rafmacalaba/datause-encoder-data huggingface dataset:
37

**Table 4a**

# **Base regression**

_(measure of current account reversals: ED4)_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||probit|probit|probit|probit|linear|linear|linear|
|CA|-9.034<br>[0.755]***|-12.494<br>[1.040]***|-12.743<br>[1.110]***|-12.760<br>[1.113]***|-2.343<br>[0.155]***|-2.356<br>[0.154]***|-2.363<br>[0.156]***|
|RES|0.095|-1.586|-1.567|-1.633|-0.157|-0.466|-0.455|
|EXTD|[0.475]<br>-0.038<br>[0.079]|[0.905]*<br>0.375<br>[0.131]***|[0.921]*<br>0.350<br>[0.145]**|[0.979]*<br>0.322<br>[0.159]**|[0.136]<br>0.080<br>[0.031]***|[0.223]**<br>0.078<br>[0.031]**|[0.223]**<br>0.109<br>[0.049]**|
|SHORTD|0.225|0.280|0.466|0.469|-0.003|-0.003|-0.002|
||[0.411]|[0.602]|[0.635]|[0.665]|[0.124]|[0.123]|[0.124]|
|CREGRO|-0.005|0.001|0.002|0.001|0.000|0.000|0.000|
|PPPGDP|[0.017]<br>0.037|[0.017]<br>0.168|[0.018]<br>0.160|[0.018]<br>0.122|[0.003]<br>0.013|[0.003]<br>0.009|[0.003]<br>0.010|
||[0.018]**|[0.084]**|[0.094]*|[0.104]|[0.017]|[0.017]|[0.017]|
|OPEN||||0.347|0.097|0.126|0.126|
|||||[0.443]|[0.072]|[0.075]*|[0.075]*|
|OT||||-0.514|-0.114|-0.099|-0.123|
|||||[1.455]|[0.296]|[0.293]|[0.296]|
|CONCD||||-0.292|-0.084|-0.076|-0.069|
|||||[0.593]|[0.095]|[0.094]|[0.095]|
|RES^2<br>EXTD^2||||||0.418<br>[0.252]*|0.419<br>[0.253]*<br>-0.008<br>[0.009]|
|Country|NO|YES|YES|YES|YES|YES|YES|
|fixed effects||||||||
|Year|NO|NO|YES|YES|YES|YES|YES|
|dummies||||||||
|Observation|1904|1904|1904|1890|1890|1890|1890|
|s||||||||
|R-squared|0.17|0.26|0.29|0.29|0.26|0.26|0.26|

_Notes_ : the dependent variable (ED4) takes the value 1 if a reduction of a current account deficit of at least 4 percentage points of GDP occurs at time t, and zero otherwise. Robust standard errors in brackets. All explanatory variables are described in Table A1 and lagged one period. For the probit models we report the pseudo – R<sup>2</sup> . * significant at 10%; ** significant at 5%; *** significant at 1%
In this paper, we document that different institutional arrangements lead to a differentiated effect of (plausibly) exogenous commodity price fluctuations on top incomes.<sup>5</sup> To do so, we combine a global panel data set from Forbes magazine on billionaires’ net worth with an index of (country-specific) commodity terms of trade shocks. Commodity shocks are significant sources of macroeconomic variation but also have important sectoral implications that elucidate linkages with concentration of income at the top. Results show that commodity booms lead to top income concentration, and the effect is economically large. Figure 1(a) globally traces the patterns of commodity shocks and the log differences of billionaire net worth and shows that they co-move. Figure 1(b) replicates the same pattern for developed (left panel) and developing economies (right panel) and shows the positive relationship between commodity price shocks and top incomes stand, regardless of the level of development. This finding is robust to accounting for sector of activity as well as the individual characteristics of billionaires as captured by billionaire fixed effects. The evidence is also suggestive that competition policy weakens the relationship between commodity booms and top incomes, and tax policy has no effect. However, we do find that commodity booms tend to lower tax revenues in the economy, hence reducing the scope for income transfers and redistribution.

In addition to the literature on top incomes, this paper contributes to several strands of literature. Specifically, the paper also relates to the so-called “resource curse” literature. The latter has provided (mixed) empirical evidence that countries with large dependence on natural resources grow slower (see survey by Ross et al., 2015) and are also more unequal (Ross, 2001; Sokoloff and Engerman, 2000).<sup>6</sup> Importantly, Mehlum, et al (2006) provide evidence that the effect of natural resources on the economy depends on the quality of institutions. Furthermore, the type of natural resource matters, with hydrocarbon and mineral resources, categorized as “point source” resources, having a more detrimental impact on growth than “diffuse” resources such as agriculture (see Isham et al., 2005). We contribute to this literature by focusing on the top incomes as opposed to general income inequality while exploring the role of different policy/institutional frameworks. We also find that commodity price shocks emanating from point source resources lead to more top income concentration than shocks stemming from diffuse resources.

