rafmacalaba/datause-encoder-data huggingface dataset:
|(6)<br>IV<br>Exports (log)<br>-0.042**<br>(0.018)<br>0.421***<br>(0.123)<br>0.046<br>(0.154)<br>0.047<br>(0.094)<br>0.576***<br>(0.088)<br>0.031***<br>(0.009)<br>0.180<br>(0.127)<br>-0.063*<br>(0.037)<br>-0.065<br>(0.080)<br>0.005<br>(0.071)<br>0.000**<br>(0.000)<br>121<br>0.949<br>121<br>NO|
|---|
|Table 7: GVC vs Traditional Trade<br>(1)<br>(2)<br>(3)<br>(4)<br>(5)<br>Regressions<br>IV - BE<br>IV - BE<br>IV - BE<br>IV<br>IV<br>Dependent variables<br>BGVC (log)<br>FGVC (log)<br>Exports (log)<br>BGVC (log)<br>FGVC (log)<br>Avg. tari¤ rate (%)<br>-0.069**<br>-0.002<br>-0.044**<br>-0.058**<br>-0.004<br>(0.028)<br>(0.026)<br>(0.020)<br>(0.023)<br>(0.022)<br>FDI in‡ows (log)<br>0.492***<br>0.292**<br>0.428***<br>0.424***<br>0.295**<br>(0.151)<br>(0.141)<br>(0.111)<br>(0.138)<br>(0.130)<br>Distance to GVC hubs (log)<br>-0.475*<br>-0.303<br>0.073<br>-0.566**<br>-0.268<br>(0.288)<br>(0.269)<br>(0.211)<br>(0.221)<br>(0.216)<br>Political stability index<br>0.088<br>-0.037<br>0.037<br>0.148<br>-0.064<br>(0.140)<br>(0.130)<br>(0.103)<br>(0.123)<br>(0.130)<br>Domestic industrial capacity<br>0.455***<br>0.696***<br>0.567***<br>0.516***<br>0.688***<br>(log)<br>(0.111)<br>(0.104)<br>(0.082)<br>(0.096)<br>(0.088)<br>Rents from resources / GDP<br>-0.004<br>0.022***<br>0.032***<br>-0.006<br>0.022***<br>(%)<br>(0.008)<br>(0.007)<br>(0.006)<br>(0.007)<br>(0.008)<br>Capital / GDP (log)<br>0.286<br>0.082<br>0.178<br>0.268*<br>0.122<br>(0.193)<br>(0.181)<br>(0.142)<br>(0.158)<br>(0.170)<br>Land / GDP (log)<br>-0.128***<br>-0.013<br>-0.067*<br>-0.119**<br>-0.011<br>(0.048)<br>(0.045)<br>(0.035)<br>(0.047)<br>(0.040)<br>Med/High-skilled labor / GDP (log)<br>-0.076<br>-0.022<br>-0.070<br>-0.098<br>0.008<br>(0.123)<br>(0.115)<br>(0.090)<br>(0.115)<br>(0.133)<br>Low-skilled labor / GDP (log)<br>0.038<br>-0.189*<br>0.018<br>0.029<br>-0.222*<br>(0.117)<br>(0.110)<br>(0.086)<br>(0.101)<br>(0.131)<br>Exch. rate appreciation<br>0.000<br>0.000<br>0.000<br>0.000*<br>0.000*<br>(0.001)<br>(0.001)<br>(0.000)<br>(0.000)<br>(0.000)<br>Observations<br>290<br>290<br>290<br>121<br>121<br>R-squared<br>0.920<br>0.925<br>0.950<br>0.924<br>0.923<br>Number of country<br>121<br>121<br>121<br>121<br>121<br>Decade ...xed e¤ects<br>YES<br>YES<br>YES<br>NO<br>NO<br>Notes: LS standard errors in parentheses for Columns (1)-(3); Robust standard errors for Columns (4)-(6);<br>BGVC is backward GVC participation in levels (logs) and FGVC is forward GVC participation in levels (logs);<br>***, **, and * indicate signi...cance levels of 1%, 5%, and 10%, respectively.|

