rafmacalaba/datause-encoder-data huggingface dataset:
domestic sectors among each other (CC dFXbc, CC dFXoc, CC dFXdc, CC dFXpd). We also know the currency composition of the Central Bank's external debt (approximately 44 percent US$, 56 percent DM), and assume that the domestic banking system's external debt had the same composition. This is sufficient to determine CC dFGc and CC dFGd.

We choose the end of 1988 cross-currency rates as the base rates. Then, it is straightforward to calculate the cross-currency effect in 1988 with the following formula: for X = FXbc, FXoc, FXdc, FXpd, FGd, FGc.

where i is defined over the set of foreign currencies, except US$<sup>1</sup> , ai is the weight of the foreign currency i in total dollar stock of external debt (X) and EERi(t) is the end of period exchange rate expressed as the amount of foreign currency per US$.

In order to calculate cross-currency effects for 1987 some additional assumptions are required if the weights ai are not known at all relevant points in time. If they are known, the calculation of the cross-currency effect can be done according to equation A1.5. If the ai are only known in the base period, two possible assumptions can be made to calculate the past effects. One hypothesis assumes that the a, simply stay constant. This implies that the country follows a borrowing strategy that offsets cross1 We assume, according to common practice, that the external debt is expressed is the US$.

10
experiment is large in scale, implements unilateral facilitation at a range of intensities, and occurs in the Philippines, one of the world’s most important sources of legal, temporary, international labor migration.

We implement our study in Sorsogon, a province that sends relatively few labor migrants overseas compared to other parts of the Philippines. These features – existing and extensive bilateral labor migration arrangements, but relatively low migration relative to other parts of the country – make our experimental context one where unilateral migration policies could potentially have a substantial positive impact. While Sorsogon residents are underrepresented among OFWs, a good share are likely to be qualified for overseas work: more than two-thirds (69%) of our sample had completed high school, and nearly half (50%) had completed at least some post-secondary school.<sup>2</sup> In the classic economic migration model, migration is an investment: individuals and households incur moving costs to generate returns via higher incomes (Sjaastad, 1962). Subsequent work acknowledges imperfect financial markets in developing countries can also create additional rationales for migrating such as to finance household investments (Stark and Bloom, 1985; Yang, 2006).

This framework suggests three main reasons why individuals do not migrate even when there are job opportunities abroad. First, individuals may have high disutility from moving and therefore may not wish to migrate internationally even though the monetary benefits outweigh the monetary costs. This is certainly not what many non-migrants say – for example, 51.1% of surveyed Filipinos aged 15 and older say they would like to work abroad if they had the opportunity (Gallup World Poll, 2010). Second, individuals may not be fully informed about the costs and benefits of migration. Perhaps because they do 4
steeper at the tails. These findings suggest that from a remittances viewpoint, African countries have more to gain from promoting migration for skilled work than for less skilled migration.

Finally, there is now a growing literature which asks whether remittances decay with time spent abroad (e.g. Brown, 1997; Simati and Gibson, 2001; Amuedo-Dorantes and Pozo, 2006; Miotti et al., 2009). This issue enters the policy debate over discussions as to whether temporary or longer-term migration episodes have greater development impact. There is a concern that as individuals spend more time abroad they will lose ties with their home countries, and remittances will fall. In contrast, we find that the likelihood of remitting and amount remitted increase over the first five to ten years of migration. While the likelihood of remitting begins to fall after 20 or so years, we have little data for such migrants, and the data we do have suggest that the total amount remitted doesn’t fall since migrants who have been abroad longer will remit more on the occasions they do remit.

# **2. Data**

Few surveys in OECD countries collect data on remittances. Data on the number and characteristics of immigrants from different countries can be readily obtained from Census microdata and labor force surveys. However, neither contains information on remittances. Instead one must rely on special purpose surveys of immigrants. With an intensive effort we have combined together all the publicly available datasets we are aware of, as well as obtained access to a number of non-publicly available datasets which have been graciously shared by the researchers or sponsoring organizations. The resulting database comprises information on more than 12,000 African immigrants from 11 surveys in 9 OECD countries (Australia, Belgium, France, Italy, Netherlands, Norway, Spain, United Kingdom and United States). The surveys cover a wide range of populations, covering both nationally representative surveys of recent immigrants (the New Immigrant Survey (NIS) in the U.S., the Longitudinal Survey of Immigrants in Australia (LSIA), and the DREES survey in France), nationally representative surveys of all immigrants (the Spanish National Survey of Immigrants (ENI)), and surveys which attempt to sample migrants from particular countries (the NiDi surveys in Spain and Italy, 4
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**Table 6: Growth Determinants for CEE Countries and Other Country Groups.**

