rafmacalaba/datause-encoder-data huggingface dataset:
available, usage of digital technologies is not always widespread. This may reflect the absence of necessary analog complements that range from regulations that enable firms to leverage digital technologies to compete and innovate, improved skills of firms and workers that place people in a position to take full advantage of digital opportunities, and accountable institutions, so that governments respond to citizens’ needs and demands (World Bank 2016). Digital technologies can augment and strengthen these complements, as seen in the case of financial inclusion and the efficiency of public spending.

There are many directions for future research to add to the evidence base. For one, there is a clear paucity of literature on developing economies, even regarding the first wave of digitalization. On average productivity growth, there is little evidence thus far on whether the use of ICT has accelerated income convergence or divergence across countries. On distributional issues, the set of studies analyzing the impact of computers, mobile phones, the internet, digital platforms, apps, and other software on jobs in developing economies is particularly thin. This may be attributable, at least in part, to the lack of data that track businesses over time. On the impacts of the second wave of digitalization powered by AI, while the literature even on advanced economies in nascent, opportunities for leapfrogging in developing economies could be a fruitful area for conducting case studies. In general, there is also need for greater discussion on whether the use of digital technologies is always consistent with factor endowments and factor prices, especially in developing economies where skills may be in short supply.

# **References**

Abebe, Girum, A. Stefano Caria, and Esteban Ortiz-Ospina. 2021. “The Selection of Talent: Experimental and Structural Evidence from Ethiopia.” _American Economic Review_ 111 (6): 1757– 806.

Acemoglu, Daron. 1998. “Why Do New Technologies Complement Skills? Directed Technical Change and Wage Inequality.” _Quarterly Journal of Economics_ 113(4): 1055-1089.

Acemoglu, Daron. 2002. “Technical Change, Inequality, and the Labor Market.” _Journal of Economic Literature_ 40 (1): 7–72.

Acemoglu, Daron, David Autor, Jonathon Hazell, and Pascual Restrepo. 2022. “Artificial Intelligence and Jobs: Evidence from Online Vacancies.” _Journal of Labor Economics_ 40 (S1): S293–340.

Acemoglu, Daron, and Pascual Restrepo. 2018. “The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment.” _American Economic Review_ 108 (6): 1488–1542.

Acemoglu, Daron, and Pascual Restrepo. 2019. “Automation and New Tasks: How Technology Displaces and Reinstates Labor.” _Journal of Economic Perspectives_ 33 (2): 3–30.

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# **1. Introduction**

The _Human Development Index_ (HDI) aims to provide a broader characterization of “development” than is possible by focusing on national income alone. For this purpose, the HDI aggregates country-level attainments in life expectancy and education, as well as income. The index has been published since 1990 in the UNDP’s _Human Development Reports_ (HDRs).

Each year’s scores and rankings by the HDI are keenly watched in both rich and poor countries. The countries that do well on the index are congratulated by each new HDR.<sup>2</sup> Politicians and the media often take note. The HDI aims not only to monitor human development, but to encourage countries to take actions that promote it. The latest (2010) HDR claims that the HDI and its various descendants “...yield many novel results—and insights—that can guide development policy debates and designs” (UNDP, 2010, p.8). UNDP (undated) documents numerous examples of the policy influence of the HDRs, including the HDI.

As in any composite index, users should know what weights are attached to the HDI’s dimensions, to properly judge if it has got the balance right.<sup>3</sup> The weight in any given dimension can be defined as the index’s first partial derivative (“slope”) with respect to that dimension. Since the units of the index are arbitrary (the HDI is normalized to lie in the 0, 1 interval) what really matters is the relative weights of its component dimensions. In other words, we need to know the assumed tradeoffs, as given by the HDI’s marginal rate of substitution (MRS), i.e., how much of one desired component of the HDI must be given up for an extra unit of another component, keeping the overall index constant. If a policy or economic change entails that one of the positively-valued dimensions increases at the expense of another dimension, then it is the MRS that tells us whether human development is deemed to have risen or fallen.

> 2 In answer to the question: “Which countries have been most successful in furthering the human development of their people?” (UNDP, 2010, p.41) the HDR looks at HDI indices over 1970-2010 for 135 countries and identifies the “top 10 movers,” defined by the rate of increase in their HDI relative to its 1970 value; the countries are Oman, China, Nepal, Indonesia, Saudi Arabia, Lao PDR, Tunisia, South Korea, Algeria and Morocco. At the other extreme, the report identifies three countries for which the 2008 HDI is lower than its 1970 value: Democratic Republic of the Congo, Zambia and Zimbabwe.

