rafmacalaba/datause-encoder-data huggingface dataset:
government and private transfers--such as pensions, scholarships and stipends--that could be considered exogenous to the household migration or labor force participation decisions. Finally, a set of variables describing the regional and ward characteristics includes variables reflecting labor market conditions in a ward.

The descriptive statistics for the main explanatory variables used in our analysis are reported in Table 1. The characteristics of women living in migrant sending and nonsending households are similar. Among the differences, women residing in non-sending households seem to be better educated. Migrant sending households are, on average, smaller, have a higher share of adult males (adjusted for the number of current migrants), possess larger land plots, and have higher non-wage incomes. The comparison of characteristics of women who participate in the labor market with non-working women demonstrates that working women are better educated, less likely to be married, and reside in smaller households and in households with a smaller proportion of adult women. Brahmin, Chhetri and Newar households are less likely to have their women working on the market compared with other castes. Land ownership has a strong negative effect on the probability of women participating in the labor market: women living in landless households are almost three times as likely to work for wages compared with women from households with less than one hectare of land. The gap between these women and the number of working women belonging to households that own larger land plots is even greater. Women living in Katmandu and other urban areas of Nepal are more likely to work for wages than women from rural Nepal.

# **5. Results**

The results of the joint estimation of equations (5) and (7) are shown in Table 2<sup>8</sup> . The coefficients on the main explanatory variables affecting household migration and

> 8 According to the likelihood-ratio test criterion, the specification that assumes an independence of the error terms in equations 5 and 7 (Table A1 in Appendix) is rejected in favor of the SPFIML estimation. The results of estimation of equations 5 and 7, under assumption of independence of the error terms between these two systems, are shown in Appendix.

> The Wald tests show that the estimated ρμ _0_ is statistically significant with ( χ<sup>_2_</sup> (1)=4.03) and ρμ _0_ is statistically significant with ( χ<sup>_2_</sup> (1)=3.88); two ρ ’s are jointly significant. The Wald test also rejects the

> hypothesis that _Cov_ [ μ , _v_ 1 − _v_ 0] = 0 .

14
# **4.2. Use of Proceeds**

The transaction-level data on capital raising activity contain detailed information on the use of proceeds from syndicated loan issuances.<sup>11</sup> There is significant heterogeneity across countries in the amount raised for different purposes. Such a heterogeneity is arguably an important factor underlying the relatively longer maturity terms for syndicated loans from developing countries in comparison to those from developed countries.

Specifically, developing country firms use the proceeds from syndicated loans more intensively for project finance than firms from the developed world, about 35 percent of all syndicated loans contracted by developing countries versus about 5 percent of those by developed countries (Figure 3, Panel B). Importantly, syndicated loans for project finance, which consists primarily of infrastructure projects, have the longest average maturity (about 11 years) across syndicated loans for all types of purposes.<sup>12</sup> Panel regressions on the transaction-level issuance data confirm the relevance of the differences in the use of proceeds for the maturity of syndicated loans. We regress the maturity of the issuances on four dummy variables that capture broad categories of how firms use the funds raised through syndicated loans: general corporate purposes, project finance, refinancing and capital structure management, and others. Acquisition financing and leveraged buyout operations is the omitted category, and thus the reference group in the estimations. The specification also includes industry-year dummies. The results, reported in column a of Table 3, are consistent with the summary statistics. They show that the maturity of syndicated loan issuances significantly varies across the types

> 11 For corporate bond market issuances, the data on the use of proceeds is much less informative. More than 60 percent of the issuances are categorized as general corporate purposes. Nonetheless, the differences in maturity at issuance between developing and developed countries is consistent across bond issuances within a given use of proceeds. Bond issuances are typically shorter in the former than in the latter countries.

> 12 In fact, most of the financing for infrastructure projects comes from syndicated loans. Engel et al. (2014) provide evidence that in the United States and other developed countries the ratio of bonds to syndicated loans for infrastructure financing is 1:5 and 1:6, respectively. The ratio in Asia (excluding China) is 1:8 and in Latin America 1:3.

12
19 effect on prices faced by consumers. The extent of sales between agriculture and non-agriculture suggests that it makes sense to examine the effects of decontrolling all intermediate prices (not just of non-agricultural goods).

