rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6. Cash Stock Regressions, Listed Firms data**

|LABELS|1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|Log of net sales|-0.00<br>(0.822)||||||
|Volatility of Sales||0.06<br>(0.202)|||||
|Total Debt to Assets|||-0.17***<br>(0.003)||||
|Interest Rate||||0.19**<br>(0.012)|||
|Gross Margin|||||0.04<br>(0.557)||
|Net Income||||||0.49***<br>(0.005)|
|year==2002|-0.03**|-0.02|-0.00|-0.01|-0.01|-0.01|
||(0.033)|(0.214)|(0.904)|(0.808)|(0.779)|(0.804)|
|year==2003|-0.03*|-0.02|-0.00|-0.01|-0.01|-0.01|
||(0.067)|(0.424)|(0.958)|(0.878)|(0.714)|(0.736)|
|year==2004|-0.02|-0.02|0.00|-0.00|-0.01|-0.02|
||(0.252)|(0.450)|(0.895)|(0.949)|(0.698)|(0.568)|
|year==2005|0.01|0.01|0.03|0.03|0.02|0.01|
||(0.644)|(0.615)|(0.483)|(0.622)|(0.586)|(0.816)|
|year==2006|0.05*|0.05|0.05|0.07|0.06|0.03|
||(0.088)|(0.121)|(0.191)|(0.277)|(0.210)|(0.477)|
|year==2007|0.05*|0.05|0.06|0.07|0.05|0.02|
||(0.085)|(0.104)|(0.162)|(0.200)|(0.197)|(0.533)|
|year==2008|0.03|0.03|0.03|0.05|0.05|0.00|
||(0.321)|(0.406)|(0.383)|(0.313)|(0.259)|(0.932)|
|Constant|0.12*|0.08**|0.14***|0.07|0.08**|0.08***|
||(0.060)|(0.017)|(0.000)|(0.192)|(0.019)|(0.008)|
||253|260|232|206|217|219|
||0.079|0.077|0.143|0.132|0.075|0.176|

38
36 good survey of the analytical issues involved in this line of research and suggests that the explicit introduction of human capital accumulation in a two-good open economy model can indeed explain the nonconvergence of rates of growth across countries. Becker and Barro (1988), on the other hand, have looked at the economics of population in order to understand the deterlminants of the rate of growth of the labor force and output.

Increasing REturns to Scale and Endogenous Technological Proeress In general, the recent efforts to construct more satisfactory theories of growth have focused on relaxing two key assumptions of the neoclassical model: (1) the constant returns to scale technology; and (2) the exogeneity of technological progress. The first assumption has been replaced by the introduction of increasing returns to scale. Work in this area has been pioneered by Romer (1986a,b, 1988), who has suggested a number of growth models with economies of scale that are either exogenous or endogenous to the firm.26 Although in both settings the presence of increasing returns makes it possible to generate an equilibrium situation with nonconvergent rates of growth across countries, the models with internal economies of scale provide a more appealing explanation. These models use an Ethier (1982)-type production function which has tbe property that a greater degree of specialization in the productive process increases efficiency. This pioneer work does not deal directly, however, with the question of the relation between trade policy and growth.

With respect to the er.dogenous technological progress, Helpman and Grossman (1988) have recently build an elegant two-country model that emphasizes the role of R&D in the growth process. This model assumes the 26Allyn Young (1928) was an early proponent of explanations of growth based on increasing returns.
# **ENDNOTES**

* Poverty Reduction Group, The World Bank, Washington DC, USA.

i It should be noted that the empirical evidence for the idea that crises catalyze the process of creative destruction is more ambiguous than theory would suggest. Studies of manufacturing firms only weakly support the hypothesis that allocative efficiency increases during downturns (see, for example, Griliches and Regev 1995; Bailey and others 1998; Foster and others 2001).