39
26 <!-- Start of picture text --> Chart V<br>TURKEY<br>55<br>50 _<br>45 -<br>40-<br>35-<br>30 _<br>25 -<br>20 -<br>10 _<br>5 19'65 I 19'67 I 19'69 I 19'71 1 19'73 I 19'75 1 i77 I 1979 19'81 4 1983 19485<br>1966 1968 1970 1972 1974 1976 1978 1980 1982 1984<br>Year<br>0 Priv.invt./GDP + Dom.Cred./GDP<br><!-- End of picture text -->
<!-- Start of picture text --> $151 re<br>Traditional<br>12 Banking<br>. $8.5<br>Shadow<br>6 Banking<br>3<br>0 rT ry, ? yj] 1 © ) t | © te op te nk nk pf ert? Peon ky<br>1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text -->
# **Endnotes**

1. Despite its potential pejorative connotation, I use “slums” in this paper because this is an official policy terminology in India. In Section 2, I discuss its definitions in the Indian context.

2. A new administrative ward has recently been added, yet this paper adheres to the 14 wards as shown in the _Pune Slum Atlas._ 3. This study does not include the areas governed by the Pune Cantonment Board and the Khadki Cantonment Board.

4. These areas include residential zones, commercial zones, and land reserved for the Economically Weaker Section (EWS).

5. The cut-off date has recently been extended again to January 1, 2000 (Kulkarni, 2014).

6. Two extra households were unexpectedly surveyed in a slum.

7. The inverse of the probability of each household being selected are multiplied as sampling weights. I refer to MASHAL (2011) to obtain the total number of households in each slum.

8. 3,128 Indian rupees is equivalent to approximately 50 USD as of 2013.

9. Virtually all the surveyed households had access to electricity. Because of the lack in variation, I did not include this variable in this study.

10. The local and state government agencies include the Pune Municipal Corporation, the State Government of Maharashtra, and the Maharashtra Housing and Area Development Authority (MHADA), which is a parastatal agency in charge of affordable and public housing development. The central government agencies include the railway and defense authorities.

11. Instead of the current 76 electoral wards, I use 144 electoral wards in reference to the _Pune Slum Atlas_ .

12. Variance inflation factors (VIF) for variables in column 1 are all less then 2, demonstrating that multicollinearity is not a serious issue in the model.

13. I use _spdep_ library (Bivand, 2014) of R software (R Core Team, 2014).

14. The number of households in an electoral ward is also a continuous variable, but it turns out that the logarithm of the variable fits very well.

15. Moran’s I test did not detect spatial autocorrelation in the residuals ( _p_ = 0 _._ 114).

27
low-case scenario, with impaired loans exceeding 5 and 8 percent of loan loss provision in 2010 in Ethiopia and Botswana, respectively.

# **Figure 4: Predicted Value of Nonperforming Loans in Percent of Loan Loss Provision following changes in Banking-level Determinant**

Predicted value of non-performing loans (in % of loan loss provisions) <!-- Start of picture text --> +/- 2 * standard deviation<br><!-- End of picture text --> <!-- Start of picture text --> High net interest margin and equity<br>in % of liquid assets<br>20<br>Benin<br>15<br>10<br>5<br>0<br>-5<br>15<br>Ethiopia<br>10<br>5<br>0<br>-5<br>-10<br>1995 1997 1999 2001 2003 2005 2007 2009<br>1995 1997 1999 2001 2003 2005 2007 2009<br><!-- End of picture text --> <!-- Start of picture text --> Low net interest margin and equity in<br>% of liquid assets<br>20<br>Benin<br>15<br>10<br>5<br>0<br>-5<br>1995 1997 1999 2001 2003 2005 2007 2009<br><!-- End of picture text --> <!-- Start of picture text --> 12<br>Ethiopia<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>-8<br>1995 1997 1999 2001 2003 2005 2007 2009<br><!-- End of picture text --> <!-- Start of picture text --> 12<br>Togo<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>1995 1997 1999 2001 2003 2005 2007 2009<br><!-- End of picture text --> <!-- Start of picture text --> 12<br>Togo<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>-4<br>-6<br>1995 1997 1999 2001 2003 2005 2007 2009<br><!-- End of picture text --> 24
24 Table II I CtAracnrdics of CompiAted Spells of Uoemployunt: Majes Aged **Tlwve and** Over, 199-91

