rafmacalaba/datause-encoder-data huggingface dataset:
Bloom, N., B. Eifert, A. Mahajan, D. McKenzie and J. Roberts. (2013). “Does management matter? Evidence from India." _Quarterly Journal of Economics_ , 128(1): 1-51.

Bloom, N., R. Lemos, R. Sadun, D. Scur, and J. Van Reenen. (2014). The New Empirical Economics of Management”, _Journal of the European Economic Association,_ 12(4):835–876 Bloom, N., R. Sadun, and J. Van Reenen. (2016a). “Management as a Technology.” Harvard Business School Working paper # 16-133 Bloom, N., R. Lemos, R. Sadun, D. Scur, and J. Van Reenen. (2016b). “International Data on Measuring Management Practices”, _American Economic Review: Papers & Proceedings,_ 106(5): 152–156 Bloom, N., E. Brynjolfsson, L. Foster, R. Jarmin, M. Patnaik, I. Saporta-Eksten. and J. Van Reenen. (2019a) “What Drives Differences in Management Practices?”, _American Economic Review_ , 109(5): 1648-1683 Bloom, N., L. Iacovone, M. Pereira-López and J. Van Reenen. (2019b) “Spillovers, Market Size and Misallocation: Management in Mexico”, mimeo Foster, L., J. Haltiwanger, and C. Syverson. (2008). “Reallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability?” _American Economic Review_ , 98 (1): 394–425.

Grover, A. and I. Torre. (2019). “Management Capabilities and Performance of Firms in the Russian Federation”, World Bank policy research working paper, _forthcoming_

- Grover, A., D. Medvedev. and E. Olafsen. (2019) High <u>Growth Firms: Facts, Fiction, and Policy Options for Emerging Economies, World Bank, Washington, DC</u>

Hall, R. and C. Jones. (1999). “Why Do Some Countries Produce so Much More Output per Worker than Others?" _Quarterly Journal of Economics_ , 114(1): 83-116.

Haltiwanger, J., R.S. Jarmin and J. Miranda. (2013) “Who Creates Jobs? Small versus Large versus Young”, _Review of Economics and Statistics_ , 95(2): 347-361 Hsieh, C.-T., and P. J. Klenow. (2009). “Misallocation and Manufacturing TFP in China and India.” _Quarterly Journal of Economics_ , 124 (4): 1403–48.

Hsieh, C.-T. and P. J. Klenow. (2014) “The Life Cycle of Plants in India and Mexico”, _Quarterly Journal of Economics_ , 129(3): 1035-1084 Iacovone, L. and M. Pereira-López. (2017) “Management Practices as Drivers of Innovation: New Evidence from Mexico”, mimeo Iacovone, L., M. Maloney and D. McKenzie. (2019). Improving Management with Individual and Group-Based Consulting: Results from a Randomized Experiment in Colombia, World Bank Policy Research Working Paper 8854.

McKenzie, D. and C. Woodruff. (2017) "Business Practices in Small Firms in Developing Countries," _Management Science_ , 63(9): 2967-2981.

Syverson, C. (2011). “What Determines Productivity?” _Journal of Economic Literature_ , 49 (2): 326–65.

Walker, F. (1887) “The Source of Business Profits.” _Quarterly Journal of Economics_ . 1(3): 265-288 28
|Wonogiri(C.Java)||7.85||6.51|=<br>.92||2.72|
|---|---|---|---|---|
|<br>Ngada,NTT|<br>8.85|<br>|4.68|2.06|<br>2.87|
|Source:BasedonLLI2data|||||

_IH.B) Ten empirical proxiesfor four dimensions of local governance_ The LLI2 instrument also elicited household responses about _desa_ government'<sup>3</sup> . We used ten specific questions about four dimensions of govemance: _information, participation, voice, and perceived responsiveness_ to local problems.

_Information._ Households were asked if they knew about three types of information associated with _desa_ government: the development programs operating in the _desa;_ the use of _desa_ funds; and funds available for development projects. If the household knew about "all three" we count them as informed. On average, information was quite widespread with between 45 and 50 percent of household having heard about any one of _desa_ budgets, use of funds or development projects and 35 percent having heard of all three (Table 3). In addition, all households were asked if information about these _desa_ government activities was "more open" than four years ago. Perhaps surprisingly given the political changes, only 20 percent thought information about all three was "more open" than four years ago.