The effect of decontrolling intermediate goods prices is the reallocation of profits between supplier enterprises and purchasing enterprises. Unfortunately, hardly any information is available on the extent of the differences between free market prices and state list prices for intermediate goods. Also, up to date information on inter-industry transactions was not available. However, table 2.7 presents illustrative calculations of the effects on industrial profitability (measured as a percentage of gross value added) of raising intermediate good prices, using the 1981 input-output table for China. The precise effects of price increases would depend on the proportions in which each industry bought each of its inputs at State list prices and free market prices. As we have no information on this, it is assumed that, for any one input, each industry has the same ratio of list price to market price purchases.

The first column reports the calculated effects of a 20 percent increase in the average price of all intermediate goods. However, not all intermediate goods are underpriced to the same extent: Dai (1989) regards the underpricing of "higher" level intermediates such as elect.icity and communications as more serious than the underpricing of "middle' level intermediates such as ferrous and non-ferrous metals. It is therefore necessary to look at examples of increasing individual intermediate good prices. The second column reports the calculated effects of a 20 percent increase in the price of electricity. The third column reports the effect of a 16 percent increase in the average price for metallurgy. The fourth column reports the same figures for a 16 percent rise in average coal prices. The fifth column reports the effects of a 10 percent rise in the average price of construction materials.

These changes in sectoral profitability in relation to value added following price reform could in principle be offset by suitable changes in the rates of VAT. However, this will not necessarily lead to a more uniform set of tax rates because price controls are not the only cause of differences in profitability; another major cause is the unequal distribution across enterprises of capital goods historically provided without charge by the State. Free markets with uniform taxes will only equalize profitability if all sources of inequality are removed.

# 3. DIRECT TAXES

# 3.1 The system of enterprise income tax

From the point of view of revenue, the income tax from enterprises is second to the Product Tax, VAT and Business Tax. Direct taxation of business income varies according to the type of enterprise. These include (i) state enterprises, (ii) collective enterprises, (iii) individual household enterprises, (iv) joint ventures, and (v) foreign enterprises.
- IICA-Instituto Interamericano de Cooperación para la Agricultura (2007).“Situación del Etanol en la República Argentina”. Informe elaborado por la Oficina del IICA en la Argentina para la Comisión Interamericana del Etanol, Abril.

- Ignaciuk A.and R. Dellink (2006). “Biomass and multi-product crops for agricultural and energy production – an AGE analysis”, _Energy Economics_ , 28, 308-325.

- Kehoe P. and T. Kehoe (1994). “A Primer on Static Applied General Equilibrium Models”. Federal Reserve of Minneapolis Quarterly Review; 18:1.

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- Kojima M. and I. Klytchnikova (2008). “Biofuels. Big potential for some...but big risks too”, _Development Outreach_ , October, World Bank Institute.

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- López, G. (2005), “Evolución y Perspectivas del Complejo Oleaginoso Argentino en Relación al de Estados Unidos y Brasil-Potencial y Limitantes”. Fundación Producir Conservando. Noviembre 2005.

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24
expressed at 2000 constant dollar measured in purchasing power parity, are also taken from the IEA (IEA, 2007f, 2007g).

Data for “Other Latin America” are taken directly from the IEA at the aggregate level. Caribbean data are either the summation of the data for the constituent countries (e.g., GDP, fuel consumption) or their weighted average (e.g., CO2 coefficients of electricity).

# **4. Results and Discussion**

All countries in LAC, with the exception of Cuba, experienced significant growth in transportation sector CO2 emissions during the 1980-2005 period. However, there remain significant differences in the magnitude of emissions growth and the factors driving it. Figure 3 summarizes the results of the decomposition of transport sector CO2 emissions growth into fuel switching, modal shifting and changes in emission coefficients<sup>7</sup> , transportation energy intensity, and economic activity (or GDP). Detailed results for each country considered are presented in the Appendix.

> 7 Only the emission coefficients of electricity changes due to different electricity generation mix over time; emission coefficients of other fuels remain constant throughout the study period.

19
opportunity cost of farm labor is rising, farm sizes need to be expanded, because the substitution of large machines for labor requires scale economies (Otsuka, 2013; Yamauchi, 2014). Promoting scale economies and mechanization is likely to involve a number of changes, including development of land rental markets (Deininger and Jin, 2005; Zhang, Qingguo and Xu, 2004), institutional development such as machine rentals (Takahashi and Otsuka, 2009) as well as removal of restrictions on labor movement from farm to non-farm sectors (Fan et al., 2003). About one-third of the world’s farmers are still in China (Christiaensen, 2012). How to promote farm size expansion while ensuring the well-being of smallholders is an important challenge which requires future research. For instance, using farm panel data from Indonesia, Yamauchi (2014) finds that while relatively large farmers tended to increase the scale of operation by substituting machines for labor and by renting more land, such a dynamic change was not observed among relatively small holders.

