rafmacalaba/datause-encoder-data huggingface dataset:
- Clarke, P.M., U. Gerdtham, and L.B. Connelly (2003) ‘A note on the decomposition of the health concentration index.’ _Health Economics_ 12, 511–516

- Curtis, S.L., and M. Hossain (1998) ‘West Africa spatial analysis prototype explanatory analysis: The effects of aridity zone on child nutritional status.’ Technical Report, Demographic and Health Survey, Macro International

- de Onis, M., E.A. Frongillo, and M. Bl ̈ossner (2000) ‘Is malnutrition declining? an analysis of changes in levels of child malnutrition since 1980.’ _Bulletin of the World Health Organization_ 78, 1222–1233

- Deaton, A. (2003) ‘Health, inequality, and economic development.’ _Journal of Economic Literature_ XLI, 113–156

- Demombynes, G., J. Elbers, C.and Lanjouw, J. Lanjouw, J. Mistiaen, and B. Ozler<sup> ̈</sup> (2002) ‘Producing an improved geographic profile of poverty: Methodology and evidence from three developing countries.’ WIDER Discussion Paper 2002/39, United Nations University

- Dibley, M.J., J.B. Goldsby, N.W. Staehling, and F.L. Trowbridge (1987a) ‘Development of normalized curves for the international growth reference: historical and technical considerations.’ _American Journal of Clinical Nutrition_ 46(5), 736–48

- Dibley, M.J., N.W. Staehling, P. Nieburg, and F.L. Trowbridge (1987b) ‘Interpretation of Z- score anthropometric indicators derived from the international growth reference.’ _American Journal of Clinical Nutrition_ 46(5), 749–762

- Elbers, C., J.O. Lanjouw, and P. Lanjouw (2000) ‘Welfare in villages and towns: Micro-level estimation of poverty and inequality.’ Timbergen Institute Discussion Paper TI 2000-029/2, Timbergen Institute

- (2002) ‘Micro-level estimation of welfare.’ Policy Research Department Working Paper 2911, The World Bank

- (2003a) ‘Micro-level estimation of poverty and inequality.’ _Econometrica_ 71(1), 355–364

- (2004a) ‘Imputed welfare estimates in regression analysis.’ _Journal of Economic Geography_

- Elbers, C., P. Lanjouw, J. Mistiaen, B. Ozler,<sup> ̈</sup> and K. Simler (2003b) ‘Are neighbours equal? estimating local inequality in three developing countries.’ FCND Discussion Paper No. 147, Food Consumption and Nutrition Division, International Food Policy Research Institute

- Elbers, C., T. Fujii, P. Lanjouw, B. Ozler,<sup> ̈</sup> and W. Yin (2004b) ‘Poverty alleviation through geographic targeting.’ Policy Research Working Paper 3419, The World Bank

- Foster, J.E., J. Greer, and E. Thorbecke (1984) ‘A class of decomposable poverty indices.’ _Econometrica_ 52, 761–766

