rafmacalaba/datause-encoder-data huggingface dataset:
61 the satellite analysis, tea cultivation areas are held fixed to the 2021 tea map, which is applied to every year. The underlying assumption is that tea planted areas do not change on an annual basis because tea is a multiyear permanent crop. We also generate a derived confidence index reflecting the precision of the satellite-derived tea maps, which we use as statistical weights in our tea yield regressions in Appendix C.D. To obtain the confidence index, we first derive a tea probability map using an ensemble random forest image classification algorithm applied to tea data. In this map, any pixel with less than 50% probability is identified as a non-tea planted pixel. We remove all non-tea planted pixels, leaving only pixels with probabilities between 51% and 100%. We then average these probabilities across the pixels within each DS and round the resulting value to obtain the confidence index (ranging from 50 to 100) for each DS.

# **A.D Agricultural Prices**

To estimate our model, we use data on producer prices for each crop (except tea) and district from the 2016-2019 edition of DCS’ Bulletin of Selected Retail and Producer Prices.<sup>101</sup> We extract average producer prices (in LKR/kg) in 2019 for rice, potatoes, cinnamon, cloves, onions, groundnuts, and maize. For onion and rice, whose prices are available for multiple varieties, we use the prices of red onions and red rice (raw). Whenever the producer price of a crop is missing for a given district, we impute it using the crop’s average price across other Sri Lankan districts. Producer prices are missing for 45% of the district-crop pairs with non-zero agricultural production. Nevertheless, the imputation procedure is unlikely to create biases since producer prices are similar across space: the coefficient of variation of (non-missing) producer prices across districts averages 0.032, ranging from 0.009 to 0.058 across crops. For tea, we use producer price data (in LKR/MT) for 2019 from the Food and Agriculture Organization of the United Nations (FAO) for Sri Lanka as a whole, which we assume to hold in each individual tea-producing district.

The 2018-2021 and 2019-2022 editions of DCS’ Bulletin of Selected Retail and Producer Prices are used to compute crop-level nominal producer price increases between January 2021 and January 2022. By subtracting from each crop’s nominal price increase the aggregate inflation implied by the National Consumer Price Index during the same period, we obtain each crop’s real price increase. This information is used as an input in the calibration of the cross-origin elasticity of substitution of agricultural crops (see Section VI.A.1).

To estimate the fertilizer coefficients of our model’s Cobb-Douglas agricultural production functions (see Section VI.A.3), we use data on national crop prices in 2022 (in LKR/kg) from the Central Bank of Sri Lanka’s Economic and Social Statistics 2023 report (except for groundnuts, whose national price comes from the 2019-2022 edition of the Bulletin of Selected Retail and 101The Bulletins of Prices can be found at: http://www.statistics.gov.lk/InflationAndPrices/StaticalInformation/Bulletins#gsc.tab=0
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employment than the intensive cultivation practices required by the new varieties; multiple cropping made use of normally underemployed workers and increased the number of work days per worker per year.<sup>19</sup> 3.48 Vlhile the adoption of new rice varieties. led to a short-term increase in demand for labor, the long-term trvc-l ' -lining share of the labor force in agriculture is an expected par: ';e structural transfcrmation. As the economy of a country shliUts from primarily agricultural to industrial, worke,s migrate to -5naLi jobs. Does crop diversification, with its associated marketing and pr. essing industries, accelerate this process by providing new opportunities? Or are there short-run risks that a shift out of rice cultivation would simply throw large numbers of hired laborers out of work? There is some concern among governments that a p.rogram of crop diversification would have adverse effects on rural employment in the short run, before the longer-term process of creating jobs in industries, whether urban or rural, is under way.

3.49 The answers to such questions cannot be known without studying the use of labor for different crop systems as well as the indirect demand for labor created by the farm households' consumption and investment that is stimulated by crop incomes. Most of the information about direct labor use comes from farm surveys, and it is difficult to draw general conclusions from them because of the differences among countries, even regions, in use of family labor, hired workers, wage rates, sharecropping arrangements, harvesting contracts, land tenure, and other factors. For rice alone, the number of labor-days per hectare to bring in a crop varies considerably, from a low of 20 days in the Sind, in Pakistan, to more than 340 days in West Java, with no absolute consistency in greater labor rec-irements for high-yielding varieties compared with traditional varieties. As mentioned previously, however, the large gains in labor use for rice came with increased multiple cropping.

