rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7 – For Some Countries, Improving the Business Environment Should Remain a Priority** Doing Business, 2012

|**Economy**|**Ease of**<br>**Doing**<br>**Business**|**Starting a**<br>**Business**|**Paying**<br>**Taxes**|
|---|---|---|---|
|Georgia|16|7|42|
|Latvia|21|51|67|
|Macedonia, FYR|22|6|26|
|Estonia|24|44|51|
|Lithuania|27|101|62|
|Slovenia|37|28|87|
|Kazakhstan|47|57|13|
|Slovak Republic|48|76|130|
|Hungary|51|39|117|
|Armenia|55|10|153|
|Montenegro|56|47|108|
|Bulgaria|59|49|69|
|Poland|62|126|128|
|Czech Republic|64|138|119|
|Azerbaijan|66|18|81|
|Belarus|69|9|156|
|Kyrgyz Republic|70|17|162|
|Turkey|71|61|79|
|Romania|72|63|154|
|Croatia|80|67|32|
|Moldova|81|88|83|
|Albania|82|61|152|
|Serbia|92|92|143|
|Kosovo|117|168|46|
|Russian Federation|120|111|105|
|Bosnia and Herzegovina|125|162|110|
|Tajikistan|147|70|168|
|Ukraine|152|112|181|
|Uzbekistan|166|96|157|

Source: World Bank, 2012a Note: Economies are ranked on their ease of doing business, from 1 – 183. A high ranking on the ease of doing business index means the regulatory environment is more conducive to the starting and operation of a local firm. This table only shows the ECA countries within the international ranking. The rankings for all economies are benchmarked to June 2011.

23
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30
<!-- Start of picture text --> Mexico<br>110 Output<br>Labor<br>110<br>105<br>105<br>100<br>100<br>95<br>95<br>90<br>90<br>85<br>1995 1996 1997 1998 1995 1996 1997 1998<br>35 Investment Net Exports<br>10<br>30<br>5<br>25<br>0<br>20<br>−5<br>15<br>−10<br>10<br>1995 1996 1997 1998 1995 1996 1997 1998<br>Model with Efficiency Wedge Model with Labor Wedge Data<br>Mexico<br>110 Output Labor<br>110<br>105<br>105<br>100<br>100<br>95<br>95<br>90<br>90<br>85<br>1995 1996 1997 1998 1995 1996 1997 1998<br>35 Investment Net Exports<br>10<br>30<br>5<br>25<br>0<br>20<br>−5<br>15<br>−10<br>10<br>1995 1996 1997 1998 1995 1996 1997 1998<br>Model with Investment Wedge Model with Risk−Premium Wedge Data<br><!-- End of picture text --> Figure 2: Data and counterfactual models with one wedge.

27
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28
group - which is therefore a poor choice of counterfactual.

Several factors which determine program selection are not observed in our data and some of the observed factors are likely to be plagued by endogeneity and measurement error. Participation to the program depends on the application decision on the part of the household and on the selection decision on the part of the government. On the household decision, we do not have any information about whether the household has applied for benefits or not. On the government decision, we know that benefits are assigned to predetermined categories of citizens such as children, the disabled and the war veterans but we cannot identify from the survey the exact categories that correspond to each benefit. For example, we do not know the category of disability or the war veteran status. Nor, we can distinguish those variables that determine the household decision from those variables that determine the government decision. For example, having many children may be a factor that induces households to apply for benefits because poor households tend to have many children but is also a categorical criteria used by the government to assign benefits.

The social benefits program also suffers from mismanagement and not all benefits are assigned to those who are supposed to be targeted. Disability status is a gatekeeper for a whole range of benefits and it is widely recognized in Moldova that the commission that grants disability status is not sufficiently transparent in its decisional process. Endogeneity may arise due to the fact that some of the unobserved criteria used for program selection may be correlated with observed variables. Also, the proxy-means tests in place for two of the existing benefits are only subsequent to the categorical criteria and result in significant targeting error type I (non coverage of poor people) and targeting error type II (coverage of rich people). Again, these are not exceptional circumstances for an evaluation of government transfers in transitional economies but the rule.

In substance, program selection is not entirely deterministic but contains several sources of stochasticity. The social assistance scheme has also expanded during the period considered and this has occurred in concomitance with output growth and poverty reduction which means that we also need to disentangle the impact of social assistance transfers from the impact of growth.