25
# **Table 7 (continued)**

|Minera|9/2/91|Court action.|
|---|---|---|
|Vapores|6/6/92|Company is fined by government.|
|Emos|10/17/93|Accident: drinking water contamination.|
|Puerto|7/23/92*|Government complains about health hazard in the vicinity<br>of the company.|
|Victoria|12/2/93|Government black list of air polluter.|
|lansa|5/29/93|One of the plants ordered to shutdown.|
|Coloso|4/1/92<br>12/2/93<br>2/5/94<br>3/11/94<br>3/18/94|Government action: fine.<br>Government action: company shutdown for few hours.<br>Court action:fine.<br>Government action: company shutdown.<br>Citizens complaint: accident.|
|Iquique|4/1/92 *<br>12/21/93<br>2/5/94<br>3/10/94<br>3/11/94|Government action: fine.<br>Government action: fine.<br>Court action: fine.<br>Government action (Company closed for 72 hours).<br>Court action for bad smell problem.|
|Lirquien|7/15/92|Government black list of air polluter.|
|Chilectra|7/11/92|Citizens complain against company expansion.|
|Molymet|1/19/92|1Governnent complaint: company major air polluter.|
|||<br>**PHILIPPINES**|
|Apex|4/24/91 *|Government action.|
|Ayala|12/8/94 *|Government warning.|
|Benguet|3/21/90<br>3/23/90|Government action: penalty.<br>Workers dismissals.|
|Lepanto|10/22/90|Pollution problem resulting in death and illness.|
|Mondragon|10/11/94|Complaint by citizens about tree cutting.|
|Robinson Land|6/15/94|Government action: company shutdown.|
|San Miguel|10/7/94|Oil spill.|
|||<br>**MEXICO**|
|Cydsasa|2/16/90<br>3/19/92<br>10/9/92|Spill causing death and injury.<br>Black list of air polluter for company's subsidiary.<br>Government action: environrmental audit.|
|Grupo Maya (A)|10/4/90<br>3/12/91<br>3/15/91<br>9/20/91*<br>1 1/27/91<br>7/29/92*|NGO's black list of air polluter.<br>Company relocation requested by Citizens.<br>Government action: warning.<br>Citizens complaint.<br>*_(1_1/25/94): Citizens and ecologists complaint.<br>Citizens complaints.|

22
For food (Table 3), the items considered are few, but we can see that subsidies can also be quite high. For flour, subsidies represent 91.3 percent of the free market price in Libya and almost 60 percent in the Islamic Republic of Iran. Libya has also the highest subsidies for bread, sugar, and vegetable oil, and the Islamic Republic of Iran has large subsidies on bread. Therefore, the oilproducing countries seem to be those that maintained the highest food subsidies. However, subsidies are also high in Morocco for flour and sugar, and in this country these products are universally subsidized and not subject to quotas.

Table 3: Food Unit Prices and Subsidies, in US$ at PPP (2014)

|||Price|Subs.|Subs.(%)|Increase(%)|Price|Subs.|Subs.(%)|Increase(%)|
|---|---|---|---|---|---|---|---|---|---|
|||||_Flour(kg)_<br>||||_Bread(kg)_||
|Djibouti||0.759|0.053|6.5|7.0|n.a.|n.a.|n.a.|n.a.|
|Iran, Islamic Rep.||0.689|1.027|59.9|149.2|1.199|1.346|52.9|112.2|
|Libya||0.130|1.360|91.3|1,044.4|0.054|1.334|96.1|2,491.9|
|Morocco|_Flour1_|1.197|0.168|12.3|14.0 %|n.a.|n.a.|n.a.|n.a.|
||_Flour2_|0.479|0.342|41.7|71.5 %|||||
|||||_Sugar(kg)_|||_Veg _|_etable oil(l_|_iter)_|
|Djibouti||0.865|0.061|6.5|7.0|1.422|0.171|10.7|12.0|
|Iran, Islamic Rep.||n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|n.a.|
|Libya||0.362|1.545|81.0|427.2|0.868|4.054|82.4|467.0|
|Morocco|_Sugar1_|1.393|0.682|32.9|49.0|n.a.|n.a.|n.a.|n.a.|
||_Sugar2_|1.393|0.682|32.9|49.0|||||
||_Sugar3_|1.077|0.682|38.8|63.3|||||

_Source:_ World Bank estimations from Household Budget Surveys. _Note_ : Subsidized flour and sugar in Morocco have different prices depending on varieties and forms; kg = kilogram; PPP = purchasing power parity.