19/ See Barker and Herdt, p.129.
problems. Previous literature has shown that immigrants are more likely to move to locations where they have a network of peers from the same country. We show below that this is true among Venezuelans in Peru as well.

Our main interest is on _β_ 1, which represent the impact of the labor market conditions in centro poblado _j_ on the discrimination experienced by Venezuelans. Venezuelans who arrive to the country clearly evaluate where to settle based on the labor market opportunities (among other reasons), and therefore to causally identify _β_ 1 we need a source of exogenous variation for the labor market at the local level. We use an instrumental variable strategy that exploits variation in the share of workers employed in different industries in 2007, along with national level shocks to trade in specific industries between Oct 2016 and Oct 2017 when the census was collected. More precisely, the first stage regression is given by: where _Sharejk_ ( _t−_ 1) is the share of workers in centro poblado _j_ employed in industry _k_ in 2007, and ∆ _Exportk_ represents the log change in national level exports in industry _k_ between 2016 and 2017. The remaining control variables are similar to those in Equation 1. The identifying assumption in this instrumental variable regression is that the change in trade in specific sectors at the national level affects the local labor market conditions without directly having effects on the the discrimination and hostilities reported by immigrants in a specific location. Importantly, we also control for other local level economic characteristics, such as the importance of agriculture and manufacturing which could be related to both exposure to export shocks and experiencing discrimination.

We then turn to examine the impact of receiving a larger population of Venezuelan immigrants on natives’ labor market outcomes and different dimensions of locals’ perceptions. To do this, we estimate the following regression: where _yipt_ represent a particular outcome for individual _i_ living in province _p_ interviewed at time _t_ , _ln_ ( _ImmigrantShare_ ) _pt_ is the (log) of the number of Venezuelan immigrants in province _p_ at time _t_ as a share of the total population of province _p_ in December 2017 and _Xipt_ include a series of individual level controls, including age, education, marital status, and household size for all models, and employment status and occupation for non-labor market 13
<!-- Start of picture text --> Figure 14: State Level Share of Children of School-going-age Not in School – Northern Regions<br>a) b) c)<br>Share Not in School at School going Age Share Not in School at School going Age Share in School at School going Age<br>- by State in North Central - MICS - by State in North East - MICS - by State in North West- MICS<br>80% 80% 80%<br>70% 70% 70%<br>60% 60% 60%<br>50% 50% 50%<br>40% 40% 40%<br>30% 30% 30%<br>20% 20% 20%<br>10% 10% 10%<br>0% 0% 0%<br>child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total child 5-9 child 10-14 child 15-17 Total<br>Benue FCT Abuja Kogi Kwara Nasarawa Niger Plateau Adamawa Bauchi Borno Gombe Taraba Yobe Jigawa Kaduna Kano Katsina Kebbi Sokoto Zamfara<br><!-- End of picture text --> <!-- Start of picture text --> d) e) f)<br>Urban Rural - Boy Girl - Share Not in School at Urban Rural - Boy Girl - Share Not in School at Urban Rural - Boy Girl - Share Not in School at<br>School going Age - by State in North Central - MICS School going Age - by State in North East - MICS School going Age - by State in North West - MICS<br>80% 80% 80%<br>70% 70% 70%<br>60%50% 60%50% 60%<br>40% 40% 50%<br>30% 30% 40%<br>20% 20% 30%<br>10% 10% 20%<br>0% 0% 10%<br>Benue FCT Abuja Kogi Kwara Nasarawa Niger Plateau Adamawa Bauchi Borno Gombe Taraba Yobe 0%<br>Jigawa Kaduna Kano Katsina Kebbi Sokoto Zamfara<br>Urban Boy 5-17 Urban Girl 5-17 Rural Boy 5-17 Rural Girl 5-17 Urban Boy 5-17 Urban Girl 5-17 Rural Boy 5-17 Rural Girl 5-17 Urban Boy 5-17 Urban Girl 5-17 Rural Boy 5-17 Rural Girl 5-17<br><!-- End of picture text --> Source: Authors’ estimates based on MICS 2016/17 data Note: Individuals with missing age were dropped from the analysis. Total refers to all individuals with known age.

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lower than those computed without age-of-entry restrictions by Docquier and Marfouk (2006), which we take as our upper-bound brain drain measure.

