rafmacalaba/datause-encoder-data huggingface dataset:
their private counterparts, consistent with previous evidence. The authors show that financial regulatory changes introduced in 2018, which tightened credit conditions, further increased credit misallocation in favor of government-run firms because of their government guarantees.

In the second strand of research, early work by Buera et al. (2011) explores the role of financial distortions in generating capital and talent misallocation, showing that distortions account for a significant part of the cross-country differences in real outcomes such as output, output per worker, and total factor productivity (TFP). Similarly, Midrigan and Xu (2014) exploit plant-level data for the Republic of Korea to evaluate the role of financial frictions in determining TFP. The parametrization of the developed model shows that financial frictions reduce aggregate real outcomes primarily through two different channels, inefficiently low levels of firm entry and technology adoption. Gopinath et al. (2017) also confirm the role of financial frictions in determining productivity losses from capital misallocation. Using data from manufacturing firms in Spain, the paper shows that the decline in real interest rates, often attributed to the euro convergence process, led to important real losses, as capital inflows were allocated to unproductive firms. More recently, Whited and Zhao (2021) extend the seminal framework of Hsieh and Klenow (2009) to analyze the effect of finance misallocation on the real economy.<sup>5</sup> The paper also quantifies the reallocation gains for China relative to the U.S., showing that state-owned firms stand to gain less in percentage terms than non–state-owned firms.

Our paper differs from both strands of research in several regards. First, we unpack the black box of financial distortions and identify one, state ownership, which generates non-trivial real losses by distorting financial markets. Second, we advance the literature and estimate the effect of such a distortion on the cost of production. Third, we use a mirror approach–finance instead of factor markets–to deal with mea5The paper shows that financial distortions reduce aggregate output through two different channels–a scale and a composition effect–as distortions create a gap between the observed and efficient total liabilities and debt-to-equity mix firms get access to.

9
of 0.7 between the CAPM beta and the ES-implied beta. Subtracting the CAPM beta, the relative usual downside beta and the relative ES-implied beta are little correlated with the CAPM beta. Therefore, it is better to use relative downside betas in the analysis to prevent the predictive power of the CAPM beta being affected by downside betas. Consistent with Ang et al. (2006), this paper finds a high correlation between the relative usual downside beta and coskewness with a coefficient of -0.3. In comparison, the relative ES-implied downside beta is not correlated with coskewness. Noticing that the relative ES-implied downside beta is mildly correlated with size and cokurtosis, Section 5.5 examines the predictive power of the relative ES-implied beta after controlling for size and cokurtosis exclusively.

# **4 Downside risk and realized returns**

Since Ang et al. (2006) document that the usual downside beta has strong predictive power for returns over the same period, but not for returns in the future, this paper presents the explanatory power of downside betas for returns over the same period first to compare the in-sample performance with the out-of-sample performance later. To investigate the explanatory power of downside betas, I sort stocks into quintile portfolios. If a downside beta determines the price of assets, there should be a pattern in the portfolio returns. Portfolios sorted by higher relative downside beta should have higher average returns. On the contrary, if the downside beta is not priced or does not characterize risk correctly, there would be no significant difference in returns across the portfolios.

I use rolling windows when sorting portfolios. Each month, I sort portfolios based on betas computed over a horizon. There is a trade-off in choosing the horizon. On the one hand, a large sample size is needed in order to have enough observations on the downside. On the other hand, too long a horizon is impossible to capture time-varying betas. Following Ang et al. (2006) and Fama and French (2006), this paper uses 12 months as the horizon. Since betas are computed at one-year horizon and portfolios are formed every month, there exist some overlapping periods. Therefore, the reported _t_ -statistics are adjusted using Newey and West (1987) heteroskedastic-robust standard errors with 12 lags.

The detailed procedure used for forming portfolios is as follows.

(1) At the beginning of each month, estimate the CAPM beta and downside betas using daily returns in the next 12 months.

(2) Sort stocks into quintile portfolios according to the beta computed above.

(3) Calculate returns of each quintile over the same period and report the average of portfolio returns.