> 5 To the extent that commodity prices and stock markets co-move, changes in commodity prices also affect billionaires’ net worth through changes in stock market valuation. See Arezki, Loungani, van der Ploeg and Venables (2014) and Ing-haw Cheng and Wei Xiong (2014) for a discussion on the respective role of fundamentals and financialization in driving commodity price fluctuations.

> 6 For an early contribution to the resource curse literature, see Sachs and Warner, 2001.

3
explanatory power for _both_ endogenous variables, institutions and trade. Consequently, the fitted values of institutions and trade from first-stage regressions are highly correlated with each other, and these specifications are at best weakly identified, even though first-stage regressions have apparently good explanatory power. In short, although the historical and geographical instruments for institutions and trade proposed by AJR and FR perform well in their respective univariate applications, they are not strong enough to identify the partial effects of these variables.

We document the identification problem using the partial R-squared diagnostic measures suggested by Shea (1997), and the minimum eigenvalue test of the null hypothesis of weak instruments suggested by Stock and Yogo (2001). In most cases, both diagnostic statistics as well as the formal tests verify the informal observation in DK that these models are not very well identified. Consequently, inferences about the structural parameters of interest, i.e. the causal effects of institutions and trade on longrun growth, cannot reliably be based on conventional t-statistics.<sup>2</sup> We therefore compute confidence intervals for these structural coefficients of interest using three methods suggested in the literature on inference with weak instruments: the AR statistic proposed by Anderson and Rubin (1949), the conditional likelihood ratio statistic proposed by Moreira (2002), and the S-statistic proposed by Startz, Zivot and Nelson (2001). These methods which take into account the low strength of the instruments produce substantially larger confidence intervals than those based on standard asymptotic theory in the specifications we consider. In many cases, confidence intervals for the parameters of interest cover (nearly) the entire real line. These results serve to formalize the informal intuition in our previous paper that the cross-country variation in trade, institutions, and their historical and geographical determinants is not very informative about their relative importance for growth in the long run.

Finally, in our previous paper we also investigated the relative contributions of institutions and trade over shorter horizons by looking at changes in decadal growth rates over the past four decades. We apply the same weak instrument diagnostics to these specifications. Although we find that dynamic regressions controlling only for trade and initial income are well-identified, the regressions which add institutional quality

**2** See Staiger and Stock (1997) for a seminal paper, and Stock, Wright, and Yogo (2002) for a recent survey of weak instruments.

2
2009). For a recent critical view on the value of randomized control trials in economic development, see Deaton (2009). In this paper, we present evidence from both experimental and nonexperimental studies, in recognition of the fact that both approaches have relative strengths and limitations.

# **2.2.2  Nonexperimental Estimators**

In the absence of a randomized experiment, evaluations must be based on nonexperimental (or observational) data. As discussed above, if the randomized experimental design was compromised in some way, nonexperimental methods can also be used to increase comparability between the treatment and control groups. Nonexperimental estimators of program impacts typically use two types of data to impute the missing counterfactual ( _Y_ 0) outcomes for program participants: data on participants at a point in time prior to entering the program and data on nonparticipants. The following types of methods are commonly used in evaluation work: (i) _Cross-section_ or _difference-in-difference regression estimators_ : These estimators evaluate the effects of the program by comparing the outcomes for a treated group to those of a nonexperimental comparison group, using regression adjustment to control for preexisting differences in observed characteristics that are thought to be determinants of the outcomes. The cross-section estimator compares participant and nonparticipant outcomes at some point in time after the program start date, whereas the difference-in-difference estimator compares the difference in outcomes between a post-program and pre-program time period (for example, the change in earnings).

(ii) _Cross-section_ or _difference-in-difference matching estimators_ : These estimators evaluate the impacts of the program by matching individuals in the treatment group to observably similar individuals in the control group on the basis of a set of observable characteristics. The overall effect of the program is obtained by averaging over the differences in participant and matched nonparticipant outcomes. A commonly used metric for matching the individuals is the propensity score, which is the predicted probability of participating in the program conditional on a set of observed characteristics.

(iii) _Control function estimators_ : These estimators evaluate the impacts of the program by a comparison of treated individuals and comparison group individuals, using statistical adjustment to control for both observed and potentially unobserved differences between the groups. These estimators usually require some assumptions on the distribution of unobservables and on their 9
To examine balance sheet data and the comparative use of debt markets across countries, we match the data set on security issuances from SDC Platinum with firm-level balance sheet information from the Bureau van Dijk’s Orbis database from 2003 to 2011. The latter covers publicly listed companies, providing a rather homogeneous sample of firms. By omitting unlisted firms from the analysis using the matched data, the sample excludes firms that are (1) relatively small and sometimes informal, (2) likely to have different accounting standards, and (3) less likely to issue in capital markets. After matching the data sets, we are left with a sample of 56,615 listed firms with 77,458 debt issuances for this part of the analysis.

