rafmacalaba/datause-encoder-data huggingface dataset:
13 by those nonworkers who indicate they might like to work, also reveals that they have restrictive and In many cases quite unrealistic perceptions of their labor market opportunities. That Is, many have only a very narrow set of jobs and wage levels they are willing to accept, and would not take jobs requiring strenuous working conditions or long commuting distances (Commonwealth Fund 1990).

A diferent insight into older persons' job market opportunities comes from information on joblessness and job finding after plant shutdowns. Older persons displaced from their jobs due to plant shutdowns in the U.S. apparently do have a more difficul **t** ime finding new work, as compared to younger employees displaced from the same firms. Further, when they do obtain employmen **t** , heir eamings are somewhat lower than younger persons. However older workers, after being displaced, appear more likely to remain in their same industry/occupation, as compared to their younger counterparts.'° Whether this illustrates employer bias, or employee reluctance to undertake investments such as skill acquis.tion and migration, is not yet known. Additional studies on other countries' experiences are needed to examine this problem in more depth.

Structural Shifts in Labor Demand: Still another explanation for why older workers' labor market attachment has declined in the last two decades is that older workers may lack computer skills, or their human capital may have depreciated so much, that they are passed over in favor of other more productive inputs. In other words, older workers may suffer disproportionate job loss as a resuit of a shift in the economic composition of jobs, due in part to structural shifts and in part to technological change. Direct tests of this hypothesis have been hampered by the difficulty of measuring structural shift and technological obsolescence, and direct measures of the degree of substitutability between older and younger workers (and capital) are rare." Evidence shows, however, that the trend toward earlier retirement in Europe and the U.S has not been concentrated in sectors experiencing the most economic growth, nor those experiencing the largest declines. Instead, eariier retirement has characterized most if not all the major industrial sectors, suggesting that this argument is less poten **t** han at first it might seem (Jacobs and others 1991a and b; Pampel and Weiss 1983).
Table 13 Redlstributive Effects of Direct Taxes

||**Cmi**|**kindx**||
|---|---|---|---|
|**Yew**|**Pe**capit<br>beforetax<br>**Income**|Pw captafter<br>taxIncorne|Redistribution<br>Effect of Taxes|
|1989|26.5|25.8|-0.7|
|1991|21.8|21.2|-0.6|
|1992|23.4|22.6|-0.8|

# 8. Trends in Welfare in Ukraine

To compare welfar levels over time, we have plotted the generalized Lrenz curve for each year. It was observed that the generalized Lorenz curve shifted upward gradually duiung the period from 1980 to 1991. But in 1992 it shifted down. This demonstrates that welae in Ukraine increased gradually untl 1991 and then it fell to a level below that for 1985. These conclusions are valid for any welfare function which is symmetric and quasi-concave in incomes.

The generalized Lorenz curve can only rank the income distributions for diffeet years without weiling the magnitudes of increase or decrease. To enable us to make statements about the magnitudes, it will be necessary to compute single measures of welfare. A single measure of weae given by equadon (1) takes into account both the size and distribution of income. The estimtes of this measme along with its growth rates are presented in Table 14.

Table 14 Welfare In Ukraine 1980-92

|**Yew**|**Avera**<br>PW**Capit**<br>Rel hoone|_Avrage_<br>PW**CUpita**<br>Wellf|**Annud**Gro<br>Per**Capita**<br>Rea_Moor_|wth**Ra**<br>**Pr Copta**<br>Wow|
|---|---|---|---|---|
|1980|123|80.4|||
|1985|135.6|92|1.97|2.73|
|1989|148.6|109.2|2.32|4.38|
|1990|159.6|119.5|7.4|9.43|
|1991|163.4|127.8|2.38|6.95|
|1992|124.9|95.7|-23.56|-25.12|

30
# **4. Example: HIV/ AIDS gap map**

The systematic review gap map on HIV/AIDS<sup>21</sup> was one of the first evidence gap maps produced by 3ie for the occasion of the 2011 World AIDS day. It was produced with the aim of providing a resource to support strategic decision making on maximizing the effectiveness and cost-effectiveness of the global AIDS response. The framework for the gap map covers key policy-relevant interventions to prevent and address HIV/AIDS in low- and middleincome countries and HIV-related outcome categories along the causal chain. The framework was drawn up based on a review of the systematic review literature, causal chain analysis and key policy documents such as the UNAIDS World AIDS reports (UNAIDS 2010, UNAIDS 2011) and the new Investment Framework for HIV/AIDS (Schwartländer et al. 2011).

