rafmacalaba/datause-encoder-data huggingface dataset:
Table 2 provides a detailed description of the types of information contained in the resulting October Inquiry files for 1983 to 1999, the period on which we focus. Here we only report on countries that reported at least one gender breakdown in the period 1983-99. In total there were 83 countries which did report at least one gender breakdown during this period. Freeman and Oostendorp (2000, 2002) provide information on all countries available in the October Inquiry files, also for the countries which did not report any male-female breakdown.

Panel A gives information on the size of the sample. It shows the maximum conceivable observations that the Inquiry would contain if each country reported a female and male wage statistic for each occupation yearly: over 227,171 male-female wage pairs.<sup>7</sup> The actual number of observations is smaller, largely because in many years most countries do not report statistics. On average, countries report male-female wages for 6.7 years out of 17 possible years. As a result, 80,500 of the potential observations are missing, because various countries did not report data in particular years. Moreover, in the years when countries did report, they did not report data for every occupation. The main point is that there are 13,020 country-year-occupation cells with female-male wage data in the 1983-99 file.

There is a further complication. Many countries report more than one female-male wage pair for a single occupation. Some give hourly wage rates _and_ average earnings. Other give female-male wages for different locations. Nearly half the observations (45 percent) contain multiple wage figures. Including multiple wages, there are 18,931 female-male wage pairs.

Panel B shows the frequency distribution of countries by the number of occupations they report; and the frequency distribution of occupations by the number of countries that report statistics on them. The distribution of countries by number of occupations shows that in most countries the gender breakdown has been reported for less than 50 observations. In 17 countries the female-male wage has been reported for less than 5 occupations, while 13 countries report between 5 and 9 female-male wages. These numbers show that in many countries there are not enough occupations with

> 7 The maximum is the multiplicand of the number of countries (83) times the number of occupations (161) times the number of years (17).

8
<!-- Start of picture text --> 100% Regions World<br>75%<br>50% é:<br>25%<br>0%<br>100%<br>75%<br>50% i<br>Fy<br>25%<br>0%<br>ea ES i we wena Nam 5A 55a Woe<br>Dh overDurationenies I](legal) roma<br><!-- End of picture text --> <!-- Start of picture text --> 100% Regions World<br>To]<br>75%<br>50% =<br>i<br>25%<br>0%<br>100%<br>75%<br>50%<br>25%<br>0%<br>(FECA)ECA (wes)ECA uc MENA Nam SA 8A Wate<br>LD cntncenTargetFi] (declacu r ed)ve FE] Pe<br><!-- End of picture text --> <!-- Start of picture text --> 100% Regions World<br>75%<br>:<br>50% i<br>25%<br>0%<br>100%<br>75%<br>50% i<br>3&<br>25%<br>0%<br>ew Py x Le wena sw sa ssa ons<br>DiTemporary eres [I]Measures: svscie2020-10-09<br><!-- End of picture text --> <!-- Start of picture text --> Regions World<br>100%<br>75%<br>50% ;<br>i<br>25%<br>0%<br>100%<br>75%<br>50%<br>25%<br>0%<br>(EECA)ECA (wes)ECA Lac MENA Nan SA 38a Wore<br>LD eninonnTarget [i] (declared)avonisie i mice<br><!-- End of picture text -->
capitalization of parent banks proved to be significant and distinct factors driving the lending behavior of foreign banks during the global crisis.

Although there is recognition that both demand and supply factors contributed to the credit contraction most of the empirical literature does not examine in more detail this critical issue. Takats (2010) and Feyen and Gonzalez del Mazo (2013) are an exception in this regard by making an effort to identify supply and demand factors. By using a panel regression framework to analyze the key drivers for cross-border bank lending to 21 emerging economies between 1995 and 2009, Takats (2010) argues that while both supply and demand factors contributed to the credit drop, the former that was more important during the crisis. Finding that a 1% increase in output is associated with an increase of around 0.2% in credit (the most important demand factor in the analysis is GDP), the paper argues that while demand and supply factors tend to be more balanced during non-crisis periods (e.g. between 2003 and 2007), during the crisis the stress experienced by major internationally active banks limited the supply of cross-border lending and the overall pace of credit creation, i.e., the impact of supply was stronger.