Table 2 reports the time-series averages of excess returns of portfolios sorted by different betas, i.e., CAPM beta " _β_ 0", usual downside beta " _βed_<sup>_−_", ES-implied downside beta "</sup><sup>_β_</sup> _es_<sup>_−_"", relative usual</sup> 10
_Table 5: Program impact on 6-36-month-old nonbeneficiary children's physical growth, overall_ _<u>and by program saturation</u>_

|H|eight-for-Age<br>Z score|Stunting|Severe stunting|Weight-for-<br>Age Z score|Underweight|
|---|---|---|---|---|---|
|_Panel A: Program impact_||||||
|Program village|-0.431**|0.123**|0.044|-0.111|0.06|
||(0.17)|(0.06)|(0.03)|(0.17)|(0.04)|
|_Panel B: Interaction with sa_|_turation _|||||
|Program village|0.043|-0.137|-0.038|1.557***|-0.381**|
||(0.63)|(0.22)|(0.14)|(0.58)|(0.15)|
|Saturation|-0.892|0.197|0.068|0.249|-0.291|
||(0.79)|(0.24)|(0.15)|(0.63)|(0.21)|
|Program|-0.773|0.428|0.136|-2.786***|0.745***|
|village*saturation|(1.00)|(0.34)|(0.23)|(0.94)|(0.25)|
|_Panel C: Interaction with a_|_bove median satu_|_ration_||||
|Program village|-0.314|0.061|0.039|0.257|-0.046|
||(0.24)|(0.08)|(0.04)|(0.22)|(0.05)|
|Above median<br>saturation|-0.397|0.074|0.048|0.066|-0.075|
||(0.29)|(0.08)|(0.04)|(0.26)|(0.07)|
|Program village*Above|-0.196|0.117|0.006|-0.798**|0.231***|
|median saturation|(0.35)|(0.11)|(0.07)|(0.34)|(0.09)|
|<br>_Panel D: Interaction with to_<br>|_p quartile of sat_<br>|_uration_<br>||||
|Program village|-0.244|0.041|0.012|0.127|0.018|
||(0.20)|(0.06)|(0.03)|(0.18)|(0.05)|
|Fourth quartile of<br>saturation|0.073|0.02|-0.009|0.498*|-0.125|
||(0.37)|(0.09)|(0.07)|(0.27)|(0.09)|
|Program village*Fourth|<br>-0.740*|<br>0.319**|<br>0.139|<br>-1.320***|<br>0.245**|
|quartile of saturation|(0.41)|(0.13)|(0.10)|(0.37)|(0.11)|
|Control Observations|145|145|145|156|156|
|Treated Observations|158|158|158|175|175|
|Control Mean|-1.124|0.317|0.069|-0.922|0.186|

note: *** p<0.01, ** p<0.05, * p<0.1 All specifications include quadratic terms for child age (in months), and municipality fixed effects. Standard errors are clustered at village level; there are 130 villages. Source: 2011 _Pantawid_ impact evaluation survey 48
Confluencia, relative to expected demand for the program. In late 1998, the average number of Trabajar participants per month represented 28% of the estimated number of people living in poor households that included an unemployed worker; the corresponding national figure was **5%.1O** However, the incidence of poverty was not unusually high; the proportion of the population living in households who had a total income per person below the poverty line and at least one unemployed person was 3.5% in Confluencia versus 4.2% nationally. In the light of these figures, the Ministry of Labor wanted to explore policy options for assisting workfare participants in Confluencia to find regular private-sector jobs.

The target population for the experiment is the set of beneficiaries of temporary employment programs, managed by the Ministry of Labor. The main program is the aforementioned Trabajar program. (There was a similar but much smaller provincial program; the beneficiaries of the latter were not included in the study.) The baseline survey aimed to cover everyone on the official list of participants in temporary programs drawn in October 1998, though some had to be dropped because they could not be located. In all, 953 households with workfare participants answered the baseline questionnaire in full.

Three roughly equal random samples were then drawn by a lottery, one getting just the voucher, one getting the voucher and training, and one reserved as the control group. All were given the sarne questionnaire as for the Permanent Household Survey, done twice a year by the statistical office of the Governnent of Argentina. Some questions were added specific to temporary employment programs. All interviews were done at the beneficiaries' homes. The experiment was not announced publicly by the Ministry of Labor. Nor were any of the

**10** For the purpose of this calculation, participants in temporary employment programs were counted as unemployed. The calculation of the number of people living in poor households with an unemployed member was done using the Permanent Household Survey for May and October 1998.