To study how firm size relates to the use of debt markets, we proxy firm size by the average size of its issuances over the entire sample period. For robustness, we explore two alternative measures of firm size. First, we examine the firms’ total assets at the time of issuance, which is available in the SDC data set. However, this information has limited coverage: 48 percent of the corporate bond issuances and 34 percent of the syndicated loan issuances. Second, we restrict the analysis to listed firms and study the total assets reported in their balance sheets (from Bureau Van Dijk’s Orbis database). The results are quantitatively and qualitatively similar to the ones reported in the paper.

We classify the sample of countries in the paper as either developing or developed, following the World Bank classification as of 2012. Developed countries are those with a gross national income (GNI) per capita in 2011 above $12,476. All other countries are classified as developing countries. The final data set comprises 82 countries, 39 of them are developed and 43 developing.<sup>7</sup> All reported statistics are in U.S. dollars at 2011 constant prices. Appendix Table 1 reports the number of issuances and issuers of corporate bonds and syndicated loans per country, splitting between developed and developing countries. The results are robust to two alternative definitions of developing countries.

> 7 Strictly speaking, and as noted in Appendix Table 1, a couple of the countries we analyze are economies rather than selfstanding countries. But for ease of exposition, we use the generic term “countries.”

8
# **Table 5. Robustness check: Correlation of country fixed effects across specifications**

||II|III|IV|V|
|---|---|---|---|---|
|Country fixed effects from specification:|All countries, 1960-<br>2019|All EMDEs|Baseline with<br>trimmed sample|Baseline with 10-year<br>averages|
|Correlation with country fixed effects from<br>baseline specification (Column I of Table 4)|0.8384|0.9991|0.9674|0.9749|

Source: Authors’ calculation.

Note: Table shows the correlation between country fixed effects from the baseline specification (Column I of Table 4) and alternative specifications reported in Columns II-V of Table 4.

27
following five topics: budgeting, savings, loans, insurance, and a final summary video. The health training videos cover topics unrelated to financial literacy: cleanliness and hygiene; midwife, maternal and child health; condoms, AIDS and syphilis; and night-blindness. See Appendix I for more details on the content of these modules. Respondents receive Rs. 50 (USD 1) show-up fee for each session they attend, and are provided with transportation to and from their home to the training center.

# **_B. Pay for Performance Treatment_**

Our 5-week video training concludes with a short follow-up survey administered two to three weeks after the final training session. The follow-up survey consists of both financial literacy and health questions, drawn from topics discussed in the video trainings. All participants are informed in advance that they would be offered the chance to earn additional compensation based on their performance on these follow-up questions. We vary which questions individuals are paid for. Half the participants (again selected at random, individually) are paid for correct answers to questions related to the videos they watched (financial literacy or health), and the other half are paid for correct answers to topics that are not covered in their video training (general knowledge or reverse questions – financial literacy training participants get paid for health questions and vice versa). Participants knew from the beginning whether health, financial literacy, or general knowledge questions would determine their compensation.

# **IV. Summary Statistics**

# **_A. Summary Statistics_**

Baseline summary statistics for our sample are provided in Table 2. Households in our sample comprise on average six members, with an average household monthly income of Rs. 5250 (USD 115). A little more than half (57 percent) of our respondents are female, and a vast majority is married. Respondents in our sample on average have limited schooling: 47 percent completed elementary school, but only 4 percent completed secondary school.

10
# Table 2: Farmers Perceptions Regarding Security of Tenure

|County|||||
|---|---|---|---|---|
|Item|Gongzhullng<br>(N-200)|Tal<br>(N-200)|Jurong<br>(N-199)|Xlajiang<br>(N-200)|
|X of farmers<br>perceiving high<br>likelihood of<br>contract disruptlon<br>before expiration<br>date|17|17|24|75|
|Xof farmers<br>percelving<br>low<br>likelihoodof<br>being reassigned<br>the same farms<br>after contract<br>expiration date|85|76|78|59|

place much later than In the other counties (November 1988). By the time of the survey, there have been several well publicized incidents In several areas of China durlng the second half of 1988 where local authorities forced consolidation of small farms. The changed Insecurity perceptions In Xlajlang are not likely to have had an impact on the Investment data underlying the present study, as these pertain to the poriod 1984-88.

