rafmacalaba/datause-encoder-data huggingface dataset:
_achievement of the millennium development goals (MDGs) and other internationally-agreed development goals_ [Technical report]. https://digitallibrary.un.org/record/840707?ln=en

- ---. <mark>(2022). Human Development Report 2021-22.</mark> _<mark>Human Development Reports</mark>_ <mark>.</mark>

- ---. (2023). Sustainable Development Goals no. 17. Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development. <u>https://sdgs.un.org/goals/goal17</u>

- Visser, H., Bruin, S., Martens, A., Knoop, J., & Ligtvoet, W. (2020). What users of global risk indicators should know. _Global Environmental Change_ , _62_ , 102068.

- <mark>Wolf, M. J., Emerson, J. W., Esty, D. C., de Sherbinin, A., Wendling, Z. A.,</mark> _<mark>et al.</mark>_ <mark>(2022).</mark> _<mark>2022 Environmental Performance Index.</mark>_ <mark>New Haven, CT: Yale Center for Environmental Law & Policy.</mark>

- Wollburg, P., Contreras, I., Carletto, C., Morales, L. G., Perucci, F., & Zezza, A. (2022). The uneven effects of the COVID-19 pandemic on national statistical offices. _Statistical Journal of the IAOS_ , _38_ (3), 785–803.

World Bank. (2020). _Statistical capacity indicators_ .

<u>https://datatopics.worldbank.org/statisticalcapacity/</u>

- ---. (2021). _World development report 2021: Data for better lives_ . World Bank. <u>https://wdr2021.worldbank.org/</u>

- Yount, K. M., Johnson, E., Sharma, K., Meinzen-Dick, R. S., & Sinharoy, S. (2022). _Conceptualizing and measuring women’s empowerment: Insights from country stakeholders in Asia_ . https://doi.org/10.2139/ssrn.4088823.

28
# **References**

- **Al-Karablieh, Yazan, Evangelos Koumanakos, and Stefanie Stantcheva.** 2021. “Clearing the Bar: Improving Tax Compliance for Small Firms Through Target Setting.” _Journal of International Economics_ , 130(1): 103452.

- **Andresen, Martin Eckhoff, and Emily Nix.** 2022. “What Causes the Child Penalty? Evidence from Adopting and Same-Sex Couples.” _Journal of Labor Economics_ , 40(4): 971– 1004.

- **Angelov, Nikolay, Per Johansson, and Erica Lindahl.** 2016. “Parenthood and the Gender Gap in Pay.” _Journal of Labor Economics_ , 34(3): 545–579.

- **Artavanis, Nikolaos, Adair Morse, and Margarita Tsoutsoura.** 2016. “Measuring Income Tax Evasion Using Bank Credit: Evidence from Greece.” _Quarterly Journal of Economics_ , 131(2): 739–798.

- **Bana, Sarah H., Kelly Bedard, and Maya Rossin-Slater.** 2020. “The Impacts of Paid Family Leave Benefits: Regression Kink Evidence from California Administrative Data.” _Journal of Policy Analysis and Management_ , 39(4): 888–929.

- **Bartel, Ann P, Maya Rossin-Slater, Christopher J Ruhm, Meredith Slopen, and Jane Waldfogel.** 2024. “The Impact of Paid Family Leave on Employers: Evidence from New York.” _Community, Work & Family_ , Forthcoming.

- **Baum, Charles L., and Christopher J. Ruhm.** 2016. “The Effects of Paid Family Leave in California on Labor Market Outcomes.” _Journal of Policy Analysis and Management_ , 35(2): 333–356.

- **Bergolo, Marcelo, and Guillermo Cruces.** 2014. “Work and Tax Evasion Incentive Effects of Social Insurance Programs: Evidence from an Employment-Based Benefit Extension.” _Journal of Public Economics_ , 117: 211–228.

- **B ́ır ́o, Anik ́o, D ́aniel Prinz, and L ́aszl ́o S ́andor.** 2022. “The Minimum Wage, Informal Pay, and Tax Enforcement.” _Journal of Public Economics_ , 215: 104728.

