rafmacalaba/datause-encoder-data huggingface dataset:
Growth of value-added shares in GDP, 2000-2015 <!-- Start of picture text --> CHN -6.6 23.3<br>IND -8.7 16.0<br>RUS -21.1 11.9<br>IDN -28.0 11.9<br>BRA -26.1 7.1<br>ZAF -35.9 5.6<br>TUR -9.9 3.0<br>HUN 2.2 9.4<br>CZE 0.5 4.0<br>POL -0.9 9.0<br>-40 -20 0 20<br>Manufacturing Services<br><!-- End of picture text --> Source: WDI; author’s calculations

**International integration and GVC participation of Turkish firms is low.** The percentage of firms that both export and import—which is a rough proxy of GVC participation—is relatively low in Turkey (left‐hand side of Figure 2.5).  Turkish firms also have low levels of foreign ownership and use fewer foreign inputs than comparator countries (right‐hand side of Figure 2.5). This suggests that Turkey may be missing opportunities that FDI and GVC participation bring in terms of new technologies and positive productivity spillovers, including in services.

**Figure 2.5: Trade and foreign ownership (2015)**

<!-- Start of picture text --> GVC participation Trade indicators<br>15<br>30 31<br>12<br>11 11<br>24<br>10<br>20<br>18<br>16<br>13<br>5 4 10<br>8 8<br>2 1 6 6 6 5<br>0 Malaysia ECA Poland China Turkey RussiaTurkey_micro0 0 Malaysia Poland ECA China Russia TurkeyTurkey_micro 2 1 1<br>Importing and Exporting Foreign ownership Importing inputs<br>Percentage of firms<br>Percentage of firms importing AND exporting<br><!-- End of picture text --> Source: World Bank Enterprise Surveys; World Bank (2017). Turkey_micro consists of firms with five or fewer full‐ time employees. All other data points include firms with five employees or more. In the left‐hand panel, firms are considered to export if they export directly at least 10% of sales. Firms that import have at least 10% foreign inputs and directly imported some of those inputs.

**Turkey’s trade in services appears relatively small and declining, with falling sophistication.** Trade in services has gone from over 10 percent of GDP in 2000 to only 7 percent in 2016, with a small uptick seen after the global financial crisis (left‐hand panel of Figure 2.6). Trade in manufacturing, on the other hand, increased from about 30 percent in 2000 to 45 percent in 2011, followed by a decline to 40 percent in 2016. Services trade as a percentage of GDP is also lower in Turkey than in many comparator countries

9
- [13] Edlund, Lena, and Evelyn Korn (2002). “A Theory of Prostitution.” Journal of Political Economy 110 (1): 181-214.

- [14] Epstein, Helen (2007). The Invisible Cure: Africa, the West, and the Fight Against AIDS. New York: Farrar, Straus and Giroux.

- [15] European Study Group on Heterosexual Transmission of HIV (1992). "Comparison of Female to Male and Male to Female Transmission of HIV in 563 Stable Couples." British Medical Journal 304 (6840): 809-813.

- [16] Ferguson, Alan and Chester Morris (2003). "Assessing the Role of Anal Intercourse in the Epidemiology of AIDS in Africa." International Journal of STD & AIDS 14 (12): 856

- [17] Gertler, Paul and Jonathan Gruber (2002). "Insuring Consumption Against Illness." American Economic Review 92 (1): 51-70.

- [18] Gertler, Paul, Manisha Shah, and Stefano M. Bertozzi (2005). “Risky Business: The Market for Unprotected Commercial Sex.” Journal of Political Economy 113 (3): 518-550.

- [19] Gray, Ronald et al. (2001). "Probability of HIV-1 Transmission per Coital Act in Monogamous, Heterosexual, HIV-1-Discordant Couples in Rakai, Uganda." Lancet 357 (9263): 1149-1153.

- [20] Gugerty, Mary Kay (2007). "You Can’t Save Alone: Commitment and Rotating Savings and Credit Associations in Kenya." Economic Development and Cultural Change 55 (2): 251-282.

