rafmacalaba/datause-encoder-data huggingface dataset:
Gautier, Peter; Gerald van den Berg; Jan van Ours; and Geert Ridder. 2002 “Worker Turnover at the Firm Level and Crowding Out of Lower Educated Workers.” _European Economic Review_ 46: 523-38.

Hamermesh, Daniel S. 1993 _Labor Demand._ Princeton, N.J.: Princeton University Press.

- Helpman, Elhanan.  2010 “Labor Market Frictions as a Source of Comparative Advantage, with Implications for Unemployment and Inequality” NBER Working Paper 15764. http://www.nber.org/papers/w15764.

- Helpman, Elhanan, Oleg Itskhoki, and Stephen Redding 2010 "Inequality and Unemployment in a Global Economy" _Econometrica_ 78(4) (July): 1239-1283.

- Hoekman, Bernard and Alan Winters. 2005 “Trade and Employment: Stylized Facts and Research Findings.” World Bank Policy Research Working Paper #3676, Washington, DC.

- Hummels, David; Rasmus Jørgensen; Jakob R. Munch; and Chong Xiang (2011) “The Wage Effects of Offshoring: Evidence from Danish Matched Worker-Firm Data” NBER Working Paper No. 17496 October.

- Jansen, Marion and Erik von Uexkull. 2010 Trade and Employment in the Global Crisis International Labour Office (Geneva) and Academic Foundation (New Delhi).

- Kletzer, Lori (2001) Job Loss from Imports: Measuring the Costs Peterson Institute for International Economics, Washington D.C.

- Reder, Melvin. 1955 “The Theory of Occupational Wage Differentials.” _American Economic Review_ 45(5): 833-50.

- Richardson, J. David. 1995 "Income Inequality and Trade: How to Think, What to Conclude" _Journal of Economic Perspectives_ 9(3) (Summer): 33-55.

- Robertson, Raymond. 2009 " Mexico and the Great Trade Collapse" in Baldwin, Richard (ed.) <u>The Great Trade Collapse: Causes, Consequences and Prospects</u> A VoxEU.org Ebook available at http://voxeu.org/index.php?q=node/4297.

- Robertson, Raymond; Brown, Drusilla; Pierre, Gaëlle; Sanchez-Puerta, Laura (eds.). 2009 <u>Globalization, Wages, and the Quality of Jobs Five Country Studies, The World Bank,</u> Washington, D.C.

- Robertson, Raymond and Donald H. Dutkowsky, 2002 “Labor Adjustment Costs in a Destination Country: The Case of Mexico” _Journal of Development Economics_ February, 67(1): 29-54.

34
example, Schott, 2004). For each product relative unit value equals the value of exports divided by volume which in turn is divided by world import value relative to volume.<sup>8</sup> Medians of unit value across countries are then plotted in Figure 6. Points along the diagonal 45<sup>0</sup> line are positions in which unit values remain constant. Thus the left-side of the diagonal line indicates positions in which values unit in 2003-2004 have increased compared to 19931994 and vice versa.

The changes in relative unit values in machinery components suggest that the Philippines is exporting more expensive machinery components, but not China.<sup>9</sup> Medians of relative unit value of export in components from developing East Asia have increased significantly over the last ten years as shown in Figure 6. The scatter plot suggests that relative unit value for components in 2003-2004 has increased compared to 1993-1994. Within this category, the Philippines have seen the highest increase in median relative unit value of export in components compared to other developing East Asian countries. On the other hand, the relative unit value of exports in components from China has not increased and has remained lower than that of other countries in the region.

As for products of finished machinery, the landscape of relative unit value of export across countries in the region tells a different story. Instead of increasing, relative unit value of export in finished machinery has declined for most of countries in developing East Asia. As shown in Figure 6, exports of finished machinery from Malaysia and Thailand had a relatively higher unit value in machinery exports in 1993/94. However, their relative unit values have decreased in the last ten years - they are located below the diagonal line. Further, relative unit value of some countries, such as Indonesia and Philippines, has not increased in the last ten years. The only country in developing East Asia that managed to increase its exports unit value of finished machinery is China. This finding suggests that

> 8 Unit value of export from country i in product k = (Xij /QXik) / (WMk /WQMk). X, QX, WM, and WQM are export value, export quantity, world import value, and world import quantity, respectively.

