rafmacalaba/datause-encoder-data huggingface dataset:
achieved their objectives, a desire to ‘modernize’ agriculture and open the way to large-scale mechanized cultivation has prompted some developing countries to also consider consolidation measures.

In light of high levels of population density, and a large number (4.8 on average) of small (< 0.2 ha each) parcels, Rwanda has taken active measures to increase security and gender equality of access to land. It has also promoted the consolidation of land holdings or prevention of further fragmentation, which has given rise to debate by experts. Many hold that, as in Rwanda, where labor costs are modest, mechanization largely absent, distances from the homestead to fields are small, variability of production high and other mechanisms to diversify risk limited, fragmentation may impose few additional costs but provide tangible benefits.

A nationally representative survey allows us to explore fragmentation-induced effects empirically and, by more precisely measuring travel times between parcels, also contribute to the methodological debate. Both a stochastic frontier production function and a yield function suggest that, by reducing the incidence of production shocks, fragmentation provides tangible benefits without increases in cost or reductions in the efficiency of agricultural production. Prohibiting fragmentation is likely to run counter to strong economic incentives and may thus bring limited benefits or even have unintended negative impacts.

While the specific results obtained here may in part be attributable to Rwanda’s conditions at the time of the data collection, the use of GIS/DEM based between parcels and homestead to parcel distance measures is applicable beyond the case at hand. Although applying it to African settings where, as a result of less severe land pressure and higher labor costs, scope for mechanization may be higher, it could help to better understand the factors at play. In doing so, it could help in understanding not only land markets, but also the factors that may facilitate or constrain use of mechanization.

14
Table 1: Summary of data

|**Country**|Admin. Unit<br>(Division level)|Survey years|1st-stage<br>observations|2nd-stage<br># locations|% national<br>population|
|---|---|---|---|---|---|
|Guatemala|Departmento (1)|2006, 2011, 2014|58,030|22|100|
|Chile|Comuna (3)|2000, 2003, 2006,<br>2009, 2011, 2013|405,221|328|99.4|
|Honduras|Municipio (2)|2004 - 2011, 2013|43,261|275|97.8|
|Peru|Distrito (3)|2000 - 2014|459,915|1,428|96.3|
|Costa Rica|Distrito (3)|2002 - 2006,<br>2008 - 2010|117,517|401|94|
|El Salvador|Municipio (2)|2014|27,117|220|93.2|
|Nicaragua|Municipio (2)|2001, 2005|24,730|116|90.2|
|Ecuador|Parroquia (3)|2005 - 2012, 2014|237,801|637|88.4|
|Uruguay|Municipio (2)|2005|15,915|13|87.6|
|Bolivia|Provincia (2)|2006, 2012|14,874|73|83.9|
|Dominican|Municipio (2)|2000 -2014|131,608|207|80.8|
|Republic||||||
|Brazil|Municipio (2)|2000|1,809,596|1,488|80.7|
|Panama|Provincia (1)|2003 - 2008,<br>2010 - 2013|186,956|9|80|
|Argentina|Aglomerado (1)|2000 - 2011, 2013|225,261|13|75.9|
|Mexico|Municipio (2)|2000, 2002,<br>2008, 2010|94,105|430|64.6|
|Colombia|Municipio (2)|2008, 2009, 2010|231,349|212|60.9|
|Total|||4,083,256|5,872|77.9|

from SEDLAC (and IPUMS for Brazil). Workers under 14 years of age are excluded from the calculation, to prevent confounding between a large share of young population and low educational averages. Robustness tests were carried out using instead percentage of workers with tertiary education, following Behrens et al. (2014) and Chauvin et al. (2017).

For most countries, the locations that our data set captures cover more than 80% of the total national population, as calculated with GPW 2015 and shown in table 1. Table 2 shows summary statistics of the data used in the 1st-stage estimation calculated using survey weights. As expected when working with developing countries, the percentage of workers with complete higher education is rather small (an overall average of 11.3 percent in our sample vs 35 percent as the average of OECD (Organisation for Economic Co-operation and Development) countries and 43 percent for the US, as reported by the OECD for 2017 (OECD, 2017)). Similarly, the working population is younger (35.8 in our sample vs a median of 41 years in the US for 2014 according to the Bureau of Labor Statistics, BLS (2017)).

