rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Steady state comparison

|Variable|Autarky|Open|Shock|Open<br>Autarky|Shock<br>No shock|
|---|---|---|---|---|---|
|_cem_|0.3|0.284|0.283|0.947|0.996|
|_cd_|1.179|1.369|1.357|1.161|0.991|
|_kem_|1.012|1.012|1.012|1|1|
|_kd_|3.974|5.188|3.974|1.305|0.766|
|_wem_|0.141|0.211|0.18|1.496|0.853|
|_wd_|1.056|0.905|0.707|0.857|0.781|
|_rem_|-0.461|-0.191|-0.31||1.623|
|_rd_|0.031|-0.191|-0.31||1.623|
|(<sup>_nx_</sup><br>_y_ <sup>)</sup><sup>_em_</sup>||0.042|0.044||1.048|
|(<sup>_nx_</sup><br>_y_ <sup>)</sup><sup>_d_</sup>||-0.097|-0.113||1.165|

**Transitional dynamics.** I solve for the equilibrium using Dynare. Figures 3 and 4 describe the transitional dynamics of the integrated equilibrium compared to the autarkic counterfactual.

Upon financial integration, the developed economy starts accumulating capital, until it converges to its new steady state. The incentive to accumulate additional capital stems from the ability to create collateral-backed liquid claims. This trend is broadly consistent with the pre-crisis boom in the US, and especially with the view that the housing boom was driven by the incentive to create MBS (which had favorable liquidity properties).

On impact, financial integration leads to a 6% increase in consumption in the developed economy, reflecting a higher permanent income due to international liquidity rents. Faced with higher private returns to capital, households in the developed economy initially sacrifice some consumption growth in favor of investment. However, consumption growth quickly recovers as it converges to its new steady state.

Interestingly, the wage rate in the developed economy falls, despite the fact that it is accumulating capital. Capital accumulation raises the “unconstrained” wage bill, as the marginal product of labor increases; however, 27
# **4.2 Panel estimates**

Table 3 presents estimates of the relationship between economic freedom and economic growth in a panel of data from 1975 to 2005. The average economic growth is measured over 5 year intervals (as the index of Economic Freedom of the World Index is only available every 5 years during 1975-2000). Data on economic freedom is available for 121 countries. Like in the cross-section estimates, the dependent variable is explained by the initial levels of the various exogenous variables (EF, CPR, and ER), the changes of those variables in the previous periods (to avoid the problem of endogeneity), and a similar set of control variables.<sup>18</sup> The panel data estimates are consistent with the cross section estimates. We find a robust positive relationship between the initial level of economic freedom and its growth in the previous period. In addition we find a similar relationship between economic growth and civil and political rights. Both the initial level and changes in the civil and political rights in the previous period contribute positively to the economic growth. This relationship is robust to the inclusion of the indicator of economic freedom. In contrast to the cross section estimates we find this time a robust negative relationship between the initial level of entitlement rights and economic growth in all specifications, while we still do not find any statistically significant impact of the change in entitlement rights on economic growth.

All the control variables are statistically significant in most specifications and have the same signs as in the cross section estimates presented in Table 2. Column 7 of Table 3 presents the results based on the fixed effects estimation. The only difference in this specification is that the growth of economic freedom in the previous period does not seem to affect economic growth in the current period. The overall fit of the fixed effects regression is poor and it does not seem to be the appropriate way to model the economic growth since the heterogeneity of the data set is driven by cross-country variation and not by time series variation.

> 18 We drop the Pop100km variable as it was consistently not statistically significant in the panel regression estimates.

