rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Program Entry and Economic Activity

|Dependent Varia|ble : Indicat|or for Opening a|New Program||
|---|---|---|---|---|
||All SCPs|Private SCPs|Public SCPs|Bachelor’s|
||(1)|(2)|(3)|(4)|
|Panel A: GDP (2004-2019); Na|rrow Entran|t Defnition|||
|L.GDP by Field|0.0018***|0.0020***|0.0007***|0.0008***|
||(0.0001)|(0.0001)|(0.0002)|(0.0001)|
|Constant|0.0006|-0.0073*|0.0211*|0.0189***|
||(0.0034)|(0.0037)|(0.0085)|(0.0033)|
|N. of Observations|68,654|52,699|15,955|89,559|
|Mean of Dep. Variable|0.023|0.022|0.027|0.020|
|Elasticities|0.39|0.47|0.11|0.18|
|Panel B: GDP (2004-2019); Br|oad Entrant|Defnition|||
|L.GDP by Field|0.0006***|0.0007***|0.0002***|0.0004***|
||(0.0000)|(0.0000)|(0.0000)|(0.0000)|
|Constant|-0.0007|-0.0021***|0.0040***|0.0026***|
||(0.0004)|(0.0004)|(0.0009)|(0.0004)|
|N. of Observations|1,004,480|770,880|233,600|999,808|
|Mean of Dep. Variable|0.0016|0.0015|0.0018|0.0018|
|Elasticities|0.71|0.87|0.20|0.41|
|Panel C: Employment (2007-2|013); Narrow|Entrant Defnit|ion||
|L.Field Relative Employment|0.4095***<br>|0.4473***<br>|0.2844***<br>|0.1629***<br>|
||(0.0364)|(0.0426)|(0.0668)|(0.0320)|
|Constant|-0.0129|-0.0203**|0.0043|0.0108***|
||(0.0056)|(0.0057)|(0.0134)|(0.0054)|
|N. of Observations|14,971|11,480|3,491|19,679|
|Mean of Dep. Variable|0.026|0.024|0.031|0.020|
|Elasticities|0.75|0.87|0.43|0.39|
|Panel D: Employment (2007-2|013); Broad|Entrant Defnitio|n||
|L.Field Relative Employment|0.0220***|0.0238***|0.0160***|0.0126***|
||(0.0017)|(0.0020)|(0.0031)|(0.0018)|
|Constant|0.0010|-0.0004|0.0054**|0.0036***|
||(0.0006)|(0.0005)|(0.0019)|(0.0006)|
|N. of Observations|323,575|248,325|75,250|322,070|
|Mean of Dep. Variable|0.0019|0.0017|0.0023|0.0019|
|Elasticities|0.61|0.71|0.36|0.34|

Note: Each column presents the coefficients from an OLS regression where the dependent variable is an indicator for whether institution _j_ opens a new program in department _d_ and field _k_ in year _t_ , for the 2004-2019 period. Elasticities of entry probability with respect to the corresponding economic activity variable, evaluated at the sample means, are reported at the bottom of each panel. GDP by field is measured in billions of COP and field relative employment is between zero and one; both variables are included with a one-year lag. All regressions include department, year, and field-category fixed effects. *,**, and *** denotes significance at the 10, 5 and 1% level. Standard errors are clustered at the institution-year level.

21
exchanges on which the firm is traded, ownership, and industry classification. Bloomberg’s industry classification is based on the Industry Classification Benchmark.

To examine corporate recovery, we use **Net Sales** defined as total operating revenue. We also use alternate measures including Output defined as Sales + Changes in Inventories and Corporate Profits computed as Earnings before Interest, Taxes, Depreciation and Amortization (EBITDA). Our main measure of credit is **Short Term Borrowings** that includes bank overdrafts and short-term debts and borrowings.

In our analysis of firm recoveries, we focus on both the balance sheet positions (that provide a snapshot of the firm’s financial resources and obligations at a given point in time) and the statement of cash flows which provides a breakdown of cash flows from operating, investing, and financing activities that helps determine the short term viability of the company. All the accounting variables are converted to real terms by deflating by CPI Index. All our results are robust to using GDP deflator instead of CPI.

# **3. Summary Statistics on the Existence of Phoenix Miracles**

# **_3.1 Macro Evidence_**

We first focus on the behavior of GDP and private sector bank credit covering a five-year window centered on troughs in output, in our sample of 3S collapse episodes. GDP is the real GDP in constant local currency units from World Development Indicators and credit is the Claims on the Private Sector by Deposit Money Banks from the International Financial Statistics (line 22d).

