rafmacalaba/datause-encoder-data huggingface dataset:
4 This seems straightforw;.id cnough. So, why so many different measures? The main reason is doubts about tGe quality and accuracy of the balance-of-payments statistics. As a result, analysts have pieferred to derive the annual piivate capital flows by using the balance-of-payments ide:itity and proxying other balance-of-payments items. How? The balance-of-payments identity implies that: A + B + _C_ + D + 8i + F + G -t H = Q.

01' C + D + E + =- (A + i + F + H) The second equation implies that p.ivate capital flows plus net errors and omissions (capital flight) is equal to (the negative of) the sum of the cuffent-account deficit, net equity flows (FDI and corporate equity), iprreases in reserves, and other long-term capital of the official resident sector. Measuring capital flight through either side of the equation give the same result. On the left hand side of the equation, however, analysts can use only balance-of-paymens for some statistics. On the right hand side, however, there is more (and presumably better) information available elsewhere. So, tP- starting point for most capital flight methods is the right hand side of the equationt.

# Residual Method.

This measures the "residual" of the "sources of funds" over the "uses of funds". Sources of funds include all net official inflows (increases in net external indebtedness of the public sector) and the net flow of foreign direct investment.<sup>5</sup> User of funds include the currei'+-account deficit and additions to reserves. Outward capital flight exists when sources of funds exceed uses of funds, and vice-versa for inward capital flight. In terms of balance-of-payments items, capital flight under the residual method is thus the sum of: A + B + F + H. By the balance of payments identity, this is also equalto-(C + D + E + G).

The residual method, however, does not rely only on balance-of-payments data. For some items, other sources are used to get a better estimate of private capital flows. Most notable is item H (other long-term capital of resident offic;al sector or net official external borrowing), for which the year-to-year change in external debt according to World Bank data (H') may be more accurate. Thus. the residual method measures capital flight as A + B + F + H'; by the balance-of-payments identity, this is equal to - [C + D + E + G + (H-H')]. Capital flight is simply the sum of identified private capital outflows (C + D + E), the net errors and omissions from the balance-of-

> **51n contrast to** much of the literature, we **inclide** net acquisition of corporate equities in **our** measure of **foreign direct** investment. Apart from establishing major ownership control, flows of corporate equities **tepresent a** claim on **a** country's resources similar to foreign direct investment.
Here, for income deciles, there is an increasing effect starting in the 6th decile and continuing to the upper deciles, with a slight reduction between the 8<sup>th</sup> and 9<sup>th</sup> deciles, and then it almost doubles from the 9<sup>th</sup> to the 10<sup>th</sup> decile. That is, natural gas is mostly an urban middle- and high-income energy good. It must be noted, though, that selection may be present in this case, since, when it is available, gas distribution is mainly found in middle- and high-income neighborhoods.

Table 4 shows the results for the consumption of electricity in KgOE units. Here, a number of appliances and other household machines, as well as the quality of the houses, are correlated to electricity consumption. For example, according to the Tobit, having air conditioning increases consumption of electricity by 20.6 KgOE in urban households, or 19 KgOE in rural households. The better the house structure, the higher the consumption of electricity. Having a male household head seems to mean that less electricity is consumed in urban areas, but no significant relation is found in rural areas. Having more dormitories in houses in urban areas leads to more electricity consumption, but it is not significant in rural areas.

For electricity, there are substantial variations between OLS and Tobit models (although as mentioned here OLS may give inconsistent parameters) for rural households. In the OLS there is a significant increasing effect of income deciles on electricity demand. For Tobit this is only true in the upper decile. For the rural households, the Tobit shows a negative effect of income deciles on electricity demand, although it is not significant except at the upper decile where it is positive and significant. For the OLS, with a sample restricted to only those having a positive spend on electricity, the deciles are positive and significant at the upper levels.