# **5. Evaluation strategy**

Our objective is to evaluate the impact of social assistance benefits on household welfare and poverty. We use four consecutive rounds of a household budget survey (2001-2004) that covers income and consumption and which measures the social assistance benefits received by households (more on the survey in the data and variables section). We also know that the social benefits program was already on-going in our period one and expanded throughout the four year period considered.

Let _i_ be our unit of interest - the household - with _i_ = ,1 2,... _n_ ; _t_ an indicator of time with _t_ = ,1 2,,3 4 ; _Pi_ a binary variable that describes whether households participate (<sup>_P_</sup> _i_ = )1 or 6
case, since the changes over time are a function of initial conditions (through an endogeneous growth model) that also influence program placement. On correcting for this bias by controlling for the area characteristics that initially attracted the development projects, the authors found significant longer-term impacts while none had been evident in the standard _DD_ estimator.

The second example is the Pitt and Khandker (1998) study of Grameen Bank. Following my interpretation above of their method of assessing the impacts of GB credit, it is clear that the key assumption is that the returns to having land are independent of village-level GB eligibility. A bias will arise if GB tends to select villages that have either unusually high or low returns to land. It seems plausible that the returns to land are lower in villages selected for GB (which may be why they are poor in the first place) and low returns to land would also suggest that such villages have a comparative advantage in the non-farm activities facilitated by GB credit. Then the Pitt-Khandker method will overestimate GB’s impact.

The upshot of these observations is that controlling for initial heterogeneity is crucial to the credibility of DD estimates. Using PSM for selecting the initial comparison group is an obvious corrective, and this will almost certainly reduce the bias in DD estimates. In an example in the context of poor-area development programs, Ravallion and Chen (2005) first used PSM to clean out the initial heterogeneity between targeted villages and comparison villages, before applying DD using longitudinal observations for both sets of villages. When relevant, pipeline comparison groups can also help to reduce bias in DD studies (Galasso and Ravallion, 2004). The DD method can also be combined with a discontinuity design (Jacob and Lefgren, 2004).

These observations point to important synergies between better data and methods for making single difference comparisons (on the one hand) and double-difference (on the other). Longitudinal observations can help reduce bias in single difference comparisons (eliminating the additive time-invariant component of selection bias). And successful efforts to clean out the heterogeneity in baseline data such as by PSM can reduce the bias in DD estimators.

**_What if baseline data are unavailable?_** Anti-poverty programs in developing countries often have to be set up quickly in response to a macroeconomic or agro-climatic crisis; it is not feasible to delay the operation to do a baseline survey.  (Needless-to-say, nor is randomization an option.)  Even so, under certain conditions, impacts can still be identified by observing participants’ outcomes in the absence of the program after the program rather than before it.  To see what is involved, recall that the key identifying assumption in all double-difference studies is

38
# **_Table F3a: Heterogeneous Impact of COVID-19 School Closures by Men’s Perceived Norms (Conservative versus Liberal Societies)_**

|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|
|Busines|s closed||Ti|me Spent on(|Hoursper d|ay)...<br>||
|||||||are an|omesc|
|(1=Yes<br>|; 0=No)<br>|Paid<br>|Work<br>|Care and<br>Responsibi<br>|Domestic<br>lities (Self)<br>|<br>Respons<br>(Spo<br>|<br>ibilities<br>use)<br>|
|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|_MARRIED_<br>_FEMALE_|_MARRIED_<br>_MALE_|
|School closure due to COVID-19=1<br>0.151|0.002|0.159|-2.337**|2.655**|0.127|1.673*|0.617|
|[0.10]|[0.06]|[1.59]|[1.12]|[1.26]|[0.70]|[0.98]|[0.98]|
|Men's Community Norm (Aggregate 0-1)<br>0.030|0.040|2.952*|0.778|1.315|3.969***|3.539***|-3.147***|
|[0.08]|[0.07]|[1.71]|[1.22]|[1.51]|[0.82]|[1.06]|[1.08]|
|School closure due to COVID-19*Men's Community Norm<br>-0.172|-0.010|-0.072|4.088**|-2.818|0.109|-2.412*|-0.166|
|[0.15]|[0.10]|[2.32]|[1.69]|[1.82]|[1.06]|[1.43]|[1.47]|
|GDP per capita (Ln)<br>0.005|-0.027***|0.034|0.317***|0.126|-0.131**|-0.405***|0.159**|
|[0.01]|[0.00]|[0.11]|[0.09]|[0.09]|[0.05]|[0.08]|[0.08]|
|Constant<br>-0.076|0.206***|6.266***|5.342***|1.629|1.991**|4.796***|5.422***|
|[0.10]|[0.08]|[1.70]|[1.33]|[1.30]|[0.84]|[1.06]|[1.15]|
|Observations<br>11692|23578|6794|13759|11692|23578|8806|17960|
|Adjusted R-squared<br>0.10|0.09|0.07|0.09|0.13|0.04|0.06|0.07|
|Mean<br>0.14|0.13|6.83|7.95|6.38|4.09|3.60|6.21|
|Sample<br>FEMALE<br>Notes:* significant at 10% level, ** significant at 5% level, *** significant at 1% level.|MALE|FEMALE|MALE|FEMALE|MALE|FEMALE|MALE|