39
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|**From a big city**|4972|0.0135|0.1153|0|1|2322|0.0159|0.1252|0|1|2650|0.0113|0.1058|0|1|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**From another country**|4972|0.0080|0.0893|0|1|2322|0.0069|0.0827|0|1|2650|0.0091|0.0948|0|1|
|**Immigrant of which**|4972|0.1267|0.3327|0|1|2322|0.1387|0.3457|0|1|2650|0.1162|0.3206|0|1|
|**Found her / his job through educational**<br>**institutions**|4972|0.0129|0.1127|0|1|2322|0.0121|0.1092|0|1|2650|0.0136|0.1158|0|1|
|**Through job fairs**|4972|0.0078|0.0882|0|1|2322|0.0052|0.0717|0|1|2650|0.0102|0.1004|0|1|
|**Through Public Employment Offices**|4972|0.0145|0.1195|0|1|2322|0.0039|0.0621|0|1|2650|0.0238|0.1524|0|1|
|**Through labor contractors**|4972|0.0050|0.0707|0|1|2322|0.0026|0.0508|0|1|2650|0.0072|0.0844|0|1|
|**Through direct call by the employer**|4972|0.0658|0.2479|0|1|2322|0.1042|0.3056|0|1|2650|0.0321|0.1762|0|1|
|**Through public job adverts**|4972|0.0084|0.0915|0|1|2322|0.0103|0.1012|0|1|2650|0.0068|0.0822|0|1|
|**Through personal network of friends and**<br>**relatives**|4972|0.2019|0.4015|0|1|2322|0.1206|0.3257|0|1|2650|0.2732|0.4457|0|1|
|**Contract of unlimited duration**|4972|0.1400|0.3470|0|1|2322|0.0986|0.2982|0|1|2650|0.1762|0.3811|0|1|
|**Contract of limited duration**|4972|0.0209|0.1431|0|1|2322|0.0129|0.1130|0|1|2650|0.0279|0.1648|0|1|
|**Seasonal contract**|4972|0.0344|0.1823|0|1|2322|0.0181|0.1333|0|1|2650|0.0487|0.2152|0|1|
|**Oral contract**|4972|0.0020|0.0448|0|1|2322|0|0|0|0|2650|0.0038|0.0613|0|1|
|**Paid no tax on her / his job**|4972|0.0736|0.2612|0|1|2322|0.0353|0.1846|0|1|2650|0.1072|0.3094|0|1|
|**Does not know whether paid tax**|4972|0.0324|0.1770|0|1|2322|0.0138|0.1166|0|1|2650|0.0487|0.2152|0|1|
|**Moonlighting**|4972|0.0177|0.1319|0|1|2322|0.0056|0.0746|0|1|2650|0.0283|0.1659|0|1|
|**Dissatisfied with her / his job**|4972|0.0815|0.2736|0|1|2322|0.0422|0.2011|0|1|2650|0.1158|0.3201|0|1|
|**Training of which apprenticeship**|4972|0.1748|0.3798|0|1|2322|0.1598|0.3665|0|1|2650|0.1879|0.3907|0|1|
|**Mentoring**|4972|0.0050|0.0707|0|1|2322|0.0039|0.0621|0|1|2650|0.0060|0.0775|0|1|
|**Training in new technologies**|4972|0.0099|0.0988|0|1|2322|0.0034|0.0586|0|1|2650|0.0155|0.1234|0|1|
|**Other type of training**|4972|0.8103|0.3921|0|1|2322|0.8329|0.3731|0|1|2650|0.7906|0.4070|0|1|
|**Union membership**|4972|0.0720|0.2585|0|1|2322|0.0556|0.2291|0|1|2650|0.0864|0.2810|0|1|
|**Firm’s size: 5 workers or less**|4972|0.1076|0.3099|0|1|2322|0.0551|0.2283|0|1|2650|0.1536|0.3606|0|1|
|**From 5 to 9**|4972|0.0346|0.1828|0|1|2322|0.0194|0.1379|0|1|2650|0.0479|0.2136|0|1|
|**From 10 to 19 size_10t19**|4972|0.0579|0.2336|0|1|2322|0.0969|0.2959|0|1|2650|0.0238|0.1524|0|1|
|**More than 20**|4972|0.0432|0.2034|0|1|2322|0.0314|0.1745|0|1|2650|0.0536|0.2252|0|1|
|**Giving a contribution to society**|4972|0.1273|0.3334|0|1|2322|0.1197|0.3247|0|1|2650|0.1340|0.3407|0|1|
|**Earning a lot of money**|4972|0.1852|0.3885|0|1|2322|0.0633|0.2436|0|1|2650|0.2921|0.4548|0|1|
|**Having a happy family life**|4972|0.3508|0.4773|0|1|2322|0.5211|0.4997|0|1|2650|0.2015|0.4012|0|1|

46
creating the best possible mask for our period of analysis. And for 2015, the two approaches result in very similar light data. Clusters are identified by removing outlier observations, averaging cells over time, and clustering areas based on their nighttime light intensity. In practice, this approach amounts to setting to zero cells that are distant from homogenous bright cores. We present results based on data cleaned with the second method, but our results are robust to both cleaning methods. For this paper, cleaned monthly data are aggregated to the district and city levels and standardized by area. Nighttime light is measured in Nanowatts/cm2 /steradian.