22
**Figure 4: Annual deforestation in the Legal Amazon (km**<sup>2</sup> **)**

<!-- Start of picture text --> 30,000<br>25,000<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>1990 1995 2000 2005 2010 2015 2020<br><!-- End of picture text --> Source: Instituto Nacional de Pesquisas Espaciais (INPE) PRODES.

that would have occurred in the absence of the evaluated intervention. The fundamental problem of causal inference is that "it is impossible to _observe_ the [potential outcome] on the same unit and, therefore, it is impossible to _observe_ the [treatment] effect" (Holland, 1986). However, researchers across different fields have developed empirical strategies to estimate these counterfactual outcomes, using randomized controlled trials, difference-in-differences, synthetic controls (Abadie, 2021) or matching.

While the optimal estimation strategy in any given context depends on the assumptions the researcher or practitioner is willing to make, all methods share the fundamental insight that the causal impact of a program cannot be observed, but needs to be estimated. Relevant for our application of deforestation in Brazil’s Legal Amazon are Baylis et al. (2016), Ferraro (2009), and Ferraro et al. (2014), who review the available causal inference methods and their suitability for applications in environmental policy.

# **4 Deforestation in Brazil’s Legal Amazon**

We apply the REACH framework to deforestation and construct an econometric benchmark model that accounts for macroeconomic factors. We study deforestation in the nine Brazilian states which comprise the Legal Amazon: Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima, Tocantins, and (parts of) Maranhão. The rate of deforestation in Brazil’s Legal Amazon is alarming (Figure 4) and constitutes one of the main sources of Brazil’s GHG emissions as well as a major source of biodiversity loss (Hanusch, 2023). Most deforestation in the area is illegal, so resourcing and incentivizing governments to enforce existing laws to protect forests (“Command and Control”) is critical.

Whether legal or illegal, deforestation is an economic choice (Hanusch, 2023). Macroeconomic factors can be key determinants of the costs and benefits of that choice and therefore, ultimately, of deforestation outcomes (de Souza Ferreiro Filho et al., 2022). On a local level, deforestation may have different immediate causes (for example cattle ranching, timber extraction, land grabbing, or mining), but on a regional or national level, the literature has found that macroeconomic factors that capture the demand for commodities (e.g. commodity prices) and the external competi12
domestic servant, ownership of agricultural land proxied using whether any member of a household has undertaken any farming activity on its household’s land in the last seven days, ownership of 14 different assets (radio, TV, telephone, refrigerator, electric stove, and so forth), main source of drinking water (piped into dwelling, piped into yard/plot, public tap/standpipe, tube well, and so forth), toilet facility type (flush toilet, ventilated pit latrine, composting toilet, and so forth) and whether the facility is private or shared, and main cooking fuel type (wood, charcoal, crop residue/leaves, and so forth). I also include the main construction material in the household’s main dwelling’s floor (mud/dung, bamboo/reed, wood planks, and so forth), walls (wood and mud, wood and thatch, wood only, and so forth), and roof (corrugated iron sheet, cement/concrete, thatch, and so forth) in the construction of the score.

I further construct and use two alternative measures of household poverty status from the two measures of welfare. Specifically, I use household sample weights, household size, and each of the measures of welfare all from the ESS2<sup>17</sup> to construct the consumption or wealth poverty line as the value of consumption expenditure per capita or wealth score, respectively, on the 40th percentile of the distribution in the population of individuals.<sup>18</sup> Then, the two measures are constructed as binary variables indicating whether the round-specific value of a household’s consumption expenditure per capita or wealth score is at most as large as the value defining the respective poverty line.

Additionally, I use five individual-level variables reflecting salaried labor market engagement in the 12 months immediately before the data collection in each round of the survey. Two of these are binary variables indicating whether an individual has had permanent or temporary salaried employment, while the other two variables capture the number of hours and days an individual has worked in permanent and temporary salaried employment, respectively. The fifth is a binary variable indicating whether an individual has had permanent or temporary salaried employment.

