rafmacalaba/datause-encoder-data huggingface dataset:
costly, consumption-smoothing mechanism relative to alternative, desperate measures (e.g., Chetty and Looney 2006).

A priori, t here are good reasons for introducing social safety-net programs to mitigate monga and other proximate causes of seasonality of poverty. Thus, assessing their benefits is essential for better targeting and resource allocation. One approach to assessing such benefits is to quantify their safety-net (social-protection) roles against their income-augmenting roles. Even though safety-net measures are geared toward mitigating economic stress caused by shocks, it is possible that easing economic stress may have a positive effect on assets and longer-term income; that is, a targeted program may reduce chronic, as well as transitory, poverty. But in some cases, the short-term benefits of safety-net programs may exceed long-term ones. Ravallion, van de Walle, and Gautam (1995), for example, showed that the effect of a social safety-net program on persistent poverty may be low, even if it contributes substantially to reducing transitory poverty. Using a twopoint (1987 and 1989) panel data set from Hungary, this study attempted to distinguish between the roles of protecting and promoting the poor, while evaluating the country’s cash benefit programs. The study’s findings suggest that, in the absence of safety-net interventions, the poverty rate would rise by 7.6 percentage points, with 6.6 percentage points due to the transition from non-poor to poor status between the two years and 1 percentage point due to the inability to move out of poverty. Thus, this safety net was found to be more effective in preventing transient poverty than reducing chronic poverty.

In contrast, Devereux (2002) found that a social safety-net program could alleviate both transitory and chronic poverty. This study distinguishes among three dimensions of poverty—lowlabor productivity, vulnerability, and dependency—and two categories of anti-poverty intervention—livelihood promotion and livelihood protection. Within this framework, social safety nets were seen as publicly-funded transfer programs with consumption smoothing, as opposed to 4
FIGURE 5 Exchange rate responses to global shocks

# A. Global demand shocks

# B. EMDEs: Global demand shocks

<!-- Start of picture text --> Percentage points Percentage points<br>Interquartile range Median<br>2.5 Interquartile range Median 4<br>3<br>2.0 2<br>1<br>1.5<br>0<br>1.0 -1<br>0.5<br>0.0<br>-0.5<br>All Advanced EMDEs Trade Exch. Inflation Energy Current<br>economies openness regime target account<br>C. Global supply shocks D. EMDEs: Global supply shocks<br>Percentage points Percentage points<br>1.0 Interquartile range Median 3 Interquartile range Median<br>2<br>1<br>0.5<br>0<br>-1<br>0.0<br>-0.5<br>All Advanced EMDEs Trade Exch. Inflation Energy Current<br>economies openness regime target account<br>E. Oil price shocks F. EMDEs: Oil price shocks<br>Percentage points Percentage points<br>1.0 Interquartile range Median 3 Interquartile range Median<br>2<br>0.5 1<br>0<br>0.0 -1<br>-2<br>-0.5<br>-1.0<br>-1.5<br>All Advanced EMDEs Trade Exch. Inflation Energy Current<br>economies openness regime target account<br>Low High Float Peg. Non-IT IT Importer Exporter Deficits Surpluses<br>Low High Importer Exporter Deficits Surpluses<br>Low High Peg. IT Importer Exporter Deficits Surpluses<br>Float Peg. Non-IT IT<br>Float Non-IT<br><!-- End of picture text --> Note: One-year impulse response of the exchange rate to global shocks (demand, supply, and oil prices) from country-specific factor-augmented vector autoregression models estimated for 29 advanced economies and 26 EMDEs over 1998-2017. Bars show the interquartile range and markers represent the median across countries. A positive number indicates an appreciation. EMDEs = emerging market and developing economies; IT = inflation targeting. B.D.F. Countries with “high” trade openness are defined as those with above median trade-to-GDP ratios; all others are considered to have “low” trade openness. Exchange rate and IT regimes are based on IMF classifications (see the Appendix for details). Energy exporters are defined as in World Bank (2018); all other countries are considered energy importers. Countries with current account deficits are those with a negative average current account balance over 1998-2017.

28
27

# **<u>Notes</u>**

1 These figures for official international remittances do not include the large – and unknown – amount of international remittances which return to developing countries through unrecorded, informal channels.

2 Recent works on the skill composition of migrants include Niimi and Ozden (2006) and Faini (2003, 2007). Faini (2007) appeared as this paper was going to press.

3 Low-income and middle-income countries are those which are classified as such by the World Bank (2000, pp. 334-335). Lowincome countries are those with 1999 GNP per capita $756 or less; middle-income countries are those with 1999 GNP per capita of $756-$9,265.