Systematic reviews included in the gap map were identified through an extensive search of relevant databases, including PubMed, the Cochrane Library, the 3ie Database of Systematic Reviews, Health Systems Evidence, International Child health Review Collaboration and the International Child health Review Collaboration. The search covered the period from 1993 up to November 2011. We identified 43 systematic reviews and 11 protocols which met our inclusion criteria across 16 broad intervention categories and 13 broad outcome categories.

We produced summaries of each included review and protocol, and assessed the quality of completed reviews using the 3ie checklist adapted from the Supporting the Use of Research Evidence checklist (2011). Evidence on different outcomes (in columns) was then mapped onto different categories of interventions (in rows). Each cell represents one systematic review. The color of each cell represents the confidence rating about the validity of the systematic review findings. Each cell in the online version of the gap map is linked to a userfriendly summary of the systematic review on the 3ie Systematic review database to allow the user to explore the main findings and quality appraisal information in more detail. Below is a brief overview of findings from a subsequent analysis of this subset of reviews.

> 21 http://www.3ieimpact.org/media/filer/2013/12/02/hiv_gap_map.pdf

16
authors assume that, under these circumstances, Mexican exports of goods and non-factor services will grow at an annual rate of 11.2 per cent, a figure which reflects the historical experience of successful liberalization by inward-oriented countries. The failure of the NAFTA negotiations, in tum, is assumed to imply a smaller rate of growth for Mexican exports (7.9 per cent), given the authors' assumption that it would foster "policy retrogression" in Mexico. This lower rate of growth is based on historical data for "collapsed liberalizations" and provides the counterfactual scenario utilized by Hufbauer and Schott in estimating the econoniic impact of the NAFTA.P Their estimates of NAFTA's influence on trade among member countries are much larger than those from conventional CGE models. Hufbauer and Schott (1992, p.60) indicate their "Mexico export gain is 50 percent larger than the most optimistic alternative model, while the US export gain is more than twice as large." The impact of NAFIA on nonmember countries, however, remains marginal (US net exports to third countries are unaffected by NAFIA, while Mexican net exports decline by US$ 3 billion). Unfortunately, the assumptions utilized in their calculations (Mexican export and import shares _vis-a-vis_ the United States are fixed at 75 per cent) limit the utility of their results for estimating trade diversion.

# D. Sectoral Analyses

Sectors with relatively high levels of protection in the United States are natrual candidates for analyses concemed with trade and investment diversion from the perspective of thX non-NAFTA countries. Statistics tabulated in this study (See Tables 2 and 4) indicate tiat 9 Histoncal data on Iibemlizauion experiments is derived from Papageorgiou er.al. (1991).
δ _v_ share parameter in the CES function for _V_ α parameter in the adjustment cost function _er_ nominal exchange rate, price numeraire<sup>_g_</sup> growth rate<sup>_i_</sup> _t_ ∗ weighted average of interest rates for commercial debt and concessionary loans<sup>_i_</sup> _taid_ interest rate for concessionary loans<sup>_i_</sup> _debtt_ world interest rate inclusive of risk premium<sup>_i_</sup> _wt_ world interest rate φ parameter in the purchase price of investment goods ρ rate of consumer time preference<sup>ρ</sup> _tc_ exponent parameter in the CES function for _Q_<sup>ρ</sup> _te_ exponent parameter in the CET function for _Q_ ρ _v_ exponent parameter in the CES function for _V_ ∗ _pet_ world export price ∗ _pmt_ world price of final imports _pmkt_ ∗ world price of capital imports �<sup>_pmn_</sup> _t_ world price of intermediate imports _said_ share of concessionary loans in external debt _tct_ rate of new tax credits to investment _tet_ export tax or subsidies rate<sup>_ty_</sup> _t_ direct income tax<sup>_tm_</sup> _ct_ import duty for final goods _k_<sup>_tm_</sup> _t_ import duty for capital goods<sup>_tm_</sup> _nt_ import duty for intermediate goods _tx t_ domestic indirect tax rate 33
# Generating Gridded Agricultural Gross Domestic Product for Brazil: A Comparison of Methodologies<sup>1</sup>