I use two household-level variables to capture self-employment in non-farm businesses in the 12 months immediately before the data collection in each round of the survey: a binary variable indicating whether a household has a non-farm enterprise and a continuous variable measuring the number of non-farm enterprises a household owns. I further use a binary variable indicating

> 17Alternatively, I constructed the poverty lines using household sample weights, household size, and household welfare measures from the ESS3. Usages of these poverty lines provide similar results.

> 18The poverty lines for consumption expenditure per capita and wealth are 3683.062 (Br) and -1.507, respectively.

11
Fama, Eugene F. (1980). “Agency Problems and the Theory of the Firm.” _Journal of Political Economy_ 88(2): 288-307.

Gosnell, Greer K., John A. List, Robert D. Metcalfe (2020). “The Impact of Management Practices on Employee Productivity: A Field Experiment with Airline Captains.” _Journal of Political Economy_ 128(4):1195-1232.

Hjort, Jonas, and Jonas Poulsen (2019) “The Arrival of Fast Internet and Employment in Africa.” _American Economic Review_ , 109 (3): 1032-79.

Islam, Asif M. and Roberta Gatti (2023). “The Human Capital of Firms and Formal Training.” _IZA Journal of Development and Migration forthcoming_ Jensen, Michael C. and William H. Meckling (1976). “Theory of the Firm: Managerial Behavior, Agency Costs and Ownership.” _Journal of Financial Economics_ 3(4): 305-360.

Lemos, Renata, and Daniela Scur (2019). “The Ties that Bind: Implicit Contracts and the Adoption of Management Technology in the Firm.” CEPR Discussion Paper 12 Mueller, Holger M., and Thomas Philippon (2011). “Family Firms and Labor Relations.” _American Economics Journal: Macroeconomics_ 3(2):218-245.

Paunov, Caroline (2016) “Corruption's Asymmetric Impacts on Firm Innovation.” _Journal of Development Economics_ 118: 216-231 Rijkers, Bob, Caroline Freund, and Antonio Nucifora (2017). “All in the Family: State Capture in Tunisia.” _Journal of Development Economics_ 124: 41-59.

Scur, Daniela, Rafaella Sadun, John Van Reenen, Renata Lemos, and Nicholas Bloom (2021). “World Management Survey at 18: Lessons and the Way Forward.” IZA Discussion Papers No. 14146.

Shleifer, Andrei and Lawrence H. Summers (1988). “Breach of Trust in Hostile Takeovers’,” in _Corporate Takeovers: Causes and Consequences_ , Chicago IL, University of Chicago Press, 33-68.

Shleifer, Andrei and Robert W. Vishny (1986). “Large Shareholders and Corporate Control.” _Journal of Political Economy_ 94: 461-488 Sraer, David and David Thesmar (2007). “Performance and Behavior of Family Firms: Evidence from the French Stock Market.” _Journal of the European Economic Association_ 5(4): 709-751.

Tsoutsoura, Margarita (2021) “Family Firms and Management Practices.” _Oxford Review of Economic Policy_ 37(3): 323-334.

Villalonga, Belen and Raphael Amit (2006). “How do Family Ownership, Control, and Management Affect Firm Value?” _Journal of Financial Economics_ 80(2): 385-417.

14
**Figure 5.1: Index of global prices of agricultural products, 1986-88=100**

<!-- Start of picture text --> 170<br>160<br>150<br>140<br>130<br>120<br>110<br>100<br>90<br>80<br>1985 1990 1995 2000 2005 2010<br>Year<br>Index<br><!-- End of picture text --> Source: World Bank Development Prospects Group web data.