||Annual Average<br>Growth Rates<br>1965-89|GNP per<br>Capita<br>1994<br>PPP Estimates|Average<br>Investment<br>as % of GDP<br>1994-95|Human<br>Capital<br>1985|1996 Modified<br>Structural<br>Framework<br>(i.e., Index Z)|Average<br>Fertility Rates<br>1984-89<br>(i.e., a + 6 + n)|
|---|---|---|---|---|---|---|
|Fastest Growing Economies|6.2%|8058|34.3%|6.73|4.26|6.4%|
|Fast Growing Economies|5.4%|10038|33.6%|5.66|3.71|7.0%|
|EU Highest|1.8%|20270|26.5%|10.33|4.22|_5.0%_|
|EU Average|2.5%|16757|18.2%|7.70|3.82|5.3%|
|EU Low|4.1%|10930|13.0%|3.83|3.27|5.8%|
|Czech Republic|n/a|8900|25.7%|n/a|4.00|5.4%|
|Hungary|n/a|6080|22.0%|10.75|3.10|4.9%|
|Poland|n/a|5480|17.1%|8.41|2.95|5.6%|
|Slovakia|n/a|6450|25.8%|n/a|3.05|_5.5%_|
|Slovenia|n/a|6230|21.6%|n/a|2.65|5.3%|
|CEE Average|n/a|6628|22.4%|9.58|3.15|5.3%|

Notes: Fastest Growing Economies includes Hong Kong, Korea, Singapore, and Taiwan. The Fast Growing Economies refers to Indonesia, Japan, Malaysia, and Thailand. The notation n/a stands for data not available. The data on human capital is from Barro and Lee (1993). No equivalent human capital data is available for Czech Republic, Slovakia, and Slovenia. We use for these countries the average from Hungary and Poland. The data on investment corresponds for all non-CEE countries to the average for 1993-94 and for all CEE countries to the average for 1994-95. The GNP per capita data for 1994 is from the 1996 World Development Report. The Index of Policy Z for CEE countries is based on the original IEF Index.

Using the econometric results discussed in Section IV, we examine how CEE countries will perform under different scenarios. More specifically, we first calculate the number of years it takes for CEE countries to reach, under different scenarios, 75 percent of the GDP per capita of the EU average country. This threshold is picked since most EU structural funds allocate resources to regions which are below it. We then perform some growth exercises and discuss how much faster the growth rate of GDP per capita for CEE countries can be relative to the EU _15_ average country, once again assuming different policy scenarios 15 The above discussion on convergence and growth paths would be incomplete if we did not highlight its three main weaknesses. First, the estimated model in this paper is based on a log-linearization of the original paths of physical and human capital. While this allows to obtain closed form solutions for the dynamics of the model out of the steady state, these dynamics are valid only in the "neighborhood" of this steady state. In practice, numerical solutions to our dynamic equations would show that the speed of convergence would decrease as a country gets closer to its own steady state rather than the constant speed of convergence p on which we currently base our analysis. Second, while we present results for different convergence and growth exercises, we assume in each of
DSM’s commercial orientation also aims to free up scarce public resources and reduce the time demands on seed system experts, allowing them to focus on improving the overall seed system and broaden the extension service in Ethiopia (Mekonen et al. 2019). For instance, DSM removes extension agents from assessing seed demand and distributing seed to farmers, allowing them to focus on providing better quality and more timely advisory services. Similarly, DSM removes seed system experts in the public sector from similar tasks, providing opportunities to reassign them to varietal development, quality assurance, and other necessary functions. Thus, DSM offers an opportunity to reallocate scarce public resources to activities necessary to make seed markets work effectively and efficiently for farmers and seed producers.

