rafmacalaba/datause-encoder-data huggingface dataset:
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In addition, from the Shephard’s lemma, the following factor share equations are obtained: where _Si_ is the cost share of input _i_ . Through the SUR model, the cost parameters are estimated in Equation (1) and one of the factor share equations (3).<sup>4</sup> The advantage of the SUR is that higher efficiency in estimation could be expected without wasting the degree of freedom (Christensen and Greene, 1976).

The marginal impact (elasticity) of infrastructure quality on firm costs is calculated by this: The used data come from the 2005 BEEPS for 26 countries in the ECA region. The total sample size amounts to about 4,300. This excludes a number of observations of which the relevant cost data are not included.<sup>5</sup> The number of observations per country in our sample varies from 50 to 650, but mostly ranging between 100 and 200.

The summary statistics are shown in Table 1. Firms look different in size and labor intensity. The operating cost ranges from US$ 3,000 to 532 million U.S. dollars with a mean of about 3 million U.S. dollars. The average wage is around US$ 5,300 per annum. In the sample the labor cost amounts to 22 percent of total costs. The number of days without sufficient electricity supply reaches 10 days per year on average. For water, there are 4 days that do not

> 4 One of the factor equations should be dropped to avoid the singularity problem.

> 5 The original sample size of the BEEPS covering 27 countries is 9,655.
20

**table** 3-s. Becteral Distribution of FDI Stoeka in 8l1vme Host Countries (percent of total)

|Country|Primary|Secondary|TertiAryX|Yea|
|---|---|---|---|---|
|Argentina|27.4|48.3|23.3t|1976-83|
|Brazil|13.5|74.7|19.2|1985|
|Colombia|61.0f|29.0'|10.0'|1985|
|Mexicod|n.a.|76.0|n.a.|1984|
|India|15.8|55.2|29.0|1974|
|Indonesia|62.5|n.a.|n.a.|1983|
|Korea, Rep. of|0.7|59.2|40.2|1986|
|Malaysia|26.9|33.5|39.Ob|1972-81|
|Thailand'|n.a.|33.0'|n.a.|1984|
|Kenya|3.1|64.3|32 <sup>. </sup>6 <sup>b</sup>|1985|
|Nigeria|28.4|30.1|41.5b|1986|

# a.a. Not Available.

Note: Figures do not add to 100.0 for some countries because of unclassified projects, imprecise estimiates, or rounding.

a. Latest year available in Becsky, Lee, and Ordu (1990). Where multiple years are shown, the stocks are based on the cumulative flows for the indicated years only.

b. Estimates computed from figures for other sectors or computed from estimates in original source.

c. Rounded estimates in Becsky, Lee, and Ordu (1990).

d. The 76 percent figure for Mexico is based on the country study estimate. of 74-;_ percent depending on the year. The latest year for the sectoral distribution is 1984.

e. The Indonesia country study estimates "nearly 10 billion" in energy out of a total of "nearly 16 billion".

f. The Thailand country study estimates "one-third" in manufacturing.

Sgogre: Becsky, Lee, and Ordu (1990). tables 2-6, 3-5, 4-3, 5-22, 6-9, 7-4, and 7-7.
plausible weighting schemes for a broadly-defined composite index of corruption include:

- Weight more heavily those sources that represent truly independent assessments.  The BEEPS would thereby receive a greater weight than WEF or ICRG.  Weighting each type of source equally, as suggested above, is consistent with this reasoning.

- Weight more heavily those sources with more extensive publicly available documentation (particularly regarding assessment criteria and methodology) and detailed justifications.  Nations in Transit would thereby receive a greater weight than EIU.

- Among survey sources, weight more heavily those with larger and more nationally representative samples, and those that include more questions on corruption.  The WEF – with many more corruption questions - would thus be weighted more heavily than the IMD.

- Weight indicators based on conceptual grounds; e.g. if an equal mix of administrative corruption and “state capture” is desired but most available indicators pertain to the former, weight more heavily those that pertain to the latter.