Figure 1A reproduces the picture of Phoenix Miracles from Calvo et al. (2006a) for 22 3S episodes where the drop in average GDP from t-2 to t was 7.8% and the drop in average credit was 15%. After t (the crisis trough), there is a steep V-shaped recovery in output to previous levels where as credit still stagnates and begins to recover only at t+1. Looking at our sample of nine 3S episodes over the 1990s in Figure 1B, we obtain a very similar picture. The fall in average GDP (bold line) and credit (dotted line) across the 9 episodes are 5.1%<sup>9</sup> and 16.1%,

> 9 The statistics reported in Calvo et al. (2006) are for the average 3S episode. Similarly here, the figure of 5.12% indicating fall in average output differs from the average fall in output across all 9 episodes which is 8.2%. The average fall in credit across 8 episodes (we exclude Korea since there was no drop in credit) is 35%.

9
38 to target a specific group. Socio-economic criteria, on the other hand, implicitly assume less mobility between groups, over the short run at least (Pyatt and Round 1976, and World Bank 1990). The second drawback is that such an approach presupposes that groups are identifiable; i.e. for example, one can physically identify the top decile. How to identify households by income level is one of the major constraints for targeting programs; a disaggregation that follows socio-economic criteria incorporates at least some identification of the households. Thus, it is easy to identify households living in the Savannah, or those that work in the traditional export sector, or even those who are government employees.<sup>8</sup> Since our targeting analyses are concerned with eliminating poverty, we need to describe the income distribution adopted in the model and how the poverty incidence in turn is derived. The functional distribution used follows Dervis, de Melo, and Robinson (1989). A log-normal distribution is constructed for each household group, with the log-variance obtained exogenously (e.g., household survey), and the arithmetic mean income from the benchmark data set.<sup>29</sup> For each policy simulation, the model provides a corresponding mean income, ° and for a given poverty line Z, the z-score is obtained and the poverty

> **28** The adoption of socio-economic criteria is equivalent to adopting a universal targeting scheme with indicators. This is similar to what Besley and Kanbur (1988) and Thorbecke and Berrian (1988) suggest in order to reduce the leakages of a universalistic transfer scheme.

**29** The log-mean is IA = _In (9) - 1⁄2o2_ where y is the arithmetic mean income, ,u is the log mean, and _a"_ is the log-variance.

> **30** The resulting mean income is expressed in 1986 prices.
**<mark>Table 9: Impacts on Continued Use of the Market for Business Services</mark>**

||Use Professio<br>Round 1|nal Services<br>Round 2|
|---|---|---|
|Assigned to Insourcing|0.252***|0.136**|
||(0.060)|(0.055)|
|Assigned to Outsourcing|0.347***|0.191***|
||(0.057)|(0.056)|
|Assigned to Training|-0.065|0.004|
||(0.057)|(0.052)|
|Assigned to Consulting|0.228***|0.185***|
||(0.060)|(0.056)|
|Mean of Control Group|0.346|0.235|
|Sample Size|670|678|
|P-value: all treatments zero|0.000|0.000|
|P-value: all treatments equal|0.000|0.002|
|Notes:<br>Regressions control for stratification batch fixed effects.|||

<mark>Robust standard errors in parentheses. *, **, *** denote significance at the 10, 5, and 1 percent levels.</mark> **<mark>Professional Services</mark>** <mark>is a dummy variable of whether they used HR, accounting, or marketing professional services in the past year.</mark> 44
# MONGOLIA: PRIVATIZATION AND SYSTEM TRANSFORMATION IN AN ISOLATED ECONOMY

# Table of Contents

|Page No.|
|---|
|I.<br>Introduction<br>...............................................<br>1|
|II. Mongolia and Its Early Reforms<br>. . . . .<br>. . . . . . . . . .<br>2|
|III. The Formulation of a Big Bang<br>. . . . . . . . .<br>. . . . .<br>_6_|
|IV. Slowing the Headlong Rush: July 1991-February 1992<br>. . . . .<br>13|
|V.<br>Picking Up the Pace: Reforms after February 1992<br>. . . . . .<br>15|
|VI. Fast or Slow? Some Considerations and the Case of Mongolia<br>24|
|References<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>33|
|Appendix 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . .<br>34|
|List of Tables and Figures:|
|Table 1<br>Mongolia: Structural Indicators<br>. . . . . . . . . . . .<br>4|
|Table 2<br>Economic and Structural Reforms<br>. . . . . . . . . . . .<br>9|
|Table 3<br>Stock Grading Volume and Value<br>. . . . . . . . . . . . .<br>17|
|Table 4<br>Spread of Shares by Provinces<br>. . . . . . . . . . . . .<br>18<br>Table 5<br>Trading Data oll Ulaanbaatar Hotel Inc.'s Share<br>. . . . .<br>20|
|<br>Table Al Model Equations and Base Values<br>. . . . . . . . . . . .<br>41|
|Table A2 Responses to External Shocks<br>. . . . . . . . . . . . . .<br>42|
|Table A3 Reverse Migration and Rural Taxation<br>. . . . . . . . . .<br>43|
|<br>Table A4 Cushioning Adjustment through Aid<br>. . . . . . . . . . .<br>44|
|Figure 1 Number of Shares Sold for Vouchers<br>. . . . . . . . . . .<br>21|
|Figure2<br>Number of Shares Sold for Vouchers<br>. . . . . . . . . . .<br>22|
|Figure 3 Share Price of Ulaanbaatar Hotel<br>. . . . . . . . . . . .<br>23|
|Figure 4 Resource Envelope and Reforms<br>. . . . . . . . . . . . .<br>31|
Table 6. Assessing Cognitive Development Gaps (5-17 years old)

