rafmacalaba/datause-encoder-data huggingface dataset:
4 share of remittances. This group of lower middle income countries, which includes many countries from the Latin America and Caribbean region, also recorded the largest percentage increase in remittances between 1998 and 2003. By contrast, the group of low income countries, which includes mainly countries from Sub-Saharan Africa,<sup>6</sup> recorded the smallest increase in official international remittances over the time period.

# 2.  Economic Determinants of Official International Remittances

It is possible to take the data set of 115 developing countries, add various variables, and then estimate an economic model of the determinants of international remittances. This seems useful in order to address several of the key policy questions concerning the economic determinants of international remittances. For example, what is the relationship between poverty, income inequality and remittances? Do the poorest countries of the world tend to produce more international migrants and receive more international remittances than other countries?

The determinants of official international remittances can be analyzed by using the type of gravity model suggested by Greenwood (1975) and Borjas (1987, 1989) for examining international migration. In general terms, such a model can be expressed as: Rij = α0 + α1yi + α2cij + εij (I = 1, . . , N; j = 1, . .,N) (1) where Rij is the flow of per capita international remittances between developing country i and remittance-sending region j, yi is the per capita income of remittance-receiving country i, cij is the cost of migrating from country i to region j, and ε is an error term.

Unfortunately, equation (1) cannot be estimated because our data set contains no information on the costs of migration (cij). Since this problem is also common to other empirical studies, a typical solution is to use the shortest air distance between remittancereceiving and remittance-sending countries as a proxy variable.<sup>7</sup> This is the solution that will be adopted here: the costs of migration will be measured by the air distance from the developing country to one of three major remittance-sending regions (United States, OECD (Europe) or the Arab Gulf).

In addition to the two explanatory variables listed in equation (1) – per capita income and migration costs – it seems likely that other economic, demographic and financial variables may also influence the receipt of international remittances. From an
- **2a** -

# **Table** 11: Correlation **Betwen** Agrkultural Productlvfty, **Populatio** and Location of Nonfarm Activity, hdian States, 1981

||C|orrelation Coef|ficient betwee|n|
|---|---|---|---|---|
||Agricultural<br>S|Income per A<br>hare In Each L|gricultural Pop<br>ocallty Slze of|ulation and|
||Nonfarm E|mployment|Popu|lation|
||All States|All Except<br>Kerala|All States|All Except<br>Kerala|
|Rural Areas|-. 23|-. 28|-. 23|-. 24|
|Rural Towns|||||
|5 -<br>100|.33|.38|.41|.41|
|5 - 250|.49|.57|.44|.45|
|Large Urban|||||
|100 +|.08|.10|.08|.09|
|250+|-. 06|-.05|-.04|-.04|

-oure: Employment and popu.ation data from India (Igelb). Agricultural income data from Economic Intelligence Service (1988).
- 30 -

# TABLE 3: MEXICO: Data Used In Estimating

# the Regressions

(In Percent)

|obs|p|pf|Y/K|QRs|
|---|---|---|---|---|
|1960|102.9630|154.1193|43.43353|37.80000|
|1961|99.28571|149.0040|42.73182|53.80000|
|1962|97.22222|145.2433|44.09813|52.50000<br>|
|1963|101.3513|141.6858|48.76482<br>|63.50000<br>|
|1964|96.81529|134.9514|49.17985|65.50000<br>|
|1965|104.3478|133.2900|49.13813|60.00000|
|1966|103.5928|131.7626|50.38022|62.00000|
|1967|104.6512|132.0489|50.48351|65.20000|
|1968|109.6591|133.6898|51.53908|64.40000|
|1969|108.7432|132.7428|51.08719|65.10000|
|1970|97.04434|125.2992|50.97790|68.31000|
|1971|93.95349|123.1190|51.74186|67.70000|
|1972|92.98246|118.9657|52.74241|66.30000|
|1973|91.86047|108.7214|53.39858|69.60000|
|1974|90.15873|101.6703|48.90191|82.00000|
|1975|93.13187|101.4508|47.96574|86.50000|
|1976|93.33333|111.7171|47.94003|90.40000|
|1977|99.64602|134.0266|43.19328|67.70000|
|1978|99.69698|124.9216|46.96928|64.20000|
|1979|102.2814|li3.7006|47.80382|60.00000|
|1980|100.0000|100.0000|47.38810|76.00000|
|1981|95.63492|93.44348|46.32153|82.80000|
|1982|104.8817|141.1739|41.34202|78.30000|
|1983|124.7540|162.4675|30.65258|80.19000|
|1984|116.5826|145.8740|32.50785|78.40000|
|1985|117.6855|145.6892|32.12053|35.10000|
|1986|119.8642|202.7307|28.49463|17.90000|
|1987|110.8495|189.9779|24.07883|13.60000|
29 expected on the basis of the cross country pattern by income, increasing over time one-third as rapidly with income as implied in the cross section. For women there was virtually no growth in employment share in manufacturing within countries whereas the cross section would have led one to expect a substantial gain. Conversely, services grew twice as rapidly within countries as in the cross section for men. Employment of women in services grew more rapidly over time than for men, but this rapid growth is more or less in line with the cross sectional evidence. The income response of the fraction of employment in commerce and transportation is roughly similar in the cross-section and within countries for both men and women.