# **4.1. Methodology**

To estimate the structure of immigration by age of entry, we collect census and register data in a sample of countries where such information is available: the US 1990 and 2000 censuses, the Canadian 1991 and 2001 censuses, the French 1999 census, the Australian 1991 and 2001 censuses, the New-Zealand 1991 and 2001 censuses, the Danish 2000 register, the Greek 2001 census and the Belgian 1991 census. Together, the countries sampled represent 77 percent of total skilled immigration to the OECD area. The sample is representative of the OECD in that it includes countries with different demographic sizes, regional locations, development levels and immigration policy and tradition.

We thus have bilateral information on immigrants' origin, age, education level and age of entry from 12 host countries' censuses for 192 sending countries. These 2304 observations allow us to compute the proportion of immigrants arrived before ages 12, 18 and 22 in the _total_ stocks of immigrants aged 25+ estimated by Docquier and Marfouk (2006). Eliminating zeros and a few suspicious observations, we end up with 1580 observations for each age threshold.

Table 3 gives descriptive statistics on the estimated proportions of adult immigrants who arrived before age J (J = 12, 18 and 22). The average shares vary across receiving countries (not shown). On the whole, the average shares are 85.7%, 78.2% and 69.1% for immigrants arrived before age 12, 18 or 22. They are usually higher for Belgium, Denmark and Greece. The lowest shares are observed in Australia, New Zealand and the United States. Canada and France are not far from the average distribution.

Obviously, an approach based on Census data is not perfect. As explained by Rosenzweig (2005, p. 9), “information on entry year ... is based on answers to an ambiguous question - in the US Census the question is ‘When did you first come to stay?’” Immigrants might answer this question by providing the date when they received a permanent immigrant visa, not the date when they first came to the US, at which time they might not have intended to or been able to stay. Only surveys based on a comprehensive migration history would provide precise data about the location in which schooling was acquired.

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manufacturers of high-technology products among the countries in developing East Asia. But the number of products with a revealed comparative advantage (RCA) index greater than 1 is larger for China than it is for other countries in developing East Asia. China is not only gaining competitiveness in hightechnology products, but is also becoming more competitive in a broader range of machinery products.

<!-- Start of picture text -->
-<br>Figure 13 China Is Catching Up in High Technology Machinery Exports<br>a. RCA index in high-technology exports b. % of high-technology exports for which RCA>1<br>China China<br>2004-05 2004-05<br>Thailand Thailand<br>1993-94 1993-94<br>Philippines Philippines<br>M alaysia M alaysia<br>Indonesia Indonesia<br>0 1 2 3 4 0 20 40 60<br>RCA index in high-tech exports percent of high-tech export with RCA>1<br>c. % of machinery products for which RCA>1  d. RCA index in machinery<br>30 3<br>China<br>China<br>Philippines<br>25 Malaysia 2.5<br>Thailand<br>20<br>2<br>Indonesia<br>15<br>Malaysia 1.5 Malaysia<br>China<br>Philippines Thailand<br>10 Thailand China<br>Indonesia 1<br>0 0.5 Thailand 1 Malaysia 1.5 2 2.5 3<br>5 Philippines<br>Philippines Indonesia<br>0.5<br>0 Indonesia<br>0 5 10 15 20 25 30<br>0<br>1993-94<br>1993-94<br>Components Finished Components Finished<br>2003-04<br>2 0 0 3 -0 4<br><!-- End of picture text -->

_Source:_ Rahardja 2006.

# **Multinational corporations: recycling comparative advantage to drive production sharing**

The development of production networks has contributed to the rise of successive waves of NIEs in East Asia, especially the emergence of the latest wave (Malaysia, the Philippines, and Thailand, and then China and Vietnam). Since the mid-1980s, firms in the most industrialized economies in the region (Hong Kong [China], Japan, Korea, Taiwan [China], Singapore) have, through FDI and outprocessing operations, gradually moved their production capacity in low-technology, labor-intensive sectors to overseas export platforms located in low-wage countries. These relocations have helped Southeast Asian countries and then China develop their comparative advantage in manufacturing industries and progressively to upgrade their industrial capacities and exports.

15
households could expect a minimum of nine years of benefits upon enrolling in the program (OPORTUNIDADES 2003).

In order to transfer the cash, OPORTUNIDADES had to verify that households actually completed the required health care visits by having medical providers from the public clinics who administered the services certify that the household had completed the requirements. A similar procedure was followed for the cash transfer associated with school attendance. About 1 percent of households were denied the cash transfer for non-compliance.