A longer-term perspective underlies the responses to a question regarding the likelihood of being assigned the same traccs of land upon contract expIration. The majority of farmers In all counties perceive low likelihood of regalning use rights to the same tracts of land. While the risk of not being asslgned the same parcels of land would clearly have a negative effect on land-embodied Investment,
increasing, and they satisfy cross-dimensional convexity (also shown to be equivalent to strong rearrangement) whereby the cumulative effect of multiple deprivations is deemed to be greater than the sum of their parts. The parameters � and � calibrate these two elements of distribution-sensitivity. Indeed, by allowing � to vary by dimension, the distribution-sensitive measures also open up the further possibility of varying degree of interdependence across any pair of dimensions.

An empirically useful property of the counting measures of multidimensional poverty is that they are additively decomposable across subgroups of population as well as across dimensions. Dimensional decomposability has been deemed particularly attractive from a policy perspective as it offers a means of assessing how deprivations in different dimensions contribute to overall poverty, thus offering guidance on dimensional priorities for poverty reduction. But simple additive dimensional decomposition is lost when cross-dimensional convexity is introduced. The paper however demonstrates how an exact Shapley dimensional decomposition can be implemented to assess the contributions of individual dimensions. The decomposition methods described in the paper are very general in nature and can be readily applied to virtually any poverty measure, not limited to distributionsensitive measures alone.

The paper has also sought to illustrate key properties of the distribution-sensitive measures with Indian data. Using the counting measures as a point of comparison, the application offers examples of how the use of distribution-sensitive measures can matter for assessing the magnitude of poverty, for the profile of the poor and subgroup contributions, and for the contribution of inequality in distribution of deprivations to multidimensional poverty. In many instances, such points of difference will also carry different implications for a variety of policy issues, including those related to dimensional priorities or the targeting of particular subgroups. The results for the Indian application are intended to be mainly illustrative. As in the case of uni-dimensional FGT measures relative to the widely-used headcount index, the use-value of distribution-sensitive multidimensional poverty measures will only be 31
It is a more pertinent benchmark than, say, the average amongst high income countries, or OECD members.

We conduct the counterfactual simulations as follows. We take 2005 as our base year. We then recalculate (for example) our maritime infrastructure indicator with the condition that those countries under the regional average for 2005 have their score increased to that value. This allows us to calculate the percentage change in the indicator for each country pair, which we map to an approximate trade impact using the gravity model elasticities.

Results for our five simulations are presented in Tables 9-13, and are compared in Table 14. In line with the results cited at the beginning of this paper, we find that the expected intraregional trade gains from improved trade facilitation are very substantial, and would appear to be greater than the gains from tariff reductions of comparable ambition. Cutting applied tariffs to the regional average would increase intraregional trade by about 2% ($6.3bn). Improving port facilities, limiting unofficial payments, and improving competitiveness in the internet services sector would boost trade by 7.5% ($22bn), 2.3% ($6.8bn), and 5.7% ($17bn) respectively. According to our results, improving air transport could increase trade by a very substantial margin: 42% or nearly $125bn. However, we consider this estimate likely high for the reasons set out earlier. As noted, air transport infrastructure quality is strongly correlated with the quality of other types of infrastructure, including maritime ports. We therefore interpret this result as an indication of the vital role that transport infrastructure can play in enhancing intra-regional trade. In terms of policy priorities, our results suggest the following ranking based on estimated trade flow impacts. First, transport infrastructure is clearly a major issue for Southeast Asia to address in ongoing and future reform programs (see _Scenarios 1_ and _2_ ). Trade flows appear to be very sensitive to transport infrastructure quality. ASEAN member countries may therefore find it

- 21 -
cognitive skills equals the extent of its dependence on household wealth and gender, respectively<sup>1</sup> . If wealth and gender inequity did not exist, there would be no detectable dependence of cognitive skills on either wealth or gender controlling for other factors.

To estimate the relationship between cognitive skills on wealth and gender, this paper utilizes data from the OECD’s Programme for International Student Achievement (PISA) for Argentina, Brazil, Chile, Colombia, Mexico, and Uruguay. This dataset provides measures of competency in mathematics, reading, and science for each student, and these will serve as measures of his or her cognitive skills in each subject area. Background information about the student, including gender and an index of household wealth, as well as background information about the student’s school, compliment these measures of competency. Unlike other major international assessments such as TIMSS and PIRLS, PISA aims to assess a student’s ability to solve practical problems using mathematics, reading, and science as opposed to a particular curriculum; these skills seem to fit the notion of cognitive skills relevant to the context of this paper.

The model used in this analysis to estimate the dependence of cognitive skills on wealth and gender stems from cognitive production function theory. In the proceeding model, both wealth and gender potentially impact cognitive skills indirectly through being correlated with household characteristics beneficial to cognitive achievement and by being correlated with better school characteristics; gender may also have a direct effect through natural ability. Consequently, wealth and gender inequity stems from two main

> 1 This is a similar concept to the measure used by Schütz et al. (2008)

4