Using a similar framework to disentangle demand from supply effects, Feyen and Gonzalez del Mazo (2013) argue that the decline in foreign lending activity of Western European banks in a large sample of emerging economies across the world—consisting of cross-border claims and claims of local affiliates—was supply driven during periods of high European financial stress. Similarly, using ECB lending survey data, Feyen and Gonzalez del Mazo (2013) also find that while falling demand was mostly responsible for the reduction in longer-term credit flows in Europe after the onset of the global financial crisis, supply factors played a significant role during periods of financial stress.

# **III. Data**

To model the interaction between our variables of interest using a PVAR approach, we compile a panel database with quarterly data covering 41 countries in various regions with about 11 years of data available for most countries. Table 1 provides a description of the variables and the sources, while Appendix Table A1 shows the availability of each of the key variables by country.

8
e¤ect of LDO restrictions on wages is likely to be biased downward toward zero. This bias would be especially pronounced when we control for labor quality across DSDs in the regression, but the land quality controls are omitted.<sup>18</sup> Thus, a statistically signi...cant and negative coe¢ cient on percentage of area under LDO leases in the OLS regression that include labor quality controls but omits land productivity controls is strong evidence in favor of an adverse e¤ect of LDO leases on wages. If OLS estimate is biased toward zero due to productivity advantage of LDO land, then adding controls for area productivity should lead to an increase in the magnitude of estimated e¤ect (negative) of LDO incidence. This provides us with a falsi...cation test.

# 4.1. Instrumental Variables Approach

The unusual productivity distribution across land with and without restrictions allows us to provide credible evidence on possible negative e¤ects of land market restrictions on equilibrium wages in Sri Lanka. It, however, does not give us an estimate of the magnitude of the e¤ects of the land restrictions. We are con...dent that the OLS estimate is biased towards zero, but have little idea about the extent of this bias. To correct for the endogeneity bias due to omitted productivity traits of land (and possibly of labor), we utilize an instrumental variables strategy. However, in addition to land restrictions, we also need to consider potential endogeneity of the distance to the nearest urban center for identi...cation of the interaction e¤ect. We thus have the following three potential endogenous variables, although our focus is on the ...rst two: percentage of land under LDO restrictions, interaction of land restrictions with travel time to urban center, and travel time to the urban center on its own.

To identify the casual e¤ect of LDO restrictions, we need an exogenous source of variations in percentage of land under LDO restrictions in a DSD. The unique role played by malaria infestation starting from 16th century till early twentieth century in the history of land policy of Sri Lanka o¤ers such an exogenous source of variations. The results from a regression of LDO incidence on district level malaria provides a coe¢ cient equal to 0.16 with a ‘t’ statistics of 37.65 after we control for province ...xed e¤ects. Even with only 15 data points on district level malaria prevalence, the results thus show a clear positive correlation between malaria intensity and LDO incidence. As noted before, we cannot use this correlation directly to identify the e¤ects of LDO 18 The potential and actual yield data used in Table 1 and discussed above are not available for the full sample.

16
coe¢ cients are higher and more signi...cant when sub-Saharan Africa is excluded. Finally, because the major oil exporters of the Middle East may occupy a special place in the world trading system, the last column of Table 5 drops these countries, leaving the results once again unchanged. All in all, the panel results are insensitive to the subsample used, as could be expected given that all of our panel speci...cations include country e¤ects. When it comes to the cross-sectional estimates, we ...nd that all of the subsample coe¢ cients are actually higher than the full sample coe¢ cients, with the exception of the non-OECD sample.

Table 6 presents the results of using alternative measures of ...nancial development.<sup>14</sup> Colulmn 1 uses the ratio of liquid liabilities to GDP instead of private credit. Both the cross-sectional and panel results are strong, and in the cross-section the signi...cance level actually improves to 1%. Column 2 uses the ratio of stock market value to GDP instead. While the cross-sectional results are signi...cant at the 1% level, the panel estimates are not. Clearly, to the extent that EFNX explains the di¤erences in the size of countries’stock markets, it does so across countries, and not within countries over time. It is important to note that the sample size is noticeably reduced when we use this measure, especially along the time series dimension. Thus, we simply may not have observations going back far enough in time to make identi...cation o¤ the time series variation. Column 3 presents the results of using the stock market turnover ratio as the dependent variable. It is de...ned as the value of total shares traded divided by the average real market capitalization. Unlike stock market value to GDP, which is a measure of market size, turnover is a measure of stock market activity. The results we obtain are similar. While the cross-sectional estimate is signi...cant at the 1% level, we cannot identify the e¤ect from the time series. Finally, we would like to use a measure of the quality of the ...nancial system rather than its size. Column 4 reports the outcome of using the net interest margin as the dependent variable. The net interest margin is de...ned as the accounting value of banks net interest revenue as a share of its interest-bearing assets.<sup>15</sup> This variable is available only post-1996, and thus we cannot estimate a panel speci...cation. The cross-sectional 2SLS estimate, however, is signi...cant at the 5% level, suggesting that there may be an e¤ect on the quality of the ...nancial system as well as its size.