> 3 On the importance of knowing the weights built into a composite index of development see Ravallion (2010a), which also discusses a number of other issues not touched on here, including the robustness of country rankings and whether aggregation of the core dimensions is useful for policy.

2
- 20 -

_Table 1 H.w_ much _poverty_ is _there in_ the developing countr,es' The situation in 1985

||||||||So|cial indicato|rs|
|---|---|---|---|---|---|---|---|---|---|
||E.:|remelypoor||Poor (Inclu|ding extremely|poor)|Under S||Net prlmary|
|Region|Number<br>(millions)|_Headcou.t_<br>index<br>(percent)|Poverty<br>S-P|Number<br>(millions)|Headcount<br>index<br>(percent)|Poverty<br>gap|mortality<br>(per<br>thousand)|Life<br>expectancy<br>(years)|enrollment<br>rate<br>(percent)|
|Sub-Saharan<br>Africa<br>|120|30||180|47|11|196|50|56|
|East Asia<br>|120|9|04|280|20|1|96|67|96|
|China|80|8|1|210|20|3|58|69|93|
|South Asia<br>|300|29|3|520|5S|iO|172|56|74|
|India|250|33|4|420|55|12|299|57|81|
|Eastern Europe<br>|3|4|_0.2_|6|8|0 S|23|71|90|
|Middle East and North<br>Africa<br>|40|21|1|60|31|2|148|61|75|
|Latin America and the||||||||||
|Caribbean<br>|50|12|1|70|19|1|75|66|92|
|All developing<br>countries|631|18|1|1.116|33|3|121|62|83|

Note The poverty line in 1985 PPP dollars is $275 capita per year for the extremely poor and S370 per capita per year for the poor The headcount index is defined as the percentage of the population below the poverty **line.** The 95 percent confidence intervals around the point estimates for the head.ount indlces are Sub-Saharan Africa. 19. 76; East Asia. 21. 22; South AsLa. 50. 53; Eastern Europe. 7, 10: Middle East and North Africa. 13. 52; Latin America and the Caribbean, 14. 30; and all developing countries. 28. 39.

The poverty gap is defined as the aggregate income shortfall of the poor as a percentage of aggregate consumption Under 5 mortality rates **are** for 1980-85. except for China and South Asia. where the period is 1975-80.

So,irce Reproduced from World Development Report 1990
and dropping CVs directly to firms, which likely require preparing an application.

**Table 5:** Effects on Frequency of Search Channel Use

||(1)<br>Empl Agency|(2)<br>Dropped CV|(3)<br>Placed Ad|(4)<br>Answered Ad|(5)<br>Searched Online|(6)<br>Fam/Friends|
|---|---|---|---|---|---|---|
|WS Basic|_−_0_._016|_−_0_._140|0_._060|_−_0_._068|0_._058|_−_0_._048|
||(0_._139)|(0_._131)|(0_._144)|(0_._122)|(0_._130)|(0_._102)|
|WS Plus|0_._362<sup>_∗∗∗_</sup>|0_._253<sup>_∗∗_</sup>|0_._153|0_._301<sup>_∗∗∗_</sup>|0_._410<sup>_∗∗∗_</sup>|_−_0_._021|
||(0_._123)|(0_._115)|(0_._119)|(0_._107)|(0_._100)|(0_._081)|
|Observations|1937|1936|1934|1927|1931|1926|
|_R_<sup>2</sup>|0_._093|0_._069|0_._028|0_._088|0_._364|0_._045|
|Control Mean|2_._125|2_._826|1_._532|3_._253|4_._203|4_._839|
|P-value|0_._010|0_._004|0_._537|0_._003|0_._006|0_._786|

Notes: * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01. Regressions use panel data over two follow-up periods. Outcome variables are a categorical frequency scale from 0 (never) to 6 (daily). Standard errors (reported in parentheses) are clustered at the individual level. P-value compares WS Plus to WS Basic. All regressions control for location-fixed effects and baseline value of the outcome variable. Outcome variables are winsorized at the 5% level.

