rafmacalaba/datause-encoder-data huggingface dataset:
We discuss in this paper various challenges related to the SDGs in terms of identification and measurement methods, and we place a special emphasis on the quantitative analysis angle.<sup>3</sup> We examine global data but focus on poorer countries. In particular, we revisit basic questions that are often assumed as settled, but that on more careful thought may actually lead to different answers. For example, is the current list of SDG indicators appropriate for the stated development goals? Is there any overlap or potential conflict among these goals? Could progress on some indicators be interpreted in one way only, or more than one way? If the latter, how best to interpret progress? Should we evaluate a country’s performance as far as we can go back with the available data, or only within a recent time period? Since it is well-known that data are unavailable for a number of indicators, could this missing data challenge affect how we evaluate a country’s performance? Also given this missing data issue (and other potential interpretation issues), what viable alternatives are there to monitoring progress on all 232 indicators: could we instead track a core group of indicators or an overall index? While we do not claim to raise all these questions for the first time (see our discussion on the relevant literature in later sections), we aim to shed new light and offer more reflections on them.

We combine our own data analysis with insights gleaned from related studies to enrich our discussion. We analyze data from various data sources, including the World Bank World Development Indicators (WDI) database, the UN SDG database, as well as the nascent (but growing) literature on the SDGs. Building on the analysis and taking into account the political settings of the SDGs, we then offer further reflections on future data collection efforts for the SDGs and other related issues. In short, we aim to expose deeper issues hidden behind the various

> 3 For recent discussions on other substantial aspects of the SDGs, see, for example, Joshi et al. (2015) on governance, Waage et al. (2015) on health indicators, and Klopp and Petretta (2017) on urban development. See also Fukuda-Parr (2019) for a recent discussion on the politics of selecting the inequality measure for the SDGs.

3
possible between 2000 and 2017. We regress the log of the average number of passengers car-km per household on the log of GDP per capita (Table 6). In the regression with year fixedeffects, a 1% increase in GDP per capita is associated with a 0.46% increase in km per car, which measures the intensity of car usage.

Table 6: OLS regression for total traveled distance per car

|_Dependent variable :_|_Log Trav_|_eled distan_|_ceper car_|
|---|---|---|---|
|Log GDP per capita|0.422<sup>∗∗</sup>|-0.173<sup>∗∗</sup>|0.466<sup>∗∗</sup>|
||(9.62)|(-3.54)|(10.49)|
|Year FE|No|No|Yes|
|Country FE|No|Yes|No|
|R-squared|0.119|0.020|0.159|
|N. of observations|686|686|686|

_t_ statistics in parentheses + _p_ < 0 _._ 10, ∗ _p_ < 0 _._ 05, ∗∗ _p_ < 0 _._ 01

# **5 Rising Incomes and Transport Sector Decarbonization**

The previous section has quantified the relations between income and transport expenditures, vehicle ownership, and vehicle usage. The objective of this section is to use these estimates to simulate the implications of rising incomes on transport CO2 emissions. For simplicity, we focus on private transport and assume that most of the increase in CO2 emissions will come from first-time vehicle buyers and an increase in vehicle use. We simulate the effects of rising incomes in 2035 on CO2 emissions through two channels: the increase of vehicle ownership (extensive margin) and the increase in vehicle use (intensive margin). The first subsection focuses on the extensive margin, the increase in vehicle ownership, the second on the intensive margin, the increase in vehicle use or car traveled distance.

## **5.1 Rising Income, Vehicle Ownership, and Carbon Emissions**

**The impact of rising income on vehicle ownership** We use the results from Section 4.5.1 to simulate the impact of predicted income changes on vehicle ownership across countries. Using the same ordered Logit model, we predict the average probabilities to own a vehicle when incomes are at the current level (panel a of Figure 19) and at the 2035’s level (panel b of Figure 19) for the countries in the LSMS sample. 2035 income levels are calculated using GDP growth predictions estimated by Fouré et al. (2012) and represent a business-as-usual scenario which does not include any change in households’ behavior adapting to the new situation. As shown in Figure 19, predicted probabilities to own no vehicle decrease as incomes expand in 2035. More households own a vehicle as they get richer. More specifically, the structure of vehicle ownership will change since more households will own any type of vehicle, a motorcycle or a

35
# 1. What is global inequality?