Timothy S. Thomas<sup>2</sup> Liangzhi You<sup>3</sup> Ulrike Wood‐Sichra<sup>3</sup> Yating Ru<sup>3</sup> Brian Blankespoor<sup>4</sup> Erwin Kalvelagen<sup>3</sup> Keywords: agriculture, regional economic activity, gridded GDP, cross‐entropy, spatial disaggregation JEL: Q10, R12

> 1 The authors thank Juha Siikämaki (IUCN), Tim Robinson (FAO), Jean‐François Arvis (World Bank), Uwe Deichmann (World Bank) and Siobhan Murray (World Bank) for earlier discussions. We also thank the participants of the Workshop on Data Integration: Realising the Potential of Statistical and Geospatial Data that took place in Belgrade, Serbia and was organised in the context of the partnership agreement between UNECE and UN‐GGIM: Europe, with the support of Eurostat and EFTA. We appreciate Juha Siikämaki sharing the non‐timber data with us. We appreciate the support of our institutions and financing from DFID under the World Bank’s Strategic Research Program ‘Big Data’. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

> 2 _Corresponding author_ . International Food Policy Research Institute (IFPRI), Email: tim.thomas@cgiar.org 3 International Food Policy Research Institute (IFPRI)

4 Development Data Group, World Bank, Email: bblankespoor@worldbank.org
FDI data for the sector-level analysis are supplied by the US Bureau of Economic Analysis (BEA). These data give the stocks of US FDI abroad.<sup>11</sup> We use the first difference of the stocks to calculate flows. BEA publishes information on 13 sectors until 1998 and 15 sectors from 1999.<sup>12</sup> We made two changes to the BEA data. We aggregated “Other manufacturing” and “Other industries” into one sector in the pre-1999 data, and “Machinery” and “Computer and electronic products” into one sector in the post-1998 data. The second change was to match sectors over time. Due to a break in the aggregation in 1998 in the BEA data, sector definitions are not exactly the same during the entire period (1989-2004). As our identification strategy is to follow sectors over time and test if post-targeting inflows are significantly higher than pre-targeting inflows (and inflows to non-targeted sectors), we would like to have long time periods before and after targeting. As the break in aggregation appeared around the middle of the period, we would typically have either very few years pre-targeting or very few years post-targeting had we not implemented the matching procedure.

After these two changes, we match BEA sectors to the sector classification used in the Census to collect targeting information. See Table 1 for the concordance and Figure 2 for summary statistics on sectors that are most frequently targeted. We have a maximum of 15 sectors per country. The stock data are available from 1989-2004 (first differenced for 1990-2004). Table 2 shows the 124 countries included in sector sample.<sup>13</sup> For additional statistics on the sectors see Table 3 and Table 4.

The US is one of the top FDI source countries, so focusing on US FDI we capture a large share of the world’s FDI stock. Figure 3, which compares the stock of US FDI to the stock of FDI from other OECD countries in 2000, demonstrates that US was the dominant source country in Latin America, East Asia and industrialized economies. An additional advantage of using the BEA data

> 11 U.S. direct investment abroad is defined as the ownership or control, directly or indirectly, by one US resident of 10 percent or more of the voting securities of an incorporated foreign business enterprise or the equivalent interest in an unincorporated foreign business enterprise. The data capture the cumulative value of parents' investments in their affiliates (source: <u>http://www.bea.gov/bea/ai/0395iid/maintext.htm). Data</u> points suppressed by the BEA for confidentiality reasons are treated as missing. Data points reported as values belonging to the range between -500,000 and 500,000 US dollars are treated as equal to 500,000 dollars. We interpolated missing information on stocks to increase the number of observations.