However, to the extent that there are diminishing returns to the use of search channels, a reallocation of emphasis to less utilized (and higher effort) channels is expected to improve job search effectiveness. Consistently, we find that contacting family and friends and placing advertisements are unaffected by the plan-making intervention. Instead, job seekers in the Workshop Plus group diversify their search strategy by intensifying the use of channels that possibly enjoy steeper returns. This, in turn, can help explain the employment effects observed in Section 3.2.<sup>13</sup> In summary, plan-making helps job-seekers optimize their search strategy. This not only increases search efficiency (as indicated by the greater number of applications submitted for a given time effort) but it may also improve search effectiveness, in terms of greater employment returns to each application submitted.

> 13Indeed, we find that the increase in hours spent searching is positively correlated with job responses and offers only for channels such as answering advertisements, visiting employment centers, or dropping off CVs, which likely require preparing an application (results not reported). In addition, job seekers in the plan-making group are significantly more likely to report that they found employment by answering advertisements.

16
Total -54,241,036 -635,646,400 -20,902,610 -106,724,543 -817,514,560 _Source:_ WB staff calculations using SUBSIM.

# **5.4. Compensating Interventions to Energy Subsidy Reforms**

A final step includes assessing the poverty and distributional effects of disbursing the total savings from the energy subsidy reform on poverty-reducing purposes. Once again, there is no clear guidance from the government of Tunisia on how these compensation programs will be implemented. For that reason, this analysis considers three hypothetical scenarios. **Simulation 1** uses total savings to provide a universal transfer to each Tunisian. This scenario is called “universal transfer” because it includes a transfer to every Tunisian without discrimination. **Scenario 2** , or “current targeting,” uses the current social assistance program, that is, the subsidized health cards, as the targeting mechanism. This name does not intend to judge the current capacity of subsidized cards to reach the poorest. Instead, it simply indicates that no additional targeting efforts take place and that authorities use existing structures to channel all the savings accruing from energy subsidy reforms. Finally, in **Simulation 3** , “perfect targeting,” all the savings are distributed exactly to those who are poor after the reform. This is an unrealistic and idealistic scenario that describes a situation in which all the poor after the reform are perfectly identified and compensated on a per capita basis. It is idealistic because it assumes perfect and costless targeting, that is, no additional resources are needed to identify the poor and distribute cash benefits to them. Although these three scenarios vary in terms of implementation feasibility, they are useful in this context where no detailed plans are announced. These results provide information on the boundaries of the distributional effects of the reform, from no compensation following the reform to the complete use of fiscal savings from energy subsidy reform to reduce poverty under perfect targeting. The true impact of the reform and of feasible compensation policies will lie somewhere in between. Table 13 summarizes the simulations’ results.

Table 13: Simulated Poverty and Inequality Impacts of Compensatory Mechanisms after Energy Subsidy Reform

||Fiscal cost of<br>compensation|Average benefit<br>transferred<br>(rounded up)|Number of<br>beneficiaries|Poverty (%)|Inequality (Gini<br>0–100 index)|
|---|---|---|---|---|---|
|Prereform|0|0|0|15.27|36.57|
|Baseline:<br>subsidy reform<br>with no<br>compensation|0|0|0|17.84|37.18|
|Simulation 1:<br>universal<br>transfer after<br>subsidyreform|TND 817.51<br>millimes|TND 75|10.9 million|14.87|36.29|
|Huye|551|118,432,522|47,927,632|166,360,154|507|
|---|---|---|---|---|---|
|Kamonyi|369|79,304,916|52,420,098|131,725,014|387|
|Karongi|230|49,324,244|53,739,182|103,063,425|311|
|Kayonza|145|31,236,863|53,929,932|85,166,795|247|
|Kicukiro|2,471|530,886,041|6,715,920|537,601,961|1,688|
|Kirehe|0|0|57,431,259|57,431,259|169|
|Muhanga|380|81,740,210|46,693,578|128,433,788|402|
|Musanze|448|96,329,234|46,285,298|142,614,533|387|
|Ngoma|130|27,941,618|55,937,075|83,878,693|249|
|Ngororero|31|6,748,616|55,898,125|62,646,741|188|
|Nyabihu|0|0|44,178,173|44,178,173|150|
|Nyagatare|230|49,521,538|72,748,696|122,270,235|262|
|Nyamagabe|246|52,749,857|55,042,094|107,791,951|316|
|Nyamasheke|0|0|65,322,461|65,322,461|171|
|Nyanza|251|53,943,836|51,869,929|105,813,766|327|
|Nyarugenge|1,671|359,038,297|12,265,948|371,304,245|1,305|
|Nyaruguru|39|8,307,465|50,150,217|58,457,682|199|
|Rubavu|814|174,951,644|44,245,292|219,196,936|543|
|Ruhango|61|13,198,341|51,091,625|64,289,966|201|
|Rulindo|20|4,224,944|48,522,489|52,747,433|183|
|Rusizi|590|126,816,350|58,703,221|185,519,570|463|
|Rutsiro|55|11,875,809|55,229,019|67,104,829|207|
|Rwamagana|363|77,958,370|49,818,099|127,776,468|408|
|**Rwanda**|**14,214**|3,053,757,518|1,526,403,540|4,580,161,058|436|

Source: WDI (2014) and NGDC (2014). Authors’ calculations.