> 9 Malaysia has been one of main suppliers of PC components for Dell, a US PC maker. At the time this draft was written, Texas Instruments was close to deciding on an expansion of its investment in high-tech electronic chip making in the Philippines.

> <sup>9</sup>
**Table 1: Countries’ AGOA eligibility dates and years of suspension**

|**Country**|**Date declared**<br>**AGOA eligible**|**Suspensions**|
|---|---|---|
|Burkina Faso|Dec-04|Jan 2023 – Present|
|_Burundi_|_Jan-06_|_Jan 2016 – Present_|
|_Cameroon_|_Oct-00_|_Jan-2020 – Present_|
|_Central African Republic_|_Oct-00_|_Dec 2003 – Dec 2016_|
|_Congo, Dem. Rep._|_Dec-02_|_Jan 2011 – Jan 2021_|
|_Côte d’Ivoire_|_Oct-11_|_Jan 2005 – Oct 2011_|
|_Eswatini_|_Oct-00_|_Jan 2015 – Jan 2018_|
|_Gambia, The_|_Dec-02_|_Jan 2015 – Dec 2017_|
|_Guinea_|_Oct-00_|_Dec 2009 – Jun 2014, 2022 – Present_|
|_Guinea-Bissau_|_Oct-00_|_Jan 2013 – Dec 2014_|
|_Madagascar_|_Oct-00_|_Jan 2010 – Jun 2014_|
|_Mali_|_Oct-00_|_Jan 2013 – Dec 2013, Jan 2022 – Present_|
|_Mauritania_|_Oct-00_|_Jan 2006 – July 2007, Jan 2009 – Jan 2010, Jan 2019 – Present_|
|_Niger_|_Aug-06_|_Dec 2009 – Oct 2011_|
|Seychelles (graduated)|Oct-00|Jan 2017 – Present|
|_South Sudan_|_Dec-12_|_Jan 2015 – Present_|
|Angola|Dec-03||
|Benin|Oct-00||
|Botswana|Oct-00||
|Cabo Verde|Oct-00||
|Chad|Oct-00||
|Comoros|Jun-08||
|Congo, Rep.|Oct-00||
|Djibouti|Oct-00||
|Gabon|Oct-00||
|Ghana|Oct-00||
|Kenya|Oct-00||
|Lesotho|Oct-00||
|Liberia|Dec-06||
|Malawi|Oct-00||
|Mauritius|Oct-00||
|Mozambique|Oct-00||
|Namibia|Oct-00||
|Nigeria|Oct-00||
|Rwanda|Oct-00||
|São Tomé and Príncipe|Oct-00||
|Senegal|Oct-00||
|Sierra Leone|Oct-02||
|South Africa|Oct-00||
|Tanzania|Oct-00||
|Togo|Apr-08||
|Uganda|Oct-00||
|Zambia|Oct-00||

_Source: United States international Trade Administration Italicized countries are identified in the analysis as suspended countries._ 10
_5_ applying cost-effectiveness to nutrition interventions. There are of course the usual practical problems even with estimating costs. Most nutrition interventions are joint with other interventions: MCH feeding and growth monitoring occur in conjunction with health services, food stamp or food subsidy targeting frequently uses the health or social welfare system, and school feeding programs use the facilities of the education system. Often the overhead costs of these other vehicles are not taken into account, such that the costs of nutrition interventions are underestimated. At the same time, if these other services are not available (as in many cases in Africa), nutrition interventions can appear prohibitively expensive, if the full cost of setting up the delivery system is assigned to the nutrition project alone. Similarly there are biases in the costs reported of those projects where aid donors (such as USAID or WFP) require (or encourage) local co-financing. Developing country governments are encouraged to assign local expenses to the nutrition intervention in order to report a desirable level of local input. If the full cost of health worker salaries are included in the cost of a MCH feeding project via health centres, this is likely to overestimate the actual cost of the nutrition intervention. Cost data from NGO's also have problems, in that these organizations often maintain cost data not by program, but by source of funding, and do not fully cost items which are supplied free or at less than market value. There exist therefore very few studies with carefully collected cost data (e.g. Anderson, 1977), other than research projects (which may be highly unrepresentative).