# 6 Conclusion

It is often argued that institutional quality in general and ...nancial development in particular are shaped largely by exogenous events in the past. It is then natural to think of the ...nancial system as an endowment, and therefore di¤erences in ...nancial development as sources of comparative advan-

> 14 All of the alternative measures come from the most recent version of the Beck, Demirguc-Kunt, and Levine (2000) database.

> 15 Unlike all of the other measures, a low value of net interest margin indicates a high quality of the ...nancial system.

18
being connected in a given year, or studying the correlation for two-year rather than oneyear lags leaves our main finding unaltered (see columns (5)-(8) in Table 2).

Table 1: Firm Linka es and Exit Probabilit <u>g y</u>

||||Ex|it|||Exit<br>Extinct<br>Parent exit|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|log wgt. degree (t-1)|-0.301<sup>_∗∗∗_</sup>|-0.289<sup>_∗∗∗_</sup>|-0.137|-0.293<sup>_∗∗∗_</sup>|-0.263<sup>_∗∗_</sup>|-0.287<sup>_∗∗∗_</sup>|-0.203<sup>_∗∗∗_</sup>|
||(0.087)|(0.092)|(0.111)|(0.099)|(0.104)|(0.093)|(0.079)|
|has only supp. ties (t-1)|||-0.602<sup>_∗_</sup><br>(0.340)|||||
|has cust. & supp. ties (t-1)|||-1.138<sup>_∗∗∗_</sup><br>(0.319)|||||
|has international ties (t-1)||||0.033<br>(0.308)||||
|has agri ties (t-1)|||||-0.212<br>(0.328)|||
|has owner ties (t-1)||||||-0.234<br>(0.369)||
|log sales (t-1)|-0.131<sup>_∗∗∗_</sup>|-0.132<sup>_∗∗∗_</sup>|-0.118<sup>_∗∗∗_</sup>|-0.131<sup>_∗∗∗_</sup>|-0.132<sup>_∗∗∗_</sup>|-0.124<sup>_∗∗∗_</sup>||
||(0.043)|(0.042)|(0.044)|(0.042)|(0.042)|(0.047)||
|Observations|18488|18488|18488|18488|18488|18488|21419|
|Year FE||✓|✓|✓|✓|✓||
|Num exiters|63|63|63|63|63|63|26|

**Note:** This table illustrates the relationship between exit probability and firm degree (number of direct linkages). Exiting firms are entities which are not observed in any subsequent year during our sample period. Columns (1)-(6) include a control for log pre-period annual revenue, measured in constant 2020 U.S. dollars. Columns (2) to (6) include year-fixed effects. The omitted category in col (3) equals one for firms with only customer ties in (t-1), relative to those with only supplier or both customer and supplier links. Column (4) includes a dummy variable that equals one if a firm has international (non-domestic) links in (t-1). Column (5) includes a dummy equal to one for firms that maintain a supply chain link to another agribusiness company in (t-1). Column (6) includes a dummy variable equal to one if a firm has ownership-links in in (t-1). Column (7) applies the restrictive definition of exiters being firms that exit our sample in a given year, which are marked as extinct by the data provider as of March 2024, and whose parent also exits the data (all subsidiaries, if any, of the ultimate parent of the exiting firm and, if applicable, the ultimate parent itself exit the data). Robust standard errors are applied.