4 Although the IMF data on international remittances include most countries, some countries do not report remittances data to the IMF. For example, in 2000 about 85 countries did not report any remittances data to the IMF; this list of countries includes at least 5 countries which are known to be large, labor-sending countries. For these 5 countries (Algeria, China, Gambia, Malaysia and Vietnam) remittances data are therefore taken from estimates made by the World Bank, Global Economic Prospects (2006: Table 4A.1.1).

5 For a recent attempt to estimate the size of unrecorded, informal remittances in the developing world as a whole, see Freund and Spatafora (2005).

6 For a full list of these 157 developing countries, see World Bank (2000, pp. 334-335).

> 7 For the purposes of this study, OECD (Europe) includes 18 countries:  Austria, Belgium, Czech Republic, Denmark, Finland, France, Germany, Hungary, Ireland, Italy, Luxemburg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland and United Kingdom.  No migration data are available for Greece, Iceland, Poland, Slovak Republic and Turkey.

8 It should also be noted that the Docquier and Marfouk (2005) data do not distinguish between permanent and temporary migrants; rather these data only count the total number of people who are “foreign born” and of working age living in the OECD.

9 To ensure compatibility across countries, all of the poverty lines in Appendix Table 1 are international poverty lines, set at estimates of $1.08 per person per day in 1993 purchasing power parity (PPP) exchange rates. The PPP exchange rates are used so that $1.08 is worth roughly the same in all countries. PPP values are calculated by pricing a representative bundle of goods in each country and comparing the local cost of that bundle with the U.S. dollar cost of the same bundle. In calculating PPP values, the comparison of local costs with U.S. costs is done using conversion estimates produced by the World Bank.

10 In this paper the terms “expenditure” and “income” are used interchangeably.
- 21 -

Krugman (1987) has suggested that we regard the "debt-overhang" as a "Debt Laffer Curve" problem, the question at hand being: does more nominal debt imply a lower market value for this debt? A test of the "debt overhang," according to this formulation, therefore amounts to deciding whether the elasticity of the market price of the debt with respect to its face value is strictly larger than one (in absolute value). Certainly if this elasticity is larger than one, then one can make the case that the lenders operate inefficiently. However, an elasticity equal to or smaller than one is not in itself sufficient to accept the hypothesis that the lenders reschedule the debt efficiently. In this section, we shall stick to Krugman's test, but certainly more work is needed in order to investigate the efficiency of the rescheduling process which has been undertaken since 1982.

Previous attempts to measure the elasticity of the price of the debt with respect to its nominal value systematically found a low estimate. A study by Purcell and Orlanki, following a previous estimate by Sachs and Huizinga, reported an elasticity of 0.34. We have estimated an equation, representative of these earlier studies, as follows: (21) Log p = 5.06 - 0.653 log D/X - 2.231 A/D - 1.016 R/D (0.152) (0.603) (0.373)

- 0.274 Dummy 1987.12

(0.132) B<sup>2</sup> * 0.560 pooled equations for 1986.12 and 1987.12 data; 60 degrees of freedom. (Standard errors in parenthesis).

p: price of the debt (cents on the dollar).

D: debt; X: exports; A: arrears; RB amount of rescheduling since 1982.
BTA came into effect on December 10, 2001, the U.S. granted MFN status to Vietnam lowering the tariff rates across the board. As a result, Vietnam’s exports to the U.S., in particular, those of labor-intensive manufacturing goods, expanded dramatically. Starting from a very low level, the U.S. absorbed 38.3 percent of Vietnam’s textiles exports, 56.9 percent of apparel, 16.6 percent of footwear/leather, and 26.2 percent of furniture and miscellaneous manufacturing exports by the year 2004 (the U.N. Comtrade System). Finally, unlike most of the cases in the literature, labor reallocation toward more labor-intensive manufacturing appears to have occurred in the aftermath of the BTA.