Since unit cost data are difficult to come by, existing studies tend to cite over and over again data from the same few projects. Some types of interventions are covered better than others. Data on unit costs of school feeding programs are relatively common (both MCH and school feeding), although usually not presented in a standardized format allowing comparison across projects, and impact data are scarce. Likewise data on costs of food subsidies exist, although usually not in the form of cost per number of calories per beneficiary, and there are almost no impact studies which can be related to costs (with the sole exception of Garcia and Pinstrup-Andersen, 1987). Cost data are
26 Table 2. Syndicate Bank and the Banking System: Some Indicators. 1968 and 1975 (in Rercent)

|||Syndic|ate Bank|Banki|ng System||
|---|---|---|---|---|---|---|
|Cat|egory|1968|1975|1968|<br>1975||
|1.|Rural Branches/<br>Total Branches|32|39|22|37||
|2.|Rural Deposits/||||||
||Total Deposits||-|12|-|8|
|3.|Agricultural<br>Advances/<br>Total Advances||12|13|0.3|10|
|4.|Advances to Small Industry/<br>Total Advances||11|14|8|12|
|5.|Advances to Small Business/<br>Total Advance||7|14|negligible|<br>8|
|6.|Share of 3+4+5 in Total<br>Advance||30|41|8.3|30|
|7.|Borrowing Accounts under<br>3, 4, & 5/Total Borrowing<br>Accounts||50|65|||
|8.|Deposit Accounts under<br>Rs. 1,500/Total Deposit<br>Accounts||90|89|||
|9.|Deposits for Deposit Accounts<br>under Rs. 1,500/Total<br>Deposit||50|50|||

Source: Same as in Table 1.
|||Page|
|---|---|---|
|I.|Introduction|1|
|II.|What is a Subsidy?|3|
||(i) Level Playing Fields|3|
||(ii) Export and Domestic Subsidies|5|
|III.|Why Subsidize?|9|
|IV.|Effects on Other Countries|14|
|V.|The Rules|18|
||ti) The Articles of GATT|18|
||(ii) The Subsidies Code|21|
|VI.|Agriculture|27|
|VII.|Countervailing<br>Duty Action|30|
|VIII.|Evaluation of the Subsidies Code|32|
|IX.|Alternatives|35|
||(i)<br>Ignore Domestic Subsidies|36|
||(ii) Ignore the Source of Injury|37|
||(III) Attack Barriers Onaly|39|
|Refere|nces|44|

# References

A revised version of this paper is to appear in a volume edited by Brian Hindley for the Trade Policy Research Centre, London. The author is on leave from his position as Professor of Economics at Monash University, Melbourne, Australia, and is editor of the The World Bank Economic Review and The World Bank Research Observer.
based on the observation that when land quality controls are introduced in the analysis, the strength of the IR often diminishes substantially or vanishes altogether. On the other hand, Barrett et al. (2010) utilize a dataset that includes laboratory measures of soil testing and conclude that only a very limited proportion of the IR can be explained by differences in land quality. Similarly, Nkonya et al. (2004) find a strong negative effect of farm size on plot output value after controlling for plot size, labor input, equipment and proxies for land quality, suggesting that not only land productivity but also total factor productivity is inversely related to scale. Carter (1984) and Heltberg (1998) control for village and household fixed effects, respectively, and still do not find that the strength of the IR diminishes. Taking these studies together, Eastwood et al. (2010: 3351) consequently posit that “the IR is immune to the land-quality objection.” Lastly, it has been suggested that the existence of the IR may be a statistical artefact stemming from measurement error in land data (Lamb, 2003). A similar explanation is provided by Barrett et al. (2010), after failing to explain the observed IR otherwise. However, the data from the UNHS 2005/06, which collected both GPS-based and farmerreported measures of plot areas, have been used by Carletto et al. (2013) to show that the estimates of the IR are robust to potential measurement error introduced by farmer’s selfreporting and that the IR is strengthened when more accurate, GPS-based measure of farm size is used in the analysis.