> 12 From 1999, the BEA-data are classified under the 1997 North American Industry Classification System (NAICS).  Previously, data were classified under the Standard Industrial Classification System (SIC).

> 13 The number of 124 countries corresponds to the baseline specification in Table 9.

16
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These ...ndings resonate well with the existing IO table-based studies, such as Koopman, Wang, and Wei (2012).

We exploit our ...rm-level data to con...rm that the increase in the DVAR not only exists within industries, but also within ...rms. Neither reallocation of resources across industries nor ...rm entry and exit contributes to the increase in the DVAR of aggregate exports. Firmlevel regressions show that the rising DVAR is due to an active substitution of domestic for imported materials by individual processing exporters. Such substitution is revealed at both the intensive margin, represented by a lower cost share of imported materials, and the extensive margin, manifested by decreasing import varieties. Behind this substitution is a continuous decline in the relative prices of domestic to imported input varieties.

We build a simple model to analyze the time-series determinants of a ...rm’s DVAR and show that during the sample period, the continuous tari¤ reduction facing upstream ...rms and the rising FDI since 2000 have contributed signi...cantly to the increase in domestic input varieties and thus the decline in their prices. These micro-level ...ndings provide comprehensive explanations about how Chinese exporters have expanded their activities along global value chains away from the ...nal stages of production. They also highlight that trade and FDI liberalization may actually raise a country’s DVAR, through input-output linkages and spillovers that go beyond the targeted industries.

While it is beyond the scope of the current paper, our approach is general enough to examine the micro-foundation and mechanism of a host of interesting economic issues. It can be used to study the relationship between ...rm DVAR and productivity, and shed light on the desirability for a developing nation to promote high value-added exports as a growth strategy. It can also be used to assess the validity of the proposal for emerging markets to “move up the value chains”or to raise the DVAR.

Regarding the last remark, we have started a new project on measuring the DVAR for a 38
However, crucially, each new FDI project also involves establishing or purchasing a production facility in the destination country (Helpman et al., 2004). The ability of firms to finance the upfront fixed costs of FDI with internal funds varies across sectors. Some sectors are technologically more dependent on external finance, meaning that firms’ desired investment levels typically exceed their internal cash flows. This may be due to a large initial project scale, a long gestation period, a short harvest period, or the requirement for continuing investment (Rajan and Zingales, 1998). Firms in these externally dependent (ED) sectors will have to rely heavily on external finance to engage in FDI since they will only be able to finance internally a small fraction<sup>16</sup> Firms’ access to external finance depends on financial development. Hence, higher SFD should have a _positive FDI-specific access to external finance effect_ on the volume of outward FDI, which ought to be larger in more ED sectors. This is even more likely to be the case if the ED sectors are poorly endowed in tangible assets that might serve as collateral.

Higher SFD can also be expected to increase the number and size of active producers, especially in financially vulnerable sectors (Rajan and Zingales, 1998). Holding other factors constant, a larger sector should be associated with larger FDI flows. This means that higher SFD can have a _positive general access to external finance effect_ on outward FDI in a given sector by increasing the scale of activity in this sector. This decomposition of the effects of SFD into general and FDI-specific channels has been ignored by the existing FDI literature.

Overall, we expect higher SFD to increase the volume of outward FDI and disproportionately so in more financially vulnerable sectors. This hypothesis should apply to greenfield FDI as well as to cross-border mergers and acquisitions (M&A FDI). It is true that the purchase of an existing firm through a cross-border M&A transaction may allow a foreign investor to save on some costs, such as the identification of distribution and

> 16External finance is a complementary source of funds for these firms because the range of their investment opportunities expands with its availability. This does not necessarily imply that external finance will be accessed after exhaustion of internal funds. Firms can raise more external funds than the level of their internal funds would dictate if they find advantageous to do so, e.g. to benefit from the tax deductibility of interest payments or simply to maintain financial slack. See Graham and Harvey (2001)’s survey of chief financial officers on their capital structure choice.