- [21] Hawken, M.P., R.D. Melis, D.T. Ngombo, K. Mandaliya, L.W. Ng’ang’a, J. Price, G. Dallabetta, and M. Temmerman (2002). “Part Time Female Sex Workers in a Suburban Community in Kenya: A Vulnerable Hidden Population.” Sexually Transmitted Infections 78 (4): 271-273.

- [22] Hudson, Christopher (1996). "AIDS in Rural Africa: A Paradigm for HIV-1 Prevention." International Journal of STD and AIDS 7 (4): 236-243.

28
<!-- Start of picture text --> Information only Information & support<br>8 studies 18 studies<br>© ©<br>a<br>© a<br>A<br>s st A<br>G<br>DB = = rN<br>YL oo © oo &<br>oO oOo<br>c=g s& a ery<br>aSoO oSOo a AQ<br><!-- End of picture text --> <!-- Start of picture text --> 5. oe a<br>SS 2 aA<br>w A 2 ae<br>S) A, oO “&<br>22<br>2co)<br>5-3-1Reported13effect5 7in percentage9 1113151719points 21 53-11Reported3effect5 7in percentage9 1113 1517points19 21<br>Filled symbols indicate statistically significant estimates.<br>Source: Tables 6.B2 and 6.B3 in annex<br><!-- End of picture text -->
_Social Capital, Household Welfare and Poverty in Indonesia_ 14 situations. Within each sub-district, four villages were selected based on location criteria (upland/lowland and near/far to growth center). Within each of the 48 villages, 25 households were selected randomly to participate in the household survey.<sup>7</sup> The subdistricts were Sarolangon Bangko and Batang Han in Jambi, Banyumas and Wonogiri in Jawa Tengah, and Timor Tengha Selatan and Ngada in NTT. In each sub-district, 200 households were interviewed for a total sample of 1,200 households.<sup>8</sup>

**Table 1: Selected Socio-economic Indicators of the Three Study Areas**

||**Jambi**<br>I<br>___<br>_I|**Jawa**<br>**Tengah**<br>_____<br> <br>|**Nusa**<br>**Tenggara**<br>t**Timiur**|**Indonesia**|
|---|---|---|---|---|
|Population ('000)<br>|2,370|29,653|3,577|194,755|
|Area ('000 km<sup>2</sup>)<br>|44.8|34.2|47.9|1,919.3|
|Population Density (people/kn<sup>2</sup>)|53|867|75|101|
|% Urbanized<br> <br>|27.2<br>|31.9<br>|13.9<br>|35.9<br>|
|% of Households<br>with Access to Electricity|30.5|71.1|14.5|57.2|
|Gross Primary Enrollment Ratio|95|97|91|95|
|Gross Secondary Enrollment Ratio|47|58|44|56|
|% of Heads of Household who are Farmers|71|67|92|**771/**|
|Household Expenditure per Capita<br>('000 Rupiah/year)<br>2'|575.3|612.4|453.8|547.12'|
|Gini-coefficient<br>|0.29|0.36|0.37|1<br>0.351_|
|1 Based on the three study areas only.<br>|||<br>|<br>|
|2. At the time of data collection (Fall 1996) the ex<br>|change rate was in th<br>|e range of $1 =<br> <br>|2,300-2,400<br>R<br>|<br>upiah.<br>|
|Sources: Statistical Yearbook of Indonesia1995; <br>Tengah, NTT-Hasil<br>Survei Penduduk A|Statistik Pendidik<br>ntar Sensus 1995; au|an<br>1994/95;Pen<br>thor's calcula|dudukIndone<br>tions.|sia,<br>Jambi, Jawa|

- 7 The data were collected in the fall of 1996, i.e. prior to the recent social and economic crisis in Indonesia. The macroeconomic evolution in the country is reviewed in Thorbecke (1991) and World Bank (1996). Tjiptoherijanto (1996) reviews the evolution of poverty and inequality. Thorbecke (1998) provides an initial assessment of the social costs of the crisis.