Table 3 provides summary statistics of the specific variables used in the second stage.<sup>25</sup>

> 25Population density for Argentina seems significantly lower than for the rest of the sample. This is explained partially by the fact that we are using an administrative level (aglomerados) for Argentina, which is generally slightly larger than the administrative units used in other countries. This choice is due to data constraints.

10
36 In an study addressing much the same issues in the context of a developing country, Behrman and Deolalikar (1989) analyzed the survival of medium and large manufacturing establishments in Indonesia over the period 1975-85.11 Their data included all manufacturing establishments with 20 or more employees in 1975. This range excludes the smallest and most vulnerable firms; in the Wisconsin data it corresponds to excluding almost two-thirds of the firms. Nevertheless, Behrman and Deolalikar report failure rates that "seem striking": the mean years of survival after 1975 is 6.4, and only about 45% of the establishments survived through 1985. As in other data sets, both age and size are positively related to survival, as are operation in a heavy industry sector, and productivity (value added/employee). Among ownership forms, the corporate-or-partnership ownership form shows lower expected survival than individual or cooperativeowned establishments when other variables are controlled. This might be attributable to the application of a more rigorously economic survival test, in particular, one that captures opportunity cost considerations more completely. Finally, foreign equity participation is significantly favorable to " Although the running head of their article is "Firm Survival Duration in a Developing Country," Behrman and Deolalikar refer only to establishments in the text. The -- relationship between firm survival and establishment survival including the question of whether an establishment death is recorded when a single-establishment firm changes location -- is not discussed. It is likely, however, that disappearances of establishments from the survey do reflect firm deaths in most cases, except for the largest establishments -- where disappearances are rare in any case.
**Table 2 Financial integration and its policy determinants** (millions of US dollars, except where otherwise indicated)

||_Interme_<br>|_diate exchang_<br>|_e rate_<br>|_Finan_<br>|_cial develop_<br>|_ment_<br>|_Sove_<br>|_reign wealth_<br>|_fund_<br>|
|---|---|---|---|---|---|---|---|---|---|
|_Item_|_No_|_Yes_|_Ratio_|_No_|_Yes_|_Ratio_|_No_|_Yes_|_Ratio_|
|_Assets_||||||||||
|FDI|4,727.6|504.0|0.11|782.1|4868.0|6.22|422.2|1,1402.8|27.01|
|Portfolio|30,001.2|4,041.4|0.13|15,605.4|26539.1|1.70|2,470.6|4,4687.5|18.09|
|Debt|54,809.7|9,340.8|0.17|14,747.5|45724.2|3.10|8,592.9|10,8749.5|12.66|
|Total|107,720.7|18,590.7|0.17|32,432.6|93895.6|2.90|17,062.7|22,2520.4|13.04|
|_Liabilities_||||||||||
|FDI|12,368.5|5,684.1|0.46|4,422.0|13510.6|3.06|5,799.2|20,892.7|3.60|
|Portfolio|11,56.6|211.9|0.18|63.6|1234.5|19.40|318.0|1,919.1|6.03|
|Debt|23,361.2|13,721.3|0.59|11,213.4|25098.9|2.24|14,103.1|33,645.8|2.39|
|Total|36,886.3|19,600.9|0.53|15,710.2|40161.4|2.56|2,0326.0|56,457.6|2.78|
|_Aggregates_||||||||||
|Capital account|173.2|94.4|0.55|212.1|87.8|0.41|163.9|91.2|0.56|
|NFA|7,0834.5|–1,010.2|–0.01|16,722.4|53734.3|3.21|–3,263.2|166,062.8|–50.89|
|Reserves|18,887.9|8,268.0|0.44|10,500.4|18516.4|1.76|6,660.5|41,055.4|6.16|

_Source_ : Authors, based on Lane and Feretti 2015. _Note_ : Countries whose credit to the private sector is above (below) the median are assumed to have a higher (lower) level of financial development.

