rafmacalaba/datause-encoder-data huggingface dataset:
Among these balancing variables, we tried to pick variables that are likely to be correlated with the outcome based on economic theory and existing data. There is, however, a caveat. As we have seen, most economic interventions have impacts measured over periods of 6 months to 2 years. While our economic models and existing data sets often provide good information for deciding on a set of variables useful for explaining _current_ levels _,_ they are often much less useful in explaining _future_ levels of the variable of interest. In practice over short time horizons, often we can not theoretically or empirically explain many changes well with observed variables – and believe that these changes are the result of shocks. As a result, it may be the case in practice that the covariates used to obtain balance on are not strong predictors of future values of the outcome of interest.

The set of outcomes we have chosen spans a range of the ability of the baseline variables to predict future outcomes. At one end are microenterprise profits in Sri Lanka, where baseline profits and six baseline individual and firm characteristics explain only 12.2 percent of the variation in profits six months later. Thus balancing on these common owner and firm characteristics will not control for very much of the variation in future realizations of the outcome of interest. School enrolment in the IFLS data is another example where baseline variables explain very little of future outcomes. For a sample of 300 students who were all in school at baseline, 7 baseline variables only explain 16.7 percent of the variation in school enrollment for the same students 3 years later. The explanatory power is better for labor income in the Mexican ENE data and household expenditure in the IFLS, with the baseline outcome and six baseline variables explaining 28-29 percent of the variation in the future outcome. The math test scores and height z- scores in the LEAPS data have the most variation explained by baseline characteristics, with 43.6 percent of the variation in follow-up test scores explained by the baseline test score and six baseline characteristics.

We expect to see more difference amongst randomization methods in terms of achieving balance on future outcomes for the variables that are either more persistent, or that have a larger share of their changes explained by baseline characteristics. We therefore expect to see least difference among methods for the Sri Lanka microenterprise

- 16 -
**Table 12. Compulsory Targeting and Privatization Prices**

**Dependent variable is net privatization price/sales**

|Variables|||Two-Step p|rocedure|
|---|---|---|---|---|
||OLS|TOBIT|OLS|TOBIT|
||(1)|(2)|(3)|(4)|
|**_1.-Firmandprivatizationcharacteristics:_**|||||
|Nettotalliabilities|-0.0888|-0.0925|-0.0641|-0.0697|
||(0.042)|(0.044)|(0.074)|(0.069)|
|ShareSold|-0.0043|-0.0042|-0.0037|-0.0034|
||(0.002)|(0.002)|(0.002)|(0.002)|
|Foreignparticipation|0.1517|0.1659'|0.145*|0.1584|
||(0.032)|(0.029)|(0.031)|(0.028)|
|Public offering|0.095|0.1356'|0.0962|0.139|
||(0.044)|(0.041)|(0.043)|(0.041)|
|DirectSale|-0.0007|0.0461|0.0004|0.0496|
||(0.048)|(0.045)|(0.048)|(0.046)|
|**_2.-LaborCharacteristics:_**|||||
|Unions|-0.147|-0.1231|-0.17099|0.1477|
||(0.036)|(0.038)|(0.036)|(0.037)|
|Strikes|-0.0171|-0.0257|0.0278|0.0197|
||(0.027)|(0.026)|(0.050)|(0.050)|
|**_3.-LaborPolicies:_**|||||
|Compulsoryage-biaseddownsizing|-0.0211|-0.0322|-0.0137|-0.0425|
||(0.033)|(0.031)|(0.100)|(0.104)|
|Compulsoryskill-biased downsizing|0.0266|0,011|0.0624|0.0722|
||(0.046)|(0.051)|(0.038)|(0.042)|
|Compulsoryfemale-biaseddownsizing|0.0709|0.1081|-0.0504|-0.0559|
||(0.071)|(0.068)|(0.065)|(0.069)|
|Employmentguarantee|-0.1003|-0.1021|-0.0989*|-0.1024|
||(0.032)|(0.030)|(0.035)|(0.032)|
|Paycut|-0.0704|-0.0786|0.2176|0.2135|
||(0.046)|(0.045)|(0.132)|(0.142)|
|**_4.-MacroeconomicVariables:_**|||||
|GrossDomesticProduct|0.0517|0.0545|0.0499*|0.0524|
||(0.009)|(0.008)|(0.010)|(0.010)|
|Inflation|0.0001|0.0001|0.0o001|0.0001|
||(0.000)|(0.000)|(0.000)|(0.000)|
|Constant|-0.7866|-1.1795|-0.4776|-0.9101|
||(0.300)|(0.242)|(0.475)|(0.460)|
|Observations|292|308|292|308|
|R-squared<br>|0.53<br>||0.53<br>||
|F|19.49||20.14||
|Prob>F|0.000||0.000||
|PseudoR2||1.3248||1.3182|
|LRchi2<br>Prob>chi2||313.74<br>0.000||312.20<br>0.000|

