rafmacalaba/datause-encoder-data huggingface dataset:
8

**Data Quality.** In Web Appx. Section A.1,<sup>9</sup> we verify that the I2D2 database generates global patterns that are broadly consistent with patterns observed when using other global databases such as the _World Development Indicators_ (WDI) database of the World Bank. More precisely, we find that our samples are globally representative in terms of per capita incomes, age structure, education, and self-employment.<sup>10</sup>

# **3. Methodology for the Estimated Returns**

We calculate _potential work experience_ – which we call “experience” in the rest of the analysis – as follows: (i) For individuals with at least 12 years of education, we assume children start school at age 6 and calculate experience as age - years of education - 6; (ii) For individuals with less than 12 years of education, we assume that experience before age 18 is inconsequential and calculate experience as age - 18. With these calculations, the mean (population-weighted) number of years of experience differs little between developing economies – 19 – and developed economies – 22. Indeed, while workers in developed economies study longer on average, they also tend to live longer.

**Estimated Returns.** For individual _i_ and sample _t_ , we use OLS to estimate the following model for _each_ country one by one:

where the dependent variable is the log of monthly wages ( _lnWit_ ). Experience is categorized into seven bins ( _expite_ ). The bins are [5-9 years] (which we call 5), [10-14] (10), [15-19] (15), [20-24] (20), [25-29] (25), [30-34] (30), and [35+] (35). The omitted bin is [0-4] (0). We control for the number of years of education ( _eduit_ ). We include sample fixed effects ( _θt_ ) to capture country-year-sample unobservables.<sup>11</sup>

**Profiles.** For illustration, we use the coefficients of the experience dummies to construct wage-experience profiles for the United States and Bangladesh (see Figure 1). In the U.S., a worker with 30 years of experience earns almost twice more than a worker with zero experience. The steepness in the profile is attributed to the first 25 years, particularly the first 15 or even 5. In Bangladesh, the profile is flatter. The same patterns

> 9 The Web Appendix is available on the website of Remi Jedwab: www.remijedwab.com.

> 10For example, we find that log mean earnings in I2D2 are strongly correlated across country-years with log per capita GDP in WDI. We show that wage variance is in line with what is expected. Finally, we verify that the resulting Mincerian R-squared are not too different across various groups of countries. 11Most countries having several samples, we use individual weights divided by the size of the sample.
# 1. Introduction

R&D (research and development) activity is generally acknowledged as a likely source of economic growth, given it is a critical input to technological progress, and given evidence from firm and industry level studies of healthy rates of private and social returns (Mansfield [1977], Jaffe [1986]). Yet except for Romer[1989] and Lichtenberg [1992], we know of virtually no other cross-country studies of economic growth that incorporate country-level data on R&D activity -- despite a recent resurgence in the growth literature, incorporating an increasing array of economic, social and political variables.' The paucity of studies reflects in part the relatively poor state of country-level R&D data that is sufficiently comparable for international comparisons. For this paper, we developed such daLa by undertaking country-by-contry adjustments to information made available by UNESCO on R&D expenditures and personnel at the country level.<sup>2</sup> On the basis of our adjusted data, we assess empirically the link between R&D activity and economic growth, and we analyze the determinants of R&D differentials across countries. We find that growth in R&D activity and economic growth are positively correlated, but only across OECD countries (conttrary to the findings in Romer [1989] and Lichtenberg [1992]). And even among OECD countries there is no evidence that R&D activity causes economic growth. Our cross country analysis does show a robust empirical link between R&D activities and the _level_ (not growth rate) of income, suggesting that R&D activities become prominent only after a country reaches a certain stage of development.

'See, for example, Easterly (1993), Levine and Renelt (1992), and Barro (1991).