Global inequality is a relatively recent topic. The first calculations of inequality across world citizens were done in the early 1980s.<sup>2</sup> This is because in order to calculate global inequality, one needs to have data on (within-)national income distributions for most of the countries in the world, or at least for most of the populous and rich countries. But it is only from the early- to mid-1980s that such data became available for China,<sup>3</sup> the Soviet Union and its constituent republics and large parts of Africa. Before we move to an analysis of global inequality, however, it is useful to set the stage by delineating what topics we shall be concerned with and what not. This is necessary precisely because of the relative underdevelopment of the topic, reflected in the fact that the same or similar terms are often used in the literature to mean different things. We need to distinguish between inequality among countries’ mean incomes (inter-country inequality or Concept 1 inequality as dubbed by Milanovic, 2005), inequality among countries’ mean incomes weighted by countries’ populations (Concept 2 inequality), and inequality between world individuals (global or Concept 3 inequality).

Concept 1 inequality deals with convergence and divergence of countries and although at first this line of work was couched in inequality terms (see Baumol 1986), most of the later work used cross-country regressions and β convergence.<sup>4</sup> In such regressions each country/year is one observation. This line of research, which has generated a huge literature, is interesting for a number of reasons but it has very little to tell us about income inequality among world citizens. This is basically because countries are of unequal population size. Thus a fast increase in income of a small poor country

> 2 By Berry, Bourguignon and Morrisson (1983), and Grosh and Nafziger (1986).

> 3 The first post-Cultural Revolution household survey in China was conducted in 1978. The first available rural and urban surveys are from respectively 1980 and 1981 (see Ravallion and Chen, 2006, forthcoming, p. 3).

> 4 Some of the initial emphasis on inequality rather than on the  β coefficients can still be seen in the use of the sigma convergence where sigma is the standard deviation of  income logs.

2
Table **6:** Variance Ratio<sup>Test of Stock Returns</sup>

||||Horiz|on|||
|---|---|---|---|---|---|---|
|**COUNTRY**|3-months|12-months|24-months|36-months|48-months|60-months<br>|
|Argentina|1.02|**0.88**|**0.70**|**0.62**|**0.60**|**0.60**<br>|
||_(0.84)_|**_(0.59)_**|**_(0.35)_**|**_(0.35)_**|**_(0.39)_**|_(0.45)_|
|Brazil|**1.01**|**0.99**|**0.82**|**0.71**|**_0.75_**<br>|**0.83**<br>|
||**_(0.93)_**|**_(0.95)_**|**_(0.57)_**|_(0.46)_|**_(0.59)_**|**_(0.75)_**|
|Chile|1.34|1.94|**2.50**|**2.88**|**3.16**|**2.97**<br>|
||**_(0.00)_**|**_(0.00)_**|**_(0.00)_**|**_(0.00)_**|**_(0.00)_**|**_(0.00)_**|
|Colombia|1.43|2.22|2.40|**2.63**|**2.76**<br>|**2.81**<br>|
||**_(0.00)_**|**_(0.00)_**|**_(0.00)_**|**_(0.00)_**|**_(0.00)_**|**_(0.00)_**|
|Mexico|**1.31**|**_1.50_**|**1.61**|1.74|**1.85**|1.84<br>|
||**_(0.00)_**|_(0.02)_|**_(0.06)_**|**_(0.06)_**|**_(0.07)_**|**_(0.11)_**|
|Peru|**1.07**|**0.80**|0.64|**0.70**|**0.82**|**0.58**<br>|
||_(0.45)_|**_(0.37)_**|**_(0.27)_**|_(0.45)_|**_(0.69)_**|_(0.42)_|
|Venezuela|**1.15**|**_1.59_**|**1.53**|**1.10**|**0.97**|**0.88**<br>|
||**_(0.09)_**|**_(0.01)_**|**_(0.10)_**|**_(0.80)_**|**_(0.95)_**|**_(0.82)_**|
|**USA**|**0.96**|**0.91**|**0.83**|**0.90**|**0.91**|0.94<br>|
||_(0.64)_|**_(0.69)_**|**_(0.60)_**|**_(0.81)_**|_(0.84)_|**_(0.91)_**|