## **3.3 Other variables**

Data on quarterly economic activity measured as gross value added (GVA) is from the Central Statistics Office. Temperature data is collected from the Average Daily Temperature Archive from the University of Dayton (2020) and based on recordings of the National Climatic Data Center. We generate an India aggregate by weighting the daily temperatures recorded in Chennai, Delhi, Kolkata, and Mumbai by population.<sup>11</sup> Data on Indian holidays is from different online sources. The information about registered COVID-19 infections at the state and district level is from Covindia (2020), the data on previous short-run inmigration and outmigration at the state and district level from the Development Data Lab (2020), and the share of employment in manufacturing and services at the state and district level is from the South Asia Spatial Database (Li, Rama, Galdo, and Pinto 2015).

# **4 Electricity, nighttime lights, and economic activity**

Intuitively, electricity consumption and economic activity are closely related since most economic activity needs electricity. Plenty of studies analyze the relationship of the two over longer periods, often with a focus on the direction of causality. Chen, Kuo, and Chen (2007), for example, study 10 newly industrializing and developing Asian countries and find a bi-directional long-run causality between real GDP and electricity consumption and a uni-directional short-run causality running from economic growth to electricity consumption. Ferguson, Wilkinson, and Hill (2000) find that correlations between electricity consumption and GDP are close to one. In Appendix A, we use a sample of 123 countries to update these correlations between economic growth

> 11The weighting is done at the regional level (according to the POSOCO classification) where Chennai represents the South, Delhi the North, Kolkata the East and North-East, and Mumbai the West.

8
The strongest overall export growth has been in machinery (SITC 7), and within this broad category it is telecoms, electrical machinery and o ffi ce machines that have experienced the highest growth and make up the largest shares within machinery. The question arises whether China is producing most of the value-added of these capital intensive goods or is it just assembling duty-free imported inputs for export? This practice is known as processing trade and does account for an increasingly large share of China’s exports, from 47 percent in 1992 to 55 percent in 2005. According to Dean, Fung and Wang (2007), imported inputs account for between 52 to 76 percent of the value of processing exports. Figure 3 graphs total exports of 2-digit machinery categories as a share of total manufacturing exports, in descending order for 2005, and the lighter bars show the portion that is classi fi ed as processing trade by China Customs. This fi gure reveals that most of the high export growth in machinery is indeed processing trade, thus only a small share of this growth is likely to be due to high value added production in machinery in China.

# 4. Skill Content of Export Growth

China’s export bundle is very di ff erent now from what it was in the early 1990s. Rodrik (2006) and Schott (2006) highlight the increasing sophistication of China’s exports, as demonstrated by an export pattern that more closely resembles high income countries than would be expected given its income level. To see whether this increased sophistication has been associated with an increase in the overall skill content of its exports, we rank industries from low to high skill intensity on the horizontal axis of Figure 4, and plot the cumulative export share on the vertical axis. Because industry skill level data for China were unavailable we based the skill intensity ranking on information from Indonesia, another emerging market that is likely to have similar technologies.<sup>2</sup> The skill intensity is measured as the ratio of non-production workers to total employment from the Indonesian manufacturing census at

> 2 Zhu and Tre fl er (2005) measure changes in the skill content of exports for all countries using U.S. industry level skill data to rank the skill intensity of industries, assuming no factor intensity reversals. Our results also hold using U.S. skill data.