- **Bjørneby, Marie, Annette Alstadsæter, and Kjetil Telle.** 2021. “Limits to Third-Party Reporting: Evidence from a Randomized Field Experiment in Norway.” _Journal of Public Economics_ , 203: 104512.

- **Carneiro, Pedro, Katrine V. Løken, and Kjell G. Salvanes.** 2015. “A Flying Start? Maternity Leave Benefits and Long-Run Outcomes of Children.” _Journal of Political Economy_ , 123(2): 365–412.

- **Cort ́es, Patricia, and Jessica Pan.** 2023. “Children and the Remaining Gender Gaps in the Labor Market.” _Journal of Economic Literature_ , 61(4): 1359–1409.

12
production, and Chad increases its employment share in agriculture. However most changes are expected to happen between subnational regions within country through worker reallocation across sectors and between regions. In this case, the aggregate changes from relocation between sectors across regions are limited because of large welfare gains from lower prices in poorer places reducing the incentives of workers to move out of the most agricultural places.

Table 7: Change in share of sectoral employment in counterfactual scenarios relative to baseline (in percentage points)

||Agricultu|ral secto<br>|r<br>|||
|---|---|---|---|---|---|
|Scenarios||Total|Cameroon|Nigeria|Chad|
|Transport (Road and Rail)||0|0.3|0|-0.1|
|Rural Electrifcation||0.9|0.8|0.7|2.5|
|Transport + Electrifcation||0.9|1.5|0.7|2|
|Transport + Border||-0.4|0.1|-0.6|1.8|
|Transport + Electrifcation|+ Border|0.5|1.3|0|4.8|
|<br><br>|<br>Manufactu|ringsec<br>|tor<br>|||
|Scenarios||Total|Cameroon|Nigeria|Chad|
|Transport (Road and Rail)||0|-0.1|0|-0.1|
|Rural Electrifcation||0.1|0.6|0.1|-0.2|
|Transport + Electrifcation||0.1|0.5|0.1|-0.2|
|Transport + Border||-0.3|0.5|-0.3|-1.6|
|Transport + Electrifcation|+ Border|-0.2|1.3|-0.2|-2|
||Service|s sector<br>||||
|Scenarios||Total|Cameroon|Nigeria|Chad|
|Transport (Road and Rail)||0|-0.2|0|0.1|
|Rural Electrifcation||-1|-1.5|-0.9|-2.2|
|Transport + Electrifcation||-1|-2|-0.8|-1.8|
|Transport + Border||0.7|-0.6|0.9|-0.2|
|Transport + Electrifcation|+ Border|-0.3|-2.6|0.2|-2.8|

**Welfare impacts** First we compute the nominal GDP impacts measured as the total nominal incomes.

with _Incomen,c_ the total nominal income in location _n_ of country _c_ . Change in national incomes do not include gains in purchasing power from lower prices.

Welfare is defined as the real income available for workers in a specific location, with nominal wages deflated by the prices for goods and housing across locations as well as an amenity from living in different places. We then compute the total welfare impact in each counterfactual and compare it to the baseline welfare: 29
Berglof and Bolton (2002), Bonin and Wachtel (2003), EBRD (2005) are among these few contributions. In this section of the paper our aim is to provide a broad picture by taking advantage of the cross-country and time-series nature of the BEEPS dataset. How has the transition process changed this important aspect of the business environment? How do the transition countries compare with the developed market economies? These are the questions that are addressed in this section.

The BEEPS surveys are very rich in questions on access to and cost of finance, relationships with lenders, financial problems of firms, etc. The BEEPS data show, for example, that when firms are asked about access to and cost of finance, firms in the pre2001 EU members have fewer problems than those in TEs, but the problems reported by firms in TEs have been declining since 1999, particularly in the SEE and CIS countries. In TEs, large firms, firms in major cities, and foreign-owned firms all face lower obstacles to obtaining finance than other firms. The pattern in the developed market economies is similar, with an interesting exception: firms in large cities in Germany and the cohesion countries do not report financing obstacles that are any different to those in smaller cities or rural areas. We take this as evidence of incomplete within-country integration in the transition economies. In these countries, the large cities grow and catch-up faster than the rest of the country, and the poorer areas lag behind, whereas the developed market economies are financially integrated internally. Similar patterns have been found in previous studies, e.g., EBRD (2005).