8
Table 3 Determinants of NAT score: Returns to School and Human Resources

|Dependent: Change in overall NAT score|(1)|(2)|(3) (4)|
|---|---|---|---|
||Non|-city|City|
|Change in PCR<br>|-0.0452<br>(2.02)|-0.0486<br>(2.16)|0.0520 0.0535<br>(1.33) (1.33)|
|Change in PTR<br>|-0.0323<br>(2.67)|-0.0434<br>(3.31)|0.0312 0.0366<br>(0.59) (0.60)|
|Change in per-pupil personnel expenditure<br>|-0.00061<br>(0.39)|-0.0019<br>(1.16)|0.0114 0.0124<br>(1.88) (1.81)|
|Including changes in numbers of principal, head<br>teachers, master teachers and teachers by ranks|No|Yes|No Yes|
|Division fixed effects|Yes|Yes|Yes Yes|
|Number of observations|16075|16075|3979 3979|
|R squared (within)|0.0018|0.0050|0.0089 0.0130|

Numbers in parentheses are absolute t values using Huber robust standard errors with division clusters.

13
|Outcome|Table|Population|Primary?|Rounds|Description|
|---|---|---|---|---|---|
|Index of Women’s<br>Divorce Prospects<br>(Standardized)|-|Core<br>Respondent|No|4|Standardized index of ability of core<br>respondent to divorce her husband and<br>maintain household property.|
|Index of Fertility|||||Standardized index of differences in core|
|<br>Empowerment<br>(Standardized)|-|Core<br>Respondent|No|4|respondent and her husband ideal degree of<br>family planning.|
|Index of Self-|||||Standardized index of core respondents need|
|Determination in Marriage<br>(Standardized)|-|Core<br>Respondent|No|4|for permission from her husband to undertake<br>certain activities.|
|Index of Frequency of<br>Social Contact<br>(Standardized)|-|Core<br>Respondent|No|4|Standardized index of core respondents’<br>frequency of travelling outside the community<br>and sending and receiving phone calls and text<br>messages.<br>Standardized index of a series of questions|
|Index of Spousal Abuse<br>(Standardized)|-|Core<br>Respondent|No|4|both asking about when it is acceptable for the<br>husband to beat his wife, as well as whether<br>the husband is violent towards the core<br>respondent.|
|Age Difference Between<br>Wife and Husband|-|Core<br>Respondent|No|4|Age difference between core respondent and<br>her husband.<br>Standardized super index of married economic<br>control, measured for core respondents who|
|Married Index of<br>Economic Control<br>(Standardized)|VI|Core<br>Respondent|Yes|4|are married at the time of the household<br>survey. It includes 4 components described<br>immediately below: agricultural decision-<br>making power, microenterprise participation,<br>livestock control, and ratio of wife to husband<br>consumption.<br>=1 if the core respondent is involved in|
|Agricultural Decision-<br>makingPower|-|Core<br>Respondent|No|4|decision-making around any of the household<br>plots.|

69
_Figure 6: Firms in Slavonia perceive education quality to be a top challenge for worker’s skills_ <!-- Start of picture text --> Main 3 Problems for Shortage of Skills: Across Regions<br>Croatian Firms, 2017<br>Other Regions Slavonia<br>Ability to Solve Problems Initiative Educational Quality<br>Discipline Initiative Ability to Solve Problems<br>Zagreb<br>Ability to Solve Problems Inititative<br>Discipline<br>25 40<br>20 30<br>15<br>20<br>10<br>5 10<br>Proportion of Firms (%)<br>0 0<br>25<br>20<br>15<br>10<br>5<br>Proportion of Firms (%)<br>0<br><!-- End of picture text -->

# **_2.4 Business Environment_**

External factors such as enabling conditions for businesses to flourish can also significantly influence firm performance. We investigate the obstacles for firms’ motivation to upgrade to better production or management processes. Figure 7 lists seven different problems in order of their importance reported by the firms. Most firms in Eastern Croatia (and in other regions, including Zagreb) perceive lack of skilled workers and political instability and corruption as the two main obstacles affecting their operations.

_Figure 7: Lack of skilled workers, political instability are among the top obstacles for firms in Croatia_ <!-- Start of picture text --> Obstacles for Production or Management Processes<br>Croatian Firms, 2017<br>Other Regions Slavonia Zagreb<br>Lack of Skilled Workers Cost of Capital<br>Small Market Size Internet<br>Flexibility of Regulations Technical Support from Govt/Private Sector<br>Corruption/Political Instability<br>40<br>30<br>20<br>Proportion of Firms (%) 10<br>0<br><!-- End of picture text --> Among the major economic problems that a firm perceives as binding constraints to their growth, Figure 8 suggests that Slavonian firms find taxes and qualification of the workforce to be a major roadblock. This is similar to the problems faced by firms in Zagreb and other regions.<sup>9</sup>

> 9 The survey asks firms to rank the top problems in their growth and operations. This does not necessarily imply that taxes, for example, vary sub-nationally but that firms in a given region perceive the noted problem as a bigger constraint.

