rafmacalaba/datause-encoder-data huggingface dataset:
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differences in principle in other CMEA countries. Trade agreements could take account of such export-embodied currency arbitrage tirough special arrangements for exports with a high CC content, by supply in kind by the export-recipient country or by direct reimbursement for CC outlays. In many instances, however, this was not feasible; there were consequently proposals to idle CMEA export capacity if CMEA exports of a good constituted a net drain on the convertible currency balance (see Koeves 1985).

fable 4. Hungary: Imported Input Content of Export (1974)

> preTR Regime TTRegimees| eeCC Regime Source: Peesi (1981). V. EVALUATION As the preceding analysis has shown, the traditional CMEA regime was no random accumulation of ad hoc rules. It exhibited a great deal of internal logic, although it was not derived from a comprehensive theoretical blueprint. It evolved heuristically over several decades of bureaucratic trial-and-error. As a result, the institutions of the traditional CMEA regime were fundamentally consistent in several directions. First, its rules were compatible with one another. Second, systematic interdependencies were internalized, that is, the principles and rules of the regime were consistent with the model of traditional central planning from which the CMEA regime was derived, and within which it initially operated, and, to a somewhat lesser extent, were consistent even with the “modified” version of central planning. But frictions started to emerge once the interdependent framework of central planning was compromised through partial reforms of other systemic rules. And third, in order to conduct mutual trade, the member countries adopted matching — essentially identical ~ rules and procedures for CMEA trade planning, implementation, and settlement. Conversely, unilateral changes out of step with other countries would create frictions. These, in turn, resulted in a need to introduce corrective measures, thereby leading to further frictions and inconsistencies. The CMEA regime, in short, was in a powerful way self-perpetuating for systemic reasons alone. In the 1980s, however, these consistency properties increasingly became shackles to reforms.

Due to its consistency, the CMEA regime was effective in handling a large volume of trade without breakdowns or excessive cyclical disruptions. But in addition to stifling system reform, it exhibited two other defects that became even more obvious.

First, the need to resort to complex procedures to make model principles operational, along with built-in incentives to maintain the bill of traded goods set in intergovernment protocols, eliminated virtually all short-run flexibility, and subjected medium-run changes to cumbersome bilateral negotiations.

Second, the CMEA model did not contain any provisions that would ensure an efficient pattern of trade. The principal reason for this defect was that the model did not reveal static gains from trade,
**Table 1 Poverty Measures According to CEQ-Income Definitions** **<u>(In percentage)</u>**

|**Poverty measures**|**Market**<br>**income**|**Net market**<br>**income**|**Disposable**<br>**income**|**Consumable**<br>**income**|
|---|---|---|---|---|
|Headcount poverty|25.00|22.77|22.77|26.35|
|Poverty gap * 100|8.24|6.73|6.73|7.80|
|Poverty severity * 100|4.10|2.97|2.97|3.45|
|Watts Index * 100|11.94|9.23|9.23|10.71|
|Sen Index * 100|11.56|9.06|9.06|10.54|

_Source_ : Authors’ estimation based on SLCHBS 2018.

Although the net balance of transfers and taxes (direct and indirect) translates into a higher incidence of poverty, the same is not true for the poverty gap (it decreases the poverty gap by about one percentage point) and other poverty measures. All of these show a reduction after the fiscal interventions.

# **5.2. How Does the Fiscal System Affect Inequality?**

The fiscal system in Grenada has heterogeneous redistributive characteristics with a significant potential to reduce inequality. The effects of fiscal interventions on income distribution are heterogeneous, as seen in Table 2. First, the direct transfers (SEED and School Feeding Programme) have low redistributive potential; between the market income and net market income, the Gini index change is below one percentage point. The potential of these transfers is better understood when examining the lower section of the income distribution through the ratio P90/P10, which decreases from 7.4 to 6.8. Second, because the PIT is concentrated in the middle- and upper-income distribution, the effect is noticeable only in the P90/P10 ratio (from 6.8 to 6.6) and on the Gini coefficient, which declines from 0.406 to 0.397. Third, though the VAT and excise taxes seem to be very neutral, they, in fact, cause all inequality measures to decline, although not significantly.

