rafmacalaba/datause-encoder-data huggingface dataset:
|Presidential system LIC countriesi,t|||-1.146<br>(2.962)||||-2.150<br>(3.562)||
|---|---|---|---|---|---|---|---|---|
|Herfindahl Index in HIC countriesi,t||||4.684<br>(9.180)<br>||||9.244<br>(8.697)<br>|
|Herfindahl Index in UMC countriesi,t||||-10.429<sup>~~**~~</sup><br>(4.654)||||-9.539<sup>~~**~~</sup><br>(5.113)|
|Herfindahl Index in LMC countriesi,t||||-16.351<sup>~~***~~</sup><br>(4.108)||||-16.016<sup>~~***~~</sup><br>(5.115)|
|Herfindahl Index in LIC countriesi,t||||-2.424<br>(5.410)||||-3.955<br>(6.898)|
|Constant<br>|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R<sup>~~2~~</sup>within<br>|0.598|0.510|0.631|0.591|0.580|0.576|0.628|0.596|
|R<sup>~~2~~</sup>overall|0.633|0.590|0.642|0.611|0.611|0.563|0.638|0.583|
|Time dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,074|1,074|915|1.024|957|957|813|908|

Notes: * = significant at 10 %; ** = significant at 5 %; *** = significant at 1 %; robust standard errors in parentheses.

33
ii

**Page** No.

# **TABLES**

|Table 1:|Economic Growth Rates by Major Sector, Indonesia, 1983-1994 ..............|.......... 25|
|---|---|---|
|Table 2:|Distribution of the Increase in Employment by Major Sector,<br>Indonesia, 1971-1994 ...............................................|25|
|Table 3:|Growth of Labour Force and Employment, Java and the<br>Outer Islands, 1986-1994 (% per annum) ...............................................|26|
|Table 4:|Indices of Real Labour Earnings of Less Educated Workers in<br>Selected Sectors and Labour Costs in Manufacturing,<br>Indonesia, 1977-1990 ...............................................|26|
|Table 5:|Population, Economic Structure and Employment by Province<br>Group, Indonesia 1971 and 1990 ...............................................|27|
|Table 6:|Index of Growth in Value Added by Sector and Province<br>Group, Indonesia 1987-1992 ...............................................|28|
|Table 7:|Distribution of the Increase in Employment by Sector and<br>Province Group, Indonesia 1987-1994 ...............................................|28|
|Table 8:|Index of Nominal Wage Growth and Differentials by Sector<br>and Province Group, Indonesia 1987-1994 ...............................................|29|
|Table 9:|Growth of Employment and Wages in all Sectors, Non-<br>Agriculture and Manufacturing, Indonesia 1987-1994 ................................|......... 30|
|Table 10:|Agricultural Employment and Wages by Region:<br>Indonesia 1987-1994 ...............................................|31|
|Table I 1:|Regression Analysis of Non-Agricultural Wage Earnings per hour<br>by Region: Less Educated Workers, Indonesia, 1994 ..................................|......... 32|
|**FIGU1RE**|**S**||
|Figure la:|Agricultural Employment 1987-1993 (millions, three year<br>moving average) .33||
|Figure Ib:|Share of Agricultural Employment 1987-1993 (%, three<br>year moving average) .33||
|Figure 2:|Index of Adjusted Male Earnings per hour among Less educated<br>Employees in Construction, Transport and Services, Major<br>Provincial Groupings, Indonesia 1977 and 1992 .34||
Another explanation relies on foreign direct investment and the resulting increase in the capital stock of developing countries. Capital and skilled labor are likely to be complements in production. The outsourcing of activities by multinationals from industrial countries is a major trend in recent years. The resulting flow of capital may be small from the perspective of industrial countries, but it represents a huge addition from the point of view of developing countries. This higher capital stock should in turn lead to a higher demand for skilled labor. Feenstra and Hanson (1 997) analyze a panel of nine industries across Mexican states and conclude that outsourcing, under the form of _maquiladoras,_ is associated with an increase in the share of non-production wages.