# A Distributional Analysis of Subsidies

As indicated in the introduction, we use the microsimulation model SUBSIM to provide a distributional analysis of subsidies and simulations of alternative subsidy reforms. The publicly available version of SUBSIM comes in two flavors, SUBSIM direct, which estimates direct effects using Household Budget Survey (HBS) data only, and SUBSIM indirect, which uses HBS data and input-output matrixes to estimate direct and indirect effects.

This paper uses a third version of SUBSIM, which is not yet publicly available and which is designed to provide comparative analyses of subsidies across countries. This version is similar to 7
# **The Economic Effects of a Borrower Bailout: Evidence from an Emerging Market**<sup>_∗_</sup>

## **Xavier Gin ́e**

World Bank xgine@worldbank.org

**Martin Kanz** World Bank mkanz@worldbank.org

> _∗_ We thank the Reserve Bank of India and the various State Level Bankers Committees for sharing data used in this paper. We thank Sumit Agarwal, Bo Becker, Patrick Bolton, Emily Breza, Baghwan Chowdhry, Shawn Cole, Rob Garlick, Rainer Haselmann, Asim Khwaja, Andres Liberman, Daniel Paravisini, Dina Pomeranz, Amit Seru, Rui Silva, James Vickery as well as seminar participants at CEPR-ESSFM Gerzensee 2014, IGIDR Emerging Markets Finance 2014, FIRS 2014, Indian School of Business, London Business School EWFC 2014, National University of Singapore, NEUDC (Harvard Kennedy School) and The World Bank for helpful comments and suggestions. Maulik Chauhan and Avichal Mahajan provided outstanding research assistance. Financial assistance from the World Bank Research Support Budget is gratefully acknowledged.
**14**

_d_<sup>2</sup> Because the first term is positive (under the natural assumption that<sup>_>_0)and</sup> _dRdk_<sup>_τ_(</sup><sup>_k, R_)</sup> given that the second term is negative, _dRd_ <u>Ψ</u> _V_<sup>cannotbesigned.Wethushavethefollowing</sup> proposition:

**Proposition 2** . _An improvement in village property rights has an ambiguous impact on the reallocation of labor between parcels located within and outside the village. If labor more efficiently reduces tenure insecurity on parcels with weaker property rights than on parcels with stronger property rights, then labor may be shifted away from parcels within the village to parcels outside the village_ .

_d_ Note that _dRdk_<sup>_τ_( ̄</sup><sup>_k −kO, RV_)</sup><sup>_F_( ̄</sup><sup>_k −kO_) is likely to be greater for lower values of</sup><sup>_RV_, implying</sup> that, following an improvement in property rights, households with initially weaker property rights in the village (e.g., female-headed households) may be more likely to shift cultivation effort to outside parcels than households with initially stronger property rights in the village (e.g., male-headed households).

# **4 Experimental Design, Data and Econometric Approach**

## **4.1 Experimental design**

The MCC-supported PFR program aimed to produce 300 ETFs in 40 _communes_ throughout Benin and deliver more than 70,000 land use certificates.<sup>17</sup> The selection in the program was done in two steps. First, villages in each of the 40 communes received an information campaign. The intention was to inform villages about the program and invite them to apply for a chance to receive one of the 300 PFRs. Second, proposals were reviewed against pre-established selection criteria.<sup>18</sup> From this review a list of eligible villages was produced. Third, each commune organized lotteries to randomly select villages within the eligible pool into the program. Overall, 1,235 villages applied for the program, out of the 1,543 that were targeted. Of these 1,235 villages, 576 met the eligibility criteria. To select treatment and control villages, 80 public lotteries were organized, two in each commune; the process started rolling out in 2008 (MCC,

> 17 _Communes_ are sub-regional units equivalent to districts. There are 77 communes in Benin.