**Table 2 Inequality Measurements According to CEQ-Income Definitions**

|**Inequality**<br>**measures**|**Market**<br>**income**|**Net market**<br>**income**|**Disposable**<br>**income**|**Consumable**<br>**income**|
|---|---|---|---|---|
|P90/P10 ratio|7.461|6.856|6.639|6.775|
|P90/P50 ratio|2.822|2.781|2.701|2.733|
|P10/P50 ratio|0.378|0.406|0.407|0.403|
|Gini index|0.416|0.406|0.397|0.395|
|GE(-1) index|0.300|0.362|0.346|0.340|
|GE(0) index|0.304|0.279|0.267|0.264|
|GE(1) index|0.302|0.288|0.272|0.270|
|GE(2)index|0.411|0.393|0.364|0.360|
|Atkinson (0.5) index|0.140|0.132|0.126|0.125|
|Atkinson (1) index|0.262|0.244|0.234|0.232|
|Atkinson(2)index|1.000|0.420|0.409|0.405|

_Source_ : Own calculation based on SLCHBS 2018.

12
# **TABLE V. Heterogeneous effects of Maternal Mental Health on Children Vocabulary at Age 5 by HH Wealth Level**

# **_Dependent variable: Standardized PPVT_**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
||IV<br>|with PDS-s<br>|elected varia<br>|bles and full<br>|regressor s<br>|et<br>|
||_Effects on c_<br>|_hildren from_<br>_HH_<br>|_wealthier_<br>|_Effects o_<br> <br>|_n children f_<br>_wealthy HH_<br>|_rom less_<br> <br>|
||MHI-1|MHI-2|MHI-3|MHI-1|MHI-2|MHI-3|
|Maternal Mental Health (-)|-2.971|-1.427|-1.509|-1.194*|-0.581*|-0.625*|
||(4.212)|(2.009)|(2.111)|(0.661)|(0.319)|(0.342)|
|Constant|1.098|1.101|1.098|1.147**|1.157**|1.158**|
||(0.869)|(0.854)|(0.839)|(0.583)|(0.576)|(0.574)|
|Observations|514|514|514|581|581|581|
|Mother controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Child controls|Yes|Yes|Yes|Yes|Yes|Yes|
|Household controls|Yes|Yes|Yes|Yes|Yes|Yes|

<mark>Table V presents estimated effects of maternal mental health on child vocabulary at age 5, separated by whether</mark> the HH is in the upper or lower wealth half of the distribution. We estimate the model selected from LASSO procedure. Selected control regressor set includes mother's age, wealth index, living in urban area, child's age, consumption of durable goods index, household size, number of children younger than 5 years in the household, and Height for age Z-score. Sample is restricted to children with available information on maternal mental health in 2002 and PPVT scores in 2006 and 2009. Robust standard errors in parentheses.

30
The remainder of the paper is structured as follows. Section 2 describes the experiment and Sri Lankan context. Section 3 estimates the mean treatment effect and return to capital by gender. Section 4 provides a simple model to detail avenues through which returns may differ by gender, and Section 5 explores these empirically. Section 6 explores non-experimental data from Brazil and Mexico to see if the pattern of returns is similar in those countries, Section 7 investigates the heterogeneity of returns across female-owned enterprises, and Section 8 concludes.

# **2. Data and Experimental Design**

We summarize here the sample of microenterprises and the experiment which randomly provided grants to some microenterprise owners. More details on the basic experimental design can be found in de Mel, McKenzie and Woodruff (2008a).