10
**<u>Table A7: Baseline characteristics of formal businesses</u>**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||**firms at**|**Ne**|**wly formal**|**ized firms**|**in...**|col|col|
||**listing**|Control||||(1)=col<br>|(2)=col<br>|
||**survey**|group|Group1|Group2|Group3|(3),(4),(5)|(3),(4),(5)|
|Number of observations|608|27|33|140|222|||
|_Firm owner characteristics_||||||||
|Female owner|0.419|0.444|0.333|0.493|0.518|0.000***|0.622|
||[0.494]|[0.506]|[0.479]|[0.502]|[0.501]|||
|Age of the owner|43.6|38.8|40|38.5|38.9|0.000***|0.959|
||[10.5]|[9.2]|[9.8]|[8.7]|[8.9]|||
|Business owner has some formal education|0.884|0.889|0.818|0.836|0.856|0.000***|0.545|
||[0.32]|[0.32]|[0.392]|[0.372]|[0.352]|||
|Business owner has some secondary<br>|0.74|0.704|0.576|0.529|0.581|0.000***|0.151|
|education|[0.439]|[0.465]|[0.502]|[0.501]|[0.494]|||
|_Firm characteristics_||||||||
|Trade|0.584|0.556|0.273|0.436|0.473|0.161|0.257|
||[0.493]|[0.506]|[0.452]|[0.498]|[0.5]|||
|Services|0.26|0.296|0.394|0.279|0.275|0.959|0.91|
||[0.439]|[0.465]|[0.496]|[0.45]|[0.447]|||
|Craft|0.09|0.074|0.212|0.257|0.216|0.000***|0.058*|
||[0.287]|[0.267]|[0.415]|[0.439]|[0.413]|||
|Firm area in m2|52.5|41.3|42.9|23.5|17.8|0.000***|0.125|
||[106.5]|[103.1]|[154.2]|[47.4]|[34.8]|||
|Business connected to electricity network|0.898|0.704|0.758|0.75|0.743|0.000***|0.62|
||[0.303]|[0.465]|[0.435]|[0.435]|[0.438]|||
|Number of employee|2.961|1.185|1.455|1.379|1.423|0.000***|0.559|
||[4.59]|[1.388]|[1.416]|[1.668]|[2.196]|||
|The firm does any form of accounting|0.642|0.222|0.303|0.257|0.243|0.000***|0.721|
||[0.48]|[0.424]|[0.467]|[0.439]|[0.43]|||
|Amount of sales in an average week|542,167|89,500|61,930|56,768|54,001|0.000***|0.001***|
||[4,434,990]|[61,392]|[57,387]|[46,359]|[53,366]|||
|Amount of profit in the last month|223,041|56,185|55,145|50,719|46,702|0.000***|0.4|
||[726,068]|[45,206]|[46,173]|[42,550]|[44,246]|||
|Firm owner owns a bank account|0.789|0.423|0.406|0.418|0.33|0.000***|0.569|
||[0.409]|[0.504]|[0.499]|[0.495]|[0.471]|||
|Firm pays taxes|0.836|0.667|0.531|0.584|0.534|0.000***|0.244|
||[0.371]|[0.48]|[0.507]|[0.495]|[0.5]|||
|Amount of taxes paid in the previous year|316,636|31,262|20,358|26,795|22,841|0.000***|0.28|
||[2,591,065]|[33,019]|[36,866]|[34,633]|[31,662]|||
|Thinks that it's difficult to know in advance|0.725|0.708|0.708|0.673|0.651|0.66|0.654|
|how much taxes she will have to pay|[0.447]|[0.464]|[0.464]|[0.471]|[0.478]|||
|Ratio tax/ annual profit for all businesses|0.128|0.076|0.047|0.066|0.058|0.000***|0.39|
||[0.221]|[0.105]|[0.083]|[0.104]|[0.088]|||
|Ratio tax/ annual profit for businesses|0.169|0.117|0.091|0.116|0.11|0.000***|0.813|
|paying taxes|[0.313]|[0.11]|[0.097]|[0.115]|[0.095]|||

Notes: sources: listing‐baseline survey March 2014 48
Households were typically invited to a centralized location, such as a school or _kebele_<sup>_4_</sup> office, to receive their stoves and preliminary instructions from field supervisors. Villagers then took the six concrete stove pieces and the clay cooking plate back to their homes. This process is shown in Figure 4. Typically, the following day enumerators came to install the stove in the kitchen area, either in the main home (43%) or in a separate kitchen (53%) by mudding together the six concrete pieces. They also gave cooks training on stove operations and the SUM device was installed on the stove using heat-resistant tape and initiated to begin logging temperature.

