rafmacalaba/datause-encoder-data huggingface dataset:
20 relationship. The first relates scarcity to the intercept of the water availability per capita of river riparians. Hence, higher values express lower scarcity levels. The second relates scarcity to the reduction in annual water availability as measured by the slope of the water scarcity of river riparians. Hence, higher absolute values indicate higher levels of scarcity. We calculated the level of maximum cooperation, using the results in Tables 7 and 8. In the case of _Water scarcity slope_ , maximum cooperation occurs where the value of annual reduction in available water per capita is around –22. In the case of _Water scarcity intercept_ , maximum cooperation occurs where the value of the intercept of available water per capita is around 190. These results suggest some policy implications discussed in the conclusion. The estimated coefficients of equations with the _Water scarcity slope_ are not presented due to space considerations.

The trade variables are consistently stable in the majority of the estimates. For example, the linear estimates of _Trade dependency_ and _Trade dependency_ (IMF) suggests a reduction range of between 0.12 to 1.8 treaties per river, on average, with increase in trade share within the ranges observed in the sample. The quadratic estimates of _Trade dependency_ (UN) and _Trade dependency_ (IMF) are quite similar. Maximum treaty cooperation is observed for both trade variables at around 0.10-0.12 and then declines as trade share increases. Observing the range of the trade variables in the Appendix suggests that the negative tradeoff between trade and treaty substitution holds for between 30-60 percent of the range of the trade variables (beyond the value of 0.10-0.12), which again supports our model of substitution between the two types of cooperation—treaty-based cooperation and tradebased cooperation. Schneider and Schulze (2003:12) define a linear relationship as “unconditional impact” and a quadratic relationship as “conditional impact” of interdependency level (e.g., trade) on conflict level among two nations.

# **CONCLUSION**

Water scarcity has been argued and shown to be a major factor explaining cooperation among two riparian states sharing a river. Our proposed models, applying the theory of _scarperation_ to the overall dataset of documented international rivers and various bilateral treaty datasets explain up to 40 percent of the variation in cooperation among the basin riparian states, depending on the way cooperation is measured. The scarcity variables are significant and their signs support our hypotheses regarding the inverted U curve scarcity-cooperation relationship.
<!-- Start of picture text --> mFormaltrade mlnformal trade<br>3.000 SD<br>c 2.000<br>2 0.806 0.853<br>‘a_—s«d1..000 O34<br>Tas<br>“>> 0.000 ia |<br>2005 2010<br><!-- End of picture text -->

<!-- Start of picture text --> mFormaltrade mlnformal trade<br>3.000 SD<br>c 2.000<br>2 0.806 0.853<br>‘a_—s«d1..000 O34<br>Tas<br>“>> 0.000 ia |<br>2005 2010<br>60 35<br>501 im 51.04 46.49 20 28.90 29.49 29.86<br>ss<br>x 25<br>a 40 ©<br>& 20<br>ad 30 a<br>—<br>S 19 11.14<br>ceg2010 14.71 14.61 10.03 2&as10&as10as1010 10511 9196,<br>0 0<br>1995 2009 1995 2009 1995 2009 1997 2009 1997 2009 1997 2009<br>World China India World China Pakistan<br><!-- End of picture text -->

<!-- Start of picture text --> 60<br>51.04<br>501 im 46.49<br>ss<br>x<br>a 40<br>&<br>ad 30<br>S<br>201010 14.71 14.61 10.03<br>0<br>1995 2009 1995 2009 1995 2009<br>World China India<br><!-- End of picture text --> <!-- Start of picture text --> 35<br>20 28.90 29.49 29.86<br>25<br>©<br>20<br>a<br>19 11.14<br>2&as10&as10as1010 10511 9196,<br>0<br>1997 2009 1997 2009 1997 2009<br>World China Pakistan<br><!-- End of picture text -->
Table 4: Returns to reading proficiency as a percentage change in weekly earnings