A quasi-experiment on the effect of the overall globalization package is the dranatic transition experienced by formerly planned economies in recent years. Countries in Eastern Europe and the former Soviet Union, as well as China and Vietnam, were suddenly confronted to the unleashing of market forces, openness to trade, and foreign direct investment. The result was an increase in the returns to education, as shown by figure 5. The observations in this figure are drawn from a variety of studies, using data sets with different coverage and relying on various econometric techniques. Consequently, the estimated returns may not be strictly comparable, not even within the same country. However, there is an upward trend in all the countries considered. In some of them, the implicit effect of the transition on wage inequality is remarkably large. For instance, if the estimates for the Czech Republic are taken literally, the wage gap between a college-educated person and a worker with primary education increased by roughly 60 percentage points in less than one decade.

MFiguire S. Retuirms to Education ni Tiransitioim Ecozonmies <!-- Start of picture text --> 10<br>-9<br>C8 > 0<br>aB7 0~~~~~~ X oHN<br>,06 [O o CZECHREP.<br>k 5 X ^a VETNAM<br>x POLAND<br>4 O A RUSSIA<br>3 2 ffi X ODA ° <0 o SLOVAKRE<br>co 0<br>-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13<br>Years n the reform process<br><!-- End of picture text --> _Source:_ Constructed using data gathered by Ramna (200 lb).

10
(1992). While we could take a computable general equilibrium (CGE) modeling approach, this would require considerable aggregation of the tariff data, which might cause us to lose information in the dispersion of tariffs within the twenty or so aggregates routinely used in CGE analysis. Instead, we have used a more disaggregated, but simpler, model with around 5000 tariff lines identified separately, and identified as substituting in Armington fashion relative to a single, domestically produced good<sup>6</sup> .

We begin by representing the relevant features of the economic structure with the welfare evaluation approach of Lloyd and Schweinberger -- a distorted trade expenditure function: _<u>B = e(p, v) - g(p, v) – zp(p,v,u).(p-p*)</u>_ <u>(5)</u> where _B_ is the balance of trade, _e_ is the expenditure function in a vector of domestic prices and total utility, _u_ ; _g_ is revenue from production as a function of domestic prices and a vector, _v_ , of resources (we might generalize this by using a profit function with variable intermediate inputs); _zp_ is the derivative of _(e-g)_ and equal to net imports of each good; and _p*_ is a vector of world prices.

If we assume a standard Armington-type structure in a small economy, with imports differentiated from domestic goods, but domestic goods perfectly substitutable with exports, then we can simplify the analysis of tariff changes by focusing only on e(p,u) and the tariff revenue function. When tariffs change, there will be no change in the realized value of _g(p,v)_ and it can be ignored, unless there are distortions such as export taxes.

To make the approach easy to apply, we write the expenditure and revenue functions using a Constant-Elasticity of Substitution functional form for the expenditure function and a tariff revenue function based on the import demand functions derived from the first derivatives of this function with respect to prices. In this situation, for a single economy, the relevant elements of the distorted trade expenditure can be written: where P = [Σj βj(pj*(1+tj))<sup>(1-σ)</sup> ]<sup>1/(1-σ)</sup> , σ is the elasticity of substitution, and the βj’s are the distribution parameters of the CES expenditure function.

We calibrate this function using data on imports from the UNCTAD TRAINS database, and data on consumption of domestically-produced goods from the World Bank’s World Development Indicators. In line with standard practice in the CGE literature, all domestic prices were initially set to unity to allow decomposition of value data into prices and quantities. This allowed the β coefficients to be determined from the value share data at domestic prices. A σ value of 4 was assigned, raising the elasticity of substitution above that in most empirical estimates of import demand

> 6 A better longer-term solution to this problem would be to utilize a two-stage aggregation approach like that proposed by Bach and Martin (2001).

14
-36-

In table 3.2 the figures for Brazil and Mexico are converted into US dollars at the average exchange rates for the year 1975 as given by IMF (8.13 cruzeiros and 12.50 pesos to the US dollar respectively). These can be compared with 1975 output values for the United States as derived from the Annual Survey of Manufactures.