||_Pea_<br>_Percen_|_body Vocabu_<br>_tile Rank (P_|_lary_<br>_VPR)_|_E_<br>|_xtremely L_<br>_(PVPR <2_|_ow_<br>_)_|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Forced Migrant|-11.50***|-11.62***|-12.54***|0.04***|0.04***|0.05***|
||(1.32)|(1.26)|(1.35)|(0.01)|(0.01)|(0.01)|
|R-squared|0.03|0.16||0.01|0.05||
|Observations|2686|2686|2686|2686|2686|2686|
|Mean Dep. Variable(Colombian sample)|67.77|67.77|67.77|0.02|0.02|0.02|
||_M_<br>|_oderately Lo_<br>_(PVPR<15)_|_w_|_(_|_Low_<br>_PVPR<50_|_)_|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Forced Migrant|0.06***|0.06***|0.06***|0.05***|0.04**|0.05***|
||(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|(0.02)|
|R-squared|0.01|0.03||0.00|0.02||
|Observations|2686|2686|2686|2686|2686|2686|
|Mean Dep. Variable(Colombian Sample)|0.04|0.04|0.04|0.20|0.20|0.20|
|Control Variables||✓|✓||✓|✓|
|Propensity Score Matching|||✓|||✓|

_Notes:_ The table presents the estimates of equation (1) using the percentile distribution of the Peabody Vocabulary Test Score as a dependent variable. The Peabody Vocabulary Test is an untimed test of receptive vocabulary for standard Spanish and is intended to provide a quick estimate of the test taker’s receptive vocabulary verbal ability. It includes 125 questions, each containing four picture options with only one correct answer. Items are organized in order of difficulty, ranging from those suitable for children as young as 2 and a half years old to more complex items for individuals over 18 years old. Control variables include children’s sex and age, the maximum level of education completed by the most educated parent and grandparent, and the wealth index is the pre-migration wealth index for the Venezuelan sample and the current wealth index for the Colombian sample. Standard errors are clustered at the household level *** * p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

naire, completed by caregivers, is designed to identify acute and chronic posttraumatic symptoms.

For children and adolescents, we use the Strengths and Difficulties Questionnaire, a 25item behavioral screening tool filled out by caregivers. It evaluates a range of areas including emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship issues, and prosocial behavior.