|VARIABLES|(1)<br>Pooled|(2)<br>Bolivia|(3)<br>Colombia|(4)<br>Ghana|(5)<br>Kenya|(6)<br>Vietnam|
|---|---|---|---|---|---|---|
|Reading proficiency|0.365***|0.095|0.724**|0.178|0.588***|0.251**|
||(0.089)|(0.239)|(0.326)|(0.143)|(0.184)|(0.122)|
|Age|0.300***|0.323*|0.246|0.213*|0.387***|0.176**|
||(0.068)|(0.179)|(0.170)|(0.110)|(0.141)|(0.088)|
|Age<sup>2</sup>|‐0.004***|‐0.004*|‐0.003|‐0.002*|‐0.004**|‐0.002**|
||(0.001)|(0.002)|(0.002)|(0.001)|(0.002)|(0.001)|
|Female|‐1.247***|‐1.912***|‐1.633***|‐0.864***|‐1.951***|0.059|
||(0.144)|(0.341)|(0.407)|(0.267)|(0.365)|(0.199)|
|Colombia FE|‐0.720**<br>(0.284)||||||
|Ghana FE|‐0.450*||||||
||(0.244)||||||
|Kenya FE|‐2.116***<br>(0.260)||||||
|Vietnam FE|0.628***<br>(0.213)||||||
|Constant|‐2.073|‐1.906|‐1.439|‐1.544|‐6.228**|0.873|
||(1.397)|(3.524)|(3.433)|(2.348)|(2.823)|(1.785)|
|Observations|8,144|1,227|1,450|1,708|1,948|1,811|
|R‐squared|0.059|0.048|0.037|0.014|0.045|0.014|

Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1 Notes: Least squares regressions weighted by sampling weights. Dependent variable: log gross weekly wage. Sample: workforce aged 25‐64. Pooled specification includes country fixed effects and gives same weight to each country. Data source: World Bank (2016a) 24
# **Abstract**

The states in the Brazilian Amazon have made progress in reducing poverty and improving social indicators in the last decade. Despite this progress, the poverty rate in the Amazon is among the highest in Brazil. As of 2000, rural poverty is the greatest challenge. In Pará, not only is the headcount poverty rate of 58.4 percent in rural areas more than 55 percent higher than headcount poverty in urban areas, but also poverty is much deeper in rural areas. The fall in infant mortality and adult illiteracy corroborate the improvement in measured income poverty. Census data from 2000 and 1991 reveal that more people left Pará than came to live in the state during the 1970s, the opposite of the 1980s. In 2000, the Gini coefficient for Pará, as in the Amazon as a whole, was 0.60. The poverty profile reveals that indigenous peoples experience a higher poverty incidence than other groups.

Census 2000 data reveal that living in rural areas in Pará does not by itself affect the probability of being poor. Individual and household characteristics are more important than geographical location. The largest statistical differences in poverty reduction between rural and urban areas are found in the effect of education, sector of employment, gender, and family size. PNAD data from 2001 reveal that living in urban areas in Pará does not by itself affect the probability of falling below the poverty line in urban areas in Brazil. The strongest poverty correlates are education, experience, race, rural location, gender, and labor market association.

2
7 Figure 1. Argentina: Growth of Total Consumption (Quarterly). Source: FIEL Database.

<!-- Start of picture text --> O.1<br>007-<br>.0.1<br>701 70 2 76.X 10.4 70.1 70.2 79.2 794 001 i60. 602 60.. 61 0.2 610<br><!-- End of picture text --> The Brazilian Cruzado Plan, implemented in February of 1986, also illustrates the important role that temporariness plays in generating speculative booms. The plan rested on the premise that the high rates of inflation were driven by inertia and inde ition.<sup>7</sup> The key elements of the plan were, thus, a strong incomes policy, with loose fiscal and monetary policies. Soon after the announcement, it became clear that the program was unsustai;able. If agents hold rational expectations, we should observe a negative correlation between the size of the speculative boom and the duration of the plan.<sup>8</sup> This prediction is borne out by the Brazilian experience. During the Cruzado plan (which lasted for less than a year) private consumption increased b, 9.3 %.

'See Cardoso (1991) for a good description of the Brazilian experience.

8 See Calvo (1986).
Figure 15 shows that boys in the affected cohort were less likely to hold a highschool degree in 2008. That effect is likely to be noise, given that, in other years, the point estimate was relatively small and imprecisely estimated. A similar pattern is observed in figure 16. Interestingly though, the estimates for 2008 and 2012 in figure 17 suggest that the affected cohort had fewer years of schooling. The estimates for 2013 and 2014 suggest they catch up later on. This would explain why, when we pooled all years together, we found no difference in completed years of schooling between the two cohorts.