> **13** That these are _household_ responses should be stressed as a considerable amount of the variation in reported governance consists of differences across individuals, not just differences across villages.

13
of the likelihood of being a treated municipality as a function of these characteristics, and taking the predicted probabilities from this estimation which form the propensity score.

The common support is the range of overlap of the distributions of the propensity score for the treatment and the control groups. In our case this ranges from 0.008 to 1. Restricting the untreated municipalities to this range of common supports eliminates 410 municipalities that are not very similar to the treated municipalities. Column 3 of Table 1, and the other plotted line in Figure 2 are then for the sample of 357 municipalities within this common support. We see this group has more registrations on average and a larger population size than the full set of untreated municipalities, but is still significantly different in size and in pre-reform trend.

We therefore further restrict the sample by using nearest neighbor matching. This selects (with replacement) the control municipality that is the closest match to each treated municipality in terms of the propensity score. This results in the use of only 29 of the 767 non-treated municipalities. The fourth column of Table 1 shows that this control group is much more similar to the treatment group. In particular, there is no significant difference in the average number of registrations or their variability, population size averages 50,000 compared to 56,800 for the treated municipalities, and there is only a marginally significant difference in the share of industry in municipal GDP. Moreover, although we only matched on pre-2011 data, the level and variability of registration rates are not significantly different over the eight pre-reform months of 2011, which provides a nice falsification test. Figure 3 plots the registration data for this nearest neighbor control group. We see it is much closer in level to the treatment municipalities, and displays similar trends. We cannot reject a common linear time trend, although do reject a common non-parametric time trend over the 44 pre-intervention months. This appears to be driven by a difference in January 2011, and so the last row of Table 1 shows that we cannot reject common time trends for all other pre-intervention months.

Finally, we also consider an alternative control group consisting of the 18 municipalities which were initially selected for the Minas Fácil Expresso program, but where it was not yet implemented by the end of our study period. The last column of Table 1 shows the characteristics of these municipalities, while Figure 3 plots their time path of registrations. We see these municipalities are slightly larger in population and had slightly more registrations per month than the treated municipalities, but not significantly so. Their time path of registrations appears to visually track that of the treated municipalities, but because the variability is slightly larger, we reject equal non-parametric trends pretreatment.

10
Table B3: Difference-in-Difference Regressions [Dependent Variable: Food Insecurity] <!-- Start of picture text --> Fractional Index 3 Fractional Index 4<br>Ethiopia Malawi Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Mild Moderate Severe Index Mild Moderate Severe Index<br>HHI 2<br>Ethiopia Malawi<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1 -1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity Effect on Food Insecurity<br>Nigeria<br>-1 -.8 -.6 -.4 -.2 0 .2 .4 .6 .8 1<br>Effect on Food Insecurity<br>Mild Moderate Severe Index<br>46<br><!-- End of picture text --> _Note:_ The figures plot results using our second fractional index that spans pre- and post-outbreak data. This index differs from our main fractional index in that it is not standardized across countries. Results come from dynamic panel and dynamic panel with interactions regression (see Equation 3.3). Horizontal lines represent 95 percent confidence intervals calculated using standard errors clustered at the household.
# **Table 13**