2Our adjustments were made on the basis of details on definitions and exclusions that are included in the footnotes to the published UNESCO data.
Benjamin and Brandt 2004). At the same time, different regions might have implemented differential policy changes and infrastructure improvements during the sample. Or, labor mobility across regions might be limited, which could lead to variation in the changes in the returns to schooling across regions. These unobserved region-specific time-varying shocks could result in spurious correlation between rice prices and economic activities. These concerns however can be addressed by interacting each region indicator with a year indicator.<sup>1</sup> Third, Vietnamese communes differ in their accessibility. If more accessible communes are better integrated into international and national rice markets, these communes might undergo bigger rice price changes. Similarly, accessibility might be associated with a range of time-varying factors affecting labor supply. The VLSS provides information on whether regular transportation is provided to a commune and whether the road to commune is paved. Based on this information, two indicator variables for a communes’ accessibility are constructed and interacted with the year indicator. Inclusion of these two interactions in the empirical framework allows for a different change in economic activities in accessible communes. Fourth, communities are interviewed at different times of the year in each round of the survey. This might generate a spurious correlation between changes in child labor and rice prices due to seasonality. For example, rice prices may be low in harvest season and demand for child labor high. Rice prices may be high in off-season and demand for child labor low. Thus, a regression of rice prices on child labor would merely capture the fact that different communities are interviewed at different times. In the empirical work, this can be controlled for by the inclusion of season indicators, an indicator for whether the interview took place at a rice harvest, and an indicator for whether an interview took place at rice planting time control for seasonal variation.

# **4.2 Empirical Framework**

> 1 There are 4 to 35 sampled communes per region (the mean and the median are both 25 communes per region).

22
# **Figure  3.   Demand for Representative Dixit-Stiglitz (IRTS) good g in Regional Market r**

## **Dixit-Stiglitz Aggregate of IRTS good g in RMr**

<!-- Start of picture text --> CES<br>σ = 3 = esub a /<br>Imports of g from Goods g from<br>Goods g from Goods g from<br>Rest of World RM q<br>RM 1 RM m<br>CES<br>CES CES<br>CES<br>σ =3<br>σ =3 σ =3 σ = 3 = esub<br>Rest of World firms Firms in RM m<br>Firms in RM 1 Firms in RM q<br>selling g in RMr selling g in RMr selling g in RM selling g in RM<br><!-- End of picture text -->

> **<u>a/</u> We take σ = 3, except based on Ivanova (2005), we take σ = 3.1 in MWO; σ = 2.6 in TPP; σ = 2.5 in CNM; and σ = 1.8 in OTI.**

66
**Narayanan, Sudha (2016).** “The productivity of agricultural credit in India.” _Agricultural Economics_ 47, no. 4: 399-409.

**Pandey, Sushil, and S. Pal (2000).** “The nature and causes of changes in variability of rice production in eastern India: A district-level analysis.” _Risk Analysis and Management in Rainfed Rice Systems_ . Los Banos: International Rice Research Institute.

**Phillips, Peter CB, and Donggyu Sul (2007).** “Transition modeling and econometric convergence tests.” _Econometrica_ 75.6: 1771-1855.

**Phillips, Peter CB, and Donggyu Sul (2009).** “Economic transition and growth.” _Journal of Applied Econometrics_ 24.7: 1153-1185.

**Prasad, Pradhan H (1974).** “Reactionary role of usurer's capital in rural India.” _Economic and Political Weekly_ : 1305-1308.

**Reserve Bank of India (2019).** “Table 10: Per capita net state domestic product – state-wise (at constant prices).” _Handbook of Statistics on Indian Economy, 2018-19_ , Mumbai, India.

**Rezitis, Anthony N. (2010).** “Agricultural productivity and convergence: Europe and the United States.” _Applied Economics_ 42.8: 1029-1044.

**Schnurbus, Joachim, Harry Haupt, and Verena Meier (2017).** “Economic transition and growth: A replication.” _Journal of Applied Econometrics_ 32.5: 1039-1042.

**Solow, Robert M. (1956).** “A contribution to the theory of economic growth.” _Quarterly Journal of Economics_ 70.1: 65-94.

**Subbarao, Duvvuri (2012).** "Agricultural credit-accomplishments and challenges." _Speech delivered at NABARD_ .

**Wu, Yanrui (1995).** “Productivity growth, technological progress, and technical efficiency change in China: A three-sector analysis.” _Journal of Comparative Economics_ 21.2: 207-229.