Although we found no impact on work incidence and log earnings, figure 19 suggests that the ban might have affected individual’s occupation to some extent. According to table 5, the affected cohort was about 2 p.p. (or 3.5 percent) less likely to have a formal job than the unaffected cohort. We also found some indication of negative effect on log earnings, but the point estimate was statistically insignificant. Even pooling seven years of survey data, it seems we did not have the statistical power to detect such small effects for log earnings and occupation.<sup>31</sup>

# **7.1. Heterogeneous Effects**

It may be the case that a particular sub-group of individuals is strongly affected by the child-labor ban but these impacts are overshadowed in our long-term estimates for the overall treated cohort. Unfortunately, we are unable to identify the group of compliers in the long-term in our database since we work with repeated cross-sections. We could still try to estimate the impact of labor-force participation in the short term on, say, log wages in the long term using the two-sample instrumental strategy originally applied by Angrist and Krueger (1992) (see Inoue and Solon 2005 for a discussion). However, since the impact of the law on participation rates is partial and we also observe an increase in school attendance among those boys banned from the labor force, we actually have two endogenous variables (participation rates and school attendance) and only one instrument (the law).

To shed some light on the long-term heterogeneous effects of the ban on log earnings, we estimate quantile regressions. Table D.1 presents the estimates for each

> 31 Using the _sampsi_ command in Stata to be able to detect a fall of 2 p.p. in formal occupation with a power of 80 percent and significance level of 10 percent, we would need a sample of 18,328 observations. That explains why the point estimates of table 5 are almost significant at 10-percent level.

24
reflect the purchase (or liquidation) of assets, at the macroeconomic level there is no simple way to ascertain the extent to which they actually finance capital, rather than current, expenditures.

In the case of M&A, one can draw a further distinction between _cross-border mergers_ , which occur when the assets and operation of firms from different countries are combined to establish a new legal identity, and _cross-border acquisitions_ , which occur when the control of assets and operations is transferred from a local to a foreign company (with the former becoming an affiliate of the latter).

In practice, world M&As have been predominantly driven by acquisitions. Cross-border mergers represented only 3 percent of cross-border M&As in 1999.<sup>10</sup> Also, over 50 percent of cross-border M&As in 1999 took the form of full (or outright) acquisitions. Minority acquisitions by foreign firms (that is, purchases of 10-49 percent participation in total capital) represented one-third of acquisitions in developing countries and less than 20 percent in developed countries (see UNCTAD 2000).

Data on FDI inflows and outflows, as well as worldwide cross-border M&As, are collected by UNCTAD’s World Investment Report (various years). We construct greenfield FDI by subtracting cross-border M&As from FDI inflows. Our sample is dictated by the availability of data on cross-border M&A transactions, which is quite limited prior to 1987. Thus, the analysis focuses on the period 1987-2001. It includes 72 countries, with a combined total of 848 country-year observations.

Tables 1 and 2 provide an overview of the major trends in the volume and composition of FDI. Table 1 documents the changing patterns of external financing to industrial and developing

> 10 In reality, even when mergers are supposedly between relatively equal partners, most are in fact acquisitions with one company controlling the other.

6
observations are in long-run equilibrium has previously been used in the literature (see, for example, Shaffer 1982 and Molyneux et al. 1996).

# C. Alternative Specification

For robustness, we estimate an alternative reduced revenue equation where we include the ratio of total revenue to total assets as the dependent variable, where total revenue is calculated as gross interest revenue plus other operating revenues, such as fee income and commission income. The dependent variable now includes also non-interest revenues, which arguable<sup>makes</sup> it a more comprehensive measure of the overall degree of competition in banking services. The other, explanatory variables remain the same. Again, we estimate the model using<sup>OLSand GLS</sup> with fixed-bank effects and refer to H3 as the H-statistic based on model estimated using OLS, and to H4 as the H-statistic estimated using GLS with fixed-bank effects. We also check whether the market is in equilibrium using the same test as for the gross interest revenue to total assets ratio.

# 3. DATA **AND RESULTS FOR THE COMPETITIVENESS INDICATOR**

# A. Data

We use bank-level data from BANKSCOPE, a database containing bank financial<sup>statements</sup> used in a number of other cross-country studies. We have panel data for the years 1994-2001 and we include all banks: commercial banks, savings banks, cooperative banks, and<sup>bank holding</sup> companies. We use data from consolidated accounts if available, and otherwise from unconsolidated accounts (to avoid double-counting).

12
- Struyk, Raymond, 1996, _Economic Restructuring of the Former Soviet Bloc: The Case of Housing_ , Urban Institute Press, Washington D.C.

- Vatne, Bjørn Helge, 2006, “How large are the financial margins of Norwegian households? An analysis of micro data for the period 1987–2004,” _Norges Bank Economic Bulletin_ , December 2006, Vol. LXXVII, No.4: pp. 173-180.

