rafmacalaba/datause-encoder-data huggingface dataset:
understate welfare gains by more than a factor of 2. In the spirit of keeping our results as conservative as possible the paper will devote a great deal of attention to adjustments, such as for migrant selectivity or compensating differentials that scale down the raw estimates of wage differentials. But we highlight the fact that our assumption that all consumption occurs in the destination country goes a long way toward ensuring that our estimates _understate_ the gaps in real wages we document.

A key question is the reliability and comparability of reported earnings. Research comparing multiple sources of income data at the individual level suggest that selfreported income is an unbiased estimator of true income, both in rich countries (Bound and Krueger (1991)) and in poor countries (Akee (2007a)). There is less certainty about comparability. Wage data for the US reflect total earnings from all jobs, whereas wage data for the 42 developing countries in our sample reflect wages from the respondent’s principal occupation. For the vast majority of formal-sector wage earners in the sample we nevertheless expect wage earnings from the principal occupation to closely reflect total wage earnings. Furthermore, wage data for the United States reflect gross earnings before taxes, and we expect that most people responding to a general question about their wages or earnings would have provided gross wages on most of the country surveys, but for a handful of countries it may be that the responses reflect after-tax wages.<sup>10</sup> If respondents provided net-of-tax instead of gross wages this would result in some upward bias to our estimated _Ro_ . This bias will be small, however, if it is present at all. Formalsector income taxes are on the order of 5% in most developing countries (Easterly and Rebelo (1993)). For the median ratio of 3.92, for example, a 5% underestimation of the denominator means that the corrected ratio is 3.73.

2.4 _Method_ We seek estimates of international wage ratios for equal-productivity workers ( _Re_ ); we begin by estimating wage ratios for observably-identical workers ( _Ro_ ). First we compare workers residing in one pair of countries at a time—the US and another country _j_ ∈ _J_ — estimating a separate regression for each country _j_ . We do this with three different regression specifications. We discuss the standard Mincer specification first since it is the easiest to describe, though we rely on it the least:

> 10 In a small number of the countries (such as Yemen) the survey explicitly requests after-tax earnings, and in a few of the others (such as Chile) custom may dictate that formal sector “wages” refer to after-tax earnings unless otherwise specified. The text of the wage question from each survey is in the Appendix.

9
(2) Measuring the effect of inequality on poverty is slightly more complex because inequality can change in infinite ways. Although intuitively progressive distributional change is likely to reduce poverty<sup>3</sup> , this result cannot be generalized without additional assumptions. For example, consider the (possibly unlikely) case of a transfer from the extremely rich to the very (but not extremely) rich. This would improve inequality levels but would not affect poverty. To make the problem of the impact of inequality changes on poverty tractable, one possibility is to assume that income follows a log-normal distribution.<sup>4</sup> Lopez and Serven (2004) compare the theoretical quintile shares according to a log-normal distribution with their empirical counterparts using data from 794 household surveys and conclude that the log normal approximation fits the empirical data extremely well. In turn, under this assumption it is possible to express the inequality elasticity of poverty φ as the elasticity of poverty with respect to the Gini index _G:_ With these elements in mind, a change in poverty due to a change in policy X can be expressed as: or using (2) and (3) above Equation (5) indicates that the impact that a change in policy X will have on poverty will depend on: (i) the impact that the policy has on growth; (ii) how growth is translated into poverty reduction; (iii) the simultaneous impact that the policy has on inequality (as measured by the Gini coefficient), and finally (iv) how inequality changes are translated into poverty reduction.

In principle, γ and φ could be considered to be independent of the policy in question and depend on the particular income distribution, the initial level of per capita income _Y_ , the poverty line, and the initial level of inequality _G_ .<sup>5</sup> Table 1 presents the theoretical elasticities of headcount poverty to growth and headcount poverty to inequality computed under the assumption that income follows a log normal distribution. These elasticities are

> 3 Strictly speaking, for the inequality elasticity of headcount poverty to be positive it is also required that the level of average per capita income is above the poverty line. Otherwise, there is the risk that progressive distributional changes increase poverty.

> 4 If the distribution of income _y_ is log-normal, then _log(y)~N(_ μ _,_ σ _)_ , with _G= 2_ Φ _(_ σ _/_ √ _2)-1_ where Φ _(.)_ denotes the cumulative normal distribution (Aitchinson and Brown (1966)), and G is the Gini index. 5 See Son and Kakwany (2003) for an excellent review of the relationship between inequality, average income and the elasticities of poverty.