Kuznets observed that the postwar employment growth in manufacturing was slower and that in services more rapid with respect to income growth of nations than would have been predicted on the basis of past cross country relationships."9 Kuznets relied on prewar data for the more industrially advanced countries to "forecast" the postwar employment changes for the less developed countries. In this study a more recent comparison of cross sectional and time series patterns is based on the same sample of countries and confirms his earlier findings. The expanding trade in manufactured goods has been ascribed a significant role in the advancement in women's employment, particularly in East Asia. 2 0 In contrast, my sample of low income countries suggests that the expansion of manufacturing may have lagged, and women's fraction of firm employment in this leading sector has not held its own, or at least has not followed the expanding path that would have been expected on the basis of cross country income comparisons. The puzzle is what has held women back from obtaining a larger share of the wage: jobs in manufacturing?
10 degree of aggregation of the data. Examining differences in cross-price elasticities across studies is likely to reflect primarily the differences in data aggreaation among the studies rather than systematic properties of crosselasticity values. Nonetheless, we selected a few cross-price elasticity estimates from studies with a relatively high degree of aggregation (thus more representative of "average" conditions). These results (from Oum, et al.) are reported for passenger and freight markets in Table 5. We reemphasize that this table draws from only a few articles and that one must be cautious in generalizing about cross-price elasticities in transport.

# B. the variability of elasticity estimates

A notable featurp of the elasticity estimates is the wide range of values in most cases. Many factors may have contributed to this diversity, among them ares

- (1) Sam studies fail to control for the presence of intermodal competition. As a result, the own-price elasticity estimates reflect, in part, the intensity of intermodal competition. If the prices of competitive modes change in the same direction as a mode's own-price, then the own-price elasticities are underestimated.

- (ii) Failure to recognle the presence of multicollinearity, autoregressive errors and other specification problems. In a few cases we feel that there is a high probability of model misspecification, hence empirical estimates may not be reliable.

- (iii) Different functional foram used. It is demonstrated in Oum (1989) that, with the same set of data, different functional forms could result in widely different elasticity estimates.
with a technological investment decision. Moreover, these firms that import both intermediate inputs and capital equipment goods improve their productivity gains suggesting a complementarity between imported inputs and foreign technology in the production process.

Our empirical analysis is motivated by a simple model of heterogeneous firms, endogenous technology adoption and imported inputs that captures these main features of the Indian data. The aim of the theoretical model is to rationalize the channels through which input-trade liberalization affects firms’ decision to upgrade foreign technology embodied in imported capital goods. Input-trade liberalization reduces the costs of imported intermediate inputs and allows firms to decrease their marginal costs and increase their profitability. In the presence of fixed cost of technology adoption, heterogeneous firms and complementarity between imported inputs and high-foreign technology, the model yields two main testable implications. First, input tariff reductions increase the probability of importing capital goods. Second, the effect of input-trade liberalization on firms’ technology choice is heterogeneous across firms depending on their initial productivity level. Firms that will benefit from input-trade liberalization are those with a high productivity level using low-technology embodied in domestic capital goods before input tariff cuts.