- Warnock, Francis and Warnock, Veronica, 2008, “Markets and Housing Finance,” Journal of Housing Economics 17, 239-51.

- Whitley, John, Richard Windram and Prudence Cox, 2004, “An empirical model of household arrears,” Bank of England Working Paper No. 214 (London: Bank of England).

- Wolswijk, Guido , 2006, “Determinants of Mortgage Debt Growth in EU Countries” _European Journal of Housing Policy_ , Vol. 6, No. 2: pp. 131-49.

- Woolridge, Jeffrey, 2001. _Econometric Analysis of Cross Section and Panel Data_ . MIT Press, Cambridge, MA.

- World Bank, 2009, _The Crisis Hits Home: Stress Testing Households in Europe and Central Asia._ Report No. 50158-ECA, forthcoming.

- Żochowski, D. and S. Zajączkowski, 2007, “The Distribution and Dispersion of Debt Burden Ratios among Households in Poland and its Implications for Financial Stability,” IFC Bulletin No. 26 (Basel: BIS).

36
21

# 6. References

- Bansal, Chahat. 2020. Describing patterns of socio-economic development at fine spatial and temporal resolutions. In _Proceedings of the 7th acm ikdd cods and 25th comad,_ 338–339.

- Beegle, Kathleen, Luc Christiaensen, Andrew Dabalen, and Isis Gaddis. 2016. _Poverty in a rising Africa._ Washington, DC: World Bank.

- Beegle, Kathleen, Joachim De Weerdt, Jed Friedman, and John Gibson. 2012. Methods of household consumption measurement through surveys: experimental results from Tanzania. _Journal of Development Economics_ 98 (1): 3–18l.

- Beegle, Kathleen, Joachim De Weerdt, Jeff Friedman, and John Gibson. 2010. _Methods of household consumption measurement through surveys._ Policy Research Working Paper 5501. World Bank.

- Christiaensen, L., P. Lanjouw, J. Luoto, and D. Stifel. 2012. Small area estimation-based prediction methods to track poverty: validation and applications. _Journal of Economic Inequality_ 10(2):267–297.

- Deaton, A., and S. Zaidi. 2002. _Guidelines for Constructing Consumption Aggregates for Welfare Analysis._ World Bank Publications.

- Development Research Group of the World Bank. Downloaded 2017. _PovcalNet: the on-line tool for poverty measurement._

- Foster, James, Joel Greer, and Erik Thorbecke. 1984. A class of decomposable poverty measures. _Econometrica_ 52 (3): 761–766.

- Gorman, W. M. 1959. Separable utility and aggregation. _Econometrica_ 27:469–81.

- ~~.~~ 1961. On a class of preference fields. _Metroeconomica_ 13:53–56.

- Head, Andrew, M ́elanie Manguin, Nhat Tran, and Joshua E Blumenstock. 2017. Can human development be measured with satellite imagery? In _Ictd._

- Jean, Neal, Marshall Burke, Michael Xie, W Matthew Davis, David B Lobell, and Stefano Ermon. 2016. Combining satellite imagery and machine learning to predict poverty. _Science_ 353 (6301): 790–794.
sales excluding those cities. Columns (4), (5), and (6) of panel (b) in table 6 present the results. The short- and long-run coefficients of PYP in those estimations are statistically insignificant, supporting the fact that the changes should have occurred in Bogotá at the time the program was implemented.

Official statistics indicate that although the number of households in Bogotá increased from 2008 to 2010, the fraction owning a car remained constant (22 percent).<sup>31</sup> The number of new households owning a car in 2010 was only 20 percent of the total new car sales in that year, suggesting that some households that already owned a car may have acquired a new vehicle. This is consistent with the results from other studies for HNC, where the households’ strategy was to acquire a second or third car (see Eskeland and Feyzioglu 1997a; Davis 2008; Gallego, Montero, and Salas 2013). Another question that arises is how in practice a household can avoid the restriction by buying a new car. Not surprisingly, drivers can easily find the license plate number of their choice in the market. Car dealers buy a group of license plate numbers, which are pre-allocated by the Transport Agency of Bogotá. Although car dealers are not allowed to choose the plate numbers systematically, customers are able to choose, implying an opportunity to avoid PYP.