# _2.1. Participants and Survey design_

The goal of our experiment was to provide a positive shock (in the form of a grant) to the capital stock of firms, and measure the return to this shock. Our target population was low-capital microenterprise owners, those with less than 100,000 Sri Lankan rupees (LKR, about US$1000) in capital, excluding land and buildings. The upper threshold assured that the grants our budget allowed us to provide would result in measurable changes in capital stock. Additionally, previous research had suggested that returns to capital were particularly high for enterprises with very low capital stocks (McKenzie and Woodruff, 2006). In addition to the capital stock threshold, a microenterprise owner had to fulfill all of the following conditions to be included in our sample:

- (a) be self-employed full-time (at least 30 hours per week) outside of agriculture, transportation, fishing and professional services;

- (b) be aged between 20 and 65; and,

- (c) have no paid employees.

Using the 2001 Sri Lankan Census, we selected 25 Grama Niladhari divisions (GNs) in three Southern and South-Western districts of Sri Lanka: Kalutara, Galle and Matara. A GN is an administrative unit containing on average around 400 households. We used the Census to select GNs with a high percentage of own-account workers and modest 5

-  -
neighborhood relations in the spatial weights are only proxies for the true neighbors. The underlying assumption is that the spatial variation among sampled “neighbors” is representative of that among the true neighbors, an assumption commonly taken in spatial hedonic models. We are comfortable that the sample design employed in these surveys supports the conclusions we draw from this analysis. However, ideally it would be desirable to employ sampling designs that consider neighborhood structure explicitly, for instance by sampling households randomly and then including a number of direct neighbors for each households. Very few surveys have employed such as design, the US American Housing Survey being an exception (Ioannides 2002).

# **IV. Data and Variables**

Detailed household data for developing country cities are scarce. Most such data are collected at the national level through Living Standards Measurement Surveys or Demographic and Health Surveys. These tend to distinguish urban and rural areas, but the number of observations in each urban area is too small for city-specific analysis. In this paper we use comprehensive and geographically referenced information from urban household surveys for 2905 households in Bangalore and 2508 households in Bhopal (for further details on this data see Deichmann et al. 2003, Lall et al. 2004, Lall and Deichmann 2006b). These surveys were conducted in 2001 and 2003 respectively. The available variables differ slightly across the two cities since the original surveys were not totally identical. The key indicator in hedonic housing market analysis is the price or rent of the dwelling unit. Due to high official transaction costs, most recorded sales in Indian real estate markets do not reflect actual transaction amounts. Rents are also often artificially low due to rent control and therefore do not always match actual market rents. The surveys therefore asked households to report what they believe would be the market rent for a similar house in their neighborhood. Therefore this variable represents an estimated value reported by the surveyed individual. The survey team compared these values selectively to transactions revealed by real estate agents in the survey cities. This confirmed that, overall, residents have a fairly good idea of market rents. In our regression analyses, we apply a log matrices based on a nearest neighbor relation among the locations, for respectively 7 and 14 neighbors. The three weights matrices are used in row-standardized form. Although the remaining analysis refers only to the results using a queen weights matrix, the results are consistent for the alternative weights matrices based on a nearest neighbor relation.
42