The dependent variable is net privatization price/sales, defined as the amount that accrues to the government after all privatization and restructuring costs are taken into account, such as government commitments at the time of sale, and other adjustments are made to the sale contract. This number is adjusted by the percentage of company shares sold and divided by the average net sales during the three years prior to privatization. The present value of the resulting number as of December 2000 is used. Columns (1), (2) consider prior restructuring measures and the rest of variable as exogenous and provide estimates from an OLS and TOBIT regressions. Columns (3), (4) show the second stage of the two-step procedure to take account of endogeneity. All regressions include sectoral controls and firm size controls. Robust standard errors are given in parentheses. a significant at I percent; b significant at 5 percent; c significant at 10 percent.

35
_L. Fox and A.M. Oviedo_ _Are Skills Rewarded in Sub-Saharan Africa?_ Table 9 shows the distribution of the workforce at the firm level according to years of education, and the distribution implied by the worker sample. We observe a systematic shift of the distribution in the workers’ sample, with far higher shares of workers that have ore than 12 years of education than what the firm reports as the average. This is particularly the case for Kenya and Uganda. In Table 10 the bias towards skilled workers seems less evident, although more comprehensive distributions (only available in Kenya, Mali, and Senegal) suggest that managers and professionals are somewhat overrepresented in the workers’ sample. However this bias is not as marked as the one for education.

Tables 11 and 12 show union membership rates across countries and for different age brackets. The rates of unionization are not very high, which seems at odds with the picture of labor market rigidity from Doing Business discussed above. However, several studies of the African labor market point out that unions have by-and-large lost ground since the 1980s.<sup>23</sup> INSERT TABLES 11 AND 12

**Figure 4**

# **Tenure years by union membership and worker age**

<!-- Start of picture text --> 4.0<br>2.0<br><!-- End of picture text --> <!-- Start of picture text -->
[20-29] [30-39] [40-49]<br>Worker age<br><!-- End of picture text --> <!-- Start of picture text -->
[40-49] [50-59] 60+<br><!-- End of picture text --> <!-- Start of picture text --> 0.0<br><!-- End of picture text --> Source: Enterprise Surveys There is substantial variation across countries, however, with very high rates in East and Southern Africa, and lower rates in Francophone countries (with the highest rate observed in Cape Verde). Unionization increases with worker age and tenure, which may be one of the reasons for the high severance pay costs in Anglophone countries (see Figure 4). Older firms have a more unionized labor

> 23 See Mazumdar and Mazaheri (2002), Fox and Gaal (2008). However, this does not imply that union members receive equal treatment as compared to non-members. See Section VI.