A disadvantage to most of these weighting schemes is that weights would be determined subjectively, in contrast to the objectively-determined weights in the WBI methodology. The larger point is that no one of these weighting choices is likely to be the most appropriate for all purposes to which an aggregate index might be applied. Greater public access to the underlying data used in the TI and WBI indexes, along with better information on how those underlying data are generated, would permit data users to customize their own indexes more appropriate to their own purposes.

# **4. Levels and Trends in Corruption for ECA Countries**

With the review of corruption indicators in section 3 as background, this section reports evidence from BEEPS and other sources on corruption trends between 2002 and 2005. We compare ECA to other regions, and compare ECA countries to each other.

Table 2 reports summary statistics for the corruption variables included in both the 2002 27
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since from 1977-78 to 1983, despite slower growth in average consumption per capita, the poverty ratio fell largely due to a decline in inequality in most states, we need to assess the role of anti-poverty programs initiated during this period. Any such assessment of their overall impact, however, must begin with the recognition of the fact that India is experimenting in many directions to reduce poverty-induced human suffering.

IRDP has been in operation long enough to be realistically evaluated. It has channelled unprecedented funds to enable the poor via loans and subsidies to obtain non-land assets. During the Sixth Plan period, assets worth some Rs 50 billion were created/distributed to about 17 million families -- a big achievement. During 1987-88, the fourth year of the Seventh Plan, another 4.2 million families were assisted with an investment of Rs4,471 per family, or Rsl9 billion overall (see Table 24). So far, IRDP reached about 25 percent of India's rural households.

State-wise shares of IRDP and NREP expenditures are compared with their shares in the population of the poor and the ultra poor in Tables 25 and 26. On the basis of the incidence of either poor or ultra poor population in 1986-87, the eastern states of Assam, Bihar, Karnataka, Kerala and West Bengal deserved higher allocations.

Most past assessments of IRDP by the government have been favorable, but micro studies have been equivocal in supporting these claims. However, the divergence between macro indicators and micro performance seems to be narrowing in recent years.

Also, "success" or "failure" depend on the criterion adopted for assessment. Thus, Subbarao (1985) argued that the usual criterion of
engagement in a technologically dynamic global economy. International cooperation through deeper agreements that are open to all trade partners would create a more secure policy environment to exploit the gains from an ever‐finer international division of labor and from reaping economies of scale and scope in large, integrated markets. These two major recommendations of this paper will have a significant development pay‐off regardless of which state of the world prevails.

# **REFERENCES**

Amiti, M. and S.J. Wei. 2009. "Service Offshoring and Productivity: Evidence from the US." _World Economy_ 32 (2): 203–20.

Arnold, J., B. Javorcik, M. Lipscomb and A. Mattoo. 2016. Services reform and manufacturing performance: Evidence from India, _Economic Journal_ , Volume 126 (590) pp. 1–255.

Arnold, J., B. Javorcik and A. Mattoo. 2011. The Productivity Effects of Services Liberalization: Evidence from the Czech Republic, _Journal of International Economics_ , vol. 85(1), 136‐146.

Baldwin, R. E. and Kawai, M. 2013. “Multilateralizing Asian Regionalism” (August 15, 2013). ADBI Working Paper No. 431.

Baldwin, R. and F. Robert‐Nicoud. 2014. “Trade‐in‐goods and Trade‐in‐tasks: An Integrating Framework,” _Journal of International Economics_ 92 (1): 51‐62.

Bastos, P. 2018. “Exposure of Belt and Road Economies to China Trade Shocks.” World Bank Policy Research Working Paper No. 8503.

Borchert, I., B. Gootiiz and A. Mattoo. 2012. “Guide to the Services Trade Restrictions Database. World Bank Policy Research Working Paper No. 6108.

Constantinescu, C., A. Mattoo and M. Ruta. 2015. “The Global Trade Slowdown: Cyclical or Structural?”, World Bank Policy Research Working Paper No. 7158, forthcoming in the _World Bank Economic Review_ .