- 8 Grootaert (1999) further discusses the demographic and economic characteristics of the sample households.
# **Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS1 033 Latin America and the Caribbean<br>Region (and Northern America)<br>Population Projections, 1992-93<br>Edition|My T. Vu<br>Eduard Bos<br>Ann Levin|November 1992|0. Nadora<br>31091|
|WPS1034 Revising Financial Sector Policy<br>in Transitional Socialist Economies:<br>Will Universal Banks Prove Viable?|David H. Scott<br>|November 1992|K. Waelti<br>37664|
|WPS1035 How Import Protection Affects the<br>Philippines' Motor Vehicle Industry|Wendy E. Takacs|November 1992|D. Ballantyne<br>37947|
|WPS1 036 Output Decline in Hungary<br>and Poland in 1990-91: Structural<br>Change and Aggregate Shocks|Simon Commander<br>Fabrizio Coricelli|November 1992|0. del Cid<br>35195|
|WPS1037 Vocational Secondary Schooling,<br>OccupationalChoice, and Earnings<br>in Brazil|Ana-Maria Arriagada<br>Adrian Ziderman|November 1992|C. Cristobal<br>33640|
|WPS1038 Determinants of Expatriate Workers'<br>Remittances in North Africa and<br>Europe|Ibrahim A. Elbadawi<br>Robert de Rezende Rocha|November 1992<br>|A. Maranon<br>31450|
|WPS1039 Education, Externalities, Fertility,<br>and Economic Growth|Martin Weale|November 1992|PHREE<br>33680|
|WPS1040 Lessons of Trade Liberalization in<br>Latin America for Economies in<br>Transition|Jaime de Melo<br>Sumana Dhar|November 1992|D. Ballantyne<br>37947|
|WPS1041 Family Planning Success Stories in<br>Bangladesh and India|Moni Nag|November 1992|0. Nadora<br>31091|
|WPS1042 Family Planning Success in Two<br>Cities in Zaire|Jane T. Bertrand<br>Judith E. Brown|November 1992|0. Nadora<br>31091|
|WPS1043 Deriving Developing Country<br>Repayment Capacity from the Market<br>Prices of Sovereign Debt|Stijn Claessens<br>George Pennacchi|November 1992|R. Vo<br>33722|
|WPS1044 Hospital Cost Functions for<br>Developing Countries|Adam Wagstaff<br>Howard Barnum|November 1992|0. Nadora<br>31091|
|WPS1045 Social Gains from Female Education:<br>A Cross-National Study<br>|Kalanidhi Subbarao<br>Laura Raney|November 1992|M. Abundo<br>36820|
|WPS1046 World Bank Project-Financed<br> <br>Research on Population, Health,<br> <br>and Nutrition|J. Price Gittinger<br>Carol Bradford|November 1992|0. Nadora<br>31091|
choose up to three 2-variables from the large pool of variables identified as potentially important for explaining cross-country growth differentials. Consequently, we restrice the total number cof explanatory variables included in any one regression to be eight or less.* The set of variables from which we draw combinations of Z-variables, however, is larger than that considered by any other investigation. The second way we Limit the extreme bounds procedure is that for every variable of interest, M, we restrict the pool of variables from which we choose 2-variables. We exclude variables that, a priori, measure the same phenomenon. For example, when we examine the relationship between growth and the rate of domestic credit creation over the 1960-89 period, we do not allow the inflation rate to be a 2-variable. These restrictions make it more difficult to implicate past findings as fragile.° The data are primarily from the World Bank National Accounts Data Base and, when available, cover the period 1960 to 1989. The data set includes 119 countries but the major oil exporters are excluded from the analysis. Detailed government expenditure and tax information are from the International Monetary Fund’s Government Financial Statistics and begin in 1974 for most countries. Data on -lack market exchange rates, measures of educational attainment, civil liberties, etc. are obtained from a variety of sources listed in the Data Appendix. We also use Barro’s (1990, 1991) data set, which is composed primarily of the Summers and Heston (1988) data set, to compare our results with 4 This total is similar to that used by Kormendi and Meguire (1985) and less than Barro (1991) who uses between eight and fourteen explanatory variables. 5 Also, before labelling a result as ‘“robust,” we conduct additional sensitivity analyses by examining different groups of countries, time periods, and expanding the number of variables that can be included as Z-variables.