# **2. Experimental Design and Data**

Our analysis takes advantage of the controlled-randomized evaluation design implemented by the Mexican Government to conduct a rigorous impact evaluation of OPORTUNIDADES. Due to budgetary and logistical constraints, the Government was unable to enroll all eligible families simultaneously. Rather, it needed to phase in enrollment over a period of time. For ease of implementation, the Government decided that it would enroll whole communities at a time and that it would enroll them as fast as possible so that no eligible household would be kept out of the program. As a result of this process, the government randomly chose 320 treatment and 186 control communities in seven states for a total of 506 experimental communities. Eligible households in treatment communities began receiving benefits in April of 1998, and eligible households in control communities were not incorporated until November of 1999 nor were they informed that OPORTUNIDADES would provide benefits to them until two months before incorporation.

The data used in this paper comes from household evaluation surveys and administrative records of the amount of money transferred to households. Detailed information on a host of topics was collected in a series of rural evaluation surveys, the _Encuesta de Evaluación de los Hogares Rurales_ (ENCEL). The ENCELs survey all eligible and ineligible households in treatment and control communities. The sub-sample used for the main analysis in this paper is restricted to eligible households classified as poor, although we also use ineligible (non-poor) households for 5
improvements in quality and efficiency, and reduced levels of inventory. Furthermore, the authors note that the main factor that impedes firms from independently adopting better management practices themselves is barriers to information. In a follow‐up paper, Bloom et al. (2018), the authors revisited these manufacturing plants in India and found that plants in the treatment group had dropped approximately half of the management practices that had been introduced as part of the experiment due to turnover of key personnel and managerial time constraints. However, nine years after the experiment was initiated, a gap remained between the management practices of treatment and control plants, and, furthermore, treatment plants continued to perform better. Finally, upon revisiting the plants, the authors found that the utilization of improved management practices had spread across plants within the same firm, but not, however, between firms.

In this paper, we focus on management practices of establishments in the LAC region. An earlier analysis of management practices in four countries in this region (namely, Argentina, Brazil, Chile and Mexico) by Lemos and Scur (2012) showed that firms in Latin America employ poor management practices when compared to firms in Europe and Asia. The authors note that one of the primary reasons for lagging managerial practices is the high prevalence of family owned and run firms in the region. Furthermore, the authors confirm that better management practices are linked to improved productivity, both at the firm and the national level. The positive impact of improved management practices on firm performance in the region is confirmed by Bruhn et al. (2018). Based on evidence from a randomized control trial of small and medium‐sized enterprises in Mexico, the authors show that firms that were given access to management consultancy services for one year had higher levels of total factor productivity and return on assets. Using administrative data, the authors also show that, five years after the treatment, those firms that had received consultancy services employed significantly more workers; showing that the impacts of improved management practices endure.

In terms of the long‐term impacts of management practices on productivity, recent analysis by Giorcelli (forthcoming) confirms what was found by Bloom et al., 2018 and Bruhn et al., 2018—that the positive impacts of improved management practices are long lasting. Studying Italian firms that received funding for management training trips to the US and more technologically‐advanced machinery as part of the _Marshall Plan_ , the author finds that both interventions (management training and improved machinery) led to increases in productivity, but that the impacts of management training lasted longer. Furthermore, Giorcelli shows that while in the short run, larger firms benefitted relatively more from management training, over time, firms with fewer than 50 employees benefitted more.

4
**Table 11 Estimation Using Three Year Average Values of Data (Between 2005 and 2007)**

||Regulations|Finance|Entry|Time to<br>Export|Infrastructure|Property|
|---|---|---|---|---|---|---|
|RealGDP(PPP)|0.973|0.981|1.087|0.948|0.977|0.986|
||(0.028)***|(0.043)***|(0.033)***|(0.031)***|(0.033)***|(0.029)***|
|Log(Area)|-0.120|-0.119|-0.198|-0.105|-0.122|-0.132|
||(0.030)***|(0.042)***|(0.037)***|(0.032)***|(0.034)***|(0.030)***|
|Remoteness|-0.309|-0.421|-0.406|-0.413|-0.381|-0.422|
||(0.141)**|(0.196)**|(0.188)**|(0.147)***|(0.157)**|(0.142)***|
|Export Growtht,t-1|-0.131|-0.097|-0.134|-0.121|-0.079|-0.117|
||(0.062)**|(0.092)|(0.082)|(0.066)*|(0.072)|(0.064)*|
|Size of Trade|0.130|0.074|0.106|0.116|0.111|0.124|
||(0.018)***|(0.024)***|(0.023)***|(0.018)***|(0.020)***|(0.019)***|
|log(TTRI)|-0.090|-0.394|-0.329|-0.307|-0.295|-0.271|
||(0.083)|(0.099)***|(0.100)***|(0.077)***|(0.087)***|(0.079)***|
|log(MATTRI)|-0.223|-1.557|0.163|0.609|-0.825|-0.830|
||(0.055)***|(0.494)***|(0.217)|(0.320)*|(0.271)***|(0.208)***|
|log(MATTRI)*Indicator|0.195|0.373|-0.037|-0.260|0.548|0.111|
||(0.065)***|(0.138)***|(0.021)*|(0.098)***|(0.210)**|(0.034)***|
|Indicator|0.302|-0.085|-0.006|-0.360|0.416|0.090|
||(0.099)***|(0.183)|(0.032)|(0.149)**|(0.270)|(0.045)*|
|Constant|3.162|5.066|2.842|6.144|3.629|3.850|
||(1.415)**|(2.219)**|(1.880)|(1.543)***|(1.643)**|(1.491)**|
|Observations|99|84|99|99|95|95|
|R-squared|0.975|0.955|0.957|0.972|0.967|0.973|