Constantinescu, C., A. Mattoo and M. Ruta. 2016. “Global Trade Watch: Trade Developments in 2015.” Washington, DC.: World Bank Group.

Constantinescu, C., A. Mattoo and M. Ruta. 2017. “Does Vertical Specialization Increase Productivity?” World Bank Policy Research Working Paper No. 7978.

Duggan, V., S. Rahardja, G. Varela. 2013. “Services Sector Reform and manufacturing Productivity: Evidence from Indonesia.” World Bank Policy Research Working Paper No. 6349.

Escaith, H., N. Lindenberg, and S. Miroudot. 2010. “International Supply Chains and Trade Elasticity in Times of Global Crisis.” WTO Staff Working Paper ERSD‐2010‐08.

48
positive income effect in the context of a CCT program (Barrera-Osorio et al. 2008). While the program increased the enrollment of recipients, ineligible siblings were more likely to drop out of school and enter the labor market. In a contrary example, evidence for a scholarship program in Cambodia that targeted poor children making the transition from primary to lower secondary school shows that the school enrollment of ineligible siblings was not affected by the program (Ferreira et al. 2009).<sup>32</sup> We exploit three different identification strategies to examine the spillover effects of the FSSP effects on current enrollment, school completion (primary and secondary), and school choice (private or public) for boys in school age (6 to 17 years old). The first strategy implements a DD analysis that compares boys in households with at least one exposed girl in stipend districts to similar boys in non-stipend districts at baseline and follow-up. The second strategy also looks at relative changes over the longer term but uses boys in households without exposed girls in stipend districts as the comparison group. Finally, the third strategy integrates both of the designs above in a triple difference framework (DDD). The analyses draw on household data from MICS (2003 and 2007-08), and the subsamples used for each of the outcomes are selected based on gender-specific age and grade restrictions.

None of the empirical models estimated for each of the research designs reveal any spillover effects of the program on the enrollment and school completion of boys. The results, summarized in Table 11, indicate that the enrollment and school completion rates of boys with female siblings who have been exposed to the FSSP exhibit statistically similar trends as boys with no eligible siblings in both stipend and non-stipend districts. The only exception is a decrease in enrollment of 3 percentage points in one of the models of the DD analysis that is based on the comparison with boys living with ineligible girls in stipend districts. Yet, this result does not hold when the analysis is extended to DDD models.<sup>33</sup>

> 32 The CESSP Scholarship Program in Cambodia provides modest transfers to eligible girls that are equivalent to approximately 2-3 percent of the total expenditures of the average recipient households. The relative size of the GSP in Pakistan is similar. The monthly stipend represents 3.4 percent of median household expenditures of the recipient households in 2004 (Ferreira and others 2009; Chaudhury and Parajuli 2006).

> 33 Results that look at the average years of schooling completed do not reveal any effects either. Comparisons for school-age boys at baseline and follow-up indicate that this result is not driven by systematic difference in of age-grade distributions.

24
<!-- Start of picture text --> Crop Productivity Index<br>28<br>Range of all sample paths of stochastic model<br>Deterministic-Baseline<br>26 average<br>10% quantile<br>50% quantile<br>24 90% quantileone sample path<br>22<br>20<br>18<br>16<br>14<br>2010 2020 2030 2040 2050 2060 2070 2080 2090 2100<br>Year<br><!-- End of picture text --> Figure 2: Crop Productivity Index _At_ at time _t_ , so they are not realized sample paths, but Figure 2 also displays one realized sample path of _At_ which is the dotted line.