6
**Figure IV.1 Index of exchange rate movements, June 97-April 98**

<!-- Start of picture text --> 041<br>0.2<br>"-. I-. ?- I- r- i= 1- r- G c c 10 c c c<br>0) 0C CD 0) 0) 0 0 CD 0) 0) 0C 0) 0 0) CD<br>N r-N -t C)C -M - C~10 4 - - e -- 1 cm 1- -- -X 040NC -X N X<br>r- 0 0 0 0 _ N _) N 10<br>Date<br>----.-- KRW - - - PHP ....... IDR S<br>MYR __THB<br><!-- End of picture text --> Secondary market spreads on sovereign bonds saw very similar and simultaneous movements for most East Asian countries during this period. While until July spreads for most East Asian countries had been close to 100 basis points, with the exception of the Philippines which was closer to 200 basis points, in the Fall spreads for East Asian countries rose simultaneously very sharply (Figure IV.2). In case of Indonesia,, the spread rose to more than 600 basis points in December, while spreads for the Philippines and Thailand rose above 400 basis points. Since then spreads have declined, and were in April 1 998 about 300 basis points for Malaysia, the Philippines and Thailand and 350 basis points for Korea. Country differences have also become more pronounced. In case of Indonesia, the spread fell somewhat in late 1997, but has since increased to about 750 basis points, and was in April 1998 about 530 basis points.

50
The agricultural sector in Ghana suffered a dual shock in 1983, as the migrants to Nigeria were expelled back into the country, and the country experienced its worst drought in history. "But once the returnees settled down in the rural areas and started fanning activities, and when the rains also returned in 1984, the situation improved considerably. The index of food output per capita rose from 80.9 in 1983 to 114.6 in 1984, 105.7 in 1985 and 113.7 in 1986 (FAO Production Yearbook, 1987, p.47)" (Tabatabai, p.720).

With the successful implementation of the ERP the economy turned around in the years following 1983. Exports, cocoa output, manufacturing and non-tradable services all showed significant real growth. We have detailed information about relative incomes from the GLSS after 1987.

**Table 6.2: Rural-Urban Differences in Income, 1987**

||Accra|Other Towns<br>Rural|
|---|---|---|
|Household Income|194.2|212.1<br>236.7|
|Income per Earner|165.7|146.8<br>140.0|
|Income per capita|62.3|59.2<br>59.6|
|Income per AEU|82.6|83.0<br>84.9|
|Median|||
|Household Income|122.9|141.4<br>160.3|
|Income per Earner|109.9|95.1<br>92.3|
|Income per capita|36.9|38.2<br>38.5|
|Income per AEU|52.5|_56.0_<br>61.1|

# _Source: GLSS 1987_

The differences in incomes between urban and rural areas, as reported in 1987, are summarized in Table 6.2. We differentiate between Accra and other urban areas. It is seen that income per earner is somewhat higher in other towns and even higher in Accra, though the extent of the difference is almost certainly not sufficient to compensate for the cost-of-living difference between town and country. But even the nominal difference is reversed when we consider household incomes. The data show the perfectly understandable fact that rural households have more earners, because of the greater opportunity of secondary members (wives, children etc.) to participate in incomeearning opportunities in and around the farm. When we move to the statistics on income per capita, the levels of income in nominal terms is almost the same in the three sectors-showing the higher burden of dependency of rural households. This is a fairly general phenomenon in developing countries, reflecting the greater incidence of single member or smaller households in the urban setting. In fact, because of the limited effect of circulatory migrants in Ghana, the quantitative importance of this effect is much 40
# **Table A6.3. The Effect of PMT on Height-for-Age of Children 24 to 60 Months: Using Binary Indicator for Program Exposure**