-6-
- 27 -

bracket figure has been put down for 1983 for it can be deduced that at some time in the past dead branchwood would be lying outside the door. Thus the walking and collecting time was practically zero. Therefore the difference between then and 1983 would be approximately equal to i)--the total walking and collectior time--and for 1988 the cumulative total would be the sum of all the previous marginal increases which in fact is the figure in line i). But of course we are assuming that when the dead branchwood was lying outside the door it had no value. Resource economists could counter this by saying that as soon as a product is used it has value. But what figure is to be put on a product that has grown without human intervention? This could be ascertained by another survey on peoples' willingness to payl 2.22 Table 2.3 also gives three stumpage value figures depending on whether the value is equated to the total standing volume, the stem volume or the volume of wood actually consumed. This is a first approximation and cannot be applied to products that are converted from one form to another, such as fuelvood to charcoal. Whether to relate the value to the stem wood or to the total above-ground volume depends on how practical it is to measure total volume. It should be noted however that because these particular trees contain useable branch wood the stumpage value based on stemwood measure is over 50% higher than the solid fuel volume value.

2.23 Table 2.2 shows that the "energy" quality of wood collected in 1983 was higher than that collected in 1988 because it was drier and thus had more useful energy per unit of measure. In our particular example the 1983 wood had an energy value of 16.4 MJ/kg and the 1988 wood a value of 15.8 MJ/kg, or about a 4 percent difference. This difference should be taken into consideration when working out a stumpage value, especially if it is based on weight. Freshly felled wood can contain over 50 percent water where as dry wood contains only about 15 percent. In volume terms there is only about 5 to 10 percent shrinkage between "green" wood and dry wood but, when burnt, green wood yields less energy per unit of dry matter because energy is required to drive off the water.

2.24 Finally like many other methods that try to fix a stumpage value for wood this method assumes, incorrectly, that there is only end use for the product.

# Differential Land Values

2.25 Sometimes, the value of a natural amenity, such as a forest, bacomes a factor in the price of marketable assets. An analysis of the price differentials of such assets may help determine the implicit value of the natural amenity. The differential land value approach is based on this principle and is applied to the valuation of timber resources, but is less commonly seen in fuelwood contexts. To use the method one needs to know the sales prices or exchange values of comparable parcels of land, some stocked with fuelwood resources and the other practically empty of trees. If the lands are otherwise similar in terms of location,
# **Table 1.The Determinants of Russian Firms’ Decision to Invest between 2011 and 2013**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|||<br>Firm|<br>implemented|<br>investment in 2|<br>011-2013,du|<br>mmy||
|Bribe incidence, BEEPS data|-0.12||-0.14||-0.36||-0.06|
||(0.27)||(0.27)||(0.29)||(0.33)|
|Bribe tax, mean, BEEPS data||0.69|0.69||0.36||0.62|
|||(0.56)|(0.55)||(0.63)||(0.50)|
|Bribe tax, variation, BEEPS data||||-0.11**|-0.14*|||
|||||(0.06)|(0.08)|||
|Raider attacks normalized by GRP||||||-0.07***|-0.07**|
|||||||(0.03)|(0.03)|
|Log GRP per capita, 2008|0.12|0.11|0.11|0.10|0.11|-0.01|-0.00|
||(0.08)|(0.09)|(0.09)|(0.08)|(0.08)|(0.08)|(0.09)|
|Employment at the firm, log|0.09***|0.09***|0.09***|0.09***|0.09***|0.09***|0.09***|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|Firm receives support from government|0.02|0.03|0.03|0.03|0.03|0.01|0.01|
||(0.04)|(0.04)|(0.04)|(0.04)|(0.04)|(0.04)|(0.04)|
|Firm receives state orders|0.10**|0.09**|0.09**|0.11**|0.10**|0.10**|0.10**|
||(0.05)|(0.05)|(0.05)|(0.05)|(0.05)|(0.05)|(0.05)|
|Controls for type of property, type of locality, and sector|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1,360|1,346|1,346|1,346|1,346|1,360|1,346|
|Probit regressions. Average marginal effects presented. Rob<br>*** p<0.01, ** p<0.05, * p<0.1|ust standard err|ors clustered at|the regional le|vel are in the pa|renthesis.|||

18
order to reduce slack or improve X-efficiency. It is possible that this seemingly simple response may make a more substantial contribution to productivity than improvements in resource allocation (see Leibenstein, 1966, 1980). Bergsman (1974), on the basis of a study of industry in six developing countries, argues that X-efficiency is several times as important as allocative efficiency in increasing incomes in these countries. Also Pack (1974), in a study of LDC manufacturing industries, and Page (1980), referring to evidence for three manufacturing industries in Ghana, suggest that factors related to X-efficiency - mainly management and capacity utilization - are more important than changes in resource allocation (via changes in relative factor prices) to improve performance (see also White, 1976).