* OLS results using the averages of variables between 2005 and 2007. All regressions control for year fixed effects. *** p<0.01, ** p<0.05, * p<0.1.

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Table 5. World Trade Growth, Output Growth, and Speed of Integration

||1"11-(d|1')0<br>Jo I|MWJ(O-1(|<br>NQ[_91J|I')'); '(,|
|---|---|---|---|---|---|
|World TIade&Growthb|7.7|3.7|6.1|4.1|8.7|
|World Output Growth|5.2|3.2|3.3|1.1|2.9|
|<br>Speed of Integration|2.6|0.6|2.8|3.0|5.8|
|<br>Higb-Income OECD|3.3|0.7|3.2|1.1|5.4|
|<br>UnitedcStates|3.1|1.9|4.0|4.2|6.9|
|Japan'|4.6|1.5|0.9|-1.7|4.7|
|<br>EU12|3.6|0.6|3.7|0.7|4.8|
|LMIC|0.3|-0.6|0.7|6.4|5.6|
|Sub-Saharan Africa|-0.9|-1.5|-0.7|0.7|0.7|
|EastAsias|_-0.5_|1.3|1.4|5.7|6.1|
|South Asia|-0.8|-0.4|-0.4|4.4|4.4|
|ECA|3.0|0.0|-1.9|6.2|9.7|
|.Europe|3.8|0.4|0.8|5.8|5.2|
|<br>.-FSU|1.7|-0.2|-5.0|-0.9|13.6|
|- MEiNrA<-|_-0.5_|-1.5|3.1|1.2|0.0|
|LatirLA6merica|-0.4|-1.6|1.9|9.5|2.6|
|- .LMICl:X<br>~~-|-0.4|-0.7|1.9|5.4|4.2|

a. Estimates and Forecast.

- b. Growth rate of export plus import volumes of merchandis **e** xcept for 1961-70. For 1961-70, national accounts data for trade in goods and services are used.

- c. Speed of integration =growth rate of world trade - growth rate of world output.

What happened in the early 1990s, often characterized as trade-output decoupling, was an enormous acceleration in the speed of LMIC trade integration into the world market, which compensated for a deceleration in trade integration among OECD, where integration is highly pro-cyclical. Table 5 shows that between two adjacent periods of smooth world output expansion (1986-90) and OECD recession (1991-93), world aggregate speed of trade integration was in fact kept, on average, at a constant level. However, the movements in different income groups were startlingly dissimilar. LMICs have seen tremendous acceleration in the speed of trade integration, from a slow pace of 0.7 percent a year to a galloping 6.4 percent a year, while OECD's pace of integration decelerated from over 3 percent a year to 1.1 percent a year in the latter half of the 1980s.

Supported by a strong recovery in Europe, accelerated market opening in Japan,<sup>22</sup> and continued robust demand in the United States and developing countries, world trade should continue to grow at a robust rate of 8-9 percent for 1994-96. LMIC's speed of trade integration will continue to be strong in the near term. The only noticeable slowdown in the pace of trade integration should be observed in LAC, where countries are forced to grapple with a problem of mounting current account deficit as a result of strong import growth during 1991-93, financed largely by a surge in capital inflow. The unstable nature of unbalanced trade integration, which accumulates the mounting and unsustainable level of current account deficit, was manifested in the recent Mexican peso crisis.

**22** See Appendix C for details.

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