11
_Table A2.4 Weighted Intention‐to‐treat effects of the original intervention on main psychological and behavioral outcomes._

||(1)<br>ITT<br>(no controls)|(2)<br>ITT<br>(controls)|
|---|---|---|
|_Main outcomes – Ps_|_ychological and beha_<br>|_vioral_<br>|
||‐0.036|‐0.022|
|Psychological wellbeing index|(0.476)|(0.646)|
|Risk index|‐0.054|‐0.062|
||(0.394)|(0.292)|
|Trust index|‐0.013|‐0.033|
||(0.811)|(0.548)|
|Crime and violence index|‐0.110**|‐0.119**|
||(0.029)|(0.023)|
|Migration index|‐0.045|‐0.036|
||(0.363)|(0.482)|
|List experiment cattle index|0.215**|0.210**|
||(0.034)|(0.037)|
|List experiment argument index|‐0.125|‐0.120|
||(0.179)|(0.201)|
|Observations||1495|

|1523|
|---|

_Note: Observations are weighted by their inverse likelihood to be in the final sample, based on who was easy to reach during the phone survey. All regression control for gender‐state fixed effects. Control variables of column (2) include all baseline controls that were significant determinants of attrition and of selection between receiving “training and grant” vs “training, but no grant”. In particular, these are age, marital status, employment status, business ownership, food consumption, non‐food consumption, formal bank account, formal loans, informal loans, education level, literacy level, numeracy level, household size, number of children, number of rooms, and number of buildings at baseline. P‐values are in parenthesis displayed below the estimated coefficients. * (**, ***) indicates statistical significance at the ten‐percent (five‐percent, one‐percent) level._ 40
<!-- Start of picture text --> Figure 3<br>Forecasted fossil energy share in 2010 according to the year of publication Forecasted shares Forcasted share =<br>0.68 x Publication Year - 1280.4<br>Robust Tukey smoother<br>R 2 = 0.51<br>Linear regression line<br>100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Fossil energy share (%x100)<br><!-- End of picture text -->
where _yit_ is the outcome of interest for country _i_ in year _t_ . _αi_ and _γt_ control for time-invariant, crosscountry differences in outcomes and year-to-year common changes to outcomes, respectively. The indicator variables 1 [ _RTAs,it_ ] measure time-since-enactment _s_ of any RTA<sup>12</sup> while 1 [ _enviros,it_ ] measure time-since-enactment _s_ of an RTA that includes environmental provisions under consideration— hence, _δs_ will measure the dynamic effects of the entry into force of RTAs without environmental provisions, _δs_ + _ξs_ will measure the dynamic effects of RTAs with environmental provisions, and _ξs_ will capture the marginal impacts of provision inclusion. RTAs with and without provisions enter into force at different times for different countries allowing us to separately identify the _δs_ and _ξs_ coefficients from the year fixed effects, as well as from each other. Countries may enact more than one RTA in our sample period which differentiates our event study framework from the RTA-level triple difference model above as well as other common applied microeconomic settings in which an event only occurs once per individual within the sample window. We allow for overlapping events in our framework and normalize all estimates to the period before enactment by omitting the corresponding indicator variable.<sup>13</sup> We also include long-run RTA indicators that correspond to four or more years before/after entry into force of an RTA and allow these long-run indicators to vary by the presence of relevant environmental provisions.

We briefly contrast this country-level approach to our agreement-level approach outlined in Section 3. Because countries enact more than one RTA in the sample, the triple difference approach is not viable in this setting. In the RTA-level approach, individual countries may enter into multiple RTAs with different trading partners at different times, but the entire RTA group enacts an RTA only once in the study window. Because of this, our country-level approach estimates the dynamics around RTA enactments—a so-called “overlapping” or “multiple” event study because treatment events can overlap in timing—and allows them to differ based on the inclusion of relevant provisions. As in our RTA-level analysis, the coefficient estimates still measure net forest loss among all counterparties; an increase in one country’s annual forest loss can be offset by a decrease in

> 12For example, if an RTA was enacted in _t−_ 1, 1 [ _RTA_ 1 _,it_ ] = 1. If an RTA is enacted in _t_ +2, then 1 [ _RTA−_ 2 _,it_ ] = 1.

> 13While there are a few options to dealing with multiple events that may occur in the same window, Sandler and Sandler (2014) argue that the approach employed here (allowing multiple indicators to be ones at the same time) is preferred to other approaches dealing with multiple events (i.e. duplicating observations, limiting treatment to first event, etc.). Other approaches may incorrectly generate pre-treatment or post-treatment trends.

