foreign companies was not affected by the currency slide in the previous years. Nowadays, these minimum wages are quoted in domestic currency and are set at 626,000 VND in the inner districts of Hanoi and Ho Chi Minh, 556,000 VND in other large cities and 487,000 VND everywhere else. So for foreign companies, the minimum wage represents between 338% and 435% of the minimum wage for domestic companies. How does this affect labor costs?

# _b) Evidence: domestic companies_

For domestic companies, the minimum wage is very low. Annually, it would yield an income of less than VND 1.73 million. This falls below the World Bank's general poverty line set at VND 1.787 million for 1998 (World Bank, 1999). As can be seen in table 17, VLSS data shows that such a minimum wage represents roughly one fourth of Vietnam's average wage and one third of rural wages. This is modest by international standards. Table 17 also shows that Vietnam's minimum wage is far below average wages in all types of employmnent, including small household enterprises. The only exception is for cooperatives, where the average wage is only about 25% above the minimum wage.

**Table 17: the minimum wage is low compared to average wages**

||Monthly<br>Average|Ratio minimum<br>wage<br>|
|---|---|---|
||wage<br>(in Dongs)|To average wage|
|Total|522,610|0.276|
|Total urban|625,420|0.230|
|Total rural|445,090|0.324|
|Total males|570,760|0.252|
|Total females|447,150|0.322|
|Government|395,650|0.364|
|State enterprises|629,690|0.229|
|Cooperatives|182,820|0.788|
|Private enterprises|623,430|0.231|
|Small household enterprises|518,390|0.278|
|Mix economic sector|846,540|0.170|
|Other|495,380|0.291|
|_Source:_VLSS 2 (GSO, 1999)|||

19
|Partnershipfor Polio Eradication Project<br>Dinesh M. Nair|
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|EmergencyEconomic RecoveryCredit<br>Susanne Holste|
|EmergencyEconomic RecoveryCredit<br>Susanne Holste|
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|EMERGENCY DROUGHT RECOVERY PROJECT<br>StanleyHiwa|
|EMERGENCY DROUGHT RECOVERY PROJECT<br>StanleyHiwa|
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|AIDS and STD Control Project(03)<br>Joana Godinho|
|AIDS and STD Control Project(03)<br>Joana Godinho|
|AIDS and STD Control Project(03)<br>Joana Godinho|
|AIDS and STD Control Project(03)<br>Joana Godinho|
|AIDS and STD Control Project(03)<br>Joana Godinho|
|AIDS and STD Control Project(03)<br>Joana Godinho|
|AIDS and STD Control Project(03)<br>Joana Godinho|
|AIDS and STD Control Project(03)<br>Joana Godinho|
|AIDS and STD Control Project(03)<br>Joana Godinho|
|AIDS and STD Control Project(03)<br>Joana Godinho|
|African Regional CapacityBuildingNetwork for HIV/AIDS PreventionDominic S. Haazen|
|African Regional CapacityBuildingNetwork for HIV/AIDS PreventionDominic S. Haazen|
|African Regional CapacityBuildingNetwork for HIV/AIDS PreventionDominic S. Haazen|
|African Regional CapacityBuildingNetwork for HIV/AIDS PreventionDominic S. Haazen|
|African Regional CapacityBuildingNetwork for HIV/AIDS PreventionDominic S. Haazen|
|African Regional CapacityBuildingNetwork for HIV/AIDS PreventionDominic S. Haazen|
|African Regional CapacityBuildingNetwork for HIV/AIDS PreventionDominic S. Haazen|
|African Regional CapacityBuildingNetwork for HIV/AIDS PreventionDominic S. Haazen|
|African Regional CapacityBuildingNetwork for HIV/AIDS PreventionDominic S. Haazen|
|African Regional CapacityBuildingNetwork for HIV/AIDS PreventionDominic S. Haazen|
|African Regional CapacityBuildingNetwork for HIV/AIDS PreventionDominic S. Haazen|
|African Regional CapacityBuildingNetwork for HIV/AIDS PreventionDominic S. Haazen|
|Great Lakes Initiative on HIV/AIDS(GLIA)Support<br>Pamphile Kantabaze|
|Great Lakes Initiative on HIV/AIDS(GLIA)Support<br>Pamphile Kantabaze|
|Great Lakes Initiative on HIV/AIDS(GLIA)Support<br>Pamphile Kantabaze|
|Great Lakes Initiative on HIV/AIDS(GLIA)Support<br>Pamphile Kantabaze|
|Great Lakes Initiative on HIV/AIDS(GLIA)Support<br>Pamphile Kantabaze|
|Great Lakes Initiative on HIV/AIDS(GLIA)Support<br>Pamphile Kantabaze|
|Great Lakes Initiative on HIV/AIDS(GLIA)Support<br>Pamphile Kantabaze|
|Great Lakes Initiative on HIV/AIDS(GLIA)Support<br>Pamphile Kantabaze|
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|Durban Landfill Gas-to-ElectricityProject<br>Xiaoyu Chang|
|Durban Landfill Gas-to-ElectricityProject<br>Xiaoyu Chang|
|EmergencyDrought RecoveryProject<br>Alex Mwanakasale|
|EmergencyDrought RecoveryProject<br>Alex Mwanakasale|
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|THE PAN CARIBBEAN PARTNERSHIP AGAINST HIV/AIDS<br>Shiyan Chao|