We then test the model implications using the Indian firm-level dataset, Prowess, over the 1989-1997 period. This data was collected by the Centre for Monitoring the Indian Economy (CMIE). The Prowess dataset provides information on imports distinguished by type of goods (capital equipment, intermediate goods and final goods). To establish the causal link between the availability of imported intermediate goods and firms’ decision to import capital goods, we rely on the unilateral trade reform that took place in India at the beginning of the 1990s as a part of the ‘Eighth Five-Year Plan’.<sup>3</sup> We depart from previous studies of input-trade liberalization by distinguishing tariffs on variable inputs from tariffs on capital equipment products. The empirical identification strategy disentangles the direct effects of tariffs on capital goods and the indirect effects of tariffs on other variable intermediate goods on firms’ decision

> 3Section 5.2 describes the policy instruments applied by the Indian Government during this reform.

3
||Table 5: Fis|cal Decentralization Variables|
|---|---|---|
|Name<br><br>|Type<br>|Definition<br><br><br>|
|oo.<br>LG verticalgap|Continuous:<br>0-100|Grants from other<br>govt's<br>(same- or upper-tier, also from<br>other countries) as%ofLG revenues m<br>1<br>- LG regulates fully (sets base and rate) at least one<br>major tax (property, income, or sales tax); 0.5 - LG partly|
|LG<br>;<br>taxation auton-<br>omy|Discrete.<br>0,<br>0.35,<br>0.4,<br>0.75, 1|regulates (sets rate or base in CG defined boundaries, or<br>.<br>only after CG approval) at least one major tax, oF fully<br>regulates some fees and minor taxes; 0 - no administration<br>of major taxes, partial administration of minor taxes; 0.25<br>or 0.75 - LG are treated asymmetrically<br>1 - at least half of transfers (to LG budgets from same-|
|.<br>EG _imeondional<br>LGexpenditure au-|Discrete:<br>0**,**<br>0.25,<br>0.6<br>0.75, 1<br>Continuous:|or upper-tiergovernments)areunconditionalandformul**a**-<br>based; 0.5 - quarter to half of transfersare uncondition l<br>and formula-based; 0 - all transfers are either conditional or<br>discretionary; 0.25 or 0.75 -LG are treated asymmetrically<br>Derivative ofLG unconditional transfers andLG vertical|
|tonomy<br>.<br>LG borrowing free-<br>dom|o-1<br>Discrete:<br>0,<br>0.35,<br>0.5,<br>0.75,1|gap. See formula (2)<br>1 - borrowing is not reculatedby CG; 0.5 - borrowing only<br>fromOG or underOG approval or regulation; 0 -borrowing<br>isnotallowed;0.25 or0.75-LGaretreatedasymmetrically|

Source: Authors’ calculations based upon data sources reported in Annex Table Al.

lg_expaut = 1 — lg_vergap * (0.75 — 0.5 * lg_transf), (2)
# _18 How_ **_Fa Ha CHasM . Indaw Gou?_**

state sector independent accounting units in 1980 and 1985 allow a trial calculation of energy consumption and energy productivity trends for collective industry durinig the period 1980/85.

NOTE: this can perhaps be extended to 1986 (using data in TJNJ 1988, pp. 424-436).

This is done in several steps:

- (1) exanine energy data for state sector independent accounting units divided into 40 branches;

- (2) collapse data for 40 branches into 15 branches;

- (3) obtair. estimates of collective sector energy consumption in 15 branches for 1980 and 1985 by subtracting energy consumption by state-sector firms from the national totals underlying Table 9.

- (4) calculate changes in energy productivity for the collective sector using derived or published figures of branch OVIO.

Energy data for 40 branches of industry in 1980 and 1985 are reproduced in Table 12, where I find no significant inconsistency between implied and publisned data for branch GVIO (readers should ignore the discrepancy for branch 7, which is trivial in size, and in the residual branch 40). Again, we see substantial increases in energy productivity over _5_ years; again, these gains depend crucially on reported increases for a small number of branches that consume large amounts of energy and report above-average productivity gains: chemicals (branch 26); machine manufacture (branch 35).