|||||Proponiro Ramini|ng Unemployed||||
|---|---|---|---|---|---|---|---|---|
|Cbnricteristics|||By Age|Group|||By fiduetioo<br>||
||||||||||
||All Make|12-20|21-30|31-40|41+|0-6|7-9|0+|
|I. S9andard Deinition UE|||||||||
|I'ropodtioof spclls ending|||||||||
|within tduccmooo<br>|.41<br>|.38|.40|.50|.43|.44|45|.34|
|within six month<br>|.69<br>|.66<br>|.69<br>|.79|.57|.76|.70|.58|
|withinnine montw|.81|.82|.82|.82|.75|.85|.84|.72|
|I'ropOrtiOOofspellsli3ing<br>**ovcr**ayear<br>|.12<br>|.09|.13|.10|.1S|.10|.09|.16|
|Meanduraion of a compklied spel (months)|5.7|5.7|5.6|5.3|6.4|5.2|5 3|6.6|
|2.**Ahcmlnvs**DernitionUE|||||||||
|P-oponiooofspells ending|||||||||
|within**_dure_rnonths**<br> "|.39<br>|.37<br>|.39<br>|.45|.35|.39|.42|.34|
|within**six**ions<br>|.61|.62|.64|.71|.43|.62|.65|.65|
|within ninemoRio"|.72|.76|.77|.74|.55|.70|.80|.69|
|Propornionof spelblating**avcr**syea|.19|.14|.15|.19|.41|.25|.12|.20|
|Mean duration of acogpktp.lspel<br>(montks)|6.4|6.1|6.0|5.9|8.4|6.7|5.8|6.8|
invisible walls. We argue that interstate mobility is inhibited by the existence of state level entitlement schemes. The non-portability across state borders of social welfare benefits, such as access to subsidized food or issuance of PDS ration cards, weakens the incentive to move for the poor and the unskilled. People are deterred from seeking education in other states because state residents get preferential access in the numerous universities and technical institutes that are under state government control. Finally, the skilled are reluctant to move to other states to seek employment because state governments are still major employers and grant de facto preferences to their own residents. We provide preliminary evidence that that the relative share of migrants moving out-of-state is linked to the importance of these entitlement schemes in each state.

This research can be taken forward in at least three ways. First, the data can be updated when the Census Bureau releases the data for 2011 and enriched in several ways. The data tables that were made available to us are two dimensional, for example, we can observe either the skill composition or the motive for migration in bilateral flows between districts but not both dimensions simultaneously. Multidimensional data would facilitate richer analysis of the determinants and consequences of internal migration in India. Second, our analysis of the reasons why state borders restrict mobility is both selective and preliminary at this stage. A fuller analysis would examine the role of other factors, e.g. such as the National Rural Employment Guarantee scheme, and for finer evidence of their relative impact. Finally, we motivate this study by noting that labor mobility enables the reallocation of labor to more productive opportunities across sectors and regions and hence promotes growth. Future analysis should assess how far India’s “fragmented entitlements” – i.e. state-level administration of welfare benefits, as well as education and employment preferences – dampen growth by preventing the efficient allocation of labor. It may also be possible to assess the impact of the implementation of a unique national identification system which will lower but not eliminate the costs of moving.