TABLE 3.2

|Grors Value of Output in Brazil,<br>Mexico an<br>at official<br>exchange<br>rates<br>(1975 U<br>...)______|d the USA in<br>S dollars)<br>____-__________|1975<br>_______________|
|---|---|---|
|Brazil<br>(million<br>dollars)|Mexico<br>(million<br>dollars)|USA<br> <br>(million<br> <br>dollars)|
|Total Manufacturing<br>Output<br>96,272.8|38,403.9|1,039,377.4|
|Grain Mill Products<br>913.7|729.9|6,469.3|
|Sugar & Sugar Products<br>1,493.5|52.7a|4,490.8|
|Malt and Malt Beverage<br>421.8|877<sup>.</sup>9<sup>a</sup>|6,232.2|
|Tobacco and Tobacco Products<br>752.6|307<sup>.</sup>8<sup>a</sup>|8,059.9|
|Textiles<br>3,618.8|1,432.8|15,770.8|
|Footwear and Leather Products<br>1,265.9|515.8|5,730.9|
|Pulp and Paper<br>1,319.9|1,168.6|17,335.5|
|Soap and Detergents<br>446.1|506.8|5,006.0|
|Paints<br>893.1|276.8<br>|5,149.9<br>|
|Agricultural Fertilizers<br>1,487.8|389.<sup>3</sup>ab|<br>6,971.0|
|Petroleum Refining and Products<br>7,137.1|2,277.1|66,429.4|
|Tires and Inner Tubes<br>886.7|397.6|7,143.1|
|Cement<br>699.7|451.9|2,334.-.|
|Bricks<br>743.1|130.9|1,229.8|
|Iron and Steel<br>6,337.7|2,626.9|42,211.7|
|Radio and TV Receivers<br>1,107.4|388.4|4,443.6|
|Motor Vehicles<br>7,108.4|3,154.1|70,031.8|
|Total in our sample<br>36,633.3|16,160.2|275,040.0|
|as%of Total Manufacturing<br>Output<br>38.0o|42.08|26.46|

a) indirect taxes and subsidies are deducted (see table 2.3).

b) includes 363.6 million US$ (excl. indirect taxes and subsidies) for petroleum refining, which are not shown in the census Resumen General but taken from Sistema de Cuentas Nacionales de Mexico.

Note: Figures are converted at the official exchange rate of 8.13 cruzeiros to the US$ and 12.50 pesos to the US$.

Source: Brazil and Mexico: derived from table 3.1; US figures from Annual Survey of Manufactures 1975-1976.
Williams, Roberton C., III; Hal Gordon, Dallas Burtraw, Jared C. Carbone and Richard D. Morgenstern (2014). The Initial Incidence of a Carbon Tax across U.S. States. National Tax Journal, Vol. 67 (4), pp. 807-29.

Yusoff, N.Y.M. and H. A. Bekhet (2016). The Effect of Energy Subsidy Removal on Energy Demand and Potential Energy Savings in Malaysia. Procedia Economics and Finance 35: 189 – 197.

33
|Rural Development Project(02)|
|---|
|Rural Development Project(02)|
|Rural Development Project(02)|
|Rural Development Project(02)|
|Babai Irrigation EngineeringProject|
|Babai Irrigation EngineeringProject|
|Hill Food Production Project|
|Hill Food Production Project|
|Hill Food Production Project|
|Hill Food Production Project|
|Hill Food Production Project|
|Hill Food Production Project|
|Hill Food Production Project|
|Agricultural Extension and Adaptive Research Project|
|Agricultural Extension and Adaptive Research Project|
|Agricultural Extension and Adaptive Research Project|
|Agricultural Extension and Adaptive Research Project|
|Agricultural Extension and Adaptive Research Project|
|Agricultural Extension and Adaptive Research Project|
|Agricultural Extension and Adaptive Research Project|
|Small Industries Project|
|Small Industries Project|
|Vocational TrainingProject|
|Vocational TrainingProject|
|Vocational TrainingProject|
|Vocational TrainingProject|
|Construction IndustryProject|
|Construction IndustryProject|
|Construction IndustryProject|
|Grain Storage Project|
|Grain Storage Project|
|Grain Storage Project|
|Grain Storage Project|
|Grain Storage Project|
|Grain Storage Project|
|Agricultural Research Project|
|Agricultural Research Project|
|Agricultural Research Project|
|Agricultural Research Project|
|On-farm Water Management Project|
|On-farm Water Management Project|
|Village Irrigation Rehabilitation Project|
|Village Irrigation Rehabilitation Project|
|Village Irrigation Rehabilitation Project|
|Village Irrigation Rehabilitation Project|
|Village Irrigation Rehabilitation Project|
|Village Irrigation Rehabilitation Project|
MINIMUM WAGES IN CAMBODIA 8 In practice, the changes in employment can manifest in both the extensive margin, through participation rates, as well as intensive margin, through hours devoted to work. If hiring and firing costs are high, then the effect might manifest more in the intensive margin than the extensive margin.