Since _doi moi_ , Vietnam has undertaken a number of reforms which introduced market forces in determining wages, including labor market reforms, privatization and rationalization of state owned enterprises (SOEs) and a variety of legal, regulatory and institutional reforms <mark>(Van Arkadie & Mallon, 2003). T</mark> his paper undertakes a strategy of isolating the effects of the U.S. tariff cut on wages from the impacts of the domestic reforms, following recent literature to explore regional variation in exposure to trade in analyzing consequences of trade reforms (see, for instance, Castilho, Menéndez, & Sztulman, 2011; Chiquiar, 2008; Coello, 2009; Hanson, 2005; McCaig, 2011; Topalova, 2010; and Wei & Wu, 2001). A contribution of this paper is to model explicitly the change in labor demand induced by exports as a mechanism through which exports would influence wages. I construct an Export Index at the province level, taking account of Vietnam’s provincial industrial composition and its export- and labor-intensities. In order to overcome potential endogeneity, the U.S. tariff cut measure inspired by Topalova (2010) is used as an instrument. Then, using the panel individuals from the _Vietnam Household Living Standards Surveys_ (VHLSS) who were interviewed both in 2002 and 2004, I evaluate how the 4
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most important sources of uncertainty associated with the dynamic allocation of land. These sources include (but are not limited to) variations in agricultural yields, liquid fossil fuels' costs, _c_<sup>_f_</sup> _,_ and the future valuation of GHG abatement, expressed through the stringency of the GHG emissions constraint, _<u>z</u>_<sup>_~~L~~_</sup> . To do this, we utilize the model to simulate the eects of the following scenarios, each of which has the potential to put greater pressure on the world's land resources:<sup>18</sup> Scenario A: The permanent decline in potential food crop yields due to adverse eects of climate change. The impact of climate change on food crop yields depends critically on their phenological development, which, in turn, depends on the accumulation of heat units, typically measured as growing degree days (GDDs). More rapid accumulation of GDDs as a result of the climate change speeds up phenological development, thereby shortening key growth stages, such as the grain lling stage, hence reducing potential yields (Long 1991). However, raising concentrations of CO 2 in the atmosphere results in an increase in potential yields due to the CO 2 fertilization eect (Long et al. 2006). When temperature increases are moderate, as we assume in the model baseline, these eects oset each other, and the food crop yields are unaected by the climate change. In scenario A we consider rapid warming, corresponding to Representative Concentration Pathways 8.5 GHG forcing scenario (Moss et al. 2008), so the former eect quickly dominates the latter. We assume that yields of celluslosic feedstocks do not change under scenario A, as they appear to be relatively insensitive to further temperature increases (Brown et al. 2000).

Scenario E: The permanent increase in growth of liquid fossil fuel costs over the medium term. Petroleum and natural gas prices are key factors aecting the competitiveness of biofuels (Hertel et al. 2010, National Research Council 2011) as well as the price of nitrogen fertilizer which is critical for boosting agricultural yields (USGAO 2003). To characterize energy price increases in scenario E in the medium term, we employ the data for the high oil price scenario from Energy Information Administration (EIA) growth projections for 2035 (EIA 2010).<sup>19</sup> We assume that the extent to which the energy prices can grow in the long term is limited by induced innovation (Popp 2002) and available backstop technologies (Nordhaus 1973)], so that it reaches its maximum of $250/bbl at

> 18We show the model sensitivity to changes in other important model parameters in technical appendix, section E.1.

> 19Of course there are many factors contributing to a potential decline of highly uncertain fossil fuel costs (Pindyck 1999). Our choice of rising fossil fuel costs in this scenario is motivated by understanding global land use decisions under greater resource scarcity.

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research (Azoulay et al 2015). In a similar vein, Ganguli (forthcoming) studies a large-scale grant program for scientists in the former Soviet Union, funded by financier George Soros, and finds that the grants more than doubled researcher publications.

Branstetter and Sakakibara (2002) and Sakakibara and Branstetter (2003) measure the impact of government-sponsored company-to-company cooperative R&D projects in Japan and the US using matched control groups of firms. They find that participating in these consortia leads to a higher number of patents. Other papers have used matching techniques or structural models to estimate the effect of EU sponsored research joint ventures, some of which include collaborations between firms and universities. These studies tend to find a positive association between participating in a research joint venture and firm productivity and profitability (see for example, Benfratello and Sembenelli 2002 and Barajas, Huergo, and Moreno 2012), but they do not distinguish between joint ventures that include universities and those that did not.

Our findings are thus in line with previous findings regarding the positive impact of R&D subsidies on publications, patents and commercialization. To our knowledge, ours is the first paper to analyze data from a survey that was specifically conducted to measure the impact of the subsidy program.<sup>4</sup> These data allow us to consider a broader range of outcomes than previous studies, and, in particular, to document the positive effect of Poland’s In-Tech program on science-industry collaboration, thus adding a new finding to the literature.

The rest of this paper is organized as follows. Section 2 describes the In-Tech program and its application process and scoring. Section 3 discusses the RD design and follow-up survey. Section 4 presents the results and Section 5 concludes.

# **2. The In-Tech Program**

Poland’s National Center for Research and Development (NCBiR) is a government entity whose programs account for around 40 percent of the national research and development budget. One of the programs it offers is Innotech, which is designed to support research entities and businesses in

> 4 Previous studies typically rely on balance sheet data and firm surveys conducted by the country’s statistical agency. They obtain information on publications or patents by matching researcher names or firm names to databases of publications and patent applications.