21
# Appendix A. Exposure Data

The exposure data layers are static inputs for the drought/welfare modeling exercise that are fixed in analysis, unlike hazard components. These exposure data layers contain information relating to various spatial characteristics as set out in table A.1.

**Table A.1. Information on exposure data**

|**Data set**|**Description**|**Source and shape files**|**Citation**|
|---|---|---|---|
|Uniform<br>Resolution Grid<br>(URG)|A country x, y grid (w|here extents are buffered to 2° from countr|y boundary).|
|World Bank –<br>country<br>administrative<br>boundaries|World Bank–approved administrative<br>boundaries (Admin0) (and polygons) including<br>international boundaries, disputed areas,<br>coastlines,and lakes.|https://datacatalog.worldbank.org/datas<br>et/world-bank-official-boundaries|World Bank Basemap Collection,<br>February 2020.|
|GADM – Database<br>of Global<br>Administrative<br>Areas (Admin0–<br>Admin6)|GADM is a database of the location of the<br>world's administrative areas (boundaries).<br>Administrative areas in this database include<br>countries, counties, departments, etc. and cover<br>every country in the world. For each area some<br>attributes are provided, including the name and<br>in some cases variant names.|https://biogeo.ucdavis.edu/data/gadm3.<br>6/gadm36_shp.zip|Global Administrative Areas. 2012.<br>GADM database of Global<br>Administrative Areas, version 2.0.|
|GAUL – Global<br>Administrative<br>Unit Layers<br>(Gaul0–Gaul1)|Information is provided at two spatial levels:<br>national (Gaul0) and subnational (Gaul1). The<br>reference spatial layers are derived from the<br>GAUL data set, implemented by the UN Food<br>and Agriculture Organization (FAO) within the<br>CountrySTAT and Agricultural Market<br>Information System(AMIS) projects.|https://data.europa.eu/euodp/en/data/<br>dataset/jrc-10112-<br>10004/resource/609a472e-7e26-4b63-<br>8003-17298cb45e2a<br>https://mars.jrc.ec.europa.eu/asap/files/<br>gaul1_asap.zip|Europa.EU<br>http://data.europa.eu/eli/dec/2011/8<br>33/oj.|
|Crop coverage –<br>Copernicus Global<br>Land Cover Layers|Mapped crop coverage data layer (2019<br>vintage).|https://land.copernicus.eu/global/produ<br>cts/lc|Buchhorn, M., B. Smets, L. Bertels, B.<br>De Roo, M. Lesiv, N.-E. Tsendbazar, M.<br>Herold, and S. Fritz. Copernicus Global<br>Land Service: Land Cover 100m:<br>Collection 3: epoch 2019: Globe 2020.|
|Population – GHS-<br>POP R2019A|This spatial raster data set depicts the<br>distribution of population, expressed as the<br>number ofpeopleper cell.|https://ghsl.jrc.ec.europa.eu/ghs_pop20<br>19.php|GHS population grid multi-temporal<br>(1975–1990–2000–2015).|

36
specific domestic producer or consumer subsidies have played a more limited role (because of their much greater cost to the treasury), with a few exceptions most notably in rich-country agriculture. With the freeing up of most foreign exchange markets over the past two decades, the phasing out of most export taxes,<sup>2</sup> and the conversion of many non-tariff trade barriers into tariffs, the task of measuring the extent of distortions to goods markets is made much easier in that attention can focus on import tariffs and agricultural subsidies. In principle services trade and foreign investment distortions also could distort incentives in the agricultural and industrial sectors, but they are ignored here partly because much controversy still surrounds their measurement and how they should be modelled, and partly because they do not seem destined for major reform under the current Doha Development Agenda of multilateral trade reform by WTO members.