These data can also be used to compute the 'energy savings' arising from the presence of lower unit energy consumption coefficients in 1985 than existed in 1980. As before, two sectors, machinery and chemicals, dominate the calculated savings, accounting for 45 percent of the total amount (Table 16). If we were to assume that the path of energy productivity in these two branches palleled the (considerably slower) gains reported for other branches of industry, the average annual growth rate for state industry during 1980/85 would decline by one percentage point, from 8.2 to 7.2 percent.' Table 13 reports the result of calculations that collapse data for 40 state-sector branches into 15 branches and derive figures for changes in energy productivity for the 15 branches between 1980 and 1985. The transition from 40 to 15 braiiches is incomplete because it is not possible to make adjustments for 18 sub-branches (as is done in a separate worksheet, DATA87.wkl); the discrepancy, however, is not large. Energy productivity for 15 branches is calculated in two ways: first, using GVIO data that is derived from the 40-branch figures shown in Table 12; and second, using data from other sources that give GVIO for independent accounting units in the S. **Thuis i** bued on **Table** 2 **and a s.paz** workshea What if **Energy** Savings **ar Trimmed?'.**
**Corporate Diversification in East Asia: The Role of Ultimate Ownership and Group Affiliation**

# **1. Introduction**

Group affiliation and large block-holders are prevalent features of public corporations in East Asia (Prowse, 1992; La Porta et al, 1999a; Claessens et al., 1998a), in contrast to publicly traded companies in the United States which are typically independent and widelyheld. This provides an opportunity to study the role of group affiliation and large blockholders in corporate policies and performance. In particular, we study how corporate diversification policy interacts with the group affiliation and ultimate ownership structure of East Asian firms. We create a unique database containing group affiliation, ultimate ownership, and diversification data for over 2,000 companies during 1991 through 1996 in nine East Asian economies.

Using these data, we test three hypotheses pertaining to the causes of diversification.' The first hypothesis is that East Asian fimns diversify to create internal factor markets which are more cost-effective in allocating resources compared to external markets (Williamson, 1985; Khanna and Palepu, 1997). 2 The second hypothesis is that corporations diversify because their ultimate owners wish to reduce their firm-specific risk (Amihud and Lev, 1981).3 We raise a third hypothesis, which has not been tested in the context of I Previous studies have extensively discussed the motivation and effects of diversification. See Montgomery (1994) for a review.

> We use the term "intemal markets" more broadly to capture within-firm markets for raw materials, labor, and financial capital. A number of recent studies discuss the costs and benefits of internal markets for financial capital. See, for example, Gertner et al. (1994), Lamont (1997), Rajan et al. (1997), Stein (1997), Scharfstein and Stein (1997), Scharfstein (1998), and Shin and Stulz (1998).

> 3 Although we are primarily concerned with large block-holders' risk reduction incentives, we do not rule out the possibility that risk reduction benefit minority shareholders. Aron (1988) and Hermalin and Katz (1993) argue that diversification alleviates the moral-hazard problem firms face with respect to their managers, because it provides multiple performance measures.

1
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Argentina is able to produce most field crops at top technical levels. It surpasses the average of the region in all crops mentioned in Table 7.1––except wheat and soybeans which is surprising––and it surpasses Chile in rice and citrus. However, not a single field crop surpasses the USA yield. LAC averages are a modest standard, however, for a middle income country with very favorable natural conditions and a long farming tradition.

Table 7.1: Crop Yields in Selected Countries, Average 2000-2002, (ton/hectare)

||**Mexico**|**Argentina **|**Chile**|**Brazil**|**LAC**|**USA**|**EU**|**India**|**China**|
|---|---|---|---|---|---|---|---|---|---|
|Cereals|2.8|3.4|4.9|2.9|2.9|5.8|5.6|2.3|4.8|
|Maize|2.6|5.8|9.8|3.0|3.0|8.5|9.1|1.9|4.7|
|Wheat|4.9|2.3|4.1|1.6|2.4|2.6|5.7|2.7|3.8|
|Rice|4.3|5.3|5.1|3.2|3.8|7.2|6.4|2.9|6.3|
|Sugar Cane|74.1|65.4|--|69.6|64.9|77.2|--|67.3|61.3|
|Cotton (Seed)|3.3|1.3|--|2.7|2.1|1.9|3.3|0.6|3.3|
|Coffee (Green)|0.4|--|--|0.9|0.8|--|--|1.0|--|
|Citrus|12.4|20.1|15.4|22.0|17.0|34.7|18.3|17.8|8.2|
|Pulses|0.8|1.1|1.6|0.7|0.8|1.9|2.7|0.6|1.4|
|Vegetables|16.5|17.2|25.6|17.9|14.9|27.1|26.7|12.9|19.2|
|Soybeans|1.6|2.5|0.0|2.6|2.5|2.6|3.3|0.8|1.7|
|<br>Source: Caballero|(2005)--c|alculations bas|ed on F|AO’s AG|ROSTAT.|||||