P-valucs shown in parentheses<sup>for the null hypothesis that the variance ratioequals</sup><sup>**1,**where the numerator is the variance of k-month returns and the denominatoris</sup><sup>**k**timesthe variance of 1-month</sup> rcturns. Ifreturns<sup>follow a random walk (i.e.,no return autocorrelation),then return variance is</sup><sup>**a**linear functionof horizon</sup><sup>**length:**the variance of returns over</sup><sup>**k**periods is</sup><sup>**k**times the variance ofreturns</sup> over one-period. If<sup>retums arc positively autocorrelated,the varianceof k-period rcturns is largerthanthe sumof one-periodreturns-variancesgrow faster than linearly. Thus, variance ratios larger than</sup> one are consistent<sup>with rationalpositive momentum trading. Alternatively, when returnsare negatively autocorrelated,the variance of k-period returns is smaller than</sup><sup>**k**timesthe variance of one-period</sup> returns. Variance ratios<sup>smaller than one wouldcall for negative momentum (or contrarian) trading.Sample: monthlyindex returns from January</sup><sup>**1975**to October</sup><sup>**1998.**</sup>

**31**
usually coverage is difficult to define, particularly in the case of broad-based PHC programes. To relate impact to inputs into<sup>an</sup> intervention usually requires consideration of the situation before and after that intervention. Country health studies could be repeated after several years but how long is an appropriate interval? A shorter period (2 - 5 years) of evaluation tends to favour<sup>those</sup> programmes which have an immediat **e** ffect (eg. immunisation, ORT etc.). Improvements in health services or sanitation may take a much longer period to reach maximum effect and over that longer period the effectiveness of the other types of interventions may decline. Can investment be delayed until long-term evaluations have been undertaken?

Ideally, meas. ement of health impact should be judged with<sup>reference</sup> to a control group, to determine whether changes observed are really due to a newly-introduced intervention or whether they would have occurred anyway eg. as a result of changing climate or improvements in agricultural extension. Where the health intervention is directed at the community, the samples for these control and intervention groups should be samples of communities and not of individuals, because individuals in a community receive the same health services/programmes, and the size of the samples is measured in numbers of communities rather than individuals. With<sup>the use of</sup> sample communities there is a serious problem of confounding variables - for example communities that have better health services are also likely to have better literacy, greater wealth, better leadership etc. In the 4 sence of randomised intervention studies very large samples would be required to overcome these confounding factors statistically.

Clearly, providing all the information required to<sup>measure</sup> consequences is a major task often beyond the efforts and time of evaluators. Instead recourse is often made to measures of intermediate output, such as the numbers of immunised children, or<sup>other</sup> utilisation and coverage data, for which information is more readily available and which are more clearly related to the intervention. Economic evaluation focused on such measures of health service output is perhaps more appropriate in the light of the difficulty of measuring health outcome.

18
cross sections. The cluster level infornation then consists of panel individuals and those not in the panel.