[1] School Closure due to COVID-19 is a dummy variable where 1 = schools closed in country at time of survey wave. Men's Community Norm is the country-level aggregate of the perceived social norm as reported by men in the sample towards gender equality expressed as a proportion between 0 and 1.

[2] OLS pooled regressions include Region of the world and Time Fixed Effects and full set of controls including Firm Controls (Sector + Size, Age and Role in Business); Mobility Controls (Movement around retail & recreation/workplaces); GDP Per Capita; and Demographic Controls (Married and Types of Dependents). Academic Break control is included in the regressions but not shown in the table.

[3] Sample is restricted to business owners who are married or living with a partner with any child dependents aged 18 years or younger in the household and is split by gender with FEMALE (Columns (1), (3), (5), and (7)) versus MALE (Columns (2), (4), (6), and (8)).

70
17 C. The Estimated Effect of Trade Reforms on Productive Efficiency Estimates of the system (1) through (3) for each industry and year are presented and discussed in appendix 1. We use the results to construct industry-specific indices of the changes in returns to scale, average efficiency level (i.e., production function intercept), and dispersion in efficiency levels between 1967 and 1979. For the jth industry (j=l, 21), these measures are: Here hats indicate estimated values, and subscripts outside brackets refer to the year of the census from which the estimates were obtained. As with our descriptive statistics of section II.C, these indices are relatively robust with respect to industry-specific distortions in estimators. Such distortions will not matter if they bias estimators by the same factor in each year, or (when comparing across industries) if the factor of bias is common to all industries.

The top panel of Table 3 presents estimated values for each parameter ratio of interest, industry by industry, along with associated PROTEC values. As in table 1, there is still no evidence of overall improvements in productive efficiency for the manufacturing sector. Of the 21 manufacturing industries analyzed, only 10 showed reductions in estimated returns to scale, only 9 showed evidence of Hicks neutral
I1 In addition, as a group, small funds invested only in half the number of companies as medium and large funds. This was their only chance to exert influence over the companies in their portfolio, given the limited number of points the small funds acquired. Of the companies held by small funds, 98% were small and medium, whereas large funds diversified their investments relatively evenly among all sizes of enterprises. In short, small investment funds, due to limited number of points and the investment strategy chosen, have a much greater ability to act as turn-around funds with stronger governance incentives. In contrast, larger funds - due to the number of points acquired and the maximum level of 20% per company - emerged with relatively less concentrated portfolios with reduced governance incentives. The analysis will discuss below to what extent the 20% limit of ownership in a single company was a binding condition for the top ten funds.

**Table 6: Rank of IPFs by book value per voucher book**

|Investment strategy|Low|-price-|shares|=|=|Hig|h-price|-shares|=|=|
|---|---|---|---|---|---|---|---|---|---|---|
|IPF 1/|YSE|PPF|AB|KB|HCC|CP|CS|CA|IB|ZB|
|Book value [CSK1,000] 2/|48.5|41.8|33.6|25.6|23.8|22.7|22.5|21.8|18.5|16.0|
|Rank by fund size 3/|10|8|9|4|3|5|1|6|2|7|
|Fund category 4/|NB|NB|DB|DB|NBB|DB|DB|FB|DB|FB|

1/ Names of funds by fund group (for bank sponsored funds categorization by sponsor): YSE=YSE; PPF=PPF; AB=Agrobanka; KB=Komercni Banka; HCC=Harvard Capital & Consulting; CP=Ceska Pojistovna; CS=Ceska Sporitelna; CA=Creditanstalt; IB=lnvesticni Banka; ZB=Zivnostenska Banka.