The picture painted by the more quantitative data is similar: privatized and new private firms in TEs have lower costs of credit than state-owned firms; small firms pay more; exporters and firms in big cities pay less. We also find that the terms of loans are, not surprisingly, longer in Germany (I) and the cohesion countries (II), followed by the EU8 members (III); and that the costs of loans have been declining in TEs between 1999 and 2005, and are approaching the levels seen in Germany and the cohesion countries. In short, we again see a pattern of convergence.

29
The corresponding studies of firms in China yield mostly insignificant estimates of the effects of private/non-state ownership on labor productivity, with only one estimate being positive. Overall, the effects of all types of private ownership on labor productivity (not controlling for non-labor inputs) are hence found to be positive or insignificant in CEE and CIS, and mostly insignificant in China.

# _4.5 Employment_

The effect of privatization on employment, like on revenues, is an indicator of the extent of restructuring brought about through privatization. As such, it provides an important empirical link to the theoretical models of transition.

Seventeen studies have examined the effect of ownership on employment or rate of change of employment, with thirteen of them tackling the issue of endogeneity/selection. The estimates indicate that there is a tendency for privatized firms, especially those with foreign owners, to increase or not to reduce employment relative to firms with state ownership, _ceteris paribus_ , where the control variables usually but not always include output (sales) and/or output and input prices. This positive or insignificant employment effect is very different from the negative employment effect found in the Mexican privatized firms by LaPorta and Lopez-de-Silanes (1999).

In general, employee ownership and control do not have a significant effect on employment, providing parallel evidence to the TFP studies that this form of ownership does not result in excess employment.

Using a large 1980-90 sample of firms in China, Julia Lane, Harry Broadman, and Inderjit Singh (1998) find a negative effect of the state and collective ownership on both job creation and job destruction.

The studies of employment hence find that privatization in the post-communist economies and China is not associated with a reduction in employment, a phenomenon that is assumed in many theoretical models and which was documented in some developing countries (e.g., Mexico). On the contrary, private owners tend to keep employment at higher levels than SOEs, _ceteris paribus_ .

_4.6 Wages_ 26
_The Optimality of Being Efficient_ _11_ the demand curve, bidder 1 begins receiving quantity, which causes a large jump up in _MRR,_ since bidder I has a high marginal revenue. Since the area in triangle A is bigger than the area in triangle B, the seller continues to award quantity until _qR_ is reached.

Figure 1 <!-- Start of picture text --> pnce<br>S<br>MRR D<br>0<br>q 1 quantty<br>MR2 R, 1 2<br><!-- End of picture text --> THEOREM 3. _Consider the mechanism (q,x) with q(t) as specified below and x(t) chosen to satisfy incentive compatibility such that the lowest type of each bidder gets a payoff of 0. Then:_

- _(i) q *(t) solves the unconstrained optimal auction ifMR is monotone and regular._

- _(ii) qR(t) solves the resale-constrained optimal auction if value is regular and MR is monotone._

- _(iii) q-R (t) solves the efficiency-constrained optimal auction if value is monotone and regular._

> PROOF. (i), (ii), and (iii) follow from Theorem 1, provided _qi(ti,t-i)_ is weakly increasing in ti in each case. In case (i), from MR regularity, as i's type increases its MR ranking improves. Since quantity is assigned in order of marginal revenue, _qi(ti,t-i)_ is weakly increasing in _ti._ In cases (ii) and (iii), from value relgularity, as i's type increases its value ranking improves. Since quantity is assigned in order of marginal value, _qi(ti,t-i)_ is weakly increasing in _ti._
- 17 -

may be anticompetitive.<sup>10</sup> Especially if foreign firms rely on consortium bidding among themselves, their bids are likely to be much higher (than the baseline). Conversely, joint bidding involving local and foreign companies looks pro-competitive. The coefficient is estimated at -6.83. Moreover, the seventh column model with multiple joint bidding variables reveals the fact that local firms have the cost advantage in road procurements but joint bidding is not always a good option. If it involves at least one local firm, joint bidding is procompetitive. When it is also composed of foreign firms, additional competitiveness could be realized.