Our study builds on the work of Carletto et al. (2013), who exclude all farming households for whom GPS-based farm size cannot be computed due to one or more missing GPS-based plot areas. As a result, approximately 50 percent of the agricultural household sample is discarded in their analysis, even if the share of non-measured plots stands at 35 percent. In what follows, we investigate the robustness of the inverse scale-land productivity relationship to complete case analysis made possible by MI. We estimate a plot-level production function to test for the existence of the IR, based on the one originally proposed by Binswanger, Deininger and Feder (1995), and not dissimilar from the approach taken by Barrett et al. (2010) and Carletto et al. (2013): where _i_ and _h_ denote plot and household, respectively; _Y_ represents value of agricultural output; _A_ denotes the plot area in acres; _P_ is a vector of plot-level variables spanning logarithm of value of input use per acre, distance from the household, tenure status, and farmer-reported soil quality and slope; _H_ is a vector of household-level attributes that might influence agricultural production, including household size, dependency ratio, and 16
the firm's optimal decisions in the formal sector and give them a cost disadvantage concerning their peers of the informal sector.<sup>9</sup>

**Table 1: Definitions of Sectors/Commodities and Data**

|**Activity/sector**<br>**No**|**Activity/sector contents**|**Presence of**<br>**the informal**<br>**sector **|**Commodity**<br>**No**|
|---|---|---|---|
|S1|Food agriculture; Breeding and hunting; Activities<br>related to culture and breeding; Fishing and fish<br>farming|Yes|C1|
|S2|Cocoa production; Other industrial or export<br>agriculture|Yes|C2|
|S3|Forestry andLogging|Yes|C3|
|S4|Extractive industries|Yes|C4|
|S5|Meat and fish production; Grain work and product;<br>manufacturing; Cocoa and coffee processing; Oilseed<br>industry; Bakery, pastry and pasta; Dairy industry,<br>fruit and vegetable industry; Beverage industry;<br>Tobacco industry|Yes|C5|
|S6|Textile and clothing industry; Leather and footwear<br>industry|Yes|C6|
|S7|Woodworking and manufacture of articles; Paper and<br>paperboard industry, printing; Furniture<br>manufacturing|Yes|C7|
|S8|Oil refining and coking|No|C8|
|S9|Chemical industry; Rubber and plastics industry|Yes|C9|
|S10|Manufacture of non-mineral mineral products|Yes|C10|
|S11|Basic metal products|Yes|C11|
|S12|Manufacture of machinery, equipment and machinery;<br>Manufacture of audio equipment and appliances;<br>Transportation equipment manufacturing|No|C12|
|S13|Electricity|No|C13|
|S14|Naturalgas|No|C14|
|S15|Construction|Yes|C15|
|S16|Transport and communicationservices|Yes|C16|
|S17|Wholesale and retail trade; Repairs; Hotels and<br>restaurants; Posts and Telecommunications; Financial<br>activities; Real estate activities; Services provided to<br>businesses|Yes|C17|
|S18|Public administration and social security; Education;<br>Health and social action; Collective, social and<br>personal services|Yes|C18|

> 9 GAMS codes of the model are available upon request to the authors.

10
- Becker, G. S., Kominers, S. D., Murphy, K. M., and Spenkuch, J. L. (2018). A Theory of Intergenerational Mobility. _Journal of Political Economy_ , 126(S1):7–25.

- Black, S. E. and Devereux, P. J. (2011). _Recent Developments in Intergenerational Mobility_ , volume 4 of _Handbook of Labor Economics_ , chapter 16, pages 1487–1541. Elsevier.

- Bossuroy, T. and Cogneau, D. (2013). Social Mobility in Five African Countries. _Review of Income and Wealth_ , 59:84–110.