The latest release of the GTAP dataset, Release 6.05, includes estimates of bilateral tariffs and of domestic and export subsidies as of 2001 for 87 countries and country groups spanning the world. This is a substantial improvement over Version 5 of the GTAP dataset, which relates to 1997. The new protection data come from a joint CEPII (Paris)/ITC (Geneva) project. The product of this joint effort, known as MAcMaps, is a HS6 tariff level detailed database on bilateral protection that integrates trade preferences, specific and compound tariffs and a partial evaluation of non-tariff barriers such as tariff rate quotas (TRQs).<sup>3</sup> The new GTAP database has lower tariffs than the previous database. This is because of the inclusion of bilateral trade preferences, as well as the major reforms between 1997 and 2001 such as continued

> 2 Apparently only a few minor export taxes remain – see Piermartini (2004) and also Thiele (2004).

> 3 More information on the MAcMaps database is available in Bouët et al. (2004) and at

> <u>http://www.cepii.fr/anglaisgraph/bdd/macmap.htm. For details of its incorporation into the GTAP Version 6</u> dataset, see <u>www.gtap.org.</u>

5
<!-- Start of picture text --> ao<br>Oo<br>ao<br>=<br>“a l |<br>oee = —<br>-2 “A 0 | 2 Bf<br>x<br>——— Growth in avg income ———_ Growth in inegu. - Bottom 10<br>——— Growth in inequ.- Bottom 20 ————Y Growth in inequ. - Bottom 40<br>——— Growth in inequ.- Bottom 90<br><!-- End of picture text -->

<!-- Start of picture text --> ao<br>Ww<br>a ui}<br>ar<br>e4<br>i"i<br>5 ——— a<br>-2 -1 0 | 2<br>x<br>——— Growth in avg income ——— Growth in inequ. - Atkinson<br>——— Growth in inequ.- Atkinson? ———— Growth in inequ. - Atkinson3<br>——— Growth in inequ.- Sen Index<br><!-- End of picture text -->
_P. Sainath, The Hindu, March 18, 2001_

**Are tribal children more likely to get sick as they get older and so more of them die?  AllIndia data indicate that tribal babies are** **_not more likely to get sick_ from diarrhea or respiratory disease, but are much** **_less likely to get treated_ than other children.** Figure 4 shows that disparities remained in treatment of illness for tribal children 3 years of age and below, compared to other children, although the incidence of disease varied only slightly.  The gap was more acute in the treatment of acute respiratory infections (ARIs). Over 41 percent of ST children received no treatment for fever or cough as opposed to only 27 percent SC, 28 percent OBC and 25 percent of children from other castes.  In the case of treatment of diarrhoea, both OBC and ST children were less likely to receive any treatment, but here again incidence of diarrhoea is roughly the same across social groups.  Clearly, therefore the major story in the health and mortality of tribal children is not so much that they get sick more often, but that they get treatment less often.

**Figure 4. Tribal children are less likely to fall sick from fever/cough, but are also much less likely to be** **<u>treated if they are sick</u>**

<!-- Start of picture text --> 45<br>41.5<br>40<br>35<br>30 28.2 27.8<br>26.9<br>24.9 24.1 24.8<br>25 22.8<br>20<br>15<br>10<br>5<br>0<br>Had fever/cough over last two weeks Received no medical treatment for fever/cough<br>Scheduled Castes Scheduled Tribes Other Backward Classes Other<br>Source : NFHS-3<br>P roportion of children <36 months old<br><!-- End of picture text -->

**Vaccine preventable diseases and other (mainly water borne and vector borne) diseases are an important proximate cause of mortality for all children.** There has been an overall improvement in immunization coverage in India, but this section documents the fact that while improvements have been larger in magnitude for tribals, absolute proportions are still low and gaps between tribal and non-tribal children remain high, especially in rural areas.