5
**<u>Table 4: Test for Short-term Policy Effects</u>**

||<br>**Parcels reg**|<br>**. first time**|**Register**|**ed sales**|**Reg. mo**|**rtgages**|**Built area**|
|---|---|---|---|---|---|---|---|
||**Total**|♀ C**o-own**|**Total**|♀ C**o-own**|**Total**|♀ C**o-own**|**Registered**|
|Syst. dummy (SyD)|9.188***|8.004***|0.081***|0.087***|0.023|0.022*|0.142***|
||(0.333)|(0.306)|(0.028)|(0.018)|(0.018)|(0.013)|(0.008)|
|Policy # regist. 2009|–0.086***|–0.046*|0.011***|0.023***|0.009***|0.006**|0.000|
||(0.017)|(0.025)|(0.003)|(0.004)|(0.002)|(0.003)|(0.001)|
|Policy # initial value||-0.042|–0.386***|–0.544***|–0.201*|-0.091|0.091**|
|||(0.064)|(0.106)|(0.093)|(0.115)|(0.151)|(0.037)|
|R-squared|0.474|0.478|0.061|0.067|0.039|0.028|0.642|
|ME: policy|–0.390***|–0.260***|0.033***|0.017***|0.036***|0.007**|0.008***|
||(0.076)|(0.073)|(0.010)|(0.004)|(0.008)|(0.003)|(0.002)|
|Mean dep. var.|0.163|0.052|0.038|0.012|0.030|0.007|0.067|
|SDdep. var|0.600|0.293|0.221|0.117|0.183|0.084|0.092|

_Note:_ All regressions are at 250m x 250m blocks in years 2000–14 with systematic first-time registration phased in over 2011–13. Initial value is the pre-program (2009) number of parcels with female co-owners (col. 2); the mean annual number of registered sales and mortgages with female co-owners (cols. 3 (4) and 5 (6); and the built-up area overlapping with registered parcels in col. 7. Controls include population density, block and year fixed effects, a constant, and, for regressions reported in cols. 1–4, the block’s share of built-up area throughout. The number of observations throughout is 22,710 (1,514 annually for 15 years). Mean and standard deviation for the dependent variable are for the pre-program period. Standard errors are in parentheses. * p<0.10, ** p<0.05, *** p<0.010.

23
> - 11 -

in exchange rates, 1/ interest rates, inflation and real activity by taking the residuals from autoregressions on quarterly data. Prices were found to respond to exchange rate surprises as predicted by the theory. That is, prices tend to appreciate with unexpected depreciations of the dollar, and vice versa. However, it was found that the interest, inflation and real activity innovation effects were relatively poorly defined. This may have been due to the fact that the measurement of news was too crude and that use of quarterly data effectively masked any news element. It was also concluded that there was evidence of weak-form inefficiency in the London Metal Exchange. This result concurs with findings by Barnhart (1988) and PH.

1/ Exchange rates were measured as a CNP-weighted index of OECD countries exchange rates with respect to the U.S. dollar. Interest rates were the U.S. Treasury bill rate. Industrial production (to account for real activity) and inflation rates were weighted indexes of OECD countries.
# African Mining, Gender, and Local Employment

Andreas Kotsadam<sup>_∗_</sup> and Anja Tolonen<sup>_†‡_</sup> March 11, 2015

> _∗_ Department of Economics, University of Oslo. andreas.kotsadam@econ.uio.no

> _†_ Department of Economics, University of Gothenburg. anja.tolonen@economics.gu.se

> _‡_ Acknowledgments: First version of this paper was circulated under the name ’Mineral Mining and Female Employment’, OxCarre Working Papers 114, June 2013. We are thankful to James Fenske, Edward Miguel, Andreea Mitruut, Måns Söderbom and Francis Teal for insights and support. We thank Christa Brunnschweiler, James Cust, Henning Finseraas, Niklas Jakobsson, Amir Jina, Jo Thori Lind, Deirdre McCloskey, Halvor Mehlum, Dale Mortensen, Hanna Mühlrad and Juan Pablo Rud for helpful comments. The paper has benefited from comments by seminar and conference participants at UC Berkeley, Gothenburg University, Oxford University, the Norwegian University of Science and Technology in Trondheim and the Institute for Social Research in Oslo, and conference participants at the 8th Annual Conference on Economic Growth and Development 2012, CSAE 2013, the Annual World Bank conference on Land and Poverty, 2013, NEUDC 2013, WGAPE fall 2013, PacDev 2014 and 9th IZA/WB conference on Development and Labor. The project is part of research activities at the ESOP center at University of Oslo. ESOP is supported by The Research Council of Norway. Anja Tolonen is thankful for project funding from Lars Hjerta Research Support. We thank OxCarre for hospitality and access to RMD data. We also thank Frikk Nesje for excellent research assistance. A significant part of the research for this paper was conducted while Anja Tolonen was visiting the Trade and International Integration team of DECRG, The World Bank.