23
FIGURE 9. RURAL AND URBAN NEONATAL MORTALITY TRENDS IN COTE D'IVOIRE, 1970-86 (LSMS 1985/1986) <!-- Start of picture text -->
| ~~~~~~~~~~~~~~~Rural<br>All<br>so<br>*~~~~~~~~%1<br>O Urban<br>0IF~~~~~~~~~~~~~~~<br>1-4 , I<br>1970 tS~17S gm0<br>Year (1)<br><!-- End of picture text -->

NOTE: (1) NNMR - YEAR H0VING AVERAGE
# **I. Introduction**

The process of creative destruction – whereby an economy reinvents itself through a continual displacement of less productive firms and old production processes with newer and more efficient ones – plays an integral role in an economy’s sustained growth and structural transformations. Through this churning, the economy’s scarce resources are reallocated to more promising firms and sectors, theoretically raising overall productivity and speeding up the process of structural transformation. There has been a growing interest in measuring the degree of creative destruction in an economy, using firm entry and exit as a proxy measure, so-called “firm turnover”. In addition to firm entry and exit, these studies also look at the reallocation of jobs, meant to capture the magnitude and speed with which labor is being shifted to more productive firms and sectors.

These studies, however, typically have relied heavily upon establishment or economic census data, often only in the manufacturing sector, and analyses have only been available for developed economies, with a handful of notable exceptions.<sup>1</sup> Moreover, the studies that do take advantage of high-quality census or registration data in developing – often upper-middle-income – economies often re-visit the handful of countries where these sources are available.<sup>2</sup> By analyzing a wide and repeated cross-section of firm (or factory)-level data, these efforts provide a panoramic view of both entry and exit flows, and they have generally shown these rates to be substantial, with significant productivity and re-allocation effects (see for instance, Disney et al (2003); Foster et al (2008); Foster et al (2001)). Nonetheless, the sparse number of comprehensive and regularly collected census data has limited broad comparability across several economies. Additionally, what these census data offer in breadth of coverage within an economy, they often lack in depth in terms of descriptions of firm characteristics and how those aspects relate to firm entry and exit.

Given the limited number of widely comparable, census-based estimates of entry and exit rates, an alternative approach, followed by some studies, has been to concentrate on just firm exit. On the assumption that firm productivity is a driving force of exit – in the sense that less productive firms exit at a more frequent rate – one can partially capture the intensity of creative destruction by looking

> 1 Examples include Liu (1993), Liu and Tybout (1996), Shiferaw (2009), Sandefur (2010), Klapper and Richmond (2011), Hallward-Driemeier and Rijkers (2013), and Kerr, Wittenberg, and Arrow (2014).

> 2 For instance, several studies leverage excellent census data from select countries where this information is available. Several of these studies are in turn limited to the same list of countries in economies such as Chile, Colombia, Ethiopia, Indonesia, and South Africa, to name a few.

2
Policy Research Working Paper 10536

# **Abstract**

This paper investigates the impacts of job displacement on subsequent labor market outcomes, focusing on differentiated effects by educational groups and gender. The findings show that job separations caused by plant closings result in sizable and long-lasting wage reductions, with an average decline of −7.5 percent over a nine-year period relative to workers who did not experience job losses. A stronger effect is estimated for highly educated workers than for low educated workers, with initial effects being 18.4 and 9 percent wage drops, respectively. For working hours, the effect on low educated workers is double the effect on highly educated workers, with 3.0 and 1.5 additional hours per week, respectively. Using the rotating panel of the survey, difference in differences coefficients are estimated, removing time-invariant individual heterogeneity. Compared to ordinary least squares, the difference in differences estimates reduce the magnitude of the average impacts of plant closing on wages, from −7.5 to −4.7 percent, and on working hours from 1.4 to 0.53 additional hours. These results suggest that the ordinary least squares estimates are upwardly biased due to omitted individual worker heterogeneity. The paper discusses another potential remaining source of endogeneity concerning the quality of the match between employers and workers.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dlederman@worldbank.org and fariasvazquez@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
income are associated with worse governance. The largest magnitude is seen for government effectiveness, measuring the quality of the bureaucracy. A one standard deviation improvement in government effectiveness (for example, moving from Chad’s level to Brazil’s level) is associated with a 5 percentage point lower passport cost as a percentage of national income. Passport costs relative to income are also higher in countries with less political stability and more corruption. When we include all of the measures together, none is individually significant, reflecting the high intra-correlation.