23
For examnple, Minot (1998 and 2000) used the 1992-93 Vietnam Living Standards Survey and a probit model to estimate the likelihood of poverty for rural households as a function of a series of household and farm characteristics. District-level means of these same characteristics were then obtained from the 1994 Agricultural Census and inserted into this equation, generating estimates of rural poverty for each of the 543 districts in the country.

Hentschel _et al._ (2000) developed a similar method using survey and census data from Ecuador. Using log-linear regression models and household-level data from a census, they demonstrate that their estimator generates unbiased estimates of the poverty headcount and show how to calculate the standard error of the poverty headcount.1 This approach has been applied in a number of other countries including Panama and South Africa (see World Bank, 2000; Statistics South Africa and the World Bank, 2000).

The earlier Vietnam study has several limitations. First, since it relied on the Agricultural Census, it generated poverty estimates only for the rural areas. Second, the use of a probit regression and district-level means, although intuitively plausible, does not necessarily generate consistent estimates of district-level poverty2. Third, in the absence of household-level census data, it was not possible to estimate the standard errors of the estimates to evaluate their accuracy.

# **1.2 Objectives**

Accordingly, this paper has three objectives. First, it explores the household factors associated with poverty in Vietnam using the 1998 Vietnam Living Standards Survey (VLSS). In this task,

> I The poverty headcount is defined as the proportion of the population with per capita expenditures below the poverty line.

> 2 Minot and Baulch (2002) show that using aggregated census data underestimates the incidence of poverty when it is below 50 percent and overestimates it when it is above 50 percent. The absolute size of the error, however, can be as low as 2-3 percentage points in some circumstances.

3
# **Table 1b MA-OTRI and MA-TTRI by Income Group, 2006**

|**Importers**|**High**<br>**income**|**Upper**<br>**middle**<br>**income**|**Lower**<br>**middle**<br>**income**|**Low**<br>**income**|**East**<br>**Asia and**<br>**Pacific**|**Europe**<br>**&Centr.**<br>**Asia**|**Latin**<br>**America**<br>**and Car.**|**Middle**<br>**East &**<br>**N. Afri.**|**South**<br>**Asia**|**SSA**|
|---|---|---|---|---|---|---|---|---|---|---|
|High|**6.3**|**5.7**|**7.9**|**9.1**|**8.3**|**5.1**|**7.0**|**4.3**|**10.4**|**4.4**|
|income|_2.7_|_1.2_|_2.5_|_2.4_|_2.6_|_1.1_|_1.5_|_0.8_|_3.1_|_0._**7**|
|UAD|**6.3**|**5.2**|**8.6**|**10.6**|**8.9**|**5.2**|**6.9**|**4.4**|**13.6**|**4.5**|
|Q|_2.1_|_0.9_|_2.5_|_2.5_|_2.7_|_0.8_|_1.2_|_0.5_|_3.3_|_0.5_|
|Upper<br>iddl|**15.6**|**11.8**<br>|**15.8**|**14.7**|**19.2**|**10.2**|**13.6**|**6.0**|**14.3**|**5.9**|
|me<br>income|_5.6_|_3.8_|5_.6_|_5.7_|_7.2_|_4.4_|_2.6_|_2.5_|_6.6_|_3.5_|
|Lower<br>middle|**12.4**|**11.1**<br>|**12.9**|**9.4**|**13.6**|**11.2**|**12.6**|**6.7**|**9.9**|**4.0**<br>|
|income|_7.1_|_4.8_|_6.7_|_5.1_|_6.6_|_6.2_|_5.1_|_2.8_|_6.2_|_2.7_|
|Low|**18.2**|**14.3**<br>|**19.5**|**25.4**|**22.2**|**17.7**|**15.9**|**16.3**|**16.2**|**16.3**<br>|
|income|_10.9_|_8.1_|_12.2_|_12.9_|_13.8_|_6.2_|_9.0_|_10.0_|_10.4_|_12.2_|

_Note_ : MA-TTRI is in italics; MA-OTRI is in boldface font. _Source_ : Global Monitoring Report (2008), World Bank.