Furthermore, relative to the moderate phase, it is unlikely that the slight indication of increased vehicle sales in the drastic phase alone explains the increase in gasoline consumption and CO levels during the morning peak. New cars generally have a TWC, removing a large fraction of CO emissions. Thus, the effect of the new vehicles on CO might be limited, unless accompanied by an increase in the number of trips. On the one hand, gasoline consumption in the drastic phase appears to have increased, indicating that households with either new or used vehicles tended to increase their 31. Data accessed from DANE. Encuesta Nacional de Calidad de Vida 2008 and 2010.

23
Table 16: Heterogeneous effects of payment delays on total spending

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Panel A:|Male<br>HH head|Any previous<br>bank use|Close to<br>branch|A<br>Assets|bove median:<br>Patience|
|1-day delay|-438.000<br>(1627.285)|3278.722<br>(2362.448)|5.191<br>(2038.724)|1150.498<br>(2047.030)|-1387.772<br>(2369.923)|
|8-day delay|-1192.477<br>(1700.539)|2282.270<br>(2161.679)|-2120.740<br>(1701.914)|569.199<br>(2041.364)|-2118.200<br>(2420.014)|
|Observations|310|152|233|226|190|
|Mean of dependent variable in Immediate group|16755.85|12634.49|17367.79|15774.29|16009.12|
|R-squared|0.296|0.443|0.341|0.301|0.286|
|P-value: one day delay = 8 day delay|0.666|0.685|0.208|0.779|0.782|
|Panel B:|Female<br>HH head|No previous<br>bank use|Far from<br>branch|B<br>Assets|elow median:<br>Patience|
|1-day delay|3048.881<br>(2339.617)|271.584<br>(1749.049)|2023.454<br>(1877.547)|583.753<br>(1876.455)|1641.853<br>(1598.743)|
|8-day delay|-5.482<br>(1856.776)|-2329.353<br>(1656.275)|484.689<br>(1838.687)|-2224.241<br>(1603.749)|-924.840<br>(1545.776)|
|Observations|162|320|239|246|282|
|Mean of dependent variable in Immediate group|12272.04|16547.96|13321.54|14874.71|14695.70|
|R-squared|0.220|0.207|0.208|0.217|0.281|
|P-value: one day delay = 8 day delay|0.123|0.130|0.463|0.125|0.088|
|P-value: equal efect of 1-day vs 0-day|0.198|0.281|0.451|0.832|0.267|
|P-value: equal efect of 8-day vs 0-day|0.621|0.074|0.282|0.262|0.663|
|P-value: equal efect of 1-day vs 8-day|0.359|0.572|0.821|0.400|0.526|

The top panel includes respondents from female-headed households and those with above-median values for number of transactions at NBS in the 90 days preceding the transfer, those who live above the median distance from the bank branch, those with above-median values of a principle-index of asset ownership, and those with above-median patience, as measured at baseline by amount required to prefer a transfer in one month instead of immediately. P-values in the bottom three rows are from tests of equality of the 1-day delay coefficients, the 8-day delays, and the differences between 1-day and 8-day delay coefficients, respectively, in Panels A and B. All specifications include village and week-of-first-survey fixed effects, and the value of the outcome measured at survey 1. Exchange rate: MK 420 per USD. Robust standard errors in parentheses. * <u>p</u> _<_ 0.10, ** <u>p</u> _<_ 0.05, *** <u>p</u> _<_ 0.01 42
In some cases workers are able to earn even more in particular weeks. For example, Vinepower workers were reported to earn NZ$18-20 per hour in their second week (Tonga-Now, 2007).

Finally, community leaders were asked what they saw as the possible benefits and downsides of the RSE program for their village. The main benefit anticipated was better incomes for the families of RSE workers. Other answers included employment opportunities for village youth, help for the village economy, more income for the church, and the new experiences and skills learned. The main downsides anticipated were the family separation involved, the chance that someone could give the village a bad name, and that there would not be enough members for the local church. In practice there have to date been several isolated incidences of alcohol abuse by workers, and pay disputes, resulting in 19 workers returning home before their contracts. However, the vast majority of workers have not experienced such problems, and the initial reports are of employers being impressed by the hard work. Indeed, one issue facing some workers has been shortages of work to do as they have finished all the work available in shorter than expected times.

# **8. Conclusions**

A survey of over 2,000 Tongans finds that the new RSE program has succeeded in opening up seasonal migration opportunities to poor, rural households in Tonga. Participation of poorer and more rural households in the program makes it more likely that the RSE will have some of the positive development impacts that form one of the objectives of the policy. The enormous interest in the RSE is evidenced by approximately

- 18 -