Adolescents between 11-17 old are assessed for the General Anxi- years anxiety using ety Disorder Scale, a 7-item questionnaire that categorizes anxiety levels from minimal 37
11 Household income = g [Human capital (Number of household members with primary, junior secondary, secondary or university education), Household Characteristics (Age of household head, Household size, Number of males over age 15, Number of children under age 5), Migration Networks) (5) In the second-stage equation the dependent variable is household expenditure, rather than household income. There are at least two reasons for using expenditure rather than income data here. First, the purpose of this paper is to estimate the impact of remittances on the marginal spending behavior of households, and therefore expenditure data is more useful than income data. Second, in low income countries, like Ghana, expenditures are often easier to measure with precision than income, because of the many problems inherent in defining and measuring income for the self-employed in agriculture, who represent such a large proportion of the labor force. For these reasons, we will use expenditure data in equation (5) and throughout the rest of the paper.<sup>11</sup> The rationale for including the various variables in equation (5) is similar to that for including them in the first-stage choice equation.<sup>12</sup> However, it should be pointed out that the model is identified from differences in the instrumental variables between ethno-religious groups, which are excluded from the second stage equation. Notice that our identification is done conditional on a set of characteristics of the ethno-religious group. This type of identification creates several potential econometric problems. First, since the instrument provides independent information by ethno-religious group, this information is shared by all individuals forming that ethno-religious group and thus generates correlation of observations within an ethno-religious group. We solve this problem by clustering standard errors by ethno-
expected to allow easier and cheaper access to infrastructure, particularly telephone and electricity (Fay and Yepes, 2003). As illustrated in Figures 6 and 7, however, neither population density nor urbanization is associated with telecommunications access improvement. This is interpreted to mean that geographic conditions might become less important for network expansion when the mobile network overwhelms the ground-based telecommunications.<sup>13</sup> Figure 6. Population Density and Telephone Penetration Rates in Africa, 1995-2005 <!-- Start of picture text --> MRT<br>50<br>ZAR<br>SOMMLI<br>NGA<br>GNQ GHA<br>40 TCDSDNCMR UGA<br>TZA<br>30 BWAGABCOGAGOMOZGINBFALSOCIVKENSENBEN TGO RWA<br>NERMDGSWZ MWIGMB<br>CAF<br>BDI<br>20 ZMBZWEZAF CPV STPSYC COM MUS<br>NAM<br>ERI ETH<br>10<br>GNB<br>0<br>0 100 200 300 400 500 600<br>Population density<br>International Telecommunication Union ; and<br>MRT<br>50<br>ZAR<br>MLI SOM<br>NGA<br>GNQ GHA<br>40 UGA TCD SDN CMR<br>TZA<br>30 BFALSORWAKEN GINMOZ TGOBENSENCIV AGOBWACOG GAB<br>NERMWI SWZMDG GMB<br>CAF<br>BDI<br>20 ZMBZWECOM MUS SYC CPVSTPZAF<br>ETH NAM<br>ERI<br>10<br>GNB<br>0<br>0 10 20 30 40 50 60 70 80 90<br>Urbanization (percent)<br>Annual average growth rate (percent)<br>Annual average growth rate (percent)<br><!-- End of picture text --> Sources: _International Telecommunication Union_ ; and _World Development Indicators_ .

Figure 7. Urbanization and Telephone Penetration Rates in Africa, 1995-2005 Sources: _International Telecommunication Union_ ; and _World Development Indicators_ .

13 In fact, the linear correlation between population density and the rate of total telephone penetration growth, as shown in Figure 6, is significantly negative, but the correlation with the fixed-line growth is positive, though still insignificant.

12
The projections of wind power development vary across studies based on the underlying assumptions and projections used in their models. In a recent study, the IEA (2008) estimates wind power development potential under two scenarios referred to as ACT and BLUE. The ACT scenario assumes that extant technologies and ones that are in an advanced state of development can bring global CO2 emissions back to current levels by 2050. The BLUE scenario assumes that CO2 emissions can be reduced by 50% from current levels by 2050. While the ACT scenarios are demanding, the BLUE scenarios require urgent implementation of unprecedented and far-reaching new policies in the energy sector. Under the ACT scenario, global wind power capacity is estimated to increase from 94 GW in 2007 to 1,360 GW in 2050. The capacity would increase to more than 2,010 GW in 2050 under the BLUE scenario. In the ACT scenario, electricity production from wind contributes 2,712 TWh/yr in 2030 and 3,607 TWh/yr in 2050. In the BLUE scenario, wind power adds 2,663 TWh/yr in 2030 and 5,174 TWh/yr in 2050. Wind power constitutes 12% of global electricity production in 2050 in the BLUE scenario compared to 2% at the baseline. Wind power production is expected to grow significantly in OECD countries, and in emerging economies such as China and India. In the BLUE scenario, China leads in wind power generation in 2050 with a 31% share. In both scenarios, onshore generation of wind power dominates, although by 2050 some 20% or more power will be generated by (more expensive) offshore wind farms.

In order to achieve a more diversified energy portfolio, the U.S. Department of Energy recently explored the possibility of supplying 20% of the nation’s total electricity demand through wind by 2030. A study commissioned by the Department (USDOE, 2008) concluded that a 20% wind scenario in 2030, while ambitious, might prove feasible if certain challenges can be overcome. First, the U.S. would require 300 GW of wind power capacity to be installed by 2030 to meet the 20% wind scenario, which is almost 18 times as high as the 2007 capacity of about 17 GW. Further, it would require construction of more than 20,000 km of high-voltage transmission lines, which is opposed by several states as it would likely increase their electricity rates (as such a network would tend to equalize rates across regions).