Additionally, the robustness of our results is affirmed even under conservative definitions of firm exit, where the sample size narrows significantly. For instance, our sample of firms considered as exiters diminishes drastically when using a stringent definition of exit (i.e. firms labeled as extinct by FactSet whose ultimate parent, including all subsidiaries, if any, also exits the data). Despite this reduction, the negative relationship between the 26
To obtain a generalized picture, we run OLS regression outcomes for the impact of war on the average years of education by region as identified by the victimization indicators. Table 5.4.2 compares the outcomes between the full sample, i.e. the North and the South sample. For the displaced household members and households that registered death due to war, the effect of war is negative and statistically significant only in the North. However, when we compare educational outcomes of households where members lost their jobs or reported declines in income, we find that war had a similar negative effect. Overall, the similarities and dissimilarities in these findings might have a lot to do with the subjective bias in responses and selection bias into victimization between the North and the South sub-populations. Nevertheless, we do not find any clear evidence in support of the North being the worst war-affected region in terms of education outcomes.

Table 5.4.2 Comparison of OLS estimates of war on years of education: Full sample, the North sample and the South sample

||Full<br>sample|North<br>sample|South<br>sample|
|---|---|---|---|
|Registered deaths|-0.128**|-0.420***|-0.049|
|Registered injury|-0.115*|-0.163|-0.102|
|Displaced|-0.033|-0.389***|0.081|
|Income dropped|-0.220***|-0.257**|-0.211***|
|Lost ownership|-0.125|-0.325|-0.062|
|Lostjob|-1.602***|-1.092*|-1.701***|
|Lost farm|-0.516|-0.659|-0.332|
|Lost livestock|-0.682***|-0.036|-1.066***|
|Lost assets|-0.200|0.049|-0.273|
|Affected bythe war|-0.058|-0.168|-0.035|
|Experienced violence|-0.240***|-0.078|-0.292***|

Notes: The household level controls include log per capita consumption expenditure, gender, gender of household head, average years of education in the household, ethnic groups and religious groups; *** implies significant at 1%, ** implies significant at 5% and * implies significant at 10%.

# **VI. Conclusion**

The relationship between education and war in Côte d‟Ivoire is complex. While anecdotal evidence from various reports and studies suggests that education has been a clear victim of war, 24
# Figure 10. Fiscal and monetary policies during global recessions and recoveries

## A. World, government expenditure

<!-- Start of picture text --> Index, t-1 = 100<br>130<br>1975 1982<br>120 1991 2009<br>Average<br>110<br>100<br>90<br>80<br>t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4<br><!-- End of picture text -->

## C. Advanced economies, government expenditure

## B. World, policy interest rate

<!-- Start of picture text --> Percent 1975 1982<br>15 1991 2009<br>Average<br>10<br>5<br>0<br>t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4<br><!-- End of picture text -->

## D. Advanced economies, policy interest rate

<!-- Start of picture text --> Index, t-1 = 100 Percent 1975 1982<br>130 15 1991 2009<br>1975 1982 Average<br>120 1991 2009<br>Average 10<br>110<br>100<br>5<br>90<br>80 0<br>t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4 t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4<br>E. EMDEs, government expenditure F. EMDEs, policy interest rate<br>Index, t-1 = 100 Percent 1975 1982<br>160 15 1991 2009<br>1975 1982 Average<br>140 1991 2009<br>Average 10<br>120<br>100<br>5<br>80<br>60 0<br>t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4 t-4 t-3 t-2 t-1 t t+1 t+2 t+3 t+4<br><!-- End of picture text --> Sources: Feenstra, Inklaar, and Timmer (2015); Haver Analytics; International Monetary Fund; Kose and Terrones (2015); Organisation for Economic Co-operation and Development; World Bank. Note: Year “t” denotes the year of the respective global recessions (shaded in gray). Average refers to an average of four global recessions with available data. Government expenditure is an index number equal to 100 in recession years. Aggregates are market-weighted. For details, see Appendix 2.

45
# 1. Introduon

The reasons for women's inferior employment and earnings position in the labor markets of developing countries have become a much debated topic in Labor and Development Economics. In this spirit, we first examine broad trends in female participation in 15 Latin America countries using population census data for the post-war period.' We subsequently present the results of female participation functions that show which characteristics influence a woman in her decision to join or not join the labor market. In addition, we utilize the latest national household surveys to assess the earnings differentials between women and men. We subsequently examine whether workers are rewarded only according to their economic characteristics, or whether their sex plays a role. If the latter, then one can legitimately talk about sex discrimination in the labor market and start thinking of ways to remove this social inequity and inefficiency. The distributional effects of such policies will be in the right direction as recent developmental work has demonstrated the "feminization" of poverty.<sup>2</sup> This is, perhaps, one of the rare examples in social policy where intervention can simultaneously achieve beneficial efficiency ad equity effects provided, of course, that the diagnosis is correct.