**8-year and 10-year Panel Growth Regressions, Restricted Sample: Non-high-income Economies**

|||**8-ye **|**arpanel**|||**10-ye **|**arpanel**||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
||**OLS**|**IV**|**FE**|**GMM**|**OLS**|**IV**|**FE**|**GMM**|
|Log of GDP pc, beg. period|-0.016***|-0.029***|-0.040***|-0.026***|-0.018***|-0.030***|-0.050***|-0.028***|
||(0.003)|(0.006)|(0.008)|(0.009)|(0.004)|(0.006)|(0.009)|(0.010)|
|Log of EXPY, beg. period|0.032***|0.075***|0.025**|0.050***|0.035***|0.075***|0.024*|0.043***|
||(0.006)|(0.018)|(0.012)|(0.012)|(0.006)|(0.020)|(0.013)|(0.013)|
|Log of % secund. school years attained., beg. period|0.007***|0.004|0.003|0.013***|0.006***|0.003|-0.001|0.013|
||(0.002)|(0.003)|(0.004)|(0.005)|(0.002)|(0.003)|(0.004)|(0.009)|
|Constant|-0.133***|-0.398***|0.135|-0.231**|-0.143***|-0.380***|0.228**|-0.145|
||(0.041)|(0.115)|(0.096)|(0.114)|(0.045)|(0.130)|(0.108)|(0.118)|
|Observations|262|257|262|257|208|204|208|204|
|Number of group(ecode)|||53|53|||53|52|
|Instruments||||34||||21|
|R-squared|0.48|0.34|0.59||0.54|0.46|0.69||
|First stage F-statistic of excluded instrument p-value (H0:<br>underidentified/weakly identified)||0.000|||||||
|Hansen J statistic p-value (H0:overidentified)||0.042||0.584||0.046||0.856|
|Stock-Wright S-statistic p-value (H0: all endogenous<br>regressors==0 and overid. rest. valid, weak-instrument-robust<br>inference)||0.000|||||||
|AB test for AR(2) in first diff. (p-value)||||0.640||||0.913|

Robust standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1% All equations include period dummies. IV regressions use log population and log land area as instruments. FE regressions include dummies for countries.  GMM is Blundell-Bond System GMM estimator using lagged growth rates and levels as instruments as well as log population and log land area.

38
# **1. Introduction**

The urbanization of the developing world’s population has been viewed in different ways by different observers. To some it has been seen as a positive force in economic development, as economic activity shifts out of agriculture to more remunerative activities. Indeed, longstanding theories of economic development, going back to Arthur Lewis and Simon Kuznets, have viewed population urbanization as a core part of the process leading to economic growth and distributional change (including poverty reduction) in poor countries. By this view, new economic opportunities in urban areas attract rural workers, who gain directly, and there may also be positive feedback effects in rural areas. To others, urbanization has been viewed in a somewhat less positive light — a largely unwelcome forbearer of new poverty problems. Advocates of this view often point to (claimed) negative externalities of geographically concentrated poverty and irreversibilities due to various costs of migration, which can mean that migrants to urban areas cannot easily return to their old standard of living in rural areas.

Empirical knowledge for informing this debate has been rather limited and problematic. We do not even have a firm grip of the basic stylized facts, including the extent to which absolute poverty in the world is urban versus rural. While the premise of much development policy making is that extreme poverty in the developing world is primarily rural, there is a perception in some quarters that this has changed appreciably in recent rimes; indeed, some observers believe that poverty is now mainly an urban problem. In an early expression of this view, the distinguished scientific journalist and publisher Gerard Piel told an international conference in 1996 that “The world’s poor once huddled largely in rural areas. In the modern world they have gravitated to the cities.” (Piel, 1997, p.58).

This paper aims to throw new light on the extent to which poverty is in fact urbanizing in the developing world and what role, if any, population urbanization has played in poverty reduction. We report our results in studying a new data set created for this paper, covering about 90 developing countries with observations over time for about 80% of them.

2
WPS4254

# **The Causes of Civil War**

Simeon Djankov The World Bank and CEPR

## Marta Reynal-Querol<sup>1</sup>

Universitat Pompeu Fabra, CEPR, and CESifo

**Abstract** The dominant hypothesis in the literature that studies conflict is that poverty is the main cause of civil wars. This paper instead analyzes the effect of institutions on civil war, controlling for income per capita. The authors set up a model in which institutions are endogenous and colonial origins affect civil wars through their legacy on institutions. The results indicate that institutions, proxied by the protection of property rights, rule of law and the efficiency of the legal system, are a fundamental cause of civil war. In particular, an improvement in institutions from the median value in the sample to the 75<sup>th</sup> percentile is associated with a reduction of 38 percentage points in the incidence of civil wars. Moreover, once institutions are included as explaining civil wars, income does not have any effect on civil war, either directly or indirectly.