# **_Customs Procedures_**

In a broad context, national customs administrations are in charge of implementing a country’s trade policy at the border. This involves, for example, levying tariff duties, verifying conformity of imported goods with regulatory requirements, and preventing the importation of prohibited or unsafe imports (e.g., illegal weapons or out-of-date medicines).

Delays in customs clearance raise trade costs. This involves opportunity costs for firms that are slower to market and may lose contracts with importers, as well as higher storage fees at the port of entry, for example. Djankov, Freund, and Pham (2008) find that each day of delay at customs is equivalent to a country distancing itself from its trading partners by an additional 85 km. Keeping customs procedures as simple and transparent as possible contributes to reducing the time needed to clear customs.

10
<!-- Start of picture text --> .13<br>.4 model (all firms) .09<br>.37<br>.34 .05<br>.31 .01<br>.28 −.03<br>.25<br>−.07<br>.22<br>.19 −.11<br>.16 −.15 data<br>.13 model (rest. sample) −.19<br>.1<br>−.23<br>.07<br>−.27<br>.04<br>data model (all firms)<br>.01 −.31<br>−.02 −.35<br>−1 0 1 2 3 4 −1 0 1 2 3 4<br>years since procurement shock years since procurement shock<br>(a) credit (b) sales to the private sector<br>cumulative growth rate cumulative growth rate<br><!-- End of picture text -->

**Notes:** This figure shows the cumulative impact of the estimate of _β_ 2<sup>_h_(anditsassociated10%confidencebands)</sup> from regression (1) for different time horizons _h_ = 0 _,_ 1 _,_ 2 _,_ 3 _,_ 4 _._ Panel (a) shows the results for the case of _x_ being firms’ total sales. Panel (b) shows the results for the case of _x_ being firms’ sales to the private sector. The line “data” shows the estimated coefficients using the real data, i.e., same results as in panel (a) of Figure 1. The line “model (all firms)” shows the results when using all firms in our simulated sample. The line “model (rest. sample)” shows the results when restricting the simulated sample to observations where credit growth is below the top 10 % and above the bottom 10%.

**Figure 8.** Procurement effect on credit and private sales

respect, both in the model and in the data, we observe a similar long-lasting effect of procurement on credit.

We note that the sharp peak observed at _h_ = 0 is mostly driven by a set of very few unconstrained firms that dramatically increase credit to expand their operations at the time of the shock, and decrease it afterwards. That is, procurement represents a large demand shock for these firms, and by the timing assumptions in our model, the only source to finance the firm’s capital expansion is debt. For example, within the set of firms that are unconstrained throughout the simulated sample, 25% of them have their credit increased by 4 times after receiving a procurement shock. These unconstrained firms operate with a capital stock which is very similar to their net worth (with credit being close to zero). When they get a procurement contract, their optimal capital stock increase at impact leads to very large percentage changes in credit. On the other hand, for constrained firms, the highest increase in credit observed in our model-simulated sample is around 85%. Our model performs significantly better once we remove the extremely responsive firms from our simulated sample. For example, the increase in credit at _h_ = 0 becomes around 9.5% once we remove the bottom and top 10% observations in terms of the change in credit. In reality, the unconstrained firms have access to other sources of funds, such as drawing down their cash positions or retaining more earnings (paying fewer dividends), so one should not expect to see such a large unconditional spike in the data. Given that the focus of our study is on the interplay between financial frictions 30
|r-squared|0.671|0.885|0.882|
|---|---|---|---|
|adjusted r-squared|0.639|0.827|0.828|
|Observations|156|156|156|

NOTES: Columns (1)–(3) show the OLS regression results with LASSO-selected controls. Robust standard errors are in parentheses. * 0.1 ** 0.05 *** 0.01. Column (4) shows the contribution of each group of realtime indicators to the overall goodness of fit of each regression, using Shorrocks–Shapley decomposition of r-squared.