**Table 3: Do Countries with better Governance have lower passport costs?**

Dependent Variable: Passport Cost as a Percentage of GNI

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Log GNI per capita|-46.198<br>(3.38)***|-45.588<br>(3.42)***|-44.706<br>(3.54)***|-50.886<br>(3.26)***|-48.110<br>(3.39)***|-46.478<br>(3.44)***|-49.513<br>(3.36)***|-46.975<br>(3.50)***|
|log GNI per capita squared|2.604|2.614|2.590|3.035|2.884|2.726|2.977|2.825|
||(3.28)***|(3.29)***|(3.38)***|(3.08)***|(3.18)***|(3.21)***|(3.20)***|(3.29)***|
|Voice and Accountability||-1.775||||||-0.470|
|||(1.53)||||||(0.46)|
|Political Stability and Absence of Violence|||-2.850|||||-1.648|
||||(1.74)*|||||(1.25)|
|Control of Corruption||||-4.036||||1.423|
|||||(1.83)*||||(0.54)|
|Rule of Law|||||-4.721|||-0.935|
||||||(1.85)*|||(0.30)|
|Regulatory Quality||||||-3.480||0.393|
|||||||(1.40)||(0.15)|
|Government Effectiveness|||||||-4.981|-4.312|
||||||||(2.10)**|(1.61)|
|Constant|201.528<br>(3.51)***|196.313<br>(3.59)***|190.697<br>(3.74)***|210.941<br>(3.48)***|198.901<br>(3.64)***|196.349<br>(3.70)***|204.182<br>(3.57)***|193.855<br>(3.74)***|
|Observations|127|127|127|126|127|126|127|126|
|R-squared|0.50|0.51|0.52|0.52|0.53|0.52|0.53|0.53|

Robust t statistics in parentheses * significant at 10%; ** significant at 5%, *** significant at 1%

# **4. Are High Passport Costs a Barrier to Migration?**

The previous two sections have shown that passport costs are high relative to per capita income in some countries, and that one explanation for high passport costs is poor

- 10 -
tails. Rewriting equation 5 as: where i indexes over the and j represents a distinct industry within a particular country. For any two firms from the same country and sector, it might be unrealistic to assume that the residuals uij and ui′j are uncorrelated. We decompose the total residual or error into a shared component between firms in the same country and sector, ξ1j, and a firm specific component, ǫij. Similarly, we can specify a country and sector specific random slope, ξ2j, that affects g2 in addition to the fixed component, β. The model becomes, where it is assumed that the random effects have zero means conditional on observables and the level-1 error term has zero mean given the covariates and random effects: Furthermore, given Xj the random intercept and random slope follow a bivariate distribution assumed to have zero mean and covariance matrix Ψ.<sup>6</sup> The model is estimated via maximum likelihood using **xtmixed** in STATA with bootstrapped standard errors after obtaining predicted values in a first-stage equation as in the IV approach outlined in the previous section.<sup>7</sup> ξij, represent the combined effects of omitted variables or unobserved heterogeneity at the country and industry level. Because of the shared components, the model accounts for within-country and sector dependence among the total residuals. The random intercepts and slopes can be interpreted as latent variables whose variance terms are estimated along with the other parameters and the variance term of the level1 residual ǫij. However, after estimating the model’s parameters, including ψ<sup>�</sup> 11, ψ<sup>�</sup> 22, ψ<sup>�</sup> 21 and θ<sup>�</sup> , we can obtain maximum likelihood estimates of the random intercepts and slopes by an auxiliary regression that relates the predicted total residuals on xij by OLS or empirical Bayesian

> 6The total residual is ξij ≡ ξ1j + ξ2jxij + ǫij and its conditional variance is V ar(ξij|xij) = ψ11 + 2ψ21xij + ψ22x<sup>2</sup> ij<sup>+θ,wherethelevel-1residualisassumedhomoskedasticandwithconditionalvariance</sup> V ar(ǫij|xij, ψij) = θ.

> 7The default case sets ψ21 and the corresponding correlation coefficient to zero but this assumption can be relaxed using the option covariance(unstructured).

11
- Delfgaauw, Josse and Robert Dur. 2007. "Signaling and Screening of Workers’Motivation." Journal of Economic Behavior and Organization, 62:605-624.