Labor productivity in Argentina’s agricultural sector is the highest in the region and way above the average for LAC countries. Labor productivity is compared across the LAC region in Figure 7.1. Argentina and Uruguay, both land-abundant countries, have the highest labor productivity, with a labor productivity of US$ 9,461 and US$ 7,807, respectively, which is far more than double the LAC average of US$ 3,368. The high agricultural labor productivity in Argentina are mainly due to factors other than public expenditure in the sector, which are only $136 per worker and lower the average for Latin America of US$ 146 per agricultural worker (calculations made in Caballero 2005).

25
Table 2: Effects of Compulsory Laws on Years of Schooling

|||w/o controls|||w/ controls||
|---|---|---|---|---|---|---|
||Pooled|Female|Male|Pooled|Female|Male|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Compulsory law|2.608**|* 2.666***|2.531***|-4.504***|-3.793***|-4.519***|
||(0.273)|(0.406)|(0.282)|(0.479)|(0.735)|(0.474)|
|Experience (imputed)||||-0.593***<br>(0.074)|-0.390***<br>(0.099)|-0.605***<br>(0.071)|
|Experience square||||0.006***<br>(0.001)|0.001<br>(0.002)|0.007***<br>(0.001)|
|Constant|8.750**|* 10.436***|8.582***|17.970***|17.368***|17.949***|
||(0.059)|(0.097)|(0.061)|(0.786)|(0.924)|(0.781)|
|N|13145|1289|11854|13145|1289|11854|
|ProvinceFE|X|X|X|X|X|X|
|Survey-Wave FE|X|X|X|X|X|X|

_Note_ : Significance level: *0.1 ** 0.05, ***0.01. The dependent variable is level of schooling in years. The coefficient can be interpreted as unit change in years of schooling.

# **5 Results**

We report our estimates of the monetary rate of return for an additional year of schooling in Table 3. Estimates are presented for both OLS and instrumental variable estimators, with the preferred estimates of interest being the 2SLS estimates reported in Columns 4 to 6. Columns 1 and 3 indicate the average rate of return for one more year of schooling among salaried workers, while the remaining columns show genderspecific monetary returns for an additional year of schooling. We find positive return rates for an additional year of schooling on average, as well as by gender, regardless of the estimator used. We find that the rate of return for women is much higher than that for men in Afghanistan.

An additional year of schooling is associated with a 2.5 to 4.0 percent increase in average annualized income among salaried workers, as seen in Columns 1 and 3 in Table 3. While the rates of return are positive, they are much lower than the global average of 10 percent (Psacharopoulos & Patrinos, 2018). The rates of return vary significantly by gender. An additional year of schooling is associated with a 7 to 13 percent increase in the annualized income of women, which is almost four times greater than that for men, which is 2 to 3 percent. Similarly, in Türkiye, a compulsory schooling reform produced a significantly lower return to schooling for men (Patrinos et al., 2021). The result holds when we include additional controls such as average years of household schooling, urban and Kuchi (Nomad) indicators, and years of experience, as shown in Table B.1. Moreover, we report survey-specific estimates in Appendix A; see Table A.1, Table A.2, and Table A.3 for survey-specific results for 2007, 2014, and 2020, respectively.

This significant disparity in rates of return by gender suggests that the marginal benefit of education is substantially higher for women in Afghanistan, subsequently their contribution to the country’s economic growth. One possible explanation is that women with higher levels of education may have better access to higher-paying job opportunities compared to their less educated counterparts, prior to the dramatic events of August 2021. World Bank (2023) indicates that unemployment has more than doubled since the Taliban 10