24
Table 3: Weighing Different Kinds of Evidence

||Pooled<br>(1)|Policymaker<br>(2)|Policy<br>Practitioner<br>(3)|
|---|---|---|---|
|Impact|1.07***|1.05***|1.10***|
||(0.01)|(0.02)|(0.02)|
|Quasi-Experimental|1.11|0.97|1.31|
||(0.13)|(0.15)|(0.22)|
|Experimental|1.40***|1.30*|1.55**|
||(0.16)|(0.19)|(0.29)|
|Diferent country, same region|1.15|1.05|1.31*|
||(0.12)|(0.15)|(0.20)|
|Same country|1.59***|1.33**|2.02***|
||(0.15)|(0.17)|(0.26)|
|Recommended by local expert|1.24***|1.28**|1.21*|
||(0.10)|(0.15)|(0.12)|
|Small C.I.|1.29***|1.06|1.61***|
||(0.11)|(0.12)|(0.20)|
|Observations|818|407|411|

This table reports the results of conditional logit regressions on which program was selected. Odds ratios are reported. “Impact” refers to the estimate of the effect associated with the program; “Quasi-Experimental” indicates whether the study associated with the program was quasi-experimental; “Experimental” indicates whether the study associated with the program was an RCT; “Different country, same region” indicates whether the study associated with the program was described as done in a different country in the same region; “Same country” indicates whether the study associated with the program was described as done in the same country; “Recommended by local expert” indicates whether the program was recommended by a local expert; “Small C.I.” refers to the estimates having small confidence intervals. The omitted categories are “Observational”, “Different region”, and “Large C.I.”. The number of observations represents the total number of choices made across individuals. Standard errors are provided in parentheses. * pă0.10, ** pă0.05, *** pă0.01.

11
capacity in DTEs yields important insights in understanding the dysfunctionality of public governance.

- (a) _Public sector vision or public sector mission and values_ .  Societal mission and values as embodied in the constitution or in annual budget policy statements, may be useful points of reference for public sector mandates and the values inherent in these mandates

- (b) _Authorizing environment_ . The authorizing environment represents the institutional mechanisms to translate constitutional mission to concrete objectives and actions. These include societal norms, formal and informal rules, procedures and organizations dealing with participation, consultation, policy making, and accountability. Legislative, coordination and oversight bodies are important elements of the authorizing environment.  These institutions ensure that the public sector is solely focused on citizen aspirations. In Switzerland, legislative bodies, fiscal rules and popular referenda on major budgetary proposals ensure that citizens preferences are respected.

- (c) _Operational capacity and constraints_ .  The skills, motivations and commitment of the executive branch represents the operational  capacity. What is authorized is not necessarily what will get done as the available operational capacity may not be consistent with the task at hand.  Further, even the operational capacity that is available may be circumvented by the bureaucratic culture or incentives that reward command and control, and corruption and patronage.

6
# **Table 7. Poverty dynamics based on synthetic panel data, bound estimates for nonparametric joint probabilities (percent)**

||Lower Bound|Upper Bound|
|---|---|---|
|Poor, Poor|10.59|3.21|
|Poor, Non-Poor|12.48|15.15|
|Non-Poor, Poor|0.08|7.46|
|Non-Poor, Non-Poor|76.85|74.18|
|All|100.00|100.00|

Note: Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Correlation estimated using household head's age cohorts (rho = 0.66).

**Table 8. Poverty dynamics based on synthetic panel data, bound estimates for nonparametric conditional probabilities (percent)**

||Lower Bound|Upper Bound|
|---|---|---|
|Poor to Poor|45.91|17.49|
|Poor to Non-Poor|54.09|82.51|
|Non-Poor to Poor|0.10|9.13|
|Non-Poor to Non-Poor|99.90|90.87|
|All|100.00|100.00|

Note: Estimated using a poverty line of 3,448 Nu/person/month in 2017 and 5,784 Nu/person/month in 2022. Correlation estimated using household head's age cohorts (rho = 0.66).