A statistical concern is endogeneity associated with the joint bidding decision. To deal with this problem, the treatment effect models are performed. The first stage regressions are generally satisfactory in all sectors. Not surprisingly, the joint bidding strategy is motivated by the size of contract. As the engineering cost estimate increases, the probability of a firm bidding jointly—regardless of coalition types—tends to increase. To a lesser extent the probability of joint bidding increases with expected contract duration. In many cases, moreover, experienced firms with one or more awarded contracts in the past are prone to relying on joint bidding practices. This may be counterintuitive from the point of view of the resource restraint hypothesis. Rather, it is consistent with the “lemon market” view. This can be interpreted to mean that inexperienced firms are faced with a typical adverse selection problem, failing to find joint bidding partners.

The second stage results are shown in Table 5. It has been found that the OLS estimates were upwards biased, since the estimated covariance _ρ_ is positive.<sup>11</sup> But the inferred bias may not be so serious; _ρ_ is not statistically different from zero. It is confirmed that only the local and foreign collaboration is pro-competitive. Joint bidding involving only foreign companies

> 10 Recall that this is consistent with the fact that joint bidding practices are relatively rare in road procurements (Figure 2).

> 11 This means that the joint bidding effect estimated by OLS is underestimated. Recall that the true effect _α_ is presumably negative if it is pro-competitive.
# **Figure 1**

**Commodity Price Indices (Real, MUV‐deflated, 2000=100)**

<!-- Start of picture text --> Korean<br>war<br><!-- End of picture text --> <!-- Start of picture text --> Oil<br>shocks<br><!-- End of picture text --> <!-- Start of picture text --> Recent<br>boom<br><!-- End of picture text -->

# **Figure 2**

**Real GDP Growth (Annual Percent Change)**

<!-- Start of picture text --> 8<br>Developing countries<br>6<br>4<br>2<br>0<br>‐2<br>‐4<br>1965 1970 1975 1980 1985 1990 1995 2000 2005 2010<br>Source: World Bank<br><!-- End of picture text -->

**_Page 27_**
14

**Table 3: Effort and Task Motivation, Controlling for Observables**

|Dependent variable:|Effort (Nu|mber of rou|nds)||
|---|---|---|---|---|
||I|II|III|IV|
|Treatment: Slider|0.048|-0.010|0.051|0.270|
||(0.29)|(0.31)|(0.24)|(0.24)|
|Treatment: Medical|3.592***|3.633***|3.718***|4.343***|
||(0.58)|(0.60)|(0.62)|(0.63)|
|Training: Midwife||-0.609|-0.331|-0.217|
|||(1.35)|(1.26)|(1.44)|
|Training: Doctor||-1.772*|-2.326**|-2.642**|
|||(0.92)|(1.04)|(1.15)|
|Education level (years)||0.837|0.875|0.858|
|||(0.59)|(0.61)|(0.63)|
|Female|||-0.842|-0.617|
|1 = Female|||(0.58)|(0.58)|
|Age (years)|||-0.041|-0.066|
||||(0.05)|(0.05)|
|Current state of personal finances|||0.055|0.268|
|4 = Excellent|||(0.46)|(0.42)|
|Risk preferences||||0.695**|
|5 = Risk seeking||||(0.27)|
|Clarity of instructions||||0.649***|
|5 = Always clear||||(0.11)|
|Constant|1.757***|-0.345|1.088|-3.201|
||(0.38)|(1.64)|(2.57)|(2.97)|
|Sigma|||||
|Constant|3.404***|3.375***|3.356***|3.210***|
||(0.38)|(0.39)|(0.37)|(0.36)|
|Pseudo R2|0.042|0.045|0.047|0.063|
|Log Likelihood|-342.6|-341.6|-340.8|-335.0|
|P|0.000|0.000|0.000|.|
|Observations|131|131|131|131|
|Right censored observations|4|4|4|4|

Note: * p<0.1, ** p<0.05, *** p<0.01. Dependent variable is effort (number of rounds respondent chose to continue the task). Tobit specification with upper censors at 16 (the maximum rounds subjects continue prior to auto-exit), with clustered standard errors (by day) in parentheses. Table reports regression coefficients. The data are restricted to treatments with no mission motivation (consistent with figure 2). Results are robust to an OLS specification.