## World Bank Policy Research Working Paper 4254, June 2007

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ 1 We are indebted to Daron Acemoglu, Antonio Ciccone, José G.Montalvo, Diego Puga and seminar participants at Universitat Pompeu Fabra, University of California Irvine, and University of Namur for their comments.

1
## **Table 1 - Descriptive statistics**

||households<br>All|Non-migrant<br>households|igrant<br>households<br>M|Differ|ence|
|---|---|---|---|---|---|
|Total income (_yuan_)|12867|10506|14360|-385<br>4 ***|8)<br>(-5.1|
|Home income ( _u_<br>_y an_)|8017|10506|6443|4063 ***|(6.39)|
|Remittances (_yuan_)|4850||7917|-7917 ***|(-24.75)|
|Per capita income (_yuan_)|3111|2810|3301|-492 **|(-2.32)|
|Number of workers|2.84|2.45|3.09|-0.64 ***|(-8.59)|
|Average number of y<br>education<br>ears of|7.02|6.56|7.31|-0.75 ***|(-4.92)|
|Number of dependents|1.31|1.42|1.24|0.19 ***|(2.82)|
|Number of children|0.14|0.10|0.17|-0.07 ***|(-3.02)|
|Land area (_mu_)|7.10|7.85|6.63|1.22 ***|(2.58)|
|Per capita land area (_mu_)|2.83|3.50|2.42|1.09 ***|(5.28)|
|Distance from household’<br>to the nearest bus statio<br>s residence<br>n (km)|5.37|7.03|4.33|2.70 ***|(5.77)|
|Distance from household’s residence<br>to the county capital (km)|2<br>0.61|2<br>3.37|1<br>8.86|4.50 ***|(4.22)|
|Distance from household’s residence<br>to the nearest rural market (km)|7.18|8.28|6.48|1.81 ***|(4.39)|
|Per capital gross output value of the<br>village (_yuan_)|3533|3473|3571|-97|(-0.85)|
|Percentage of non-farm production<br>of the village (%)|27.4|26.3|28.1|-1.8|(-1.44)|
|Cultivable land per capita of the<br>village (_mu_)|1.14|1.15|1.13|0.01|(0.37)|
|Percentage of paddy field of the<br>village (%)|21.8|19.7|23.2|-3.5 ***|(-3.55)|
|Percentage of vegetable field of t<br>village (%)<br>he|21.0|21.2|20.8|0.4|(0.36)|
|Nu<br>rvations<br>mberofobse|1208|468|740|||

Note: (1) t-statistics are in brackets. *** significant at 1%; ** significant at 5%; * significant at 10%. (2) One _yuan_ = 0.12 US$; one _mu_ is equal to 1/15 hectares.

# **7 Results and discussion**

Our empirical results are presented in two parts. First, we estimate the participation and income equations to identify the factors that determine participation in migration and per capita income, and to simulate income obtained in a counterfactual scenario without migration and remittances. Second, we compare Gini coefficients and poverty indices to examine the effects of 17
power sector, have a considerable impact on the transport sector’s share of national CO2 emissions (see Table 1). For example, the transport sectors in China and India exhibit exceptionally low shares of total national emissions because power generation in these countries is heavily reliant on emission intensive fuels, mainly coal. On the other hand, while the transport sector share of total national emissions in Mongolia has hardly changed, the striking rise in its power sector’s share is directly attributable to a drastic drop in industrial output (and industry sector emissions), reflecting the recession that followed the end of Soviet assistance in 1990-91. Low emissions from the power sector typically signify the exploitation of renewable resources such as hydro-power as opposed to fossil fuels. Of the twelve Asian countries considered, only Sri Lanka demonstrates a very low share of emissions from the power sector, and only until 1996, when it started to supplement its almost exclusive reliance on hydro-power with substantial usage of fossil fuels in its generation mix.