The USDOE study estimates that upwards of 600 GW of wind generating capacity could be installed at a cost of $60 to $100 per megawatt-hour (MWh), including the costs of connecting to the extant transmission system (USDOE, 2008, p.9). The federal government’s 7
of male household heads, proportion of households lacking basic services in energy, garbage collection, subject to contamination by stagnant water and trash accumulation. As a final step, only one municipality, the first or the second closest, was selected based on consultation with the program implementers as a “control” for each municipality in the treatment group. Thus, a total of seven treatment and seven control municipalities were selected into the study. It is important to bear in mind that the participatory budgeting process is also present in the control municipalities.<sup>5</sup> It was not possible to ascertain the willingness of the local mayors in the control municipalities in developing information systems and opening offices of information as was done in the process of selecting the seven treatment municipalities. Table A.1, in the Appendix, lists the municipalities which entered the quantitative impact evaluation and indicates those selected for the qualitative baseline study.

Quantitative data were collected in two rounds for a broad range of indicators from the seven ATI municipalities, and from seven comparable municipalities that did not receive project support.<sup>6</sup> The baseline survey round took place between December 2010 and January 2011, prior to the implementation of the ATI pilot program. The follow-up round took place in June 2012, six months after the completion of the intervention activities (in December 2011). The study was designed to detect a 10% relative increase in the primary and related outcomes indicating knowledge of the specific rights under the ATI law, satisfaction with requested information from public offices, civic participation, local governance, trust in public and non-public institutions, and perception about main public services. This resulted in a sample size of 1,932 households (966 households for the treatment and 966 for the control group), representing seven municipalities per treatment group, 10 clusters ( _estratos_ ) per municipality, and around 13 to 14 households per cluster.

> 5 Additional details on the potential role of participatory budgeting in the impact evaluation of the ATI pilot are provided below.

> 6 Gallup Dominican Republic was contracted to conduct the field work for the baseline and end-line surveys. All instruments were translated into Spanish, and pre-tested prior to use in the field. Data collection instruments were administered to respondents in Spanish by native speakers.

7
# **Table 6:  Performance Differences Conditional on Sector Dummies**

|**Performance Differences Conditional on Sector Dummies**|
|---|

|||**Quantile**|**Regressions**||||**OLS**|
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|_Quantile_|**10**<br>coef/se|**50**<br>coef/se|**90**<br>coef/se|**95**<br>coef/se|**99**<br>coef/se|**Simple**<br>coef/se|**Weighted**<br>coef/se|
||**Depen**|**dent Variab**|**le: Market Sh**|**are(Output)**||||
|Ben Ali firm (BA)|0.000***|0.004|0.083***|0.107***|0.129***|0.038**|0.024|
||(0.000)|(0.003)|(0.013)|(0.005)|(0.010)|(0.015)|(0.038)|
|BA*Highly Regulated (de Jure)|0.000|0.002|-0.012|0.139***|0.758***|0.044|0.358**|
||(0.000)|(0.003)|(0.013)|(0.009)|(0.015)|(0.051)|(0.165)|
|Firm Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Weights|None|None|None|None|None|None|Output|
|Number of observations|49,446|49,446|49,446|49,446|49,446|49,446|49,446|
|R2|0150|0189|0247|0220|0176|0288|0872|
|<br>|.<br>**D**<br>|.<br>**ependent Va**|.<br>**riable: Profit**<br>|.<br>**s Z-Score**<br>|.<br>|.|.|
|Ben Ali firm (BA)|-0.605***|-0.016|0.603***|1.209***|3.159***|-0.096|-4.822|
||(0.136)|(0.011)|(0.206)|(0.289)|(0.150)|(0.255)|(6.124)|
|BA*Highly Regulated (de Jure)|0.176|0.051***|0.672***|1.838***|161.855***|6.535|38.016|
||(0.142)|(0.017)|(0.221)|(0.335)|(0.395)|(5.684)|(25.970)|
|Firm Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Sector Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year Dummies|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Weights|None|None|None|None|None|None||Net Profits||
|Number of observations|43,128|43,128|43,128|43,128|43,128|43,128|41,824|
|R2|0.009|0.011|0.017|0.016|0.039|0.064|0.836|

Note: *,**,*** denote significance at the 10%, 5% and 1% significance level respectively. Standard errors are clustered by sector. The sample is confined to 2006-2010 and to firms which report using hired labor, producing more than 1600 USD worth of output and having labor productivity in excess of 400 USD, operating in sectors governed by the investment code in which at least one Ben Ali firm is contemporaneously active. Standard errors are clustered at the 5-digit sector level. Highly Regulated (de Jure) is a dummy variable indicating whether the particular 5 digit sub-sector the firm operates in is subject to both authorization requirements and FDI restrictions at time t. Firm controls include offshore, foreign and log firm age.

33