The results are displayed in Table 3. The central findings are in the first and second rows, which compare the effort expended by subjects on the slider or medical tasks relative to the (omitted) blank screen task. Across all specifications, subjects expended more effort on the highmotivation Medical task relative to the Blank task ( _p_ <0.01 across all specifications). There are no differences in effort between the low-motivation Blank and Slider tasks ( _p_ =0.87 in model I – no controls – and _p_ =0.26 in model IV – full set of controls).
The research agenda on income inequality in China (and other developing counties) could productively move in the following direction. A distinction can be made between inequality that is based on rewards for productive characteristics and inequality that is based on market discrimination or segmentation and unequal access to income opportunities. In 2004, Whyte (2010) conducted a sociological survey of Chinese attitudes toward income inequality and concluded that Chinese people were not averse to the degree of inequality that they observed, particularly if it was based on merit, effort, or risk taking. Indeed, income inequality appeared to offer people incentives or other opportunities for improving their economic positions. This interpretation corresponds to the first stage of the “tunnel effect” (see below) hypothesized by Hirschman and Rothschild (1973).

By contrast, inequality based on unfairness or inequity in access to opportunities was generally disliked. Whyte (2010) found that farmers, despite being the poorest group, were the least discontented. Actual income is not necessarily a good guide to perceived distributional injustice because people’s information sets and aspirations not only matter but also vary. It is an important question whether China will enter the second stage of Hirschman’s tunnel effect—that is, whether or when a critical mass of people will begin to see inequality not as a sign of available opportunities but as a sign of unequal opportunities and distributional injustice.

For the first quarter century of economic reform, China’s leaders gave overriding priority to the achievement of rapid economic growth, even at the cost of rising 27
recommendations such as marketization, privatization, and opening up (Yao 2015; Brandt et al. 2016; Megginson, 2017; Wu, 2018; Zhang 2019). Zhang (2019), for instance, offers evidence that _improvement_ in the level of marketization is strongly and positively associated with China’s provincial GDP growth rates. Relatedly, Brandt, Kambourov and Storesletten (2016) offered evidence that the reduction of the state sector contributed to narrowing regional disparity in the past two decades. Understanding the extent and effects of privatization is thus critically important for guiding other developing countries to achieve economic growth, and understanding what China needs to do next. Indeed, Rodrik (2006) implicitly interpreted China’s experience in the 1990s as evidence against the importance of privatization for economic success as follows: “Rapid economic growth in China, India, and a few other Asian countries has resulted in an _absolute_ reduction in the number of people living in extreme poverty. The paradox is that that was unexpected too! China and India increased their reliance on market forces, of course, but their policies remained highly unconventional. With high levels of trade protection, _lack of privatization_ (emphasis added), extensive industrial policies, and lax fiscal and financial policies through the 1990s, these two economies hardly looked like exemplars of the Washington Consensus. Indeed, had they been dismal failures instead of the successes they turned out to be, they would have arguably presented stronger evidence in support of Washington Consensus policies” (Rodrik 2006, p. 975).

Given the importance of sound policy recommendations based on the Chinese experience, the continued misunderstanding of China’s privatization, it is important to identify the causal effect of privatization in China. This requires using comprehensive Chinese SOE data and a credible identification strategy.