|Short‐term technical worker|
|---|

|Short‐term technical worker|0.4881*<br>**|0.4993*<br>**|0.5066***|0.47005***|
|---|---|---|---|---|
|Long‐term technical worker|32.19<br>0.5566*|33.25<br>0.5626*|33.84<br>0.5668***|37.51<br>0.5312***|
||**|**|||
||38.19|38.83|39.74|44.43|
|Professional secondary school|0.5787*|0.5832*|0.5833***|0.55974***|
||**|**|||
||37.36|37.69|37.8|42.96|
|College diploma and above|0.8133*|0.8130*|0.8099***|0.76219***|
||**|**|||
||51.35|52.7|54.59|65.25|
|Days ill in bed, senior household<br>members|0.0001|0.0001|0.0001|0.0000|
||0.71|0.67|0.6|0.47|
|Days ill in bed, adult household<br>members|‐0.0002|‐0.0002|‐0.0002|‐0.0002***|
||‐1.42|‐1.45|‐1.57|‐2.07|
|Days ill in bed, children|0.0001|0.0002|0.0002|0.0002|
||0.74|0.84|0.84|0.94|
|Own a house (1=yes; 0=no)|0.1393*<br>**|0.1415*<br>**|0.1493***|0.1592***|
||9.01|9.28|10.31|15.40|
|Own land (1=yes;0=no)|‐|‐|‐0.6848***|‐0.7334***|
||0.7036*<br>**|0.6848*<br>**|||
||‐21.83|‐21.67|‐21.77|‐37.63|
|Areas of land owned (squared|0.0782*|0.0763*|0.0769***|0.0883***|
|metres)|**|**|||
||19.81|19.68|19.85|38.29|
|**_Community characteristics_**|||||
|1 if sanitary drinking water in|‐0.0704|‐0.0754|‐0.0694|‐0.0723*|
|enumeration area|||||
||‐1.5|‐1.54|‐1.52|‐1.74|
|1 if sanitary living water in<br>enumeration area|0.0853*<br>*|0.0881*|0.0854**|0.1014***|
|Strengtheningthe Capacityof Public Sector Internal Audit|Agnes Albert-Loth|
|---|---|
|Strengtheningthe Capacityof Public Sector Internal Audit|Agnes Albert-Loth|
|Federal UniversityHospitals Modernization Project|Andre C. Medici|
|Federal UniversityHospitals Modernization Project|Andre C. Medici|
|Afghanistan Agricultural Inputs Project(AAIP)|Amanullah Alamzai|
|Afghanistan Agricultural Inputs Project(AAIP)|Amanullah Alamzai|
|Afghanistan Agricultural Inputs Project(AAIP)|Amanullah Alamzai|
|Afghanistan Agricultural Inputs Project(AAIP)|Amanullah Alamzai|
|Afghanistan Agricultural Inputs Project(AAIP)|Amanullah Alamzai|
|Afghanistan Agricultural Inputs Project(AAIP)|Amanullah Alamzai|
|Afghanistan Agricultural Inputs Project(AAIP)|Amanullah Alamzai|
|Afghanistan Agricultural Inputs Project(AAIP)|Amanullah Alamzai|
|Afghanistan Agricultural Inputs Project(AAIP)|Amanullah Alamzai|
|Afghanistan Agricultural Inputs Project(AAIP)|Amanullah Alamzai|
|Afghanistan Agricultural Inputs Project(AAIP)|Amanullah Alamzai|
|Afghanistan Agricultural Inputs Project(AAIP)|Amanullah Alamzai|
|Afghanistan Agricultural Inputs Project(AAIP)|Amanullah Alamzai|
|Afghanistan Agricultural Inputs Project(AAIP)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|AF On-Farm Water Management(OFWM)|Amanullah Alamzai|
|Second Public Expenditure Development PolicyLoan|Ewa Joanna Korczyc|
|Second Public Expenditure Development PolicyLoan|Ewa Joanna Korczyc|
|IDF-VN StrengtheningProcurement Audit|Adu-Gyamfi Abunyewa|
|IDF-VN StrengtheningProcurement Audit|Adu-Gyamfi Abunyewa|
|Public Financial Management Reform II|Syed Waseem Abbas Kazmi|
|Public Financial Management Reform II|Syed Waseem Abbas Kazmi|
|Public Financial Management Reform II|Syed Waseem Abbas Kazmi|
|Public Financial Management Reform II|Syed Waseem Abbas Kazmi|
|Public Financial Management Reform II|Syed Waseem Abbas Kazmi|
Figure 5: Intervention Impacts on Payment Behavior <!-- Start of picture text --> A. Impact of Engagement on B. Impact of Targeted Enforcement on<br>Proportion of Paying Customers Proportion of Paying Customers<br>2018.8 2019 2019.2 2019.4 2019.6 2019.8 2018.8 2019 2019.2 2019.4 2019.6 2019.8<br>Date Date<br>Engagement Control Enforcement Control<br>C. Impact of Engagement on D. Impact of Targeted Enforcement on<br>Total Amount Paid (US$) Total Amount Paid (US$)<br>2014 2016 2018 2020 2014 2016 2018 2020<br>Date Date<br>Engagement Control Enforcement Control<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>Proportion of paying customers Proportion of paying customers<br>150<br>80<br>100<br>40<br>50<br>Total amount paid (US$) 0 Total amount paid (US$) 0<br><!-- End of picture text --> _Notes._ All figures present the impacts of the experimental interventions on payment behavior using utility billing data over time. Panel A shows the cumulative distribution function (CDF) of the proportion of people making at least one payment over time (post-intervention), comparing property owners in the control (red) and engagement intervention (blue) group. Panel B shows the CDF comparing property owners receiving the enforcement intervention vs. control. Panel C shows the total cumulative amount paid by property owners over time, comparing the engagement and control groups. Panel D shows the cumulative payments of the enforcement vs. control group. Black dashed vertical line is the date at which the enforcement intervention began. All monetary amounts are in USD using an exchange rate of 100:1, unless otherwise stated.