# 2. Female Labor Force P_aricipation Trends

Latin America and the Caribbean have twice the number of countries examined in this paper. However, our countries account for more than 90 percent of the 120 million total (female and male) labor force in the region.<sup>3</sup> In this respect, any conclusion drawn from our sample should be fairly representative of the whole region. Table 1 shows the female participation rate in the countries under consideration for two time periods (sometime in the 19,50s and the 1980s depending on the country). The aggregate (all-ages) labor force participation rate is potentially misleading, especially in cross-country comparisons, as it is affected by differences in the age-pyramid of the population. We have, therefore, broken down the information into three crucial age groups: the young (those below the age of 20 years), the prime-age (20 to 59) and older (60+) . .en.

Bearing in mind that there is sume variation in the way national statistics draw the distinction between work and non-work,<sup>4</sup> the table clearly suggests that women's labor force

**1** The countries studied are Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, Guatemala, Honduras, Jamaica, Mexico, Panama, Peru, Uruguay and Venezuela (for more information on the data sources see Psacharopoulos and Tzannatos, 1991).

- 2 Tokman (1989).

3 ILO (1990) provides information for 35 Latin America and Caribbean countries but many of the countries not included in the present analysis are too small to alter the general picture. The most populous country excluded from the present analysis is Cuba (with about 3.5 million workers) which does not have, however, a market oriented economy.

- 4 See Bowers (1975), Standing (1981), Psacharopoulos and Tzannatos (1989).
low‐quality lint left over from the ginning process—are used in various manufac‐ turing products, including yarn, plastics, and filling material (see ICAC 2000 for a detailed description of uses of cotton by‐products.) Sometimes cottonseed is used unprocessed, directly for animal feed, especially in the dairy industry. This paper will discuss the two key by‐products: oil and meal.

Though the composition of seed cotton by weight depends on numerous factors, typically one ton of seed cotton yields between 35 percent and 40 percent lint, about 10 percent cotton oil, and about 30 percent cotton meal.<sup>**3**</sup> The relative proportions partly depend on the ginning outturn ratio, which ranges between 30 percent and 40 percent of the weight of seed cotton, and the cotton oil extrac‐ tion ratio, which ranges between 10 percent and 16 percent of the weight of cot‐ tonseed depending on the method of crushing (Blasi and Drouillard 2002). Figure 1 shows the current composition of the US cotton industry.

# **Figure 1**

**Weight Composition of Cotton and Potential By‐Products**

<!-- Start of picture text --> Cotton<br><!-- End of picture text --> <!-- Start of picture text --> Lint (35%)<br><!-- End of picture text --> <!-- Start of picture text --> Seed (65%)<br><!-- End of picture text --> <!-- Start of picture text --> PROCESSED<br>(crushing facilities)<br><!-- End of picture text --> <!-- Start of picture text --> NOT PROCESSED<br>(animal feed)<br><!-- End of picture text --> <!-- Start of picture text --> MEAL (45%)<br>[used for animal feed,<br>competes with other<br>meals]<br><!-- End of picture text --> <!-- Start of picture text --> OIL (16%)<br>[mostly human<br>consumption, competes<br>with edible oils]<br><!-- End of picture text --> <!-- Start of picture text --> HULL (27%)<br>[used for animal<br>feed, often mixed<br>with meal]<br><!-- End of picture text --> <!-- Start of picture text --> OTHER (12%)<br>[linters , i.e., low<br>quality lint (8%) and<br>waste (4%)]<br><!-- End of picture text -->

> **3** On average one metric ton of seed cotton in the US generates about 350 kgs of lint and 104 kgs of cotton oil. Of the remaining 546 kgs, meal accounts for more than half (293 kgs); hulls and linters account for 176 kgs and 52 kgs, respectively; and 26 kgs is waste. However, in WCA, one ton of cotton generates about 420 kgs of lint (because of the higher ginning out‐turn ratio) and 70 kgs of cotton oil (when less technologically advanced methods of oil extraction are used).

**_Page 3_**