> 18The following criteria were used: poverty index, potential for commercial activities, regional market integration, local interest in promoting gender equality, infrastructure for economic activities, adhesion to the PFR application procedure, incidence of land conflicts, and the production of main crops.
**Table 19. Marginal Effects of Having a Certain Type of Activity af er Completing CONALEP**

||**Prob[Acdvity=**|**j****_I_**X,**_dummykl_**|**Marginal**||**Prob[ActivityV**|**-j**<br>**_I_**_X, dummy=kJ_|**Marginal**|
|---|---|---|---|---|---|---|---|
|**Variable**|**k=1**<br>**Survey 98**|**kre**<br>**Survey 94**|**Effect**|**Variable**|**k=1**<br>**Survey 98**|**kSv9**<br>**Survey 94**|**Effect**|
|_j= Work_||||_j = Searching forjob_||||
|98 Survey|0.77|0.59|0.183|98 Survey|0.08|0.26|-0.174|
|Women|0.64|0.80|-0.162|Women|0.10|0.11|-0.012|
|Age|||0.004|Age|||0.000|
|South-East Area|0.78|0.74|0.040|South-East Area|0.09|0.11|-0.027|
|Center-South Area|0.81|0.74|0.076|Center-South Area|0.08|0.11|-0.030|
|North-East Area|0.85|0.73|0.122|North-East Area|0.08|0.12|-0.039|
|North-West Area|0.82|0.73|0.085|North-West Area|0.06|0.12|-0.055|
|Center-North Area|0.84|0.73|0.111|Center-North Area|0.06|0.12|_-0.055_|
|Pacific Area|0.84|0.73|0.116|Pacific Area|0.04|0.12|-0.081|
|92 Cohort||0.75||92 Cohort||0.10||
|93 Cohort||0.76||93 Cohort||0.09||
|94 Cohort|0.46|||94 Cohort|0.37|||
|96 Cohort|0.66|||96 Cohort|0.20|||
|97 Cohort|0.43|||97 Cohort|0.41|||
|Lower-secondary HH|0.73|0.75|-0.016|Lower-secondary HH|0.11|0.11|0.003|
|Upper-secondary HH|0.68|0.75|-0.069|Upper-secondary HH|0.11|0.11|0.001|
|University - + HH|0.73|0.75|-0.012|University - + HH|0.08|0.11|-0.035|
|Do not know HH|0.74|0.75|-0.003|Do not know HH|0.09|0.11|-0.021|
|GDP per capita|||0.062|GDP per capita|||-0.057|

Base categories for independent variables: 94 Survey, men, Center area (D.F. and Mexico), 91 cohort, none or primary school education of household head (HH). Units for GDP thousands of 1998 pesos.

43
These first and second stages can also be estimated using maximum likelihood based on conditional distributions of earnings for teachers and non-teachers. The likelihood function of teacher earnings is The likelihood function for non-teacher earnings is: With consistent estimates of β 1 and β 2 , we use predicted values of _W_<sup>_a_</sup> and _W_<sup>_na_</sup> for each individual to estimate the structural model of the following:

# **V. Data and results**

We use the 2001-2008 Indonesia Labor Force Survey (SAKERNAS) to look into the occupational choice of working cohorts with college education. Our sample includes 40,019 workers with college education from year 2001 to 2008. Overall, only around 3% 15
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# **4. Policy Implications and Priorities**

We believe the manufacturing sector can contribute to the reversal of Africa’s poor economic performance. In this section we outline what, in view of what we have learned from the firm surveys, we think must be done in order to maximize the likelihood of this happening. A necessary condition for such a strategy to be successful is that it is firmly based on the current reality. This is where we begin.