- Dixit, Avinash. 2002. "Incentives and Organizations in the Public Sector." Journal of Human Resources, 37(4): 696-727.

- Dur, Robert and Otto Swank. 2005. "Producing and Manipulating Information." Economic Journal, 115: 185-199.

- Ellingsen, Tore and Magnus Johannesson. 2008. "Pride and Prejudice: The Human Side of Incentive Theory." American Economic Review, 98(3): 990-1008.

- Easterly, William. 2003. "Can Foreign Aid Buy Growth?" Journal of Economic Perspectives, 17(3): 23-48.

- Easterly, William and Tobias Pfutze. 2008. "Where Does the Money Go? Best and Worst Practices in Foreign Aid.". Journal of Economic Perspectives, 22(2): 29-52.

- Farrell, Joseph, 1995, Talk is Cheap, American Economic Review, 85:18690.

- Francois, Patrick and Michael Vlassopoulos. 2008. "Pro-social Motivation and the Delivery of Social Services." CESifo Economic Studies, 54:2254.

- Gerardi, Dino and Leeat Yariv. 2008. "Costly Expertise." American Economic Review,98:187-93

- Ghosh, Suman and Eric Van Tassel. 2008. "A Model of Mission Drift in Microfinance Institutions." Mimeo, Florida Atlantic University.

26
**Figure 1 : Consumption Compared to the Time to General Agricultural Producers’ Market**

<!-- Start of picture text --> 0 2 4 6 8 10 12 14<br>Time to Market (Days)<br>140<br>120<br>100<br>80<br>60<br>40<br>Total Consumption (1,000 Shillings)<br>20<br>0<br><!-- End of picture text --> Note: Red represents walk, green taxi, blue bus, purple motorcycle, gold bicycle, and brown boda-boda. The upper five percent of time to market and of total consumption dropped The community survey also provided another element to compare results, access to paved roads. Logically it was thought that paved roads lead to cities and/or markets, allowing this to serve as a proxy to further support the results of the first graphs. Again the surveyor asked if there was access to a paved (tarmac) road within the local community<sup>16</sup> . This portion of the survey suffered from some of the same issues the section on markets did. Nonetheless, a comparison was still made between time to the paved road and consumption of the households; the results are presented in Figure 2.

that reported a travel time of 60,000 minutes, which is 1,000 hours or a little more than 40 days. To combat some of these discrepancies the analysis was conducted on a trimmed sample, the upper 5 percent of the time sample was dropped. Additionally, to make the data more manageable and more readable, the time to market was converted from minutes to hours than to days.

> 16 If the answer was no then information was collected on closest location to next paved road, time, common transportation method and time to the road.

20
**_Energy demand models for policy_** **_<u>formulation</u>_**

- Linear Programming based method (used for its interesting and useful economic interpretations through dual problem and thus providing “a natural link between process and economic analysis”).

- Input –output approach (used for its ability to capture the sectoral interdependence but faces difficulties in terms of restrictive assumptions relating to fixed technology, zero price elasticity, long time delays in data availability).

- Econometric method (for its ability to represent and validate economic theories and laws).

- Process models

- System dynamics and

- Game theory.

Pandey (2002) has used a set of attributes to classify energy models. Table A1.1 captures his classification.

Table A1.1: Classification of energy-economy models

|Paradigm|Space|Sector|Time|Examples|
|---|---|---|---|---|
|Top-down/<br>simulation|Global;<br>national|Macro-<br>economy,<br>energy|Long-term|AIM, SGM2,<br>I/O models|
|Bottom-up<br>optimisation/<br>accounting|National,<br>regional|Energy|Long-term|MARKAL,<br>LEAP|
|Bottom-up<br>optimisation/<br>accounting|National,<br>regional,<br>local|Energy|Medium<br>term,<br>short<br>term|Sector models<br>(power, coal,|

Source: Pandey (2002) Nakata (2004) has considered the modelling approach (top-down and bottom-up), methodology (partial equilibrium, general equilibrium or hybrid), modelling technology (optimisation, econometric, or accounting) and the spatial dimension (national, regional and global). This leads to another classification of models. He uses a Meta-Net approach for energy system modelling and demand analysis, further information on which is available in Kanagawa and Nakata (2006, 2007 and 2008), Ashina and Nakata (2008) and Wang and Nakata (2009).