In principle, a gap between the demand and the supply for food could be diminished by a protection policy that raises the price of all agricultural products relative to non-agricultural products. However, this would have the undesirable effect of reducing the access of some poor people to the staple foods that they need. In any event, as shown in Figures 12a and 12b, it appears that per capita demand for key food staples, such as rice and wheat, is now declining quite sharply in both urban and rural settings. This suggests that it is unlikely that the gap between food demand and supply in China will manifest itself as large net imports of these staples. Given this, there does not seem to be a self-sufficiency argument for increased protection of these staple foods.

Protection for the feedstuffs demanded by China’s rapidly growing livestock production sectors could reduce demand for these products, but would do so at the risk of hindering the 27
surveys, rather than working with consumption data in only the base survey, as with the estimates for Table 1.) Estimation results confirm that, for the model specifications that provide estimates within the 95 percent confidence interval of the true poverty rates, the changes captured by the characteristics are closer to 100 percent. For example, under Specification 4 with the random effects probit regression, the change due to the coefficients is the most negligible; this specification also provides a point estimate of poverty (29.7 percent, Table 1) that is closest to the true poverty rate.

We turn next to impute poverty for 2011/12 with the estimated parameters from the 2009/10 survey round.<sup>15</sup> We have preferred specifications for analysis but we also show estimates for all the other specifications for comparison in Table 3. Our preferred specifications show that the imputation-based poverty estimates can be range from 22.9 percent (Specifications 3 and 4, the probit model) to 25 percent (Specification 2, the linear regression model). Interestingly enough, except for Specification 5 that could be excluded due to overfitting concerns, all other estimates— including even Specification 5 with the probit model—fall within this range.

These imputation-based estimates are larger than the design-based estimates of 22 percent, and the differences are statistically significant (outside the 95 percent confidence of the latter). However, considering all specifications together, the difference between the probit estimates and the design-based estimate is between one and two percentage points, while that between the normal linear regression estimates and the design-based estimates is between two and three percentage points. Thus according to our imputation-based estimates, while the design-based estimate may

> 15 We use estimated parameters from the 2009/10 round, rather than the 2004/05 round, to impute poverty in the 2011/12 round since these parameters may change over time. Indeed, the null hypothesis of the equality of the estimated parameters in these two survey rounds is rejected with significantly large value from a Wald test (results available upon request). More generally, survey rounds that are closer in time are more appropriate for imputation.

15
|_________<br> <br>Table 1:|___________________<br> <br>Countries included in t|_____________________________________________<br> <br>he international comparison and data sets used|_________________<br>|____________________________________<br>|
|---|---|---|---|---|
|Country<br>_________|Unit of analysis<br>___________________|Coverage<br>_____________________________________________|Years<br>_________________|Contact<br>______________________________________|
|Austria|Firm|Manufacturing firms with at least 20 employees|1999 – 2005|Stefano Schiavo<br>stefano.schiavo@ofce.sciences-po.fr|
|Belgium|<br>Firm|All firms|1996 – 2005|Mauro Pisu<br>mauro.pisu@nbb.be|
|Chile|Establishment|All establishments with at least 10 workers|1990 – 1999|Roberto Alvarez<br>ralvarez@bcentral.cl|
|China|Firm|All state firms and all non-state firms with sales<br>above RMB 5 million|1998 – 2005|Jo Van Biesebroeck<br>jovb@chass.utoronto.ca|
|Colombia|Establishment|All establishments with at least 10 workers.|1981 – 1991|Alberto Isgut<br>isgut@un.org|
|Denmark|Firm|Universe of firms with minimum economic<br>activity|1999 – 2002|Ulrich Kaiser<br>uka@sam.sdu.dk|
|France|Firm|All firms (not establishments) with at least<br>20 active persons|1990 – 2004|Lionel Nesta<br>lionel.nesta@ofce.sciences-po.fr|
|Germany|Establishment|All establishments with at least 20 active persons<br>(including owners) plus smaller establishments that<br>are part of a multi-establishment enterprise with<br>at least 20 active persons|1995 – 2004|Joachim Wagner<br>wagner@uni-lueneburg.de|
|Italy|Firm|Universe of firms with 20 or more workers|1989-1997|Chiara Tomasi<br>c.tomasi@sssup.it<br>|

27
MONGOLIA'S INFORMAL SECTOR Size and Trends 10 household survey conducted by the SSO, add strong support to the key finding that _most Mongolian informals do not consider themselves employed, and are thus omittedfrom official statistics._ Some statistics on ablebodied people of working age, employment and "not-employment" are depicted graphically in Figure 1.