29
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<!-- Start of picture text --> T oO 40 35.4 25<br>20<br>30<br>[ed 22.4 Xs<br>Poo z== ne<br>22<br>ats gr<br>0 53<br>Ss 53 5.0 .<br>a a 0 | [|27 02 | | o [21 aos —os<br>1 2 2 4 5 Total 1 2 2 4 5 Total<br>35<br>20 288<br>25<br>=Zz@& 20<br>=<br>afisFa Be<br>2<br>es 54<br>0.2<br>3| | =7<br>12 2 4 5 Total<br><!-- End of picture text -->
behavior (extreme risk aversion, risk neutrality, and risk loving behavior) occur more frequently than random choice would suggest.

# **3.2 Instrument Validity**

To address concerns about the validity of hypothetical measures of risk aversion, we test whether individual responses to our hypothetical lottery choice questions explain observed variation in behaviors that we might expect to be driven (at least in part) by risk attitudes. We focus on two such behaviors suggested by the literature: entrepreneurship (Schumpeter 1934, Schumpeter 1939, Skriabikova, Dohmen, and Kriechel 2014) and migration in search of employment (Jaeger, Dohmen, Falk, Huffman, Sunde, and Bonin 2010, Bryan, Chowdhury, and Mobarak 2014). Both of these actions can be seen through the lens of risk tolerance: for an unemployed or underemployed young adult residing in a rural area, operating one’s one business and migrating are two high risk but potentially profitable strategies for improving one’s long-term income prospects. Both behaviors are also relatively uncommon: only 12.4 percent of KLPS2 respondents were operating their own business at the time of the survey, and only 3.5 percent had ever moved for a job or in search of work.

To explore the association between these risky but potentially profitable activities and responses to our lottery choice questions, we construct a simple index of risk tolerance: a count of the number of times (out of five lottery choice questions) that a subject chose the riskiest and highest expected value lottery.<sup>13</sup> This index has two important strengths. First, it obviates the need to impose functional form assumptions on the structure of risk preferences. In the decision problems included in our study, all lotteries involving risk have two equally likely outcomes, and the highest expected value lottery always has the highest variance and the lowest minimum payoff (i.e. the lowest payoff in the bad state),<sup>14</sup> so any parametric restriction that permits an ordering of utility functions in

> 13 Focusing on the number of risky choices allows us to include those subjects whose decisions were not consistent with a CRRA utility representation in our analysis. As discussed in Loomes (2005) and Choi, Kariv, M ̈uller, and Silverman (2014), most subjects implement their choices with error — and we would expect relatively high levels of error in our sample because of the relatively low level of formal schooling attained by our subjects. Importantly, if the likelihood of making consistent choices is associated with risk aversion, omitting the inconsistent subjects could bias our results. However, all of our findings are robust to the omission of the inconsistent types from the analysis. 14The one exception to this is that the final lottery choice question included one alternative designed to distinguish between risk loving and risk neutral types. In this choice problem, we classify both the highest expected value lottery and the highest variance lottery as risky, though we obtain similar results of we do not classify the highest expected

12