5
Last but perhaps most importantly, governments can improve access by increasing competition in the financial sector. As financial institutions find their traditional business coming under competition, they seek out new lines of profitable opportunities, including lending to the SMEs and the poor. Given the right incentives, private sector can develop and make use of new technologies – like credit scoring – to reach the underserved segments. Foreign banks’ role in improving the competition environment and improving access is important. There is accumulating evidence that foreign banks can enhance access (Clarke et al, 2001 and 2003).<sup>22</sup> Indeed, multinational banks have been leading the way in expanding access all around the world.<sup>23</sup>

# **IV.  Challenges for Low-Income Countries**

Should all countries follow these recommendations? While the general messages will not be dissimilar, the directions in which financial sector needs improvement in different countries will be based on their initial conditions (World Bank, 2001). These reforms are the most challenging for low-income countries, where the legacy of financial repression and state ownership has generally hampered the development of a vigorous private financial system, where the underlying legal and information infrastructure is weak, and achieving minimum efficient scale will be difficult. Supported by stable and sustainable macro policies, a priority for the state would be to divest itself of bank holdings and attract reputable international banks. If democracy is weak and ethnic conflict is high, a significant level of uncertainty will prevail, which will likely deter

> 22 Also see the country examples discussed in Claessens (2005).

> 23 Studies have found that while foreign banks with small local presence do not appear to lend much to small businesses, large foreign banks in many cases surpass large domestic banks.  See for example, Sanchez et al. 2006.

33
39

|1980|198.1|22.4|47.7|20.2|9.7|
|---|---|---|---|---|---|
|1985|404.7|29.7|31.9|30.5|7.9|
|1989|727.4|39.9|27.3|27.2|_5.5_|

# **II. Private plans**

Noninsured:

|1950|$7.1|_15.5%_|39.4%|32.4%||
|---|---|---|---|---|---|
|12.7%||||||
|1955|18.3|33.3|43.2|16.4|7.1|
|1960|38.1|43.3|41.2|7.1|8.4|
|1965|74.4|54.8|_30.5_|4.0|10.6|
|1970|112.0|59.9|26.2|2.7|11.2|
|1975|225.0|48.0|18.6|8.0|25.4|
|1980|469.6|47.6|16.5|10.8|25.1|
|1985|848.4|46.4|14.3|12.3|27.0|
|1989|1,163.5|57.3|12.4|12.3|18.0|
|Insured:||||||
|1950|4.8|4.2|41.7|22.9|31.2|
|_1955_|10.1|4.0|43.6|9.9|42.6|
|1960|16.8|4.8|43.5|6.0|45.8|

Olivia S. Mitchell - Public Pension Governancc and Performance
estimation strategies and describe the data that are used in the analysis. Drawing on the results of the growth regressions, Nigeria’s growth rates are predicted assuming different scenarios in Section 6. Section 7 concludes.

# **2 Natural Resources, Institutional Quality, and Growth**

Starting with the observation that resource-abundant countries are often performing worse than resource-poor countries, Sachs and Warner (1995) study empirically the impact of natural resources on economic growth using worldwide cross country data. In their seminal contribution, they find evidence for a detrimental effect of natural resources on growth, and in subsequent studies they further confirm the robustness of this result<sup>2</sup> —a controversial finding that triggered an ongoing scholarly discussion about the _curse_ and the _blessing_ of natural resources.

In response, various analyses were dedicated to deepen the understanding of this result, and in particular, to identify the mechanisms through which natural resources result in low economic growth rates. One of the most common arguments is that high shares of natural resources can lead to overvaluations of the real exchange rates. A consequential contraction of the tradable sector can weaken economies’ development prospects, especially if this sector exerts economies of scale, by learning-by-doing, for example (Torvik, 2001; Atkinson and Hamilton, 2003). Sachs and Warner (1995, 1997, 2001) mainly attribute their empirical findings to these so-called _Dutch-disease_ effects.

Another strand of the literature argues that natural resources may lead to a ”crowding-out” of investment in human capital. Strong primary sectors lower the incentives to dedicate sufficient resources to other, more education-intensive sectors. Gylfason, Herbertsson, and Zoega (1999) highlight that school enrollment rates are lower in countries with a high share of the labor force engaged in the primary sector. Low human capital accumulation can translate into poor growth rates—directly via the channel of productivity, or through indirect effects on political stability, health, or democracy. Empirical evidence suggests, that a considerable part of the negative impact of natural resources on growth can be attributed to lower educational attainment in resourceabundant countries (Gylfason, 2001).