As evident from the title of this paper, we also tried to estimate poverty levels and changes based on nightlights data. The estimated associations were however not robust, and more time and effort would need to be invested in examining this relationship in a more detailed fashion. This will be pursued in further work.

# **6. Conclusions**

In this paper, we have adapted the methodology of Henderson, Storeygard and Weill (2012) to examine the association between night lights and GDP for a sample of 46 countries in Sub Saharan Africa. Overall, we find a fairly strong correlation between the intensity of night lights as measured by satellites and country-level GDP, with even small and temporary economic downturns being reflected in the intensity of lighting. As expected, we find that the association between lights and economic activity is stronger in countries where agriculture is a smaller part of GDP (where more economic activity is lit). We apply the regression results to estimate subnational GDP levels and growth in Kenya and Rwanda. To our knowledge, this is the first time such an exercise to estimate county-level GDP levels and growth for Kenya and Rwanda has been attempted.

23
**Table 3. Serbia: Changes in poverty rates due to government intervention**

|||**Market**<br>**Income**<br>**+**<br>**Pensions**|**Disposable**<br>**Income**|**Consumable**<br>**Income**|
|---|---|---|---|---|
|Poverty|||||
|Headcount|$1.9 PPP|5.1%|2.8%|4.9%|
||$3.2 PPP|8.0%|5.9%|10.3%|
||$5.5 PPP|15.3%|14.4%|22.4%|
||60% of median equivalized disposable||||
||income|22.9%|22.8%|34.4%|
|Poverty Gap|$1.9 PPP|2.7%|1.4%|2.4%|
||$3.2 PPP|4.2%|2.6%|4.5%|
||$5.5 PPP|7.3%|5.7%|9.4%|
||60% of median equivalized disposable||||
||income|10.2%|8.8%|14.0%|
|Poverty severity|$1.9 PPP|1.9%|1.0%|1.7%|
||$3.2 PPP|3.0%|1.7%|2.8%|
||$5.5 PPP|4.9%|3.3%|5.6%|
||60% of median equivalized disposable||||
||income|6.5%|4.9%|8.1%|

Source: Own estimations using HBS and SILC data <mark>The finding that fiscal policy is estimated to increase poverty in Serbia is similar to that in most available analyses in Europe and Central Asia. Only in some countries in the CEQ sample, such as Russia and several countries in Latin America, poverty is reduced or remains mostly unchanged after accounting for direct and indirect taxes and transfers.</mark><sup>13</sup> <mark>Poverty, measured as per-capita income below $5.5/day in 2011 PPP, is estimated to increase by about 7 percentage points in Serbia, from 15.3 percent in market income (including pensions as deferred income) to 22.4 percent in consumable income. In Armenia, poverty increases by 6.6 percentage points while in Romania 1.5. Other studies using slightly different international poverty lines also indicate poverty increases after fiscal interventions in Albania, Montenegro, Croatia, and Poland.</mark>

# **V. Progressivity and marginal effects of individual taxes and transfers**

This section discusses how each component of the tax and benefit system contributes to the system’s overall impacts on poverty and income inequality. The influence of individual fiscal interventions may be different from the influence of the overall fiscal system. Both the size and progressivity of a tax or expenditure item determine its marginal effects on inequality and poverty measures. For example, a highly progressive but small government transfer may have a small marginal effect on inequality and, therefore, small redistributive impacts. We first show how taxes and transfers affect household income across the distribution to illustrate their distributional impact. We then analyze the marginal effect of each individual tax and benefit component on the Gini coefficient and the poverty headcount.

> 13 Based on the data from the CEQ data center: https://commitmentoequity.org/datacenter/.