The potential productivity improvements from these types of reactions are probably larger in the less developed countries than elsewhere, simply because the initial inefficiencies are often larger. On the other hand, local firms in the less developed countries may be too weak to mount a competitive response to foreign entry, whereas the locals in industrialized host countries can often be expected to reply competitively. Various defensive corporate agreements, such as amalgamations among local firms or cooperative ventures with other foreign firms, may improve the local firms' competitiveness, even in developing countries (Blomstr6m, 1986b; Lall, 1979; Evans, 1977), but there are no direct cross-country comparisons available, and there are not enough case studies for more comprehensive conclusions. Exactly what the reaction is - and how important the spillover benefits are - is likely to depend on the initial conditions in the market, and how much of an impact MNC entry makes on concentration and competition. However, the evidence seems to suggest that there is a larger risk that foreign MNCs crowd out local firms in developing countries. We will return to this question in section 4 below.

# _Statistical Testing of Spillovers_

Although there is plenty of empirical evidence of spillovers from the studies reviewed above, there are only few direct analyses and tests of the existence and significance of spillovers in a more general setting, presumably because of measurement problems and lack of suitable data. 12 In addition, most of the studies that are available focus on intra-industry effects. An early 20
# **Appendix 2: The Survey Instrument**

# **Survey of Detailed Skills**

**2021**

# **INTRODUCTION**

**FOR JOB INCUMBENTS** :  The survey is being conducted by the General Statistics Office on behalf of the World Bank. The purpose of this questionnaire is to create an accurate description of your job as a [ **OCCUPATION TITLE** ] for a project that the World Bank is undertaking about working in Vietnam.

Because you know your duties, responsibilities, and skills better than anyone else, we need your help to create this description. The questionnaire only asks what your job requires you to do. The questionnaire does not ask about your job performance.

Please complete this questionnaire as honestly, completely, and accurately as you can. Base your answers on what you normally do in your current job, not on special projects or temporary assignments unless they are a regular part of the job. When answering the questions, imagine you are describing what you do to a neighbor, friend, or someone just hired for your position.

Your responses are anonymous. All responses will be kept confidential. If you have any questions or comments, please contact our survey information center at XXXX. We appreciate your active participation in this important study.

**FOR OCCUPATION EXPERTS** :  The survey is being conducted by the General Statistics Office on behalf of the World Bank. The purpose of this questionnaire is to create an accurate description of work as a [ **OCCUPATION TITLE** ] for a project that the World Bank is undertaking about working in Vietnam.

Because you are familiar with the duties, responsibilities, and skills of a [ **OCCUPATION TITLE** ], we need your help to create this description.

Please complete this questionnaire as honestly, completely, and accurately as you can. Base your answers on what is normally done in this job, not on special projects or temporary assignments unless they are a regular part of the job. When answering the questions, imagine you are describing the job to a neighbor, friend, or someone just hired for the position.

Your responses are anonymous. All responses will be kept confidential. If you have any questions or comments, please contact our survey information center at XXXX. We appreciate your active <u>participation in this important study.</u> 36
- 61 -

Stock Exchange of Thailand. In some cases, the firms are extraordinarily unprofitable and in other cases quite profitable. Given that the Thai company tax regime is not ridden with fast writeoffs for capital, it is surprising to find so many companies not paying taxes. A frequent concern raised by many foreign companies is that their competitors are not being taxed at the same level. In part, some companies, especially closely-held ones, avoid paying company taxes by not declaring income either for book or tax purposes, yet the firms may in fact be profitable. Perhaps there is a case for a 'minimum tax" that would be creditable against normal company taxes paid by the firm. Such a minimum tax would add eomplexity to the tax system but it could ensure that all companies bear some tax. The two minimum taxes that could work in Thailand are a tax on distributed profits (dividends, possibly interest) or a tax on the capital assets of the subsidiary (such as the Canadian "Large Corporation Tax"). The minimum tax on distributed profits would level the "playing field" across domestic and foreign companies. If structured correctly, the tax could be creditable against home country taxes on remitted dividends from Thailand.