Rampant rent-seeking behavior is a further often-investigated transmission mechanism. The rents generated by natural resources cause an increased number of agents engaged in rent-seeking, as opposed to pursuing productive activities, and this _voracity effect_ destroys the rents generated by natural resources. Lane and Tornell (1996), for example, develop a model that shows that in countries with powerful groups and low institutional quality, growth rates reduce due to natural resource windfalls, as higher productivity increases the demand for transfers, and these redistributional effects may outweigh the growth-enhancing effects of resource endowments. Hodler (2006) provides theoretical as well as empirical evidence that increased rent-seeking behavior weakens

> 2See Sachs and Warner (1997) and Sachs and Warner (2001).

3
estimated by a production function using actual input quantities. Productivity estimates can be obtained from a regression of the type:

> where _Yi_ is firm’s output, K and L are capital and labor, β _k_ and β _l_ are capital and labor shares and<sup>ε</sup> _i_<sup>is the error. In this model, TFP, the estimated residual, is obtained</sup> ˆ ˆ

> as the difference between actual and predicted output, or ε _i_ = ln _Yi_ − ln _Yi_ .

The simplest model can be estimated by OLS. However, econometric issues arise because firm productivity can affect input choices. For example, firms that receive a productivity shock may alter their mix of inputs. This implies that the error and the regressors in (1) might be correlated and that coefficient estimates obtained with OLS might be biased. A number of solutions have been proposed in the literature to overcome this problem. These include using firm-level fixed effects, that would deal with time-invariant individual effects, and instrumental variable strategy for input choices. Following Olley and Pakes (1996), Levinsohn and Petrin (2000) argued that using information on intermediate input choices such as demand for electricity – which tracks productivity shocks quite closely and cannot be stored – one can effectively control for productivity shocks and thus obtain consistent and unbiased estimates of β k and β l (see discussion in Hallward-Driemeier et al., 2002).

We use several estimates of TFP to check for robustness and minimize possible biases. The simplest measure is obtained from a pooled OLS regression, in which all 3 main sectors (manufacturing, construction and services) are pooled together. This 8
food is with agriculture. In the base-year dataset the food industry accounts for 27 percent of total demand for agricultural output.<sup>25</sup> As the increase in food production increases with increased mobility, the positive impact on intermediate food demands for agricultural output comes to dominate the negative impact on agricultural exports from appreciation. Interestingly, this input-output relation between agriculture and food dominates the labor reallocation from agriculture to food that would have been expected given the close proximity between them (see Table 4 in Section 2.3).

In Table 9, all contracting activities show the same negative changes in physical and efficiency units since all of the departing workers were assigned to the activities from they depart (their proximities were equal to one, i.e., _a_ = _a_ ’). On the other hand, given that the proximities for movements from contracting to expanding activities here always are less than one, the positive employment changes are always smaller in efficiency units than in physical units.

**Table 6.** Real macro indicators (percent change from base).

|Item|baseyear*|zero|low|medium|high|perfect|
|---|---|---|---|---|---|---|
|Absorption|109.7|0.9|1.4|1.7|1.9|2.0|
|Private consumption|77.1|1.3|2.1|2.5|2.7|2.8|
|Exports|16.0|4.3|4.0|3.9|3.4|2.9|
|Imports|25.7|6.5|7.1|7.7|7.9|8.3|
|GDP at factor cost|91.4|0.0|0.5|0.8|1.0|1.0|
|Real exchange rate|1|‐4.5|‐8.1|‐7.5|‐7.0|‐6.9|

_Note: * In this column, the unit is % of GDP at market prices except for the real exchange rate, which is indexed to 1._ _Source_ : Authors' calculations.

> 25 Other major demands for agricultural output is for household consumption, exports, and for use in the “Trade, hotels and restaurants” sector.

21
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