17
36 Table 3: Contributions to Trade Reduction Index for covered products by different policy instruments,<sup>a</sup> by region,<sup>b</sup> 1980-84 and 2000–04 (percent)

# (a) Production side of economy

||||1980-|84|||||200|0-04|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Africa**|**Asia**|**LAC**|**DCs**|**HIC**|**World**|**Africa**|**Asia**|**LAC**|**DCs**|**HIC**|**World**|
|**All measures**|**24**|**37**|**21**|**33**|**35**|**33**|**16**|**10**|**7**|**10**|**27**|**17**|
|**Border measures**|**22**|**33**|**20**|**30**|**35**|**32**|**13**|**9**|**7**|**9**|**28**|**17**|
|Export tax|24|27|21|26|0|14|15|2|6|3|0|2|
|Export subsidy|-4|-1|-1|-1|-3|-2|-3|-1|-3|-2|-1|-2|
|Import tax|10|9|6|8|38|22|7|10|5|9|29|17|
|Import subsidy|-8|-2|-5|-3|-1|-2|-6|-1|-2|-1|0|-1|
|**Domestic taxes & subsidies**|**2**|**4**|**1**|**3**|**0**|**1**|**3**|**1**|**0**|**1**|**-1**|**0**|
|Production tax on output|2|3|0|2|0|1|3|1|0|1|0|0|
|Production subsidy on output|0|0|0|0|0|0|0|0|0|0|-1|0|
|Farm inputnet subsidies|0|0|1|1|0|0|0|0|0|0|0|0|
|(b) Consumption side of ec|onomy||||||||||||
||**Africa**|**Asia**|**LAC**|**DCs**|**HIC**|**World**|**Africa**|**Asia**|**LAC**|**DCs**|**HIC**|**World**|
|**All measures**|**12**|**35**|**16**|**29**|**41**|**34**|**12**|**11**|**11**|**11**|**30**|**19**|
|**Border measures**|**12**|**34**|**16**|**29**|**41**|**34**|**11**|**11**|**10**|**11**|**31**|**20**|
|Export tax|14|26|15|23|0|12|11|1|4|3|0|2|
|Export subsidy|-3|-1|-1|-1|-2|-1|-2|-1|-2|-1|-1|-2|
|Import tax|13|11|8|11|43|25|9|12|11|11|33|21|
|Import subsidy|-12|-3|-7|-4|-1|-3|-6|-1|-2|-1|0|-1|
|**Domestic taxes & subsidies**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**1**|**0**|**-1**|**-1**|
|Consumption tax|0|0|0|0|0|0|0|0|2|0|0|0|
|Consumptionsubsidy|0|0|0|0|0|0|0|0|-2|0|-1|-1|

Source: Authors’ compilation based on data in Anderson and Valenzuela (2008).
_Note:_ In panel A, active is status of mine in birth year; in panel B, active is active status of mine in survey year. Robust standard errors clustered at the DHS cluster level in parentheses. All regressions control for year and district fixed effects, urban dummy, age, and years of education. *** p<0.01, **p<0.05, *p<0.1. OLS = ordinary least squares.

An active mine is associated with a decrease in cough among children under age five (panel B of table 5), and children are also (insignificantly) more likely to have a health card. An active mine is associated with insignificant decreases in the anthropometrics measures (World Health Organization measures in standard deviations), such as height-for-age and weight-for-age. However, the standard errors for these coefficients are very large relative to the estimated coefficients, which is why the effects are imprecisely estimated.

# **5.3 Spatial heterogeneity of results**

Thus far we have used a cutoff distance of 20 km. Panel A of figure 4 shows that the largest treatment effect for services for women is found within 10 km of a mine, with an 8 percentage point increase in the probability that a woman works in the service sector. This is equivalent to a 22 percent increase in service sector participation. However, this effect is only statistically significant at the 10 percent level, possibly due to the small sample size within that distance. This is in contrast to the dummy for 0-20 km which is insignificant, pointing highly localized effects on service sector employment for women. Using distance bin of 30 km, we estimate zero treatment effect on the probability of service sector employment. Panel B of figure 4 shows the results for cash-earning opportunities, and similarly, we estimate positive treatment effects within 20 km.

In panel C of Figure 4, infant mortality is re-estimated using different distance spans. The decrease in child mortality decreases almost linearly with the increase in distance bins, an indication that the effects are only found close to a mine. The largest drop, and the only significantly negative drop, is found for the distance bin 0–20 km.

**Figure 4 Varying the cutoff distance: Service sector employment, cash earnings, and infant mortality**

**Panel A Service and sales Panel B Cash earnings                        Panel C Infant mortality**

20