Let us look at the pattern of intra-household time use by per capita consumption. We provide histograms for male-female time use split along the lines of consumption deciles in figures 1-4. Figure 1 shows the hours per week allocated to all types of work activities by men and women in the sample. The first striking fact is that the total time worked is not as high as one would expect in a country like Peru. In none of the deciles does work exceed 60 hours per week. However, there are significant differences between urban and rural subsamples, with men and women in the latter working more than those in the former (see table 1 for details). The other striking fact in figure 1 is that women tend to work almost 15-20% more per week than do men. Female work varies by consumption decile. Women in the bottom two deciles work the most, but men in these deciles do not appear to have their work burden any different from men in the rest of the deciles. This seems to indicate that the burden of poverty may be falling on the women in the form of high work needs. Total work patterns tend to mask the disparities that exist between the differing nature of male and female work. Figure 2 graphs the shares of housework in malefemale work. Here the contrast between men and women is stark (as expected). Housework accounts for on average 70% of the work effort of women in Peru. It is only 25-35% of the work effort of men. These differences are particularly stark at the bottom end of the consumption distribution (about 73% for women and 23% for men) but less so at the top end (68% for women and 36% for men). The interesting pattern that emerges from this figure is that men's housework seems to vary somewhat across deciles (though not showing any pattern). However, the share of housework in total time of females stays unchanged-there is little difference for instance between this share for women at the bottom and top deciles.

panel. In order to avoid the complications of using an unbalanced panel, the subset used in this paper is restricted to the 1994 and 1997 data sets.

13
**25 |** P a g e

surveys of exporters could give precise information on wages of those involved in export‐oriented industrial activities, some insights can be obtained from an examination of the exports content in terms of labor skills. Higher skills tend to be better rewarded: hence the shift toward products requiring high labor skills would indicate also the shift towards higher paid jobs.<sup>11</sup> In order to shed light on labor skills underlying BiH exports, we use a taxonomy developed by Austrian Institute of Economic Research (Peneder 1999). It classifies two‐digit SITC items in terms of embodiments in labor skills. Four levels of skills are distinguished: products requiring **_low‐skill_** labor (e.g., most foods and feeds, fabrics, iron and steel, non‐ferrous metals, clothing, footwear); **_medium skill blue‐collar_** (e.g., energy, vegetable oils and fats, wood products, automobiles, and other vehicles); **_medium skill white‐collar_** (e.g., fertilizers, cosmetics, paper articles, power generating equipment, telecommunication and electrical equipment, scientific instruments); and **_high‐skill_** labor intensive sectors (e.g., pharmaceuticals, industry special machines, metalworking machinery, industrial equipment, office and data processing machines). Table 16 presents the results of this analysis.

**Table 16: Labor content of BiH exports and EU imports from BiH in terms of skill in 2003 and 2007 (in percent)**

|Level of labor skills|EU<br>2003|-27<br>2007|Index, 2007<br>2003=100|R<br>2003|OW<br>2007|Index, 2007<br>2003=100|Share o<br>2003|f the EU<br>2007|Index 2007<br>2003=100|
|---|---|---|---|---|---|---|---|---|---|
|Low-skill|58.5|53.4|91|48.6|58.2|120|79.1|57.1|72|
|Medium skill blue-collar|27.1|25.6|94|34.5|25.2|73|71.3|59.5|84|
|Medium skill white-collar|9.7|8.8|90|11.8|11.9|100|72.1|51.8|72|
|High-skill|3.8|11.4|300|4.5|3.2|71|72.6|83.7|115|

Source: Own calculations based on BiH trading partners’ reporting to UN COMTRADE database.

The contrast between two export baskets—EU‐27 versus ROW—has become huge five years into the current export expansion phase. While EU‐oriented exports in 2003 created more employment opportunities for low‐skill labor force, ROW‐oriented exports were characterized by the presence of labor with higher skills: the share low‐skill labor products in EU‐destined exports was 59 percent as compared to 49 percent in ROW‐oriented exports (Table 16). By 2007, the change has been rather dramatic: the share of low‐skill labor intensive products in ROW exports increased to 58 percent and that in EU‐oriented exports fell to 53 percent.