Importantly and fortunately, the manner in which Chinese SOEs were selected for privatization grants us a unique opportunity to identify the causal effects of privatization. The literature has focused on the selectivity of privatization from the perspective of investors, where private and foreign investors tend to purchase firms with good performance (Estrin et al. 2009). The Chinese privatization case differs in this aspect. In China, the SOE restructuring program was decentralized to the local governments, which implemented the privatization programs of ``` 5 ```
37 2. The number of households for which there is complete information on household weights, household size, and all expenditure items. This set of households is used to provide of the tabulations concerning the number of households using LPG, and their average expenditure (cleaned sample).

3. The number of households for which there is also complete information on all the variables used in the Heckman equations reported (Heckman sample). This number is divided into those purchasing LPG (selected) and those not purchasing LPG (censored).

Table A2.1: Sample size for countries analyzed

|Country|Full|Cleaned|Heckman|LPG selection|
|---|---|---|---|---|
|Guatemala|13,686|13,656|13,185|5,830|
|India|124,644|120,427|108,003|35,806|
|Indonesia|10,575|9,928|8,942|651|
|Kenya|13,212|12,754|9,601|436|
|Pakistan|14,744|14,700|11,546|1,107|
|Sri Lanka|20,682|18,473|16,007|4,674|
|Albania|3,600|3,420|3,420|2,298|
|Brazil|55,970|43,435|34,116|31,310|
|Mexico|29,468|25,068|25,068|14,197|
|Peru|26,598|20,414|16,469|9,632|

_Source:_ Authors’ calculations Table A2.2 shows the total population (rural and urban) and the equivalent number of households at a national level based on the household weights.

Table A2.2: Population estimates based on returns from cleaned data sample in survey year

||**Total**<br>**national**|**Total po**|**pulation, cle**|**aned data**|**Total ho**|**useholds, cle**|**aned data**|**Samp**<br>**cl**|**le house**<br>**eaned da**|**holds,**<br>**ta**|
|---|---|---|---|---|---|---|---|---|---|---|
|**Country**|**population**|**Rural**|**Urban**|**Total**|**Rural**|**Urban**|**Total**|**Rural**|**Urban**|**Total**|
|Guatemala|1.30E+07|6.73E+06|6.24E+06|1.30E+07|1.22E+06|1.42E+06|2.64E+06|7,857|5,799|13,656|
|India|1.09E+09|7.10E+08|2.39E+08|9.49E+08|1.45E+08|5.49E+07|2.00E+08|76,783|43,644|120,427|
|Indonesia|2.19E+08|1.14E+08|9.18E+07|2.06E+08|2.89E+07|2.29E+07|5.18E+07|5,631|4,297|9,928|
|Kenya|3.63E+07|2.77E+07|6.68E+06|3.44E+07|5.08E+06|1.67E+06|6.75E+06|8,270|4,484|12,754|
|Pakistan|1.54E+08|8.87E+07|4.12E+07|1.30E+08|1.30E+07|6.21E+06|1.92E+07|8,894|5,806|14,700|
|Sri Lanka|1.99E+07|1.56E+07|2.73E+06|1.84E+07|3.89E+06|6.28E+05|4.51E+06|13,874|4,599|18,473|
|Albania|3.14E+06|1.56E+06|1.43E+06|2.99E+06|3.51E+05|3.98E+05|7.49E+05|1568|1,852|3,420|
|Brazil|1.93E+08|2.39E+07|1.17E+08|1.41E+08|6.56E+06|3.63E+07|4.28E+07|9768|33,667|43,435|
|Mexico|1.06E+08|1.96E+07|7.30E+07|9.26E+07|4.50E+06|1.84E+07|2.29E+07|5409|19,659|25,068|
|Peru|2.92E+07|9.96E+06|1.83E+07|2.82E+07|2.30E+06|4.42E+06|6.72E+06|8092|12,322|20,414|

_Sources:_ National population from WDI, others from authors’ calculations.

# **Guatemala (ENCOVI 2006)**

The data for this study are taken from the 2006 Living Standards Survey (ENCOVI) administered by the National Statistical Institute of Guatemala. The fieldwork for the survey was carried out between March and October. The survey did not provide information on items consumed but not purchased, except for food. The total non-purchased food was equivalent to 14