37
7

**Table** 1: SklUmary **or** Data oan Clsses and **Clssretoms** in **BuWgkia's** Munkipaities

|Y,|123.34|198.06|8|1694|
|---|---|---|---|---|
|(nuimber oftotalclassos in towns)|||||
|r,|64.51|-49-.33--70|_|399|
|(number ortoutlclasses in village)|||||

The problem at hand is to explore whether it is possible to expand the number of classes in towns and villages by a common rate, subject to the constraint that the number of classrooms required, is less than or equal to the existing number of classrooms in towns and villages.

# III. Results of DEA Analysis

Table 2 presents the distribution of efficiency scores. Most observations fall **ih:** the score range between 0.60 and 0.89, the total of 147 observations or 74% of the total. The total of 41 observations, or a little over 20%, has the efficiency scores of between 0.9 and 1.0. Of these, 22 observations or 11% of the total are the frontier points with highest efficiency score of 1.0. On the lower end of the distribution, we find 11 observations or 5.6% of the total.
|Table1 Estimates of futu<br>|re disasterp<br>|robabilities<br>|
|---|---|---|
|Number of incidences:|None|Once Twice Three times|
|Bangladesh: Flood<br>|0<br>(453)|0.14 0.29<br>(323) (7)|
|Ethiopia: Drought<br>|0<br>|0.20 0.40 0.60<br>|
||(594)|(394) (215) (54)|
|Malawi: Drought<br>|0<br>(389)|0.25 0.50 0.75<br>(228) (101) (36)|

Numbers of households are shown in parentheses. Probabilities are defined as the empirical average of disaster incidences (measured yearly) in the period between the initial round and the final round.

23
|**tive VAT p**|**rogressivity a**<br>Reynolds-|**nd redistribu**<br>Kakwani|**tion in O**<br>t<br>|
|---|---|---|---|
||Smolensky||1 −t|
|AUT|0.0056|0.0428|<br>0.1300|
|BEL|0.0082|0.0738|0.1117|
|CHE|0.0020|0.0419|0.0469|
|CHL|-0.0009|-0.0080|0.1099|
|CZE|0.0018|0.0114|0.1572|
|DEU|0.0049|0.0477|0.1017|
|DNK|0.0064|0.0382|0.1665|
|ESP|0.0032|0.0401|0.0799|
|EST|0.0024|0.0153|0.1558|
|FIN|0.0043|0.0346|0.1237|
|FRA|0.0040|0.0457|0.0867|
|GBR|0.0029|0.0277|0.1059|
|GRC|0.0040|0.0394|0.1010|
|HUN|-0.0010|-0.0059|0.1651|
|IRL|0.0019|0.0215|0.0876|
|ITA|0.0063|0.0822|0.0766|
|LUX|0.0031|0.0464|0.0667|
|LVA|-0.0001|-0.0007|0.1550|
|NLD|0.0054|0.0561|0.0961|
|NZL|-0.0025|-0.0188|0.1301|
|POL|0.0069|0.0562|0.1228|
|PRT|0.0039|0.0378|0.1026|
|SLV|0.0053|0.0478|0.1103|
|SVK|0.0010|0.0067|0.1520|
|SWE|0.0074|0.0631|0.1175|
|TUR|0.0010|0.0106|0.0988|
|**Average **|**0.0034**|**0.0328**|**0.1138**|

**Table 2: Effective VAT progressivity and redistribution in OECD countries**

Source: Thomas (2020)

# 2.3.3 Other taxes

Effective progressivity studies can also be undertaken for excise taxes (including health- and energyrelated taxes) where microdata is available on consumption by different households across products subject to excise taxation. Excise taxes are often found to be regressive, although this is not always the case (e.g., Flues and Thomas, 2015, find transport fuels in some OECD countries to have some progressive impact). Furthermore, beyond the pure fiscal impact, health- and energy-related excise taxes, through eliciting behavioral changes, can have broader progressive impacts (see, e.g., Fuchs et al., 2019); Heine and Black, 2019; Sassi et al., 2018).

16