7. The company-income tax incentives used in Thailand to attract foreign capital raise important issues. The current incentive system uses a company and withholding tax holiday, lasting from three to eight years. As discussed by Mintz (1989), the Thai tax holiday is biased against long-lived capital since the assets are written down during the holiday. The remaining depreciation allowance after the holiday may be too low relative to true economic depreciation, resulting higher effective tax rates than the statutory rates. The Revenue department, however, does not review the income statements of tax holiday firms. It
prices) was reduced from 33 percent to 13 percent; for the rich countries, the reduction was from 16 to 7 percent. The pattern of reduction for both poor and rich countries has been very similar to the one shown in Figure 3: not only are average tariff rates less in 2000 than some twenty years ago, but the differences between the countries is much smaller too (in other words, the distribution of average tariff rates across countries is much more compressed now than in 1980).

To account for the possibility mentioned above that individual countries are “globalized” not directly through their own policies but through the effect that policies of their trading partners have on themselves, we create a special globalization variable. It is calculated as follows. For each country, we take its three largest importers and calculate their import shares (for the United States, for example, they would be Canada, China and Mexico). These import shares are then multiplied with a variable denoting presence or absence of trade reform in the three countries.<sup>26</sup> The result gives an import-weighted indicator of presence of trade reforms in country’s most important trading partners. Presumably, if a country’s key trading partners are engaged in reforms, this puts pressure on the country in question to reform as well.

One problem when trying to link tariff liberalization reforms to domestic outcomes such as wage distribution is that they are seldom undertaken in isolation. Most frequently, pro-openness trade reforms are accompanied by other “globalization” policies that may well affect labor market outcomes: for example, easier direct or portfolio investment by foreign residents or more liberal regulation of international labor flows. And just as frequently trade reforms are accompanied by domestic reforms that impact directly on labor markets: “flexibilization” of the labor market, changes in the minimum

> 26 This is a three-way variable: there could be a pro-openness reform, anti-openness reform, or no change. The calculation is based on Free the World  (FTW) measure of trade taxes: if they decrease sufficiently, a country is deemed a reformed over the entire five-year period (FTW data are available at five-year itervals). “Sufficiently” is defined as reduction in trade taxes/trade ratio by more than 1⁄2 of the world standard deviation in that variable. In other words, if world’s standard deviation of trade taxes/trade variable is 5 percent, than if country’s trade taxes/trade ratio goes down by 5 percentage points, it is deemed a proopenness reformer. The opposite is the case for anti-openness reforms. Free the World data are available at <u>www.freetheworld.com. The report is issued by the Fraser Institute, Vancouver (see http://www.fraserinstitute.ca/shared/readmore.asp?sNav=nr&id=537).</u>