2/ per voucher book invested (average book value of privatized property per voucher book: CSK32,400).

3/ in terms of investment points acquired in the first wave.

4/ NB=non-bank; DB=domestic bank sponsored; FB=foreign bank sponsored. Source: Fund interviews.

Looking at our set of the top ten Czech funds one can see they behaved very distinctively with respect to the outlined high-price vs. low-price investment strategy. YSE fund acquired an average book value of shares three times as high as Zivnostenska Banka's, in other words, Zivnostenska Banka's fund spent three times as many points per share in the portfolio (see Table 6). Seven funds opted (on average) for high-price shares (low book value per voucher book) whereas only three funds tried to focus on low-price shares (high
and Shleifer, 2014). Contrary to the situation in manufacturing, other services utilize mostly laborintensive technologies and have very low emissions compared to other sectors (Figure 1). The existence of a large informal sector further reduces the use of capital, resulting in a negative relationship between emissions and informality. Relative to the case with an insignificant coefficient observed in aggregatelevel regressions, a negative correlation implies that emissions per unit of value added in the formal sector relative to the informal sector can be either higher or lower depending on the differences in their labor productivities. While there is some variation in the control variables as well, the significant ones are in line with those recorded in Table 1.

**Table 2. Sectoral Regression Results for CO2 Emissions per Output.**

||**(1)**<br>**Agriculture**|**(2)**<br>**Trade**|**(3)**<br>**Construc-**<br>**tion**|**(4)**<br>**Finance**|**(5)**<br>**Manufac-**<br>**turing**|**(6)**<br>**Mining**|**(7)**<br>**Services**|**(8)**<br>**Transport&**<br>**Communication**|**(9)**<br>**Utilities**|
|---|---|---|---|---|---|---|---|---|---|
|Share of<br>Informal<br>Workers|0.599<br>(0.66)|-0.599<br>(0.75)|-1.044<br>(1.51)|-1.838<br>(1.02)|-1.440**<br>(2.45)|-0.291<br>(0.41)|-1.77**<br>(2.24)|0.386<br>(1.33)|-1.253<br>(0.84)|
|Log of<br>Capital<br>per Worker|-0.187<br>(0.44)|0.107<br>(0.63)|-0.231**<br>(2.03)|0.004<br>(0.02)|-0.075<br>(0.43)|-0.268<br>(1.67)|0.27<br>(1.62)|0.036<br>(0.38)|-0.236<br>(0.77)|
|Share<br>of<br>Sectoral<br>VA in total VA|-11.11***<br>(5.16)|-4.75<br>(1.43)|-5.39*<br>(1.69)|-21.24**<br>(2.36)|-3.54**<br>(-2.25)|-3.41<br>(1.46)|-6.73***<br>(3.69)|-4.92**<br>(2.51)|-21.5**<br>(2.41)|
|Imported<br>Intermediate<br>Goods/Total|0.529<br>(0.25)|3.291**<br>(2.37)|2.204***<br>(2.90)|2.804<br>(1.32)|-0.634<br>(0.60)|1.605*<br>(1.73)|2.39**<br>(2.22)|1.336***<br>(4.64)|0.354<br>(0.33)|
|Number of<br>Observations|57|68|69|52|69|55|63|68|56|

Each column shows the OLS estimation results for separate regressions and different sectors. These regressions include the following control variables: natural logarithm of capital per worker, share of the respective industry value added in countries’ total value added, and share of imported intermediate goods in total intermediate goods. Meanwhile t statistics are in parentheses are in brackets. * shows significance at the 10% level, ** at the 5% level and *** at the 1% level.

The findings on sectoral differences can also help explain the environmental Kuznets curve widely discussed in the literature in relation to income and emissions. As shown in Grossman et al. (1991), the transition from an agricultural economy to a manufacturing economy to a service economy can cause an inverse-U shape in emissions in relation to development. An agricultural economy in the early stages of development has low levels of CO2 emissions, which increase as the economy grows and shifts towards manufacturing. With a further transition into a developed economy, the manufacturing sector declines, 16