29
# **Table 4 Job creation and job destruction by sector, 2001**

|Sector<br>Gross job<br>creation<br>Gross job<br>destruction<br>**Ownership**<br>**All units**|Net job<br>creation<br>(employment<br>growth)<br>re|Job<br>allocation<br>|Share in<br>employment<br>(sample)|
|---|---|---|---|
|State a)<br>3.0<br>8.4|-5.4|6.0|30.0|
|Collective b)<br>7.0<br>13.2|-6.2|14.0|60.9|
|Private<br>16.5<br>8.6|7.97|17.1|9.1|
|**Non-agricultural business sector**||||
|State a)<br>2.6<br>7.0|-4.5|5.2|42.1|
|Collective b)<br>8.9<br>11.6|-2.7|17.8|46.4|
|Private<br>11.6<br>7.5|4.1|14.9|11.4|
|**Firm size**||||
|Micro (1-10)<br>37.3<br>9.5|27.9|18.9|3.4|
|Small (11-50)<br>13.5<br>12.6|0.8|25.3|12.9|
|Medium (51-250)<br>6.8<br>15.4|-8.6|13.5|34.0|
|Large (251+ )<br>2.8<br>9.0<br>**Activity**|-6.2|5.6|49.7|
|Agriculture<br>3.8<br>13.8|-10.1|7.5|34.1|
|Business sector<br>7.9<br>9.9|-2.1|15.8|61.0|
|Non-busines sector<br>11.9<br>11.7<br>**Industry**|0.3|23.3|5.0|
|Agriculture<br>3.8<br>14.0|-10.2|7.5|34.0|
|Manufacturing c)<br>7.4<br>11.9|-4.5|14.7|33.3|
|Services<br>9.0<br>9.2|-0.2|17.9|32.8|

Note: Data refer to continuing firms which are registered legal persons. State ownership includes municipal and mixed state/private ownership.

b) "Collective" ownership includes joint-stock companies, cooperatives, and social or political organizations. Joint-stock companies are either state-owned or privatized.

c) Including mining and construction.

d) Including mining, electricity, gas and water.

e) Social, community and personal services. _Source: Annual Survey of Enterprises, 2000 and 2001; author's calculations._

# **_Firm ownership_**

One of the key firm characteristics is ownership, which influences governance and is reflected it the firm’s behavior. Data on job turnover by firm ownership strongly 18
∗ ∗ ∗ ∗ ∗ _pmt M t_ + _pmkt M K t_ + _pmnt M N t_ + _i DEBTt_ ≡ _pet Et_ + _FGRS t_ + _EFLOW St_ + _Bt_ (57)

# **Labor Market**

# **Goods Market**

# **Glossary**

# **_Parameters_**

α _v_ shift parameter in the CES function for _V an_ coefficient of intermediate imports _an_ coefficient of capital imports<sup>_d_</sup> _g_ share of public external debt δ depreciation rate of capital<sup>δ</sup> _tc_ cost share parameter in the CES function for _Q_<sup>δ</sup> _te_ cost share parameter in the CET function for _Q_ 32
PROVISION OF WATER TO THE POOR IN AFRICA

**Table 3.8    WAIO in Maputo**

||Coverage<br>(%)|Price<br>(US$/m3)|
|---|---|---|
|Adem connection|22.8%|0.44|
|SSIP connection|11.5%|0.88|
|HH reseller|25.8%|0.81|
|Public standpipe|14.3%|0.58|
|SSIP standpipe|11.4%|0.92|
|Pvt B/H /neighbours|6.9%|0.99|
|Own B/H|5.6%|0.00|
|Public handpump|1.9%|0.00|
|Total|100%||
|Source: BA Maputo 2006|||
|WAIO (total)|1.62||
|WAIO (unconnected)|1.82||

The lack of attention to standpipe pricing and management issues, together with the constraints that utilities face in expanding service to a rapidly growing population, means that in low coverage countries, the poor remain largely outside of the formal sector. This indicator provides a more accurate picture of the reality faced by all of the urban population in low coverage countries than formal coverage and prices alone, and can therefore provide a useful tool for monitoring purposes before and during reform processes.

36
19 footnote 3, typically the status quo is adopted as the baseline case by modelers when in fact rising agricultural protectionism – and hence higher estimated welfare gains from reform – may well be more realistic.

Surveys of attempts to explain agricultural policies are available in Binswanger and Deininger (1997) and de Gorter and Swinnen (2002), while surveys focusing on explaining trade policy more broadly include Rodrik (1995) and Gawande and Krishna (2003). Following the pioneering theoretical work by Grossman and Helpman (2001, 2002), substantial econometric progress has been made in recent years to test elements of that theory but, as Ethier (2006) warns, that should not be the only approach. It is possible also to use economy-wide models to back out politicians’ preferences from the estimated impacts of the protection structure on industry value added. Balaoing and Francois (2005) adopt that approach in analyzing EU trade policy. To use it for developing countries first requires bringing together comparable time series estimates of the structure of distortions in those countries. Then it will be possible to test hypotheses about trends over time and across industries, about changing choices of policy instruments (including the relative importance of exchange rate measures, indirect border measures such as manufacturing tariffs, and various agricultural price, trade and direct farm income measures), and about fluctuations in distortions around longer-run trends.