Furthermore, if hiring and firing costs are low, then jobs in the garment sector become more desired. Garment firms could use the opportunity to replace low-productivity workers with higher productivity workers. This could manifest in terms of changed (skill-)composition of those who are employed in the garment sector.

For this analysis, I use data from the annual Cambodia Socio-Economic Surveys (CSES) from years 2007 to 2014.<sup>11</sup> The CSES are nationally representative surveys that collect data or each month of the survey year. Hence, the annual series provides a picture of how the labor market has evolved month to month for the study period. This feature of the survey design will be extremely important in identifying how the outcome measure evolves after a minimum wage increase is enacted.

Since minimum wage regulations is only applied for the garment and footwear industries, the expected share of workers paid above minimum wage should be higher for workers in garment and footwear relative to other jobs in the private sector. Indeed, as Figure 3 indicates, the share of workers earning above the monthly minimum wage is higher for the garment sectors. The proportion of workers earning higher than minimum wages increased from about 87 percent in 2007 to about 95 percent in 2014, though there may have been a decline in 2015. The comparative proportions were 62 and 74 in other private sectors. The share of female workers in other private sector is even lower at 48 percent in 2007 to 62 percent in 2014.

As Figure 3 shows, compliance to minimum wages is quite high in the garment sectors. Before the minimum wage change in Oct 2010, 95 percent of the workers in the garment and footwear sectors were paid above the prevailing minimum wages. This compliance rate was at slightly higher level of 96 percent before the changes in May 2013, and February 2014. There was some bunching of monthly earnings at the minimum wage value, particularly for the latter rounds, but the share of the individuals at the minimum wage level was quite small. Furthermore, a large share of the workers in the garment and footwear sector had wages that are higher than the minimum wages that would be enacted in the subsequent months. The share of worker getting paid above the future minimum wage is between 76 and 83 percent. If minimum wage legislation had only a mechanical impact, it would affect

> 11Survey data are not regularly available for earlier years. However, given that most of the (large) increases in minimum wages occurred during this period, the lack of data availability for earlier is unlikely to affect the interpretation of the results.
In addition, the applied CEMAC, WAEMU and ECOWAS CET rates display an escalating (differential resulting from higher import duties on semi-processed products than on raw materials, and higher still on finished products) structure, distorting away from tradable toward non- tradable, and are misaligned with peer regions. CEMAC duties on intermediates and capital goods necessary for any industrial activity are above other regions and significantly above EAC and other regions such as ASEAN, the latter of which is now among the most diversified and prolific exporters of industrial products and deeply integrated into global value chains (Table 3). Providing increasing neutrality in incentives and aligning closely with world prices have been foundations of ASEAN structural transformation. In contrast, the CET of the 3 MCNG regional blocs shows much higher tariffs on final products than on primary and intermediate inputs, a structure that is designed to promote outdated import substitution by providing tariff protection to the industrial production of final goods. High CET tariffs on consumer goods, with an aim of creating incentives for regional substitution, may encourage greater regional production, but at a high cost to consumers and at the expense of export diversification, both within the regional market and into global value chains. And while escalation is common across most African countries, variation is less pronounced in the Sahel than in other SSA countries and regions, including EAC (Figure 12).

Table 3: Comparative Simple and Weighted Tariffs in Regional Groupings (2016)