**Table 1: CO2 Emission Mix by Sector**

||||1980<br>Indus-|Trans-||||2005<br>Indus-|Trans-||
|---|---|---|---|---|---|---|---|---|---|---|
|Country|Total|Power|try|port|Other|Total|Power|try|port|Other|
||(Mt of<br>CO2)|(%)|(%)|(%)|(%)|(Mt of<br>CO2)|(%)|(%)|(%)|(%)|
|Bangladesh|7|21|41|14|24|36|35|29|12|24|
|China|1,403|20|51|6|23|5,060|48|37|7|9|
|India|292|26|39|19|16|1,147|52|30|8|10|
|Indonesia|69|10|39|26|26|341|28|39|22|11|
|Korea|122|20|32|12|37|449|35|31|19|15|
|Malaysia<br>|23|32|34|28|6|138|33|35|28|3|
|Mongolia<sup>#</sup>|12|48|25|11|16|10|70|8|12|10|
|Pakistan|26|16|37|25|22|118|30|37|22|11|
|Philippines|32|27|39|15|18|76|37|19|37|7|
|Sri Lanka|4|8|22|55|16|12|28|16|45|11|
|Thailand|34|33|23|28|16|214|30|37|26|7|
|Vietnam|14|24|36|14|26|80|24|37|25|14|

Note: The note for Figure 1 also applies here.<sup>#:</sup> 1985 data are used instead of 1980. Other includes residential, commercial and agricultural sectors. Source: IEA (2007a) 7
Fewer firms receive subsidies in the form of exemptions on value-added taxes. These exemptions increased until 2003, then declined. It is clear from these tables that income tax holidays were a more pervasive form of incentives until the 2008 tax reform.

# **III. Estimation and Results**

# **A. Baseline Results**

We begin the analysis by estimating the model described in equation (1) using ordinary least square (OLS) with and without firm fixed effects. Columns (1) and (2) of Table 3 are estimated with the dependent variable as the log of the firm‘s deflated output. To study the impact of FDI spillovers on the performance of domestic firms, we are interested in how FDI invested in other firms affect the domestic firms located in the same sector. Therefore, the key parameters in the above specification are  7 ,  8 and  9 .

One possibility that has not been explored in the literature on vertical and horizontal linkages is that foreign investment shares are proxying for different trade policies across sectors. Protected sectors may be more likely to receive foreign investment as these firms may be motivated to relocate in order to circumvent tariff or non-tariff barriers (―tariff-jumping‖ foreign investment, which leads to immiserizing effects as modeled by Diaz Alejandro (1977)). In this case, the gains from foreign investment could be under-estimated due to omitted variable bias.

To control for the effects of trade policies, we have created a time series of tariffs, obtained from the World Integrated Trading Solution (WITS), maintained by the World Bank. We aggregated tariffs to the same level of aggregation as the foreign investment data, using output for 2003 as weights. We also created forward and backward tariffs, to correspond with our vertical FDI measures. Table 1 and Table A-5 show basic summary statistics for these tariff variables. During the sample period, average tariffs fell nearly 9 percentage points, which is a significant change over a short time period. While the average level of tariffs during this 12
Policy Research Working Paper 5373

# **Abstract**

This paper seeks to assess the extent to which a country’s overall level of development and that of its financial sector, in particular, are factors that attract private capital into infrastructure projects. The authors investigate these effects in a 1990–2007 dataset on the power sector in 37 developing countries. The results suggest that economic growth is a key determinant of private investors’ investment in infrastructure projects, and that investors tend to take countries’ governance quality into account in their decisions to invest. The empirical results highlight that the development of the financial sector also plays a significant role in private investors’ decisions to enter infrastructure sectors. In particular, the degree of country risk and exchange rate volatility is found to be negatively related to the volume of private sector investment in power projects. Furthermore, when the banking sector and the capital market are separately treated in the analysis, the existence of a well functioning capital market is the main attracting factor. In addition, the existence of an independent energy regulatory authority significantly improves the level of private investors’ implication in energy projects. When accounting for the interactions between the overall economic development and the financial sector development variables, the effects of these variables are still significant and the results also confirm the importance of an independent energy sector regulator.

This paper—a product of the Sustainable Development Department, Middle East and North Africa Region—is part of a larger effort in the department to promote infrastructure development in client countries through applied research targeting cutting-edge policy, regulatory and infrastructure finance issues. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at pnoumbaum@worldbank.org.

_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