||Poole|d OLS|Com<br>Fixed|munity<br>-Effects|
|---|---|---|---|---|
|Year (2000=1)|0.012|0.042|0.071|0.086|
||[0.136]|[0.137]|[0.187]|[0.187]|
|Program since ‘98 (Yes=1)*Year|-0.100|-0.125|-0.120|-0.139|
||[0.133]|[0.134]|[0.191]|[0.192]|
|Gender (boy=1)|-0.014|-0.012|-0.005|-0.006|
||[0.040]|[0.040]|[0.041]|[0.041]|
|Age|0.006***|0.005***|0.005**|0.005**|
||[0.002]|[0.002]|[0.002]|[0.002]|
|Mother education (years)|0.019***|0.018***|0.013**|0.013**|
||[0.005]|[0.005]|[0.005]|[0.005]|
|Father education (years)|-0.004|-0.005|-0.002|-0.002|
||[0.005]|[0.005]|[0.005]|[0.005]|
|Mother height (cm)|0.056***|0.055***|0.054***|0.055***|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Father height (cm)|0.034***|0.033***|0.034***|0.034***|
||[0.004]|[0.004]|[0.004]|[0.004]|
|Head of HH (male=1)|0.026|0.030|-0.025|-0.017|
||[0.067]|[0.067]|[0.069]|[0.069]|
|Number of children 5-14 yo in HH|-0.055***|-0.051***|-0.032|-0.031|
||[0.019]|[0.019]|[0.020]|[0.020]|
|HH owns private toilet (Yes=1)|0.141***|0.126***|0.118**|0.109**|
||[0.043]|[0.044]|[0.048]|[0.048]|
|HH has sanitation (Yes=1)|0.383***|0.304***|0.356***|0.318***|
||[0.053]|[0.063]|[0.071]|[0.076]|
|Type of community (urban=1), C|-|0.143**||-0.018|
|||[0.058]||[0.092]|
|Distance to district capital (km), C|-|-0.001||-0.002|
|||[0.001]||[0.001]|
|Distance to bus station (km), C|-|0.001||0.002|
|||[0.003]||[0.005]|
|Land w/ technical irrigation (%), C|-|0.026||-0.182|
|||[0.115]||[0.182]|
|Comm. has asphalt road (Yes=1), C|-|-0.006||0.198**|
|||[0.053]||[0.087]|
|Comm. has sewerage system (Yes=1), C|-|-0.060||-0.014|
|||[0.047]||[0.075]|
|Comm. has piped water (Yes=1), C|-|<br>0.017||<br>0.014|
|||[0.049]||[0.077]|
|Constant|-15.717***|-15.473***|-15.423***|-15.607***|
||[0.813]|[0.817]|[0.873]|[0.890]|
|R-squared|0.15|0.15|0.26|0.26|

Notes: n is 3221. (C) indicates community-level variables. Robust standard errors are in parentheses. (*), (**) and (***) indicate significance respectively at 10%, 5%; and 1%.

39
# **1. Introduction**

One of the enduring debates in the development and agricultural economics literature is the inverse relationship (IR) between farm size and agricultural productivity.<sup>2</sup> The IR continues to draw the attention of policy makers and researchers concerned with the unrelenting fall in farm sizes in much of the developing world, persistent poverty (largely concentrated in rural areas and associated with agriculture as the primary livelihood), and insufficient progress in structural transformation (see, e.g., Collier 2008, Collier and Dercon 2014). This paper seeks to contribute to this debate with empirical insights from Bangladesh.

Bangladesh provides a particularly interesting setting – it is one of the most densely populated and cultivated countries in the world, with farm sizes among the smallest in the world. Yet, Bangladesh’s agricultural performance has been remarkable since the mid-1990s, with impressive productivity growth (appropriately defined as total factor productivity or TFP). This seemingly paradoxical coexistence of very small – and declining – farm sizes and consistent high productivity growth is itself thought provoking in the larger IR debate. Nevertheless, this casual observation raises conceptual, measurement and methodology issues, leaving open the question on whether the IR holds in the setting of very small farms that characterize rural Bangladesh.

Several unique features of Bangladesh’s agriculture make an investigation of the IR particularly interesting and important. A combination of active factor markets, the availability of a householdlevel panel data set on agricultural production, and application of recent advances in estimation methods (specifically employing the stochastic production frontier approach to jointly estimate the production frontier and technical inefficiency for unbiased and consistent estimates) allows overcoming several of the limitations (conceptual and empirical) identified in the literature on testing the IR.