# **5 Model Results**

This section describes the results of the impact of crop yield uncertainty on the optimal path of global land use based on the dynamic stochastic model simulations. While it is unlikely that the world’s land will be optimally allocated in the coming century, knowledge of this path can provide important insights into how uncertainty and irreversibility shape the desired path for global land use decisions. We solve the model over the 400 years with 5-year time steps and present the results for the first 100 years to minimize the e↵ect of terminal period conditions on our analysis.<sup>25</sup> We first present the results of the perfect foresight model, wherein the optimal land allocation decisions are made based on the values of the crop productivity index in the absence of climate and technology shocks. This deterministic analysis is a useful reference point for further discussion when the uncertainty in food crop yields is introduced. We then present the results of the dynamic stochastic model, where the impact of the intrinsic climate and technology uncertainty is brought into the model optimization stage. Specifically, we generate 1,000 sample paths of optimal global land use under di↵erent realizations of the stochastic crop productivity index. The results are presented as the di↵erence between the stochastic path and deterministic reference solution. **5.1 Optimal Path of Global Land Use under Crop Yield Uncertainty** Figure 3 depicts the optimal allocation of global land use over the next century. The left-hand side of Figure 3 shows the deterministic paths of di↵erent types of land considered

> 25The model converges to its stationary state around 2150. The di↵erences in land use allocations between 2100 and 2150 are small and therefore not reported.

15
10 the estimata without grant, a measure that will correctly reflect progress toward sustainable internal balance. Almost all adjustment programs aim at reduction of the relative size of the budget deficit which is key to containing effective demand and releasing resources for the pnvate sector. Here the projections have been somewhat ambitious. The average budget deficit to GDP ratio, 'or countries for which data was available, was just about 11 percent in 1986.bi For these countries, the average budget deficit to GDP ratio was projected (using the latest available projection from PFPs) to come down to 10% in 1987. Instead, the average budget deficit to GDP ratio went up to about 12% in that year. This implies that the expected efforts to reduce budget deficit did not come forth. In other words, the commitment of governments to reduction of budget deficit as assumed in the PFP projections, was in fact weak. Either the commitment of government improved in later years or the projections became much more realistic. The aggregated record of the subsequent years also shows that generally projected (or targeted) values for budget deficit were lower than actual outcomes, but the difference is much smaller than earlier years. The years 1990 and 1991 have seen progress both in reducing budget defi^its and in getting the deficit projection closer to the target level. In both years, the projection exceeded the outcome by only about 0.5% of GDP per year, a substantial improvement on earlier years.

{/As noted, most budget deficit estimates are without grants. In one or two cases, as noted in the graph, the data on budget deficit without grants are not available. There I have used grant-inclusive data. The awegate figures reflect unweighted averages. However, in the case of budget deficit, there are two small countries (Rwanda and Lesotho) where the deviations between projected and actual values are unusually high. Since inclusion in the aggregate measure will distort the picture, they have been excluded from estimating the aggregated simple average measure. It should also be noted that the figures for the budget deficit for Guinea-Bissau are not comparable between successive PFPs. I used the current budget deficit from the 1989 PFP.
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<!-- Start of picture text --> MOAN<br>\<br>\<br>\<br>\<br>‘<br>+\<br>\<br>2‘<br>8£2c3 as* Ss<br>ee SAL<br>& mse<br>ra ween<br>fo) == ==<br>Zz 0 Se BS =<br>_<br>0 5 10 15 20 25 30<br>Age in Years<br>=== Frontier ms | AfTICa a =| AC<br><!-- End of picture text -->
have become important trading partners of the Africa region over the past quarter-century. Although China and India’s average export and import shares with Sub-Saharan Africa barely exceeded 1 percent at the start of the 1990s, these shares increased steadily over the past three decades. By 2018, average export and import shares of the region with China and India amounted to 13.1 and 6.5 percent, respectively. Second, trade between Africa and the United States + euro area aggregate has declined steadily over the past 25 years. Specifically, it decreased from about 35 percent at the beginning of the 1990s to nearly 18 percent in recent years. Finally, intra-regional trade more than double, from 7 percent at the beginning of the 1990s to approximately 15 percent in 2018. Overall, there is an increase in the number of trade destinations of Sub-Saharan African products.