128
# **Figures and Tables**

Figure 1: Zero-emission Vehicle Market Shares and Targets <!-- Start of picture text --> Norway Netherlands UK Fance Canada Germany<br>100%<br>Historical Japan<br>Targeted<br>80%<br>60%<br>US<br>40%<br>South Korea<br>India<br>China<br>20%<br>0%<br>2010 2015 2020 2025 2030 2035 2040 2045 2050<br>Share of zero-emission vehicles of new sales<br><!-- End of picture text --> _Notes_ : The market shares in 2020 and targets of zero emission vehicle (ZEV) in major EV countries. ZEVs include EVs and hydrogen vehicles.

18
the next subsection, this paper examines in more detail the relationship between women’s decision-making power and caloric availability.

# **4.3. Caloric availability and women’s bargaining power**

Although the results presented so far show no significant association between food budget shares and women’s power for the overall sample, this finding is not automatically linked to the relationship between women’s power and food quantity and quality, which are more relevant to human development. Women may spend more efficiently on food consumption, purchasing more nutritious foods with less money, for example. Some evaluations of conditional cash transfer programs show that when women’s power increases as cash transfers going directly to women, households consume more calories (Attanasio and Lechene 2002; Djebbari 2005) and more nutritious calories (Hoddinott 1996; Hou 2010).

This paper uses total caloric availability and caloric availability from different sources, such as calories from grains, calories from vegetables and fruits, calories from animal products, and calories from other sources to examine the relationship. Figure 4 shows the relationship between women’s power and caloric availability after controlling for per capita expenditure. After controlling for per capita expenditure, when women’s power increases, per capita calorie availability actually decreases. Calories from vegetables and fruits and calories from other sources increase, but calories from grains and animal products decrease.

After controlling for other variables, the regression analysis (Table 4) shows similar results except for per capita caloric availability from animal products, which shows a strong positive correlation with women’s power. The breakdown of urban and rural analyses reveals similar relationships. These patterns suggest that when women have more power, households 13
where the subscript _s_ denotes the state and _t_ the year, _Hst_ is the total stock of the skilled workers in state _s_ year _t_ , _θst_ is a vector of other control variables (discussed below) and _ust_ is the error term. The equation also includes lagged values of the dependent variable as an explanatory variable to capture possible convergence or divergence from the mean. In separate regressions, the outcome variable, _Yst_ , equals GDP and value added in agriculture, total manufacturing, registered manufacturing, unregistered manufacturing and the services sector. In section 3 the dependent variable is value added in the various sectors within registered manufacturing and services sector (pooled together).<sup>1</sup> Our data covers the fourteen major states of India over the 1980-2000 period.<sup>2</sup> One problem with estimating (1) directly is that data on the stock of skilled labor ( _Hst_ ) at the state level are not available. What we do have is data on the number of students enrolled in higher education (colleges and universities) which is a flow measure since current students add to the stock of human capital in the future. To use this flow measure we make two assumptions and modify equation (1) to an appropriate form.

First, we assume that, from the date of enrollment, it takes about four years for a student to complete her higher education and find a job. This is a reasonable assumption since the data on enrollments that we use below covers students at the undergraduate level (Bachelor of Arts, Science and Commerce) which is a three year program in all the states.<sup>3</sup> The second assumption relates to inter-state migration of the highly skilled. If such migration were significant then it would weaken the case for using enrollments as a

> 1 In the regressions, the dependent variables are appropriately deflated by the total population of the states. 2 List of the 14 major states is provided in Table 3.

> 3 Further, enrollments in a given year relate to the academic year September to August while data on the outcome variables (GDP per capita, etc.) relates to the financial year beginning in April implying an additional lag of about 4 months before the full impact of new graduates is felt on output. If we assume that it takes about 5-6 months to search for a job we get that it takes about four years from the date of enrollment before a student's skills have any significant effect on output.

3