# **4. Econometric Specification**

To examine the determinants of the accumulation of NFA and its components, we introduce several policy variables. First, we include a variable measuring the existence of an SWF. For long-term investors, SWFs can play a stabilizing role in financial markets by supplying liquidity and reducing market volatility. Second, we include _Ln_ ( _Credit_ ), a variable measuring financial development (the share of credit to the private sector), as countries with a higher level of financial development are more likely to attract both FDI and portfolio investment. Third, we control for the exchange rate regime ( _Interm_ ) as well as whether the country is a net oil exporter ( _Oil_ ). The econometric specification is as follows: _Yit_ = _α0_ + _α1 SWFit_ + _α2Intermit_ + _α3 Ln_ ( _Credit_ ) _it_ + _α4Oilit_ + _εit_ Where _Y_ = the share of FDI to GDP (assets and liabilities), debt to GDP (assets and liabilities), portfolio investment to GDP (assets and liabilities), total assets and liabilities, reserves to GDP, and the capital account, and _εit_ is the discrepancy term.

Two remarks are worth mentioning. First, we run all regressions using a random effects estimator, as the Hausman test showed that it is more efficient. Second, in some regressions, we distinguish between oil exporters and oil importers, as in general oil exporters have more assets and oil importers more liabilities.

12
||(0.569)|(0.508)|(-0.145)|(-0.135)|(2.130)<br>(-0|.701)|
|---|---|---|---|---|---|---|
|Test b4+b6= 0|-3.118|-3.828|-5.849|**-12.84****|-3.737<br>-2|3.83|
|χ<sup>2</sup>(1)|0.736|1.289|0.314|5.231|0.615<br>2|.051|
|χ2 > Prob|0.391|0.256|0.575|0.0222|0.433<br>0|.152|
|Observations|1,929|1,929|1,929|815|815<br>|815|
||Local rainfall va<br>|riabilityin non-flo<br>|odprone Upazilas<br>|(Continued)<br>|||
||(13)|(14)|(15)|(16)|(17)|(18)|
|Dependent variables|Both self|employed in the s|ame sector|Both|self employed in agri|culture|
|Policyinteraction term:|Credit|SafetyNet|Market|Credit|SafetyNet|Market|
|CV Rain CRU (b4)|**-11.19****|**-10.10****|**-15.20****|**-12.37****|**-12.22*****|**-14.79***|
||(-2.436)|(-2.411)|(-2.225)|(-2.411)|(-2.678)|(-1.887)|
|Credit X CV Rain CRU (b6)|4.837|1.947|23.05|1.144|0.521|11.79|
||(0.786)|(0.397)|(1.082)|(0.163)|(0.0947)|(0.492)|
|Test b4+b6= 0|-6.350|**-8.150***|7.853|**-11.22****|**-11.70****|-2.998|
|χ<sup>2</sup>(1)|1.928|3.685|0.249|5.391|6.305|0.0297|
|χ2 > Prob|0.165|0.0549|0.618|0.0202|0.0120|0.863|
|Observations|1,929|1,929|1,929|1,929|1,929|1,929|

Robust z-statistics in parentheses based on PSU level clusters *** p<0.01, ** p<0.05, * p<0.1. Full estimation results are in annex Table A7, and Table A8 16
constant that also does not vary across ...rms within the same industry: Note that the cross-sectional distribution of DV AR does not depend on the distribution of ...rm productivity under CES preferences, as long as markup is constant across ...rms. Empirically, if we observe varying DV AR across ...rms within the same industry-year, it indicates that the CES preference assumption is not supported and that the industry is likely not perfectly competitive.