|Region|Type<br>of<br>Goods|Simple<br>Average|Weight<br>ed<br>Averag<br>e|
|---|---|---|---|
|EAC|Primary|9.64|3.58|
|EAC|Intermediate|8.75|5.35|
|EAC|Consumer|17.07|7.72|
|EAC|Capital|4.97|4.48|
|ECOWAS|Primary|13.42|19.11|
|ECOWAS|Intermediate|9.96|9.37|
|ECOWAS|Consumer|18.36|13.78|
|ECOWAS|Capital|7.69|7.79|
|CEMAC|Primary|18.09|10.04|
|CEMAC|Intermediate|14.87|12.2|
|CEMAC|Consumer|24.84|19.99|
|CEMAC|Capital|12.68|12.82|
|SADC|Primary|4.37|0.65|
|SADC|Intermediate|4.49|2.26|
|SADC|Consumer|11.96|8.71|
|SADC|Capital|2.82|2.16|
|WAEMU|Primary|17.76|25.38|
|WAEMU|Intermediate|10.70|11.50|
|WAEMU|Consumer|17.51|14.47|
|WAEMU|Capital|7.94|7.76|
|ASEAN|Primary|5.33|2.72|
|ASEAN|Intermediate|4.49|4.08|
|ASEAN|Consumer|9.39|6.16|
|ASEAN|Capital|4.89|1.96|
|EU|Primary|5.64|n.a|
|EU|Intermediate|0.03|n.a|
|EU|Consumer|2.34|n.a|
|EU|Capital|0.00|n.a|

_Source_ : Pitigala et al. (2017, 2018a,b).

11
_-6-_ endogeneity between family planning or health programs and socio-economic variables, especially education levels, in countries where family planning or health program efforts may<sup>be focused on</sup> states/districts with higher levels of socio-economic development. Such endogeneity may be a less serious problem in cross-country analysis than in studies y_ithin a country. To test<sup>for endogeneity between</sup> socio-economic development and program effort at the cross-country level, we regressed an index of socio-economic development (WFS and UN 1987) on family planning services across countries,<sup>and</sup> found the correlation to be weak (R = .38). Such correlations with other socio-economic and human development indices were found to be even weaker.<sup>2</sup> In this paper, therefore,<sup>the possible endogeneity</sup> between program supply and socio-economic development is ignored.

2.10 Female Education and T,otal Fertilit: The first equation presented in this study is intended to explain the total fertility rate CM'7R) - the number of children that would be born to a woman if she lives to the end of her childbearing years and beare children at<sup>each age in accordance with</sup> prevailing age-specific fertility rates. The hypothesis is that the TFR depends<sup>in a non-linear way on</sup> female and male secondary education (FED and MED) and in a linear way<sup>on per capita GDP (GDP),</sup> the rate of urbanization (URB), population per physician (PHYS), and the level of<sup>a family planning</sup> services score (SERV), and potendal interaction between education<sup>and family planning services:</sup> TFR = ao + alFED + a2FED^2 + a3MED + a4MEDA2 +a5GDP+ a6URB + a7SERV + a8(FED*SERV) + a9PHYS + alODAFR + allDAS + al2DLAC + ei (6)

> 2 **Mauldin** and Ros **(1991)** found **sdmilar resuk.**
of global consumption poverty” (World Bank, 2017, p. 144). The purpose of this paper is to propose such a measure. Borrowing language from Atkinson, we offer a “societal poverty line” (SPL) that builds on the work of Atkinson and Bourguignon (2001) and Ravallion and Chen (2011, 2013), but one that we argue is better aligned with national assessments of poverty in low, middle-, and high-income countries. Our approach is informed, but not completely determined, by countries’ assessments of basic needs as exhibited in their national poverty lines.

We offer two parameterizations of the SPL. Our preferred parameterization is one that has an intercept of $1 (in 2011 PPPs) and a gradient of 50 percent of median consumption or income. We also consider a variant of this line that includes a lower bound set at the international poverty line of $1.90. The intercept, or the $1.90 lower bound in the variant case, provides a fixed, absolute element to the SPL. The slope of 50 percent adds a relative element to the SPL, rising in value as median consumption levels increase. This economic gradient of the SPL corresponds with a view of relative poverty that is well accepted in much of the rich world and coincides with SDG indicator 10.2.1 on inequality, which tracks the proportion of people living on less than 50 percent of median income.<sup>4</sup> The remainder of the paper is laid out as follows. In the next section, we describe existing conceptualizations of global relative poverty and then describe the data used in support of our proposed our SPLs. Section III assesses the fit of the data and our proposed SPLs, and section IV discusses the geographic and temporal profiles of societal poverty.

> 4 For details, including metadata and indicators, on each of the 17 SDGs, see <u>http://unstats.un.org/sdgs/iaeg-sdgs/metadata-compilation/ .</u>

4
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