Since its inception, the DSM program's performance has been frequently monitored and assessed to gauge its impact on the timely provision of quality seed to farmers in appropriate quantities and at competitive prices. While results from operational assessments have been critical in informing government actors—who were initially hesitant to concede seed marketing responsibilities to private agents and facilitate the program’s expansion—these assessments do not examine higherorder outcomes such as crop productivity. This study aims to fill this important empirical gap.

# **3 Data and Descriptive Statistics**

We used three rounds of the Ethiopian Agricultural Commercialization Clusters (ACCs) panel survey, which interviewed a total of 13,302 rural households in 221 districts across the four agriculturally important regions (Amhara, Oromia, Tigray, and SNNP) in 2012, 2016, and 2019. The coverage of the ACC survey expanded between 2012 and 2019 both across districts and households following the program expansion. The number of sample districts increased from 99 in 2012 to 153 in 2016 and 154 in 2019. Similarly, the number of sample households increased from 3,000 in 2012 to 4,991 in 2016 and 5,311 in 2019. Despite the gradual expansion of the sample, about 74 percent of the households (9,769 observations) were re-interviewed in at least two survey rounds. The sample households were selected following a three-stage sampling procedure. First, the districts were stratified into ACCs defined by the ATA, and five sample districts were randomly selected from each ACC. Second, two _kebeles_ were randomly selected from each district to be part of the surveys. Finally, in the third stage, 15 farm households were randomly selected from each sample _kebele_ based on household lists maintained by local administrations. In addition, about 20 percent of the sample was selected from neighboring 8
on the other performance variables are close to zero and not significant, exception made for energy efficiency that is positive, as in the case of Mexico. We return below to the possible interpretation of the more muted effect of electricity prices on performance among Indonesian plants.

A comparison with the OLS counterparts of these estimates (see Table A2 in the appendix) reveals two interesting patterns. First, the endogeneity appears to exert a downward bias to the estimated fuel coefficient, which tends to be positive but smaller than the IV coefficient for Indonesia and close to zero for Mexico. This could be the case for instance if an unobserved positive productivity shock to the plant (e.g. the hiring of a good manager) improves performance and reduces the fuel price paid by the company (e.g. the manager is able to source fuel more cheaply). Second, for Indonesia, the endogeneity bias goes in the opposite direction. This would be consistent for example with a positive shock (e.g. an increase in demand) leading to an increase in the plant’s installed electricity capacity, which moves the plant to a higher electricity tariff. On the other hand OLS estimates are very similar to the 2SLS coefficients for Mexico. That is expected, because as discusses in Section 4.1 energy prices are computed on the basis of institutional factors and so less subject to plant-specific sources of endogeneity.

One important difference between the two samples is the longer time period covered in the Indonesia dataset (1998-2015) as opposed to Mexico (2009-2015). Table A3 of the appendix shows that similar results hold when we restrict the Indonesia data on the period 2009-2015.<sup>36</sup> .

To the best of our knowledge, such an asymmetry in the impact of prices across different sources of energy has not been documented before. In particular, the result that an increase in fuel prices can boost performance is remarkably similar across the two countries in spite of the differences in the data, the instrumental variables and the structure of the two economies.

This asymmetry is consistent with the fact that fuels and electricity tend to power different types of capital equipment. Old technology is embodied in capital vintages that are more likely to be powered by fuel, while new technology tends productivity indicator.

> 36This restricted sample also addresses the possible concern that the longer time period includes the post-East Asian crisis and Indonesia’s democratic transition, which may affect the estimated

25
2007. In addition to the increase in production costs, transport costs also increased due to higher fuel costs and the margin between domestic and export prices reflect this cost (Table 2). However, these margins also include handling and other charges, such as insurance, which increase with crop prices. The margin for corn between central Illinois cash and the Gulf Ports barge increased from $0.36 to $0.72 per bushel for an increase of 15.5 percent, while the margin between Kansas City and the Gulf Ports wheat increased only $1 per metric ton. An export weighted average of these prices suggests that transport costs could have added as much as 10.2 percent to the export prices of corn and wheat. Comparable data was not available for soybeans. Thus, the combined increase in production costs and transport costs for the major U.S. food commodities—corn, soybeans and wheat—was at most 21.7 percent, and this amount likely overstates the increase, because transport costs are not estimated separately. It therefore seems reasonable to conclude that higher energy and related costs increased export prices of major U.S. food commodities by about 15-20 percent between 2002 and 2007.