6
Table 4: Balance on School Characteristics

|||Mean|(SD)|||P-Value||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||Overall|Control|GS|GS + R|GS vs. Control|GS + R vs. Control|GS vs. GS + R|
|Total students in F2|132.595<br>(10.229)|143.86<br>(17.626)|132.964<br>(21.954)|120.768<br>(12.703)|0.699|0.289|0.631|
|Total qualifed teachers in F2|4.832|4.638|4.946|4.951|0.545|0.412|0.992|
||(0.184)|(0.262)|(0.435)|(0.275)||||
|Student-teacher ratio in F2|28.228|29.151|28.054|27.341|0.812|0.626|0.876|
||(1.696)|(2.646)|(3.767)|(2.592)||||
|Does this school have two shifts?|0.602<br>(0.046)|0.583<br>(0.083)|0.667<br>(0.076)|0.553<br>(0.082)|0.463|0.793|0.311|
|Form 2 pass rate in 2015 for English|50.99|50.108|51.582|51.351|0.861|0.88|0.978|
||(3.342)|(5.724)|(6.088)|(5.849)||||
|Form 2 pass rate in 2015 for Math|44.153|38.796|47.474|47.194|0.33|0.309|0.974|
||(3.477)|(6.023)|(6.445)|(5.541)||||
|Form 2 pass rate in 2015 for Science|48.731|46.033|47.703|53.597|0.85|0.329|0.504|
||(3.411)|(5.43)|(6.901)|(5.428)||||
|Average teaching experience in month|150.491<br>(6.724)|139.479<br>(13.055)|149.542<br>(9.991)|162.742<br>(11.91)|0.541|0.19|0.397|
|Observations|187|62|64|61||||

_Notes_ : This table reports the balance test for various school level variables captured in baseline survey. Means, standard deviations and p-value of differences is reported by GS (Goal-Setting), GS + R (Goal-Setting + Recognition) and Control group.
5 75 developed and developing countries in the World Bank’s recently released Distortions to Agricultural Incentives database (Anderson and Valenzuela 2008) over the period 1955 to 2007.

In addition to being useful to summarize the agricultural and food policy regime in an individual country, the Anderson-Neary scalar index measures can be usefully adapted to summarize two other aspects of agricultural policy: they can be computed for individual policy instruments, to show the relative contributions of different policy instruments to reductions in trade and welfare (Croser and Anderson 2010); and they can be computed to measure the trade- and welfare-reducing effects of policy in a single global or regional commodity market (Croser, Lloyd and Anderson 2009). In this paper we utilize the methodology to estimate all three types of indexes. In doing so, we extend the theory and analysis to include nontradables, which have not been addressed in previous studies.

# **_Country level trade- and welfare-reduction indexes_**

To capture distortions imposed by each country’s border and domestic policies on its economic welfare and its trade volume, we adopt the methodology from Lloyd, Croser and Anderson (2010). Those authors define a Welfare Reduction Index (WRI) and a Trade Reduction Index (TRI) and estimate them by considering separately the distortions to the producer and consumer sides of the agricultural sector (which can differ when there are domestic measures in place in addition to or instead of trade measures). As their names suggest, the two indexes respectively capture in a single indicator the (partial equilibrium) welfare- or trade-reducing effects of all distortions to consumer and producer prices of farm products from all agricultural and food policy measures in place. The WRI and TRI thus go somewhat closer to what a computable general equilibrium (CGE) can provide in the way of estimates of the trade and welfare (and other) effects of price distortions, while having the advantage of providing an annual time series. Fortuitously, estimates of the actual price distortions are available in the NRAs and CTEs of the World Bank’s Distortions to Agricultural Incentives database.

The derivation of the two indexes for _n_ import-competing industries (see Appendix) leads to the expressions for the TRI and WRI for the import-competing sector of a country shown in Box 1.