# _Moving forward_

To progress this research agenda, a newly launched research project at the World Bank is revisiting the Krueger, Schiff and Valdes study but for a larger sample of countries. Its first phase is focusing on measuring the extent of direct and indirect
Evans, D.S. and L. S. Leighton (1989), “Some Empirical Aspects of Entrepreneurship,” _American Economic Review_ 79(3): 519-535.

Evans, D. S., & B. Jovanovic (1989), An Estimated Model of Entrepreneurial Choice under Liquidity Constraints. _Journal of Political Econom_ y, 97(4), 808–827.

Fajnzylber, P., W. F. Maloney and G. Montes Rojas (2007) “Micro-Firm Dynamics in Less Developed Countries: How Similar Are They to Those in the Industrialized World? Evidence from Mexico” _World Bank Economic Review,_ Vol. 20:3 pp389-420.

Fields G. S. (2006) "A Guide to Multisector Labor Market Models". World Bank Labor Market Conference, Washington, DC November 18-19, 2004 Fiess, N. and M. Fugazza and W. Maloney (2006) “Informal Labor Markets and Macro Economic Fluctuations,” mimeo, World Bank.

Flinn, C. J. and J. J. Heckman (1983) “Are Unemployment and Out of the Labor Force Behaviorally Distinct Labor Force States, _Journal of Labor Economics_ 1(1):28-42.

Flinn, C. J. and J.J. Heckman (1982a) “Models for the Analysis of Labor Market Dynamics,” in _Advances in Econometrics_ , R. Basmann and G. Rhodes, eds. Greenwich, Conn.: JAI 1982.

Flinn, Christopher J. and J. J. Heckman (1982b), “New Methods for Analyzing Structural Models of Labor Force Dynamics,” _Journal of Econometrics_ 18 (1982) 11568.

Fougère, D. and T. Kamionka (2003), "Bayesian Inference for the Mover-Stayer Model in Continuous Time, with an Application to Labour Market Transition Data," _Journal of Applied Econometrics_ , 18: 697-723.

Fujita, S. and Ramey, G. (2007) “The Cyclicality of Separation and Job Finding Rates,” _International Economic Review_ , forthcoming Fujita, S. and Ramey, G. (2006), “The Cyclicality of Job Loss and Hiring” Working Papers 06-17, Federal Reserve Bank of Philadelphia.

Fugazza, M. and J. F. Jacques, 2004, “Labor Market Institutions, Taxation and the Underground Economy, _Journal of Public Economics,_ 88: 395-418.

Geweke J., R. Marshall and Gary A. Zarkin (1986) “Exact Inference for Continuous Time Markov Chain Models,” _Review of Economic Studies_ 53(4). Pp653-69 Hall, R. (2005); “Employment Efficiency and Sticky Wages: Evidence Flows in the Labor Market,” _Review of Economics and Statistics_ 87(3)397-407.

Hart, K. (1972). "Employment, Income and Inequality: A Strategy for Increasing Productive Employment in Kenya". Geneva: International Labor Organization.

27
**<u>Table 4: Intensity of Input Use and Resource Endowment by Credit Market Participation Status</u>**