Industrial development in the world in recent years has been characterized by a rapid globalization of production, facilitated by liberalization of international economic relations and changes in the nature of corporate activity and international investment. Production systems have become increasingly integrated across national borders. Firms world-wide have been exposed to increased competitive pressure and in large parts of the world production and international trade have grown rapidly. In many ways, this is a new global reality for Africa’s manufacturing firms who not long ago were protected from international competitive pressure. On the domestic scene, the state of the business environment is still very much a legacy of those past times when being efficient and internationally competitive was not all that important. Poor infrastructure and high utility costs hold back competitive production and drive up costs, thus undermining the potential for output expansion. Excessive regulations result in entrepreneurs forming very small and informal firms rather than formal organizations that have export potential and contribute to tax revenues. The fact that key market institutions - such as laws, courts, business associations, lobbies, quality control, protection of property rights and enforcement of contracts – are underdeveloped means that personal relationships play a central role when firms make their output and input decisions, which hampers market integration.

In this reality, African policy makers face a choice between three broad alternatives. The first is to revert to essentially isolationist policies that protect the industrial sector from international competition and encourage supply to the domestic market. Such policies have been widely implemented in Africa in the past without any obvious success, and we can see no reason why they would yield better results this time around. The second option is status quo. So far, Africa has failed to take advantage of the new opportunities arising from the globalization of production. This is partly because the process of liberalization and opening-up has been slower and less pervasive in Africa than in other regions, but also because key elements of the 34
**Table 5: The Impact of Migration on the Education of Males Aged 16 to 18**

||(1)<br>OLS|(2)<br>2SLS|(3)<br>Ordered<br>Probit|(4)<br>IV-Ordered<br>Probit|(5)<br>Censored <br>Ordered<br>Probit|(6)<br> IV-Censored<br>Ordered<br>Probit|
|---|---|---|---|---|---|---|
|Child is in a Migrant Household|0.151|-1.366|0.041|-0.613|-0.012|-0.653|
||(1.34)|(1.99)*|(0.87)|(2.92)**|(0.16)|(2.81)**|
|Age of Child|-3.945|-3.921|-1.716|-1.709|-1.137|-1.096|
||(0.84)|(0.82)|(0.76)|(0.75)|(9.41)**|(9.11)**|
|Age of Child Squared|0.120|0.119|0.053|0.053|0.033|0.032|
||(0.87)|(0.84)|(0.80)|(0.78)|(8.15)**|(7.70)**|
|Mother's Years of Schooling|0.340|0.337|0.139|0.138|0.149|0.149|
||(12.98)**|(12.52)**|(15.88)**|(15.09)**|(12.38)**|(12.34)**|
|Proportion of rural households owning land in 1910|-0.107|-0.043|-0.039|-0.011|-0.022|0.006|
||(1.98)|(0.71)|(1.71)|(0.55)|(1.05)|(0.31)|
|Male School Attendance in 1930 (6 to 10 year olds)|-0.003|0.007|-0.002|0.002|-0.005|-0.001|
||(0.25)|(0.48)|(0.40)|(0.51)|(1.23)|(0.26)|
|Gini of Income in 1960|-2.173|-1.475|-0.793|-0.495|-0.167|0.142|
||(1.56)|(1.04)|(1.43)|(0.99)|(0.35)|(0.30)|
|Number of Schools per 1000 population in 1930|0.483|0.005|0.209|0.004|0.355|0.149|
||(1.90)|(0.01)|(2.01)*|(0.03)|(2.91)**|(1.08)|
|Gini of Male Years of Schooling for 15-20 year olds in 1960|-5.168|-4.690|-1.878|-1.681|-2.295|-2.117|
||(1.76)|(2.13)*|(1.58)|(2.02)*|(2.84)**|(2.64)**|
|Average Male Years of Schooling in 1960 for 15-20 year olds|-0.574|-0.528|-0.204|-0.185|-0.218|-0.198|
||(1.98)|(1.95)|(1.67)|(1.97)*|(0.034)|(1.92)*|
|Observations|4094|4094|4094|4094|2047|2047|

# Robust t-statistics in parentheses clustered at the state level. Instrument is the 1924 state-level migration rate.

Censored ordered probit regressions are carried out on a 50% random sample, and use school attendance as censoring variable. * significant at 5%; ** significant at 1% 37
11

- (iv) Different definitions of variables **used.** For esample, some studies use real vehicle operating costs while others use the nominal values, and some studies normalize costs by income while others do not.