- Chapman, B. and Suryadarma, D. (2013). Financing higher education: The viability of a commercial student loan scheme in Indonesia. _Education in Indonesia_ , pages 203–215.

- Chetty, R., Hendren, N., Kline, P., and Saez, E. (2014). Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States. _The Quarterly Journal of Economics_ , 129(4):1553–1623.

- Corak, M. (2013). Income Inequality, Equality of Opportunity, and Intergenerational Mobility. _Journal of Economic Perspectives_ , 27(3):79–102.

- Durlauf, S. N. (2000). The memberships theory of poverty : the role of group affiliations in determining socioeconomic outcomes. Working papers 14, Wisconsin Madison: Social Systems.

- Echazarra, A. and Readinger, T. (2019). Learning in Rural Schools: Insignts from PISA, TALIS, and the Literature. OECD Education Working Paper Series 196, OECD.

- Emran, M. S., Greene, W., and Shilpi, F. (2018). When Measure Matters: Coresidency, Truncation Bias, and Intergenerational Mobility in Developing Countries. _Journal of Human Resources_ , 53(3):589–607.

- Emran, M. S., Islam, A., and Shilpi, F. (2020a). Distributional Effects of Corruption When Enforcement is Biased: Theory and Evidence from Bribery in Schools in Bangladesh. _Economica_ , 87(348):985–1015.

- Emran, M. S., Jiang, H., and Shilpi, F. J. (2020b). Gender Bias and Intergenerational Educational Mobility : Theory and Evidence from China and India. Policy Research Working Paper Series 9250, The World Bank.

- Emran, M. S. and Shilpi, F. (2011). Intergenerational Occupational Mobility in Rural Economy: Evidence from Nepal and Vietnam. _Journal of Human Resources_ , 46(2):427– 458.

- Emran, M. S. and Shilpi, F. (2015). Gender, Geography, and Generations: Intergenerational Educational Mobility in Post-Reform India. _World Development_ , 72:362–380.

29
47

# TABLE **I** THE AVERAGE DISTRIBUTION OF INDUSTRIAL EMPLOYMENT BY ESTABLISHMENT SIZE AND BY LEVEL OF PER CAPITA INCOME (% OF TOTAL)

|GNP Per<br>Capita($)<br>_<br>_<br>_<br>_<br>_<br>_<br>_|Number of<br>Cournries<br>_<br>_<br>_<br>_<br>_<br>_|Cottage<br>Shops<br>_<br>**~~(1-4)_**|Smail<br>(5-19)<br>_<br>_|Medium<br>(20-99)<br>_<br>_<br>__|Large<br>(100 +)|Total<br>_|
|---|---|---|---|---|---|---|
|.$100-500|**6**|64|7|4|25|100|
||$50 ,000|**7**|41|12|10|37|100|
|$1,000-2000|7|1 1|13|14|61|100|
|**$2,000-5,000**|<br>9|8|11|17|64|100|
|**1$5,000**+|5|4|6|20|70|100|

Source: Donald E Snodgrass, "Employment and- Enterprise Policy in Structual Transformation," Harvard Insitte of Intemational Development, draft manuscript, July 1992, page 63.

Notes: Based on industrial data from 34 countries.
- 14 -

# D. EMPIRICAL EVIDENCE AND POLICY IMPLICATIONS

11. The behavioral equations (1-7) are estimated for Indonesia using quarterly data over the 1983-87 period. The lag structure is obtained empirically by testing for the relative importance of a specific lag. All the equations are estimated using OLS. The use of simultaneous equation technique was constrained by the non-linear nature of some of the equations and the smnall number of degrees of freedom. The detailed results and a glossary of variable definitions are as follows.

# Estimation Results

(la) IS3 = 0.190 DISC + 0.174 DISC(-l) + 0.193 IFA3(-2) + 0.501 IFA3(-3) (3.387) (2.982) (1.546) (3.602) 0.021 POIL(-3) + 0.177 INF(-l) + 2.943 EXD3 (-2.185) (1.850) (4.690) Sample Period: 1984.2-1987.4, R2 = 0.916, D.W. = 1.744, t-ratios in parentheses.