We measure immunization coverage using two indicators - _breadth_ of coverage (percentage receiving any basic vaccination) and _intensity_ or _quality_ of coverage (percentage receiving all basic vaccinations). Our analysis using the NFHS data suggests that both indicators registered substantial improvement between 1992 and 2005, especially among Scheduled Tribes, thus narrowing the differential between tribal and non-tribal populations (Table 6). At the all-India level, of the 12-23 months olds born to ever married women in the age group 15-49 years, the proportion that received any of the basic vaccines expanded from 71 to 95 percent (a 34 percent increase). The corresponding increase for Scheduled Tribe children was 53 percent - from 58 to 13
|**Table 4.**<br>**Physical Capita**|**l and Invest**|**ment b**|**y Trader**|**s in Be**|**nin and**|**Mala**|**wi**||
|---|---|---|---|---|---|---|---|---|
|||Benin||||Mal|awi||
|%**Traders who own:**|Mean|S.d.|Median|<br>N|Mean|S.d.|Median|N|
|Scales|8%|0|0|663|29%|0|0|738|
|Processing equipment|6%|0|0|663|1%|0|0|738|
|Non-motorized transport|13%|0|0|663|58%|0|1|738|
|Motorized transport|_15%_|0|0|663|6%|0|0|738|
|Shop and storage facility|10%|0|0|663|11%|0|0|738|
|Telephone|4%|0|0|663|1%|0|0|737|
|**Average number by trader:**|||||||||
|Scales|0.13|0.52|0|663|0.51|1.71|0|736|
|Processing equipment|0.11|0.58|0|663|0.01|0.18|0|738|
|Non-motorized transport|0.20|0.57|0|663|0.82|0.96|1|737|
|Motorized transport|0.32|1.04|0|663|0.08|0.39|0|738|
|Shop and storage facility|0.13|0.44|0|661|0.13|0.37|0|738|
|Telephone|0.03|0.18|0|657|0.01|0.12|0|737|
|**Average number a year ago:**|||||||||
|Scales|0.13|0.52|0|663|0.47|1.68|0|738|
|Processing equipment|0.11|0.59|0|663|0.01|0.18|0|738|
|Non-motorized transport|0.19|0.56|0|663|0.78|0.93|1|738|
|Motorized transport|0.30|1.02|0|663|0.07|0.38|0|738|
|Shop and storage facility|0.12|0.43|0|663|0.12|0.36|0|738|
|Telephone|0.03|0.18|0|663|0.01|0.12|0|738|
|**Current value of:.**|||||||||
|Scales|61|305|0|661|46|249|0|736|
|Processing equipment|32|281|0|663|64|1274|0|738|
|Non-motorized transport|21|83|0|659|34|57|23|736|
|Motorized transport|3277|24603|0|657|357|3224|0|738|
|<br>Shop and storage facility|106|723|0|648|188|1174|0|737|
|**Value a year ago:**n|||||||||
|Scales|61|304|0|663|39|186|0|736|
|Processing equipment|33|281|0|663|16|218|0|738|
|Non-motorized transport|20|82|0|663|35|112|20|737|

a US$ equivalent. At the tirne of survey, exchange rates used are 1 US $= CFA 600 and 1 US $ = MK 45.

12
<!-- Start of picture text --> Latin America2000-2030& Carib. Mid. East2000-2030& North Afr. South2000-2030 Asia<br>30 350 30<br>3 300 300 300<br><!-- End of picture text --> <!-- Start of picture text --> esa 2 1% dy bats 2 150 2 1% TTP lili<br>100 HH fl i 100 10 18 f a2<br>oO a oO Se — oO Bees Bate<br><!-- End of picture text --> <!-- Start of picture text --> 1 S§28 858 S898 F998 E828 F938<br>qu ess “E85 gas “885 E55 “ESE & Batey<br>a} \ aae rd e 4 2 Fg « = Dry beans<br>5 TAwo AliI. ssP4 SSPS ssP4 SsP5 ssP4 SSP5 == eoCassava<br><!-- End of picture text --> <!-- Start of picture text --> 500 Ae Europe & Cent. Asia Sub-Saharan Africa East Asia & Pac. = Cottonseed<br>= Millet<br>till| | || 3 60 0 2000-2030 3 50 0 2000-2030 3 50 0 | 2000-2030i | = m GroundnutsPotatoesRape<br><!-- End of picture text -->