Finally, the conclusions derived from tables 5 and 6 may be substantiated by taking a comparative view of Bhutan’s case. Estimates of joint transition probabilities based on synthetic panels are available for a number of countries: a selection of the most relevant to the context at hand is shown in Table 9. Bhutan’s recent experience emerges almost unequivocally as the most favorable in the bunch. A natural comparison is with earlier mobility estimates for Bhutan itself (World Bank 2014): the picture that refers to 20072012 is remarkably similar to the one obtained for 2017-2022 – if anything, the country’s performance improved, as the probability of chronic poverty (poor to poor) decreased, and upward mobility (poor to non-poor) increased. Compared to other countries, Bhutan’s overall mobility (the sum of the two cross-state transitions, poor to non-poor and non-poor to poor) appears low: Viet Nam 2006-2008 is the only example where aggregate mobility is lower (though the shorter time period considered should be factored into the comparison). However, mobility in Bhutan is overwhelmingly positive, that is, it is mostly 15
14 Table 2: QUARTERS RANKED BY VALUES OF DESCRIPTIVE STATISTICS

||_Ranking_||_Inflation_||_var_||_sk_||_wvar_|
|---|---|---|---|---|---|---|---|---|---|
||**1**|**90Q1**|1.319|**89Q3**|1.615|**89Q1**|6.523|**89Q3**|17.433|
|five|2|**89Q4**|1.230|**89Q4**|1.015|**96Q4**|5.195|**89Q4**|5.661|
|highest|3|89Q3|1.053|90Q1|0.980|94Q4|5.183|90Q1|4.740|
||4|89Q1|0.295|89Q1|0.275|89Q4|4.542|**89Q1**|0.342|
||_5_|91Q1|0.257|91Q2|0.081|95Q4|3.936|89Q2|0.219|
||29|95Q4|0.047|93Q1|0.003|96Q2|-0.997|95Q4|0.021|
|five|30|96Q2|0.047|96Q2|0.002|93Q3|-1.497|96Q4|0.034|
|lowest|31|96Q4|0.041|**96Q1**|0.002|96Q3|-4.009|96Q1|0.020|
||32|95Q3|0.025|95Q2|0.002|95Q3|-4.429|96Q2|0.013|
||33|96Q3|0.023|97Q1|0.001|94Q1|-5.071|97Q1|0.013|
|||_Ranki_|_ng_|_wsk_||_Tvar_||_T_**_-sk_**||
|||1|96Q4|6.192|89Q3|243.370|**89Q1**|0.721||
|||2|**94Q2**|4.490|**90Q1**|_55.905_|**92Q1**|0.652||
||five|3|94Q1|4.340|89Q4|27.502|94Q4|0.447||
||highest|4|95Q4|4.308|89Q1|9.481|96Q4|0.424||
|||5|89Q3|3.943|**91Q1**|7.068|91Q1|0.367||
|||29|90Q3|0.151|93Q1|0.431|**91**<br>Q3|-0.107||
||five|30|<br>93Q3|-1.790|<br>96Q1|0.265|<br>93Q3|-0.227||
||lowest|31|91Q3|-2.203|96Q2|0.238|96Q3|-0.297||
|||32|96Q3|-2.641|97Q1|0.198|95Q3|-0.349||
|||33|95Q3|-3.863|<br>95Q2|0.169|94Q1|-0.399||

_source: author 's calculations using GUS data_ The basic message that emerges from the tables is intuitionally obvious: the distribution of individual inflation rates is most heavily skewed and dispersed during initial stages of reform when most profound relative price changes took place. In the case of all statistics but one, second and third quarter of 1989 turn out to be the periods during which the measures peak. By contrast, more recent observations (1994-1997) tend to be characterized by lowest variance and skewness. To give a better idea of these outlier distributions, Figure 1 presents histograms of individual price changes for 89Q3 and 89Q4 (high positive skewness an(d variance) as well as 95Q3 and 96Q3 (high negative skewness).
<!-- Start of picture text --> 100<br>90<br>80<br>= 70<br>£cy<br>a<br>= 60<br>3=&<br>ag 50<br>- 40<br>4<br>5<br>=z 30<br>20<br>10<br>0 1360 1365 1970 1975 1380 1985 1950 1955 a0 2008 ame ams<br>—IMF —IBRD —Paris Club — Private<br><!-- End of picture text -->
**<u>Table 4</u>** <u>: Difference-in-difference estimates of effect of VUP on livestock accumulation (2009-2014)</u>