> 2 The contemporary debates on the relationship between farm size and productivity date back to a 1962 note by Sen seeking to interpret three broad findings on Indian agriculture emerging from the Studies in the Economics of Farm Management conducted by India’s Ministry of Food and Agriculture.  One of these findings was that “by and large, productivity per acre decreases by farm size” (Sen 1962).  The general interest in the relationship between farm size and productivity, however, dates further back to the late 1800s and early 1900s, in the context of the debates at the time on how to deal with “peasant” farming, specifically in Russia (Thorner, Kerblay and Smith, 1966).

2
if labor markets do not function effectively by matching skills sets. Both the ESI and ASI in this sense provide a more comprehensive picture of the process by which human capital is translated into economic output.

Second, measurements for each pillar in the process (Skills Formation, Skills Activation, Skills Matching) offer other insights. Skills formation, for instance, constitutes what is traditionally meant by human capital. By summarizing the 3 pillars built from several underlying indicators, the 3 pillars as well as the overall ASI, are easier to interpret than a battery of separate indicators. They offer a composite and succinct measure.

Third, the ASI like many composite indicators provides cross-country comparisons (both within and across pillars) that can illustrate complex issues. Such comparisons can often highlight why a particular policy followed by one country has led to superior results compared to another.

The motivation for composite indicators is neatly summarized by Sharpe (2004, p.9): _“The aggregators believe for two major reasons there is value is combining indicators in some manner to produce a bottom line. First, they believe such a summary statistic can indeed capture reality and is meaningful. Second, they stress that bottom lines are extremely useful in garnering media interest and hence the attention of policy makers”._ Finally, the underlying motivation for the ASI is similar to the ESI. _“The ESI is intended to measure the performance of EU-27+4 countries’ skills formation and matching systems to enable a comparative assessment across EU 27+4 countries. The concept of a skills system is a multifaceted and complex one, and there is no single all-encompassing measure of the system’s performance”_ (2020 European Skills Index, Technical Report).

5
2

# **1. Introduction**

Between 2005 and 2008, driven by rapidly rising demand during a global economic expansion, the world prices of many staple food commodities rose substantially.<sup>2</sup> Rice prices rose by 25 percent, wheat prices by 70 percent, maize prices by 80 percent, and dairy prices by 90 percent (Ivanic and Martin, 2008). Price increases of this magnitude for basic foodstuffs, over a relatively short period, led to widespread concern about possible impacts on hunger and deprivation. A number of governments, including those of India and Vietnam, resorted to export restrictions in order to guarantee domestic supply, while international organizations fretted about possible reversals and delays in meeting the first Millennium Development Goal, related to the eradication of hunger and extreme poverty.<sup>3</sup> After falling during the “Great Recession” of 2008-2009, international food prices resumed their upward trend in mid-2009. The increase accelerated in late 2010, with the World Bank food price index rising by 15% between October 2010 and January 2011, to a level just 3% below its previous (2008) peak. Some international agencies began to describe the prevalence of higher average levels _and_ volatility for world food prices as the “new normal” (World Bank, 2011).

But it has also been argued that the present concern with the effect of higher food prices on household well-being may be fundamentally misguided, because it tends to ignore or underplay income gains to farmers or farm workers, who are numerous among the poor in many developing countries. As Swinnen (2010) notes, this represents a striking reversal in the mainstream opinion among development economists: “Only a few years ago the widely shared view was that low food prices were a curse to developing countries and the poor. However, since 2008 the vast majority of analyses and reports state that high food prices have a devastating effect on developing countries and the world‟s poor” (p.30). As he suggests: “This reversal of opinion [...] raises questions about the correctness of [both] old and new arguments, and about the proposed remedies” (p.3).

Food price increases represent a positive terms-of-trade shock for net food-exporting countries and hence an aggregate income gain. Argentina and Brazil, for example, were net food exporters in

> 2 A yet unresolved debate remains as to the relative contributions of various different demand factors: rising food consumption in emerging Asia, competing demand for crops from biofuel production, and even financial speculation on commodity futures markets.

> 3 See e.g. United Nations (2008).