- (v) Different time periods and locations. It is well-known that a long run elasticity is higher than a short-run elasticity because users have more time to adjust to price changes. In addition, data drawn from different countries may show markedly diiferent elasticity estimates. In general, we expect that elasticity estimates for developing countries tend to be less elastic due to their less competitive market structure compared to industrially advanced countries.

- (vi) The degree of aggregation. As more disaggregated markets are investigated, the range of elasticity estimates tend to widen because individual estimates will reflect quite unique market conditions. Aggregation 'averages out' some of the underlying variabilities of price sensitivity in different markets.

The many sources of variability and differences in interpretation of elasticity estimates make it difficult to generalize about probable values for elasticities. Nonetheless, Tables 2 and 3 include our estimaLss of *most likely" values for own price elasticities in various markets.

# C. Most likely values of price elasticities

In Tables 2 and 3, we construct a 'most likely' range of elasticity estimates. It is subjective but based on a number of considerations in reviewing the many demand studies. First, some of the extreme values for elasticities were eliminated. We did not automatically eliminate references just because their results seemed out of line. Rather we reviewed the approach or types of data employed to see if that might influence the magnitudes of elasticity estimates. For example, in the case of air passenger travel, the elasticity estimates of Hensher and Louviere (1983) were omitted from consideration because the study was based on inflight interview data of a single airline for a single route. These results are not directly comparable to other studies which estimate a
|**Table 1: Treatment Assig**|**nment**<br>Number of <br>Blocks|Number of<br>Firms Listed|Firms Surveyed<br>at Baseline|Control <br>Random|Communication<br>ization Among Bas|Free Cost<br>eline Firms|Control<br>Ra<br>f|Inspector In<br>ndom Sample<br>irms not in Ba|direct Inspector<br>of Listed<br>seline|
|---|---|---|---|---|---|---|---|---|---|
|Control Block|201|2810|689|689|0|0|709|0|0|
|Communication Block|202|2609|659|0|331|328|0|0|0|
|Inspector Block|202|2376|0|0|0|0|0|577|593|

33
24

**Table 3:** The effect of visa provision on bilateral stocks of migrants by type of country pair.

|Dep var:|Bilateral Sto|ck of Migrants||
|---|---|---|---|
||(1)|(2)|(3)|
|Visa|0.212***|-0.008|-0.018|
||(0.068)|(0.033)|(0.033)|
|Visa _×_ North-South|-0.105<br>(0.120)|0.107**<br>(0.054)|0.124**<br>(0.054)|
|RTA|0.784***|-0.039|-0.035|
||(0.067)|(0.033)|(0.033)|
|RTA _×_ North-South|0.269**|0.130***|0.130***|
||(0.093)|(0.046)|(0.046)|
|Schenghen|||0.197***<br>(0.040)|
|Distance (ln)|-1.268***<br>(0.026)|||
|Colony|1.934***<br>(0.068)|||
|Language|0.943***<br>(0.069)|||
|Religion|0.552***|||
||(0.095)|||
|GDP diference (ln)|0.011|0.003|0.002|
||(0.019)|(0.008)|(0.008)|
|Estimator|PPML|PPML|PPML|
|Country-Year Fixed Efects|Yes|Yes|Yes|
|Country-Pair Fixed Efects|No|Yes|Yes|
|Sample period|1990-2020|1990-2020|1990-2020|
|Observations|199,514|61,613|61,613|
|Pseudo R-squared|0.82|0.99|0.99|

Note: Dependent variable is the stock of immigrants in country _j_ originating from _i_ . In moving from column (1) to (2) the number of observations reduces considerably because country-pair fixed effects perfectly predict the outcome variable of time-invariant bilateral migration stocks, and the concerned observations are dropped. Robust standard errors in parentheses. ***, **, * significantly different from 0 at the 1%, 5%, and 10% levels respectively.
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