31
MAKING SENSE OF AFRICA’S INFRASTRUCTURE ENDOWMENT: A BENCHMARKING APPROACH

**Figure 2.1   Infrastructure endowments plotted against national income, by world region**

<!-- Start of picture text --> a. Percentage of households with improved water b. Percentage of households with improved sanitation<br>LCR<br>ECA ECALCR MNA<br>MNA<br>EAP<br>EAP<br>SAS<br>SADC SAS<br>EAC<br>SADC<br>EACECOWASSSA<br>ECOWASSSA<br>CENTRAL CENTRAL<br>0 1000 2000 3000 4000 0 1000 2000 3000 4000<br>Income Per-Capita Income Per-Capita<br>c. Percentage of households with electricity d. MW installed generating capacity per 1 million people<br>MNA<br>ECA<br>LCR<br>EAP<br>SAS MNA<br>LCR<br>SADC EAP<br>ECOWASCENTRALSSA<br>SAS SADC<br>EAC SSA<br>EACECOWASCENTRAL<br>0 1000 2000 3000 4000 0 1000 2000 3000 4000<br>Income Per-Capita Income Per-Capita<br>e. Total roads (km/km2) f. Paved roads (km/km2)<br>MNA<br>LCR<br>LCR<br>MNA<br>ECA<br>ECA<br>SAS<br>EAP SAS<br>SADC<br>ECOWASCENTRALSSA SADC<br>EAC ECOWASCENTRALSSA EAP<br>EAC<br>0 1000 2000 3000 4000 0 1000 2000 3000 4000<br>Income Per-Capita Income Per-Capita<br>90 80<br>70<br>80<br>60<br>50<br>70<br>40<br>% HHolds With Improved Water<br>% HHolds With Improved Sanitation<br>60<br>30<br>1000<br>80<br>800<br>60<br>600<br>40<br>400<br>MW per 1 Mil. People<br>% HHolds With Electricity 20<br>200<br>0 0<br>800 500<br>400<br>600<br>300<br>400<br>200<br>Tot. Road (km/km^2)<br>200 Paved Road (km/km^2)<br>100<br>0 0<br><!-- End of picture text --> 9
<!-- Start of picture text --> 8 Expenditures per consumption unit versus time to tarred roads (min)<br>8<br>o .<br>%<br>¥<br>ig<br>EF<br>20£o<br>BS<br>=.<br>3o .td<br>2 a<br>5s ° .<br>= a ~ «<br>5O fee %<br>gn je, s H * Hy<br>w te 8 we *<br>i be e *. ee of « bd<br>2 ; .r j : ee?i 8 *.j eee bd<br>S Miia bin oid: Wat -<br>0.00 100.00 200.00 300.00 400.00<br>Time to tarreds roads(min)<br><!-- End of picture text -->
**Table 6b: Drought Impacts on Child HAZ*100 (OLS with Cluster x Month Fixed Effects)**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||All|Mozambique|Malawi|Zimbabwe|Tanzania|Zambia|
|Drought: Birth Season|-19.83***|-33.31***|8.89|-24.82***|-13.59**|-28.70***|
||[-5.13]|[-3.81]|[0.96]|[-2.80]|[-2.18]|[-3.80]|
|Drought: Season after Birth|-10.63**|5.88|-21.51**|-13.75|-11.29*|-21.15***|
||[-2.57]|[0.48]|[-2.15]|[-1.55]|[-1.79]|[-2.59]|
|Mother ht between 152 and 160 cm|-39.77***|-39.95***|-37.50***|-40.78***|-42.84***|-30.06***|
||[-11.33]|[-3.95]|[-3.69]|[-7.35]|[-8.13]|[-4.33]|
|Mother ht below 152 cm|-75.07***|-64.73***|-79.33***|-82.95***|-81.98***|-69.08***|
||[-17.79]|[-5.69]|[-7.08]|[-8.28]|[-13.29]|[-7.65]|
|Mother under 19 yrs|-21.58***|-10.72|-14.01|-8.80|-30.48***|-6.58|
||[-3.85]|[-0.84]|[-1.00]|[-0.81]|[-3.33]|[-0.59]|
|Age:7-24 months|-80.25***|-60.92***|-47.24***|-95.11***|-80.60***|-106.15***|
||[-15.05]|[-4.41]|[-3.13]|[-8.95]|[-10.32]|[-9.73]|
|Age:25-59 months|-104.15***|-88.39***|-77.60***|-119.63***|-106.90***|-112.55***|
||[-20.02]|[-6.72]|[-5.78]|[-12.35]|[-13.87]|[-10.68]|
|No Livestock|-5.55|0.21|-8.17|10.40|-1.99|-20.15***|
||[-1.43]|[0.03]|[-0.87]|[1.40]|[-0.25]|[-2.76]|
|Does not treat water|-4.07|-1.38|1.87|-22.23**|-4.64|2.66|
||[-1.16]|[-0.08]|[0.22]|[-2.46]|[-1.02]|[0.32]|
|Mother underweight|-14.36***|-35.45**|-15.26|-12.08|-8.95|-13.31|
||[-2.72]|[-2.47]|[-1.10]|[-0.86]|[-1.13]|[-1.53]|
|Mother able to read part of sentence|1.04|-7.36|-1.27|-4.08|10.68|-13.25|
||[0.20]|[-0.54]|[-0.08]|[-0.42]|[1.31]|[-1.18]|
|Mother cannot read at all/Unknown|-0.15|-6.33|-18.93*|3.45|4.48|-6.06|
||[-0.04]|[-0.64]|[-1.84]|[0.27]|[0.90]|[-0.96]|
|Constant|29.17**|22.04|9.21|29.49|48.16***|-8.48|
||[2.52]|[0.69]|[0.27]|[1.17]|[2.64]|[-0.33]|
|Observations|28,017|5,002|3,618|4,400|9,358|5,639|
|R-squared|0.49|0.47|0.56|0.51|0.49|0.48|

Notes: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors are clustered at the DHS cluster level. All regresssions employ cluster x month fixed effects. The weights used are person weights adjusted for the national populations. The first regression is the same as the first regression in table 3. Additional controls are for roof, floor and wall quality, cooking fuel, employment in agriculture, source of water, type of toilet, ownership of radio, tv, bicyles, refridgerators, cars, trucks, birth order, birth interval, gender of child and the gender of the household head. All metrics are based on children being measured below -2 standard deviation thresholds.

29