2. Time-series movement of DV AR within ...rms Eq. (36) shows that the time-series movement of DV AR is determined by the price of imported inputs to domestic inputs, PPt<sup>D</sup> <u>t</u><sup>I,whichiscommonacross...rmswithinthesame</sup> industry-year. Factors that a¤ect PPt<sup>D</sup> <u>t</u><sup>I</sup> will a¤ect a ...rm’s DV AR over time. It is worth emphasizing that factors that do not a¤ect PPt<sup>D</sup> <u>t</u><sup>I</sup> directly, such as the ...rm’s wages (w) or productivity (�i), do not directly a¤ect the time-series movement of DV AR within ...rms.<sup>57</sup>

# References

- [1] Ahn, J., A. Khandelwal, and S.J. Wei (2011) “The Role of Intermediaries in Facilitating Trade,”Journal of International Economics, vol 84, 73–85.

- [2] Cebeci, Tolga, Fernandes, Ana, Freund, Caroline. and Martha Pierola (2012). “Exporter Dynamics Database,”World Bank Policy Research Working Paper 6229.

> 57Domestic wages can still indirectly a¤ect ...rm DV AR through a¤ecting the price of domestic materials. In the regression analysis below, controlling for the relative price of materials, we should expect no impact from wages on ...rm DV AR.

10
the conditions for the Todaro paradox to hold.<sup>38</sup> At best empirical tests have provided microeconomic evidence consistent with the migration incentives present in the HarrisTodaro model, measuring for instance how rural dwellers respond to an increase in the wage differential (see Section 3). But _this type of evidence does not provide a real test of the link between urban unemployment and migration_ . In other words, the validity of the model has not been clearly established even though it certainly influenced policies for decades.

Many countries have implemented policies aimed at forbidding migration from rural to urban areas. In South Africa, the Apartheid system (1948-1994) used extreme controls to monitor what was meant to be the temporary migration of rural workers to cities. In Indonesia, the ‘transmigration program’, a policy of resettlement from high to lowdensity areas has been implemented over several decades, resulting in the relocation of more than eight million people between 1969 and 1995 (Humanitarian Policy and Conflict Research, 2002). Similarly, in China, the _Hukou_ or household registration system initiated in 1955 required that all residents live in the places where they were born and obtain permissions to move (see Cheng and Selden, 1994, for more details). Until the 1970s, migrants were even transferred back to their village in order to prevent ‘overurbanization’. Some of these programs did meet their objectives of restricting rural to urban migration, but very few have been subjected to a rigorous analysis of their welfare costs. Au and Henderson (2006)’s empirical finding suggest that migration restrictions in China have maintained surplus labor in rural areas and led to insufficient agglomeration of economic activity in cities, resulting in GDP losses.

Other policies aimed at providing people with incentives not to migrate have been less extreme. In India and Malaysia, nativist policies favored persons locally born (Waddington, 2003). Attempts have also been made to ensure that urban development

> 38 The literature provides mixed and controversial evidence. For instance, Barnum and Sabot (1977) believed that the Todaro paradox did occur in Tanzania. But Collier (1979) later argued that Barnum and Sabot’s test was limited to the extent that it treated urban unemployment as an exogenous explanatory variable. Basing his own study on a general equilibrium model for Tanzania, Collier suggested that the informal sector expanded because of a rise in primary education which increased the number of workers eligible for unskilled wage employment but not because of formal employment expansion as a Todaro explanation would have implied.

47
income effect. However, if the shock also results in lower wages, the implication can be a substitution towards school enrollment through a substitution effect due to a reduction in the opportunity cost of remaining in educational activities.<sup>24</sup> In the end, the balance between income and substitution effects is an empirical matter.

Yet another element closely linked to the availability of education and labor market opportunities is the demographic transition. Generations that are greater in number may exacerbate competition for jobs or for high quality education, and inherently exclude larger sectors or the population in atypically large generations.<sup>25</sup> On the demand side, critical factors include the economic returns to education, opportunities in the labor market, the global demand for different kinds of skills, sector growth patterns, and labor legislation that defines the conditions in which economic activity takes place, among others. The perceived returns to education play a critical role as they provide information on how the labor market rewards and values human resources of different types. For instance, relatively low returns to upper Secondary education relative to primary or lower Secondary may deter adolescents from investing more years in school as opposed to entering the labor market directly, precisely when they are aware of the differential returns. However, low returns to upper Secondary combined with higher returns to tertiary education may have the opposite effect. Similarly, higher returns to upper Secondary may motivate adolescents to complete this level.