||**E**|**ntire sam**|**ple**||**Gojjam on**|**ly**||**Wollo onl**|**y**|
|---|---|---|---|---|---|---|---|---|---|
||**Total**|**Cons.**|**Uncons.**|**Total**|**Cons.**|**Uncons.**|**Total**|**Cons.**|**Uncons.**|
|**Endowment ratios**||||||||||
|Cult. area for select crops (ha)|1.32|1.26|1.44|1.80|1.71|2.02|0.90|0.84|1.00|
|Male adults per ha owned land|1.92|1.90|1.96|1.26|1.26|1.28|2.49|2.50|2.48|
|Female adults ha of owned land|1.83|1.80|1.90|1.18|1.18|1.17|2.40|2.38|2.45|
|Oxen owned per ha owned land|1.45|1.36|1.62|1.26|1.16|1.49|1.61|1.55|1.72|
|**Intensity of input use**||||||||||
|Used fertilizer?|0.41|0.39|0.45|0.82|0.76|0.94|0.06|0.05|0.08|
|Quantity (kg/ha)|31.03|29.16|34.78|63.23|58.10|74.76|2.82|2.01|4.29|
|Used manure?|0.70|0.69|0.71|0.58|0.58|0.58|0.80|0.79|0.80|
|Quantity (kg/ha)|591.77|542.04|691.79|138.96|147.51|119.71|988.38|912.05|1128.03|
|Used pesticides?|0.13|0.12|0.16|0.22|0.18|0.31|0.05|0.05|0.04|
|Quantity (kg/ha)|0.30|0.19|0.52|0.38|0.11|0.98|0.22|0.26|0.16|
|Oxen days (pair/ha)|20.99|21.34|20.30|18.02|17.66|18.84|23.59|24.78|21.42|
|Male family days/ha|113.77|116.48|108.31|60.63|58.81|64.73|160.31|170.57|141.54|
|Female family days/ha|59.83|60.01|59.47|31.55|31.49|31.68|84.60|86.76|80.66|
|Hired labor?|0.26|0.24|0.31|0.29|0.25|0.37|0.24|0.22|0.26|
|Hired labor days/ha|5.52|5.37|5.81|2.62|2.16|3.68|8.05|8.39|7.44|
|Number of observations|1587|1060|527|741|513|288|846|547|299|

**Source:** Own computation from AAU/Gothenburg/WB survey 2006/07.

19
To show how countries with different levels of regulatory quality would benefit from an improved regulatory improvement, we estimate the average marginal effect of the Regit variable by country and then simulate the gains in productivity growth across countries if they all engage in a sustained reform effort aimed at achieving the best practice of the countries in the sample. Following a reasonable conjecture, this hypothetical improvement would not have the same magnitude in all countries, as it would be conditioned on the last observed value for regulatory conditions in 2010. Specifically, the reform would imply, for each country, augmenting the Regit variable from its observed value in 2010 to the value of the country with the highest observed value in the same year, which is Denmark (with a value of 1.9). In other words, the benchmark for regulatory reform used in this exercise is the implementation of the regulatory settings related with the country with the best environment in 2010. The closer a country is to this 2010 benchmark the less ambitious would be the reform it would implement.

It is worth acknowledging that the simulated values produced by this kind of exercise should be only considered as indicative parameters. Regulatory reform is not assumed to change the estimated average parameters of the model, and for this reason, it would not change the estimated average relationship between the outcome variable and the other covariates. Nevertheless, this kind of exercise can be a useful instrument to have a sense of the magnitude of the impact on the economy of a possible policy change.

Figure 3 presents the results of a simulation that considers the impact on productivity growth due to a regulatory reform initiative that improves the country’s regulatory environment to the level of Denmark in 2010. The average increase in annual productivity growth ranges from 0.5 percent for Finland to 15.5 percent for Bulgaria. Romania stands out with the second highest productivity dividend (14.1 percent).

**Figure 3: Increase in average annual productivity growth (over 1995-2010) given a move to a regulatory environment equivalent to Denmark in 2010**

<!-- Start of picture text --> 18.00<br>16.00<br>14.00<br>12.00<br>10.00<br>8.00<br>6.00<br>4.00<br>2.00<br>0.00<br>percent<br>Finland Netherlands Ireland Sweden United Kingdom Austria Germany Norway Belgium Malta France Estonia Cyprus Spain Iceland Italy Czech Republic Portugal Greece Hungary Slovenia Slovakia Poland Lithuania Latvia Romania Bulgaria<br><!-- End of picture text --> _Note: The regulatory environment is measured by the regulatory quality indicator (WGI data). Results for Switzerland and Luxembourg are not presented as the corresponding marginal effects of regulatory quality were not statistically significant. Table IV, Annex, presents the average marginal effect of regulatory quality for all countries. The figure is based on the estimation of Eq(1) with_ Open_trade _variable. Results are robust to the use of_ Open_tradeEU _variable._ 9