**Table 1. Cost of production for corn, soybeans and wheat, 2002 vs. 2007 (dollars per acre)**

||Cor|n|Soybe|ans|Whe|at|
|---|---|---|---|---|---|---|
||2002|2007**|2002|2007**|2002|2007**|
|Operating costs:|||||||
|Seed|31.84|48.93|25.45|38.27|6.65|9.51|
|Fertilizer|42.51|93.96|6.79|13.94|17.71|33.33|
|Chemicals|26.11|24.67|17.12|14.79|7.13|9.23|
|Custom operations|10.79|10.93|6.16|7.25|5.67|6.93|
|Fuel*|18.93|30.98|6.98|16.98|8.67|19.20|
|Repairs|13.91|14.86|9.76|11.93|10.15|12.78|
|Other|0.22|0.12|0.63|0.15|0.61|0.34|
|Interest|1.17|5.16|0.61|2.37|0.48|2.14|
|Total Operating|145.48|229.61|73.5|105.68|57.07|93.46|
|Allocated overhead:|||||||
|Hired labor|3.06|2.22|1.84|2.15|2.53|2.52|
|Unpaid labor|25.74|23.86|15.59|17.02|16.72|21.97|
|Capital recovery|55.26|69.99|43.30|54.00|48.97|53.86|
|Land|87.44|95.44|80.74|92.72|39.19|42.93|
|Taxes & ins.|5.42|7.39|5.66|6.93|3.90|7.24|
|Overhead|11.91|13.83|11.37|12.90|7.25|8.78|
|Total Allocated Overhead|188.83|212.73|158.5|185.72|118.56|137.3|
|Total Costs ($per Acre)|334.31|442.34|232|291.4|175.63|230.76|
|Yields|134|151.5|40|40.5|27.9|34.4|
|Total Cost ($/bu)|2.49|2.92|5.80|7.20|6.29|6.71|

Source: USDA Cost of Production Surveys and Forecasts, July 2008. *Fuels include lubricants and electricity. ** is USDA’s forecast.

6
**Table 3 Random effects matching regressions for performance, size, and subsidies variables**

||_(1)_|_(2)_|_(3)_|_(4)_|
|---|---|---|---|---|
|_Variable_|_ROAA_<br>_A_|_djusted ROAA_|_EBITDA to_<br>_revenues_|_EBITDA to_<br>_average_<br>_assets_|
|SOEs that do not receive|–2.101**|–7.268***|–34.87***|–7.062***|
|subsidies|(1.015)|(1.084)|(4.806)|(1.211)|
|SOEs that receive subsidies|0.416|–3.844***|–25.84***|–3.476***|
||(0.753)|(0.790)|(2.927)|(0.758)|
|Leverage|–0.0491***|–0.0576***|0.0588*|–0.0146*|
||(0.00790)|(0.00833)|(0.0321)|(0.00837)|
|Size (ln(assets))|–0.0174|0.0366|3.660***|0.675***|
||(0.109)|(0.116)|(0.513)|(0.115)|
|World Bank region dummies|Y|Y|Y|Y|
|Year dummies|Y|Y|Y|Y|
|Sector dummies|Y|Y|Y|Y|
|World Bank income level|Y|Y|Y|Y|
|dummies|||||
|Constant|Y|Y|Y|Y|
|Observations|3,383|3,381|3,728|3,371|
|Number of firms|570|570|590|567|
|Mean of dependent variable<br>_Note_: EBITDA:Earnings before in|2.098<br>terest, taxes, depre|1.590<br>ciation, and amorti|18.84<br>zation. ROAA: Ret|6.526<br>urn on average assets.|

_Note_ : EBITDA: <mark>Earnings before interest, taxes, depreciation, and amortization. ROAA: Return on average assets.</mark> Standard errors in parentheses. Errors are clustered by strata derived from the Coarsened Exact Matching procedure. * _p_ < 0.1, ** p < 0.05, *** _p_ < 0.01.