# _Empirical results: Out of School and Out of Work Youth, community and macro factors_

In order to verify the influence of community and macro factors over the share of Osow youth, we estimate equation (12) by pooling all cohort trajectories for all countries and survey years. The dependent variable is the change in the share of Osow youth for each cohort at each spell – depending on the country and year of each household survey- and this is linked with independent variables from two types of sources. A first set is computed directly from the same household surveys, from which the cohort trajectories are derived -including the returns to different levels of schooling, and labor force participation. The second set is defined using the World Development

> 24 There is a large number of studies documenting the effects of economic shocks on school dropout at these critical ages, including the Ferreira and Schady (2009), Patel (2009), Mendoza (2009), Ramesh (2009), Mehrotra (2009), Keane (2009), Friedman and Levinsohn (2002), and Shang and Wu (2003), among many others. Duryea, Lam, and Levinson (2007) find evidence of the negative effects of shocks on educational and labor market outcomes in Brazil.

> 25 Newhouse and Wolff (2013) analyze this empirically with a database of 83 developing countries and conclude that youth born in smaller cohorts are more likely to find jobs earlier, and they also tend to have access to more productive jobs. This effect, however, fades away over time.

23
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# **1 Introduction**

The surge in elephant poaching in the past decade has led to a dramatic decline in the African elephant population, which now stands below 600,000 animals (Thouless et al., 2016). Conservation policies, while targeting every segment of the supply chain, have privileged a ban on the trade of elephant ivory. The Convention on International Trade in Endangered Species (CITES) enacted a ban on international trade in elephant ivory during its 7th Conference of the Parties in October 1989. More recently, following a September 2015 commitment “to enact nearly complete bans on ivory import and export (...) and to take significant and timely steps to halt the domestic commercial trade of ivory,” the U.S. banned commercial ivory trade in June 2016 and China followed suit in January 2018.<sup>1</sup> On the other hand, it has long been understood that optimal regulations “depend not only on the differences between the social and private values from consumption, but also on [the demand and supply] elasticities” (Becker et al., 2006). We therefore undertake the estimation of one key elasticity, the price elasticity of poached ivory supply. This elasticity is crucial for evaluating the efficacy of conservation policy. Interventions that combat trafficking and decrease consumption ultimately lead to a reduction in the (expected) price offered to poachers. The supply elasticity estimated in this paper determines how much illegal poaching decreases in response to a fall in price. This same elasticity is also informative on how poachers will react to increased anti-poaching measures.

We combine a novel dataset on ivory prices with measures of the extent of elephant poaching. With ivory price information available in both producing countries and a major consumer market – China – we estimate the supply elasticity with respect to both domestic and international prices. Our main finding is that the supply of illegal African ivory is price inelastic, with an elasticity of 0.4 with respect to prices in either African range states or in China. Accordingly, the ivory price pass-through from the consumer markets in China to the producer markets in Africa is found not to be economically or statistically different from unity.

To conduct the analysis, we assemble a unique database of ivory prices from a variety of published and unpublished sources. The resulting dataset contains 4,873 raw ivory price observations in 72 producing, consuming, and transit countries, and spans the time period 1970-2014. To our knowledge, this is the largest comprehensive global database of ivory prices

> 1Respectively, U.S. Department of the Interior ruling, citation number “81 FR 36387”, `https://www.gpo. gov/fdsys/pkg/FR-2016-06-06/pdf/2016-13173.pdf` ; China State Council announcement, index number 2016-00266, `http://www.gov.cn/zhengce/content/2016-12/30/content_5155017.htm` (in Chinese).

2