<!-- Start of picture text --> 2993Esee 289g"25erd §=5 100TM 5== 90]100 safeBeme Ssse;SEs Healt]= S07100 SRERaHaeBSE_ =eco© supecaneSunflower<br>ssP4 SSP5 07K0 See 00 scaa onanBees 5 0 SweetWheat potatoes<br><!-- End of picture text --> <!-- Start of picture text --> 3samg Bssag 3sng B5ag 35mg B5ag<br>& & & 2 2 &<br>ssP4 SSP5 ssP4 SSP5 ssP4 SSP5<br><!-- End of picture text -->
international reserves relative to countries’ shares of the global economy (Figure 2(a) and (b)). Unless a new reserve currency or international substitutes arise, then the scenarios laid out above for economic growth imply that the misalignment, as well as volatility in asset prices and capital flows, will only grow over time.

**Figure 2(a) and (b): Currency composition of international reserves and economic indicators.**

<!-- Start of picture text --> Global currency share (%) Global currency share (%)<br>70<br>Reserves Share 70<br>Reserves Share<br>Turnover Share<br>60 Turnover Share<br>60<br>50 USA USA<br>50<br>40<br>Euro Area 40<br>30<br>30<br>Euro<br>20<br>Japan 20 Area<br>UK<br>10 10 UK Japan<br>India China<br>0 India<br>Brazil Russia 0<br>0 3 6 9 12 15 Russia Brazil China<br>Percent of world exports 0 10 20 30<br>Percent of world GDP<br><!-- End of picture text --> Source: Reproduced from World Bank (2011). GDP, export shares and reserves shares are for 2009. Currency turnover is for 2010.

**Implications for the International Monetary System** .  What does this scenario mean for the evolution of the international monetary system?  At the time of the Bretton Woods Conference, a number of more radical proposals for international system reform were proposed—including proposals for a new international currency; however, these more comprehensive and radical reforms were not accepted, and a dollar-centric system was established.  In all likelihood, this system was established for the following three reasons: (i) the United States’ position as a dominant economy with over 50 percent of global GDP; (ii) the United States’ exorbitant privilege (see Box 1) of being a dominant reserve currency implied large economic gains for the United States (so there was a strong incentive  against relinquishing or even diminishing the dollar’s global role); and (iii) the United States’ economic growth and fiscal position was strong, thus generating confidence in the US dollar.

10
<!-- Start of picture text --> 2 55<br>n GRE<br>o FF g :<br>3 ae<br>va 22%283 3 22sé<br>cS ee 333 a Eg<br>gg g BE<br>op § c__ mean - EE Z<br>Oo s ee om 2 woe 6 33<br>eo 8 ‘3 — 8 33 "<br>iG a (IS<br>= Sao li TTT) | igs<br>G P| ‘ & sigs<br>ae<br>op 5es TUBE % 5 ® g Ss 3<br>2¢ 8 ¢ 82 8 2 °@ s 8 E ERS<br>= on mite<br>Q * tiB gh e y<br>— 5 as ~3<br>gfneis<br><!-- End of picture text -->
66 TABLE 7: Number of Farm Enterprises by Tenure Category 1981

||Prtvate||Ejldo||Mixed||
|---|---|---|---|---|---|---|
|Holding size (has)|'000<br>farms|%|'000<br>farms|%|'000<br>farms|%|
|0-2|398|39.7|647|30.8|66|34.9|
|3-5|168|16.7|573|27.3|55|29.1|
|6-20|213|21.2|809|38.5|55|29.1|
|21-50|101|<br>10.1|58|2.8|8|4.2|
|51-100|54|<br>5.4|4|0.2|2|1.1|
|100-1000|58|<br>5.8|9|0.4|3|1.6|
|1001-2500|6|<br>0.6|*|*|'|*|
|Over 2500|5|0.5|-|-|||
|Total|1003|100.0|2100|100.0|189|100.0|

<!-- Start of picture text --> Holding size (has)<br><!-- End of picture text --> SOURCE: Same as Table 1.