Ml (2a) Log --= - 3.702 + 0.471 Log GDPQ - 0.005 IS3 WPI (-3.125) (2.553) (-3.045) Sample Period: 1983.2 - 1987.4, R<sup>2</sup> = 0.781, D.W. = 2.008.

QMD (3a) Log --= - 28.968 + 0.013 IS3 + 0.034 IS3(-2) - 0.029 IFA3(-3) WPI (-10.095) (1.770) (4.894) (-2.827) - 0.016 INF(-2) + 5.522 Log GDPQ(-2) + 0.002 POIL (-2) (-1.141) (9.453) (1.477) Sample Period: 1982.4 - 1987.4, R2 = 0.982, D.W. = 2.107.
To determine the level of these benefits we run the PROST (Pensions Reform Options Simulation Tool) model<sup>11</sup> which feeds SSELMA with the evolution of dependency ratios and average replacement rates (the ratio between the average pension and the average wage in the economy). SSELMA uses these two inputs to compute total pensions and other social security expenses, which are proportional to the total wage bill.

# **_The income and expenditures block_**

Households earn their income from wages, returns to capital, interests on bonds and transfers (mainly government and social security transfers and migrants’ remittances). Their expenditures are composed of consumption of goods and services, social security contributions, interest payments and transfers. The composition of their basket of goods and services is determined through the maximization of a Linear Expenditure System (LES) function under their budget constraint.

The government earns income from various taxes (income taxes, corporate taxes, tariffs, production and consumption taxes) and from foreign transfers. Its expenditures consist of government consumption (mainly civil servants wages), social transfers and interest payments on public debt.

# **_The foreign trade block_**

The allocation of output between domestic and foreign markets is modeled as a Constant Elasticity of Transformation (CET) function. On the demand side, the Armington assumption is adopted to describe imperfect substitution between domestic products and imports. The small country assumption holds for imports, which implies that world import prices are exogenous, however, an export demand function is modeled, which means that Moroccan exporters can reduce their prices if they want to increase their market share on international markets.

# **_The closures of the model_**

SSELMA has 5 closures: a macro closure, a government closure, an external balance closure, a closure of the social security system and a labor market closure.

Concerning the macro closure, this version of SELMA is savings driven (households’ marginal propensity to save is exogenous). The government closure chosen consists in fixing government

> 11 Developed by the World Bank Human Development Network..

13
_Other data from the survey_ “Female” is a dummy variable that takes the value of one for women. “Age” is a dummy variable that takes the value of one if a subject is more than 29 years old. “Private university” takes the value one if the participants in the experiment were from the private university. “Montenegrin” denotes whether or not a subject states that his or her nationality is Montenegrin. “Age missing” and “Trust missing” are variables taking the value of one if receivers have not answered either question.

# **7. Results**

# _7.1 Transfers_

Table 2 summarizes the results of the trust experiments.

- P-initiators sent large amounts to both P and S-receivers.

- S-initiators send smaller amounts than P-initiators but sent more to P- receivers.

- P-receivers and S-receivers both exhibited reciprocating behavior.

We note that transfer of S-initiators to S-receivers of 4.8 is a common amount in trust experiments.<sup>25</sup> Table 2. Transfers (in euros)

|Average|Initiator sent|6.9(0-10)|Receiver returned|11.1(0-30)|
|---|---|---|---|---|
|||Private univ<br>|ersity<br>||
|P-initiator sent|to P-receiver|8.2=24.6|P-receiver returned|14.9|
|P-initiator sent|to S-receiver|8.8=26.4|S-receiver returned|14.5|
|||State unive|rsity||
|S-initiator sent|to P-receiver|5.9=17.7|P-receiver returned|8.3|
|S-initiator sent|to S-receiver|4.8=14.4|S-receiver returned|6.3|

was: The average share of earnings returned _within the group_

- S-receivers returned 44 percent of earnings to S-initiators

- P-receivers returned 61 percent of earnings to P-initiators

> 25 See Sapienza et al. (2007), Cox et al. (2009), Greiner et al. (2012).

25