Figure 4. Mirt Stove Recipient taking Stove Home

# 3.2 _Behavioral Treatments_

The participating households received the stoves under six mutually-exclusive randomized behavioral treatments. There were three aspects to each treatment, with only two levels of each aspect (present or absent) divided equally so one-third of all sites received each treat Source: Randall Bluffstone ment use; aspect. These aspects are: 1) payment for stove 2) cost of the stove and 3) networking.

In the first treatment aspect sites were randomly chosen for households to receive a 50 Birr payment if the SUM devices indicated that Mirt stoves were used at least twice per week during the first monitoring period. The 50 Birr payment was made after checking the recorded SUM data at the end of the first round (about 6 weeks after installation of the SUM device). In a manner similar to Charness and Gneezy (2009), this treatment aspect tests the hypothesis that use incentives increase fuelwood savings.

The second treatment aspect is cost. One-third of the sample paid 25 Birr for their Mirt stoves and the remainder received their stoves for free. This is about 13% of the real stove cost. This treatment aspect tests the same type of hypothesis examined by Cohen and Dupas (2010) that

> 4 _Kebele_ is the lowest administrative unit in Ethiopia and translates to “peasant association.”

**12 |** P a g e
high degree of political and administrative centralization was imposed by the military regime (1964-1985), and the subsequent democratic government promoted decentralization by increasing federal transfers to states and municipalities, mostly via the 1988 Constitution.

According to The World Bank (2008a) the substantial increase in federal transfers to states and municipalities, as well as in state transfers to municipalities, could be justified on several grounds. First, as the economic cost of raising municipal taxes tends to be higher than that of federal taxes, it makes sense for more tax revenue to be collected by federal and state governments and transferred to the municipalities. Second, welldesigned conditional federal transfers can encourage states and municipalities to invest in budget items that produce positive externalities, such as education and health care. Third, transfers can be used to redistribute income from richer areas to poorer ones, thereby contributing to the reduction of regional disparities. While Brazil’s system of intergovernmental transfers has enjoyed partial success in each of these three areas, its evolution suggests that problematic are issues have also arisen.

Conditional transfers are used in Brazil to ensure that different states and municipalities are able to achieve similar levels of expenditure on selected public goods and services such as health and education. The financing of public health, for instance, is based on conditional matching grants through a large and complex system of multi-government transfers. States and municipalities are obligated to earmark a fixed percentage of their revenues for health spending, and the same is true for education. These revenues are then complemented by federal transfers. As a result, public health spending has achieved a high degree of equality among different regions of the country (Table 2).

**<u>Table 2: Public Health Expenditures by Region, 1986 and 2005</u>**

|**Region**|**% of tot**<br>**expend**|**al health**<br>**itures**|**% of n**<br>**popul**|**ational**<br>**ation**|**Per capi**|**ta index**|
|---|---|---|---|---|---|---|
||**1986**|**2005**|**1986**|**2005**|**1986**|**2005**|
|North|2.3|7.2|5.5|8.0|0.41|0.90|
|Northeast|18.1|28.1|28.8|27.7|0.63|1.01|
|Southeast|59.3|43.1|43.8|42.6|1.35|1.01|
|South|15.1|14.4|15.1|14.6|1.00|0.98|
|CenterWest|50|7.2|6.8|7.1|0.74|1.02|
|Brazil|100|100|100|100|1.00|1.00|

Source: World Bank (2008a).

# **4. Empirical Evidence at the Sub-national Level**

In order to study the cyclical behavior of sub-national revenues and expenditures, annual data on revenues, expenditures, primary balances, and gross state product have been collected for the 26 states and the Federal District of Brasilia for the period from 1990 to 2006. Revenue and expenditure data have also been broken down into different categories—total revenue, tax revenue, intergovernmental revenue transfers, sales tax revenue ( _ICMS_ ), total expenditures, primary expenditures (total expenditures excluding 8