<!-- Start of picture text --> "Not-Employed" in Ulaanbastar<br>by Reason<br>100,000<br>60,000 --------------<br>40,000 .<br>20,000<br>0 1 1<br>199, 1992 1993 194 1995 199<br>jJ3clearly not in labor force seasoral<br>nv<br>mmnotfiedingprofessiw o rk nal [left anyorex-convic<br>*taidoff Jnor<br>Source: SSO<br>Able-bodied People of Working Age<br>and Official Employment in Ulnanbaatar<br>330,000 IS0,000<br>310,000 -------- ---- 1,00060---------------------------<br>290,000 - ------ 140,000<br>270,000 ------- 120,000<br>250,000 ------- 100,000<br>230,000 80,0------- ,<br>210,000 ------- 60,000<br>190,000 - -- - - - 40,000<br>170,000 ------- 20,000<br>150,00 1991 1992 1993 1994 1995 1996<br>-a- Able-bodied people of working age _- Officially Employed<br>-Officially Unemployed (right axis)<br><!-- End of picture text --> 20. The changes over time are 0 dramatic.'<sup>5</sup> In only six years the number of able-bodied people of jJ3clearly working age in Ulaanbaatar has grown nv by 12%. During the same period of time, the percentage reporting that they *taidoff are employed has declined from 73% to 59%, and the percentage of the not- Figure 1 employed who do not provide a reason _Source: SSO_ has grown from 38% in 1991 to 76% in 1996. Furthermore, the percentage of the not-employed that are registered at 330,000 the labor exchange has fallen from 310,000 -------42% to 13%. The dramatic increase in 290,000 - the level of people "not-employed," 270,000 coinciding with relatively low official 250,000 unemployment rates, is suggestive of 230,000 marked changes in the labor market 210,000 ------that may be escaping the official 190,000 - statistics.

21. Figure 2 depicts the changes 150,00 over time in the number of able-bodied people of working age, the level of -aofficial employment, and the level of -Officially official unemployment for Ulaanbaatar I city.'<sup>6</sup> At the same time that the level **Figure 2** of employment has decreased, and the _Source: SSO_ number of people not-employed in the annual censuses has increased, the number of registrations, upon which the official unemployment rate is calculated, have actually fallen slightly since 1991. Unemployment as registered at Ulaanbaatar's EROs stood at about 10,600 at the end of 1996, whereas the census of December 1996 counted 83,100 as notemployed, 63,400 of which "without any reason."'<sup>7</sup> 22. The story being told by Mongolia's employment, unemployment, and not-employment statistics, is that a large portion of the able-bodied, working age, population (particularly in Ulaanbaatar) has fallen out of the calculations of official labor statistics, counted as neither employed nor unemployed. One may
-20 -

commercial banking sector and private sectr, and was chaired by a commercial banker. There was fairly broad consultation on changes in the Central Bank's regulations for the auction, and rules and amendments to rules were published. Uganda similarly operated with a high degree of trasparency, with public opening of the bids.

Fijh. one final set of empirical analyses reveals the presence of considerable non-stationarity and regime shifts for most of the auction data from the four countries. This is unsurprising in view of the above stylised facts which emphasise nrle changes, the non-normality of auction data and the importance of anticipations ofpolicy changes. The non-parametric variance ratio test CCochrane, 1988) showed that the share of the random walk component in the total variance of the rate of change in the auction rate, while large for all of the counties, is noticeably smaller for Nigeria and to some extent, Zambia. The rate of convergence also strted rather more rapidly in these two countries. This finding is again consistent with the use of a stochastic reserve prioe in Ghana and Uganda (i.e. based on the non-stationary bureaux series), as opposed to the frequent and direct policy interventions that charactsed the Zambian and Nigerian auctions. These results on non-stationarity have profound implications for the way in which auction data are analysed: random shocks have permanent effects on economic variables, and consistent ing of the behavioural propositions of auction theory requires modelling to account for these data characteristics.

In our companion paper (Axon and Elbadawi, 1994b) we buid on the results of this paper, and estimate dynamic models influenced by auction dteory for the determination of the auction rae in these four countries, using weekly micro-auction data. Our modelling methodology aes account of the non-stationarity of the auction data as well as regime changes and policy interventions. As motivated above, we use dtese models to test the impact on the level of the auction rate of increased competition amongst bidders, the effect of uncertainty (proxied by a volatile supply of foreign exchange to tie auction), and dift pricing mechaims (Dutch and marginal pricing).