Products using high‐skill labor have been the main lever of BiH export growth in 2003‐07, although their share in total exports has remained relatively low. The share of high‐skill labor in total

> 11 Wages in the BiH industrial sector were in 2005 among the highest in the region: only those employed in Croatia earned more. The ratio of wages of skilled to unskilled was 1.4 in 2005 (MIGA 2006).
Primary deficit <!-- Start of picture text --> 4%<br>y = -0.1466x + 0.0091<br>© Mexico R? = 0.2248<br>2%<br>@.. El Salvador @ Honduras<br>0% “ittes-.@,, Guatemala - .<br>‘@ Dominican Republic @ Panama ® Colombia @ Uruguay<br>ace @ Nicaragua<br>-2% @ Peru re teers @ Brazil<br>© Costa Rica @ Ecuador ee,<br>-4%<br>@ Argentina<br>-6% -<br>@ Bolivia<br>-8%<br>0% 5% 10% 15% 20% 25% 30% 35%<br>Rigid spending<br><!-- End of picture text --> <!-- Start of picture text --> % of GDP<br>45%<br>40%<br>35%<br>30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>SSSSGSLSESLESESSSESLESTESLSLLLELTLELTTESVELELLEEgs o 38 8 Besegeg $88 ee 2 sR y EERPCESTESCESELTE ETRER<br>FEULTOCESBCs 2 LACEUIOSEECEELECSITTSE sf ssge"s- g g428 “3572TE RCESUTESSaEdeé gape ST LIISIge2e*TE EEE2<br>= s 3 &<br>6§==<br><!-- End of picture text -->
Black, S. and P. Strahan (2001): “The Division of Spoils: Rent-Sharing and Discrimination in a Regulated Industry”, _American Economic Review_ , 91(4), pp. 814-31.

Blau, F. and L. Kahn (2001): “Understanding International Differences in the Gender Pay Gap”, NBER Working Paper No. 8200.

Busse, M. and C. Speilmann (2003): “Gender Discrimination and the International Division of Labour"” HWWA Discussion Paper 245.

Cagatay, N. and G. Berik (1991): “Transition to export-led growth in Turkey: is there a feminization of employment”, _Capital and Class_ , vol.43.

Diamond, J. (1997): _Guns, Germs, and Steel_ . New York: W.W. Norton & Co.

Dollar, D. and Kraay, A. (2001): “Trade, Growth, and Poverty”. Working Paper, March, Development Research Group, The World Bank.

Edwards, S. (1998): “Openness, Productivity and Growth: What Do We Really Know?”, _Economic Journal_ , 108, pp.383-98.

Filmer, D. (1999): “The Structure of Social Disparities in Education: Gender and Wealth”, Policy Research Report on Gender and Development, Working Paper Series No.5.

Fontana, M. and A. Wood (2000): “Modeling the Effects of Trade on Women, at Work and at Home”, _World Development_ , 28(7), pp.1173-90.

Freeman, R. and R. Oostendorp (2000): “Wages Around the World: Pay Across Occupations and Countries”. NBER Working Paper Series, 8058.

Freeman, R. and Oostendorp (2002): “The Occupational Wages around the World Data file”, _International Labour Review_ , 140(4).

Gallup, J., J. Sachs, and A. Mellinger (1998): Geography and Economic Development”, NBER Working Paper 6849.

García-Cuéllar, R. (2000): “Is Trade Good for Women? Evidence for the Lower-Skilled in Pre- and Post-NAFTA Mexico”, chapter 1 in PhD Dissertation, Harvard University.

Grown, C., D. Elson and N. Cagatay (2000): “Introduction”, _World Development_ , 28(7), pp.1145-56.

Hall, R. and C. Jones (1999): “Why Do Some Countries Produce So Much More Output 32
# Appendix A4: Economic Reform News

To compile our database of economic reform articles, we use carefully selected keywords in the 28 languages available in the Dow Jones database. In particular, we search for all articles which contain the words ‘economy’ or ‘economic’ _and_ ‘reform’ or ‘reforms’ in the text of the article. Table A4.1 presents our translations of these terms in the 28 languages.