||2011|||2014|||2011 & 2014|||
|---|---|---|---|---|---|---|---|---|---|
||VUP<br>Beneficiary|DS<br>Beneficiary|PW<br>Beneficiary|VUP<br>Beneficiary|DS<br>Beneficiary|PW<br>Beneficiary|VUP<br>Beneficiary|DS<br>Beneficiary|PW<br>Beneficiary|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|_Mean at baseline_||||||||||
|2011 (=1)|0.036|0.042|0.041|||||||
||(0.072)|(0.072)|(0.073)|||||||
|Beneficiary in 2011 (=1)|-0.230***|-0.251*|-0.198*|||||||
||(0.080)|(0.107)|(0.085)|||||||
|2011*Beneficiary|0.120|0.102|0.128|||||||
||(0.089)|(0.094)|(0.114)|||||||
|2014 (=1)||||-0.321***|-0.318***|-0.325***||||
|||||(0.076)|(0.078)|(0.078)||||
|Beneficiary in 2014 (=1)||||-0.351***|-0.375***|-0.307***||||
|||||(0.079)|(0.085)|(0.094)||||
|2014*Beneficiary||||0.398***|0.450***|0.296||||
|||||(0.105)|(0.140)|(0.222)||||
|Beneficiary (2011 & 2014)|||||||0.521***|0.564***|0.27*|
||||||||(0.122)|(0.200)|(0.160)|
|_Controls_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|_Yes_|
|N|1,228|1,027|1,086|1,231|976|1,095|1,845|1,806|1,840|
|Pseudo-R2|0.213|0.210|0.200|0.189|0.196|0.198|0.190|0.187|0.185|

_Notes:_ Robust, clustered standard errors in parenthesis. * p<0.10; ** p<0.05; *** p<0.01. In all specifications we control for the characteristics of the household head, i.e. headship, age, education level; the household composition, and wealth proxied by a housing index and land holdings. _Source:_ VUP household survey, 2009, 2011, 2014.

27
an easy time circumventing capital controls, most countries have not imposed capital controls, and a significant part of the capital raising activity has gone to investment and growth (Didier et al., 2015; Calomiris et al., 2017). Carry trade has its risks as it increases vulnerability to currency depreciations and spikes in international interest rates (Chui et al., 2014). In fact, the more recent economic slowdown and currency depreciations in the developing world coupled with tightening monetary policy in developed countries have raised concerns because they could negatively affect the capability of highly-leveraged corporations to pay back their debts (Alfaro et al., 2017).

Tax avoidance might also explain the borrowing pattern by developing country corporations. As the world became more globalized, large corporations started to increasingly use within-firm financial transactions (intra-firm debt, royalty payments, dividend repatriations, etc.) to transfer profits to related parties located in low-tax countries (also known as “tax havens”) to reduce their tax burden (Desai et al., 2006). These corporate financial arrangements have translated into increasing cross-border financial flows between developing countries and international financial centers (IMF, 2014). For example, companies can shift profits across jurisdictions by lending to firms in high tax jurisdictions through related parties in low tax countries, which translates into increasing debt flows (Arena and Roper, 2010). Data on the Coordinated Portfolio Investment Survey (CPIS) show that in 2016, 9 percent of foreign debt securities liabilities of Brazil were held in Cayman Islands and 8 percent in Luxembourg. Similarly, Luxembourg and Mauritius ranked third and six, respectively, as the top holders of debt securities liabilities of India, accounting for 9 and 5 percent of all liabilities, respectively. Tax avoidance is particularly important for developing countries that have weak tax legislations (Arezki et al., 2013). Still, a significant part of the borrowing activity of corporates has taken place in countries that do not necessarily present tax advantages nor are regarded as tax havens, such as the United Kingdom and the United States, especially when compared with the offshore markets mentioned above.

17