**FIGURE 2: Average trade share of Sub-Saharan Africa with regional and international partners**

_(percent of GDP)_ <!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>199019921994199619982000200220042006200820102012201420162018<br>China India US & Euro Area Intra-Regional SSA<br><!-- End of picture text --> _Source: Direction of Trade Statistics, International Monetary Fund._ The rising export and import shares with China over the past 25 years has been accompanied by a shift in the composition of goods that the region exports and imports from the East Asian country. For instance, exports of “ _crude materials, inedible, except fuels_ ” and “ _mineral fuels, lubricant and related materials_ ” amount to US$ 7.2 and US$ 6.2 billion respectively in 2015-18, up from US$ 262 and US$ 38 32
# **B.  Robustness of Results**

Overall, the results are robust to the inclusion of additional variables known to explain cross-sectional differences in capital structure and the use of FC debt such as family group ownership structure, a foreign equity listing, foreign currency cash flows, capital intensity, and the level of business risk. We do not include these because the variables are not available for all firms and they do not appreciably change the results. We also examine other measures of legal effectiveness such as creditor rights (from LSSV, 1998) and a categorical variable for legal origin (i.e., English=1, German/Socialist=2, and French=3) and obtain results similar to those reported.

Another concern is that the results are specific to East Asia or the time period studied. Robustness checks using other years and countries are hampered by a lack of detailed data for FC debt. However, we examined an additional sample that provides some additional insights. We take all non-financial firms in the WorldScope database in 2001 and split the sample by the level of foreign sales (as a percent of total sales). We assume that firms with low foreign sales (less than 10%) will on average face greater constraints in accessing foreign debt markets (as discussed in the next section).<sup>8</sup> We repeat the regressions reported in Table 3 and find that for all countries, all developing countries, and only East Asian countries in 2001 the coefficient on legality is on average three times greater in magnitude for firms with low foreign sales. However, the difference is only statistically significant for East Asian countries. In sum, this analysis provides some additional evidence consistent with the main hypothesis, but the results are weak probably because of less precise data on foreign currency debt.

> 8 Allayannis et al. (2003) find that firms with FC debt also have higher levels of foreign sales, income, and assets though many firms without FC debt also have nonzero values for these characteristics.  In addition, some firms without foreign sales access FC debt markets, thus foreign sales is a reasonable but imperfect measure for firm use of FC debt.  We chose the 10% threshold because it splits the sample of EA firms roughly in half.

16
# **C.2 Heterogeneous Effects of Livelihood Diversification**

In the main paper we address two pre-specified research questions. In this section, we explore our third pre-specified research question: does income diversification have disparate impacts on different country subgroups in the context of the COVID-19 pandemic? Specifically, we investigate heterogeneous impacts for male- and female-headed households as well as urban and rural households. To detect these potentially disparate effects, we include binary interactions term in our ANCOVA and difference-in-difference specifications indicating head-of-household gender and household sector. Because we use only the ANCOVA and difference-in-difference specification to answer this question, we restrict our analysis to just the indices that rely on baseline data (fractional indices 3 and 4 and HHI 1 and 2).

Table D2 displays coefficient estimates for the ANCOVA specification with a head-of-household gender interaction. In this context, male-headed households are the comparison group. While nearly every coefficient of interest is not significant, coefficients tend to be slightly positive coefficients, suggesting that households headed by women may experience increased food insecurity when household incomes are more specialized. As seen in Table D3, using the difference-in-difference estimator also produces null results with a similar tendency for most coefficients to be slightly positive.

We also test for heterogeneous impacts across rural and urban populations. Similar to the results for differences based on gender of the head-of-household, all of the results for differences between urban and rural households are statistically insignificant. Unlike the head-of-household gender results, the specifications that include urban/rural indicators do not point to a consistent relationship in terms of sign. For these specifications, rural households serve as the comparison group.

As seen in Table D4 and Table D5, coefficients for the interaction term do not evidence a differential impact of livelihood diversification on food security for urban versus rural populations. Coefficient estimates are never statistically significant and do not follow a discernible trend across countries.

Overall we do not find any significant differences in how livelihood diversification impacts welfare outcomes based on the gender of the head-of-household or whether the household is rural or urban. There is a slight pattern of female headed households experiencing worse outcomes than male headed households when female headed households are more specialized. No pattern emerges for urban/rural households.

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