**Table A4.1 Keywords for economic reforms articles**

|Language|Economy|Economic|Reform|Reforms|
|---|---|---|---|---|
|Arabic|اقتصاد||الصلحات<br>||
||اقتصادیة||اصلحات<br>||
||اقتصادي||إصلحات<br>||
||||لصلحات<br>||
||||الصلح<br>||
||||إصلح<br>||
||||لصلح<br>||
||||اصلح||
|Malay|ekonomi|ekonomi|pembaharuan|pembaharuan|
|Indonesian|ekonomi|ekonomis|pembaruan|reformasi|
|Bulgarian|икономика|икономически|реформа|реформи|
|Catalan|economia|econòmic|reforma|reformes|
|Chinese - Simplified|经济|经济|改革|改革|
|Chinese - Traditional|經濟|經濟|改革|改革|
|Czech|hospodářství|hospodářský|reforma|reformy|
|Danish|økonomi|økonomisk|reform|reformer|
|Dutch|economie|economisch|hervorming|hervormingen|
|||economische|||
|English|economy|economic|reform|reforms|
|Finnish|talous|taloudellinen|uudistaa|uudistuksia|
|French|économie|économique|réforme|réformes|
|||économiques|||
|German|wirtschaft|wirtschaftlich|reform|reformen|
|||wirtschaftsreformen|reform|reformen|
|Hungarian|gazdaság|gazdasági|reform|reformok|
|Italian|economia|economico|riforma|riforme|
|||economica|||
|||economici|||
|||economiche|||
|Japanese|経済|経済的|改革|改革|
|Korean|경제|간결한|개정|개혁|
|Norwegian|økonomi|økonomisk|reform|reformer|
|Polish|ekonomia|ekonomiczny|reforma|reformy|

36
Central region, one can expect to observe a relatively high increase in irrigation water shadow prices in this region.

**Table 4.15.Change in factor prices**

||**Ba**|**se**||**% Change**||
|---|---|---|---|---|---|
||**Le**|**vel**||||
||**Region**<br> <br>**(T**|<br>**RY)**|**Sc. 3.a**|**Sc. 3.b**|**Sc. 3.c**|
|**Labor***||1.00|-6.21|-7.13|-5.90|
||**West**|0.64|20.20|15.04|17.19|
||**Central**|0.26|21.06|15.43|0.48|
|**Irrigated Land**|**East**|0.27|22.06|16.76|7.65|
||**Southeast**|0.53|13.53|8.67|-3.07|
||**LSCB**|0.70|19.79|14.12|10.74|
||**West**|0.27|-3.63|2.21|11.24|
||**Central**|0.11|0.28|5.69|-0.01|
|**Rainfed Land**|**East**|0.11|-1.04|3.95|5.89|
||**Southeast**|0.23|3.79|8.20|2.17|
||**LSCB**|0.14|-3.74|3.49|21.86|
||**West**|0.13|21.82|15.88|18.85|
||**Central**|0.11|27.14|21.65|0.48|
|**Water**|**East**|0.05|26.09|20.83|9.60|
||**Southeast**|0.09|3.34|-1.35|-13.82|
||**LSCB**|0.13|10.46|5.54|2.25|
||**Urban**|0.81|-7.31|-9.27|-7.88|

_Source: Results from TACOGEM-W. *Labor employment is taken as value added created by labor_ Employment of irrigated land increases for wheat, vegetables and fruits, and crops classified as other grains such as rice. This is due to the shift from rainfed land in these activities towards irrigated land as an alternative. Employment of irrigated land in sugar beet, cotton, potatoes and maize production fall at different rates. We mentioned sugar beet, cotton and potatoes are grown only on irrigated land. As production of all crops shifts from rainfed land to irrigated land due to the yield shock in rainfed activity, the price of irrigated land increases in all regions at rates ranging from 13 to 30 percent. With this increase in the price of irrigated land, a drop in employment in irrigated-land-only activities can be expected.

As can be inferred from the discussion above, production volume in all rainfed crop activities drop at various rates. For example, production of fruits in rainfed land drops by 39 percent, while production of oil seeds drop by 36 percent from the base as a result of the 30 percent drop in yield. Overall, considering the sum of both irrigated and rainfed land production 55