Table 3 reveals two interesting findings. First, it shows that SOEs that receive subsidies perform better than firms that do not. Firms that receive subsidies have no significant difference in ROAA relative to private firms. Netting out the subsidies and comparing private ROAA with SOEs’ adjusted ROAA, however, shows that SOEs that receive subsidies perform 3.8 percent worse than private firms—a large difference relative to the sample mean of comparable ROAA of 1.5 percent. This finding means that subsidies are covering up the unprofitability of SOEs by making up for their losses. Similar results are evident when looking at EBITDA to revenues and EBITDA to assets (columns 3 and 4). If we view EBITDA to revenues and EBITDA to assets as a measure of efficiency, then it is clear that SOEs that receive subsidies perform worse than private firms. EBITDA to revenues is 25.8 percentage points lower and EBITDA to assets is 3.4 percentage points less for SOEs than for private firms.

The second finding of table 3 is puzzling. SOEs that do not receive subsidies perform worse than private firms and therefore also worse than SOEs that receive subsidies. SOEs that do not receive subsidies have an ROAA that is 2.1 percent lower than private firms and an adjusted ROAA that 19
The results in this section reinforces the potential of latent export baskets in providing countries with a tool to cope with external volatility. Indeed, while countries tend to open new export lines in industries in which they had a factor-endowment driven comparative advantage in the short-run, this link weakens in the long-run. Hence, over long-periods of times the industry composition of initial export baskets may differ substantially from the industry composition of the goods added to the latent export basket, which in turn results in latent basket that are more evenly distributed across industry clusters.

# **8. Conclusion**

This paper introduces the concept of latent trade diversification, which measures trade diversification based on the number of export lines that have been active in a given time period (instead of currently active only). Differences in diversification across countries are much smaller when looking at latent diversification than when looking at cross-sectional diversification. Moreover, latent diversification has a significant and economically meaningful impact on volatility in terms-of-trade and indirectly on GDP per capita growth. These results are robust to the estimation of numerous variations of our baseline specifications.

Our results open the door to further research on the concept of latent diversification and its implications for volatility. This paper presents a highly stylized and simple measure of latent diversification, which is easily implementable. This simplicity notwithstanding, the measure has some limitations. It only takes into account products that have been exported within a finite time frame, which may be arbitrary depending on data availability to researchers. This implies that the proposed measure of latent diversification might miss products that are in the latent basket but that have not been exported in a country’s history within the data sample.

More importantly, the findings call for an explanation of changes in an economy’s basket of exported products as the paper took a shortcut in this regards. We utilized Leamer’s (1995) industry clusters as a proxy for products with similar production functions in terms of factor proportions. This highlights the connection between this paper and the literature on dynamic comparative advantage. Future research could aim to parameterize Ricardian and factor-proportions models of comparative advantage enhance our understanding of the forces that could explain the relationship between the products in an economy’s latent export basket and the basket observed in a given period of time.

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generation this calculation is certainly eystem specific so that the inetalled technology plays an important role in determining fuel choice. Capital costs and technological characteristics are also key variables in the analysis. The specificity of the technologies chosen also allow a precise evaluation of the pollution characteristics of the plant or system.

The data required for engineering analysis at a plant level are those which a firm would take into account - capital costs of alternative technologies, likely fuel prices, technical performance, likely demand growth etc. For the government it is likely that such data would be available economy wide for the power sector, since this is often state controlled. Whatever the form of ownership, the management will optimize over its system as a whole when taking investment/fuel choice decisions.

For industry the information required for government intervention to affect fuel choice is much more diverse and very unlikely to be available for the economy as a whole. It would be necessary to have data on existing technology by capacity and performance as well as plant specific fuel prices for every plant in the economy. For a given industry if it were narrowly enough defined the range of potential technologies and costs could be assembled but where a large number of plants were in operation, the necessity to make a separate evaluation of the price at which fuel switching would take place would be very demanding. in summary it appears that engineering studies are likely to be much more reliable in producing a plant level analysis of the sensitivity of fuel choice to prices. Econometric studies cannot be expected to give accurate answers, but because of the lesser level of demand for data, may be able to provide some guidance to the order of magnitude of response that may be produced. There is an apparent distinction between power systems and industry analysis. The former is homogeneous in terms of the technologies used (all sites face the same choices) and is often owned and optimized by a single authority. The data for an engineering approach is likely to be avallable and certainly should be utilized if possible. Even a plant by plant analysis (ignoring system interrelationshipe) is likely to be a more trustworthy guide than econometric