rafmacalaba/datause-encoder-data huggingface dataset:
# CONTENTS

|EXECUTIVE SUMMARY<br>..................<br>...................<br>i|
|---|
|I.<br>Why Study The Determinants of Public Expenditure on Infrastructure? ...<br>.....<br>1|
|II.<br>The Objectives of the Analysis ................................<br>2|
|III. Conceptual Framework and Model Specification ..................<br>...<br>.<br>3|
|A. Dependent Variable: Per Capita Infrastructure Spending ............<br>.<br>3|
|B. Independent Variables .......<br>..................<br>4|
|1. Characteristics of the Economy .....<br>.............<br>.<br>4|
|1.1. Existing Stock of Infrastructure ..................<br>4|
|1.2. Population Density ..........................<br>5|
|1.3. Urbanization ..............................<br>5|
|1.4. Urban-Rural Balance .........................<br>6|
|1.5. Labor Force Participation Rate ..................<br>6|
|2. Other Variables Reflecting the Structure of the Economy .......<br>.<br>7|
|2.1. Level of Development ........................<br>7|
|2.2. Internal Balance ...........................<br>8|
|2.3. External Balance ...........................<br>8|
|2.4. Size of the Foreign Sector .....................<br>9|
|2.5. Terms of Trade Shifts ........................<br>9|
|2.6. Debt Obligations ............................<br>10|
|2.7. Institutional Development ......................<br>10|
|3. The Level and Mix of External Funding<br>..................<br>10|
|3.1. The Level of Foreign Funding ...................<br>11|
|<br>3.2. The Mix of Foreign Funding ....................<br>11|
|4. Government Objectives ..........................<br>...<br>12|
|4.1. Poverty Commitment ........................<br>12|
|IV.<br>Results ...............................................<br>13|
|A. Summary Statistics<br>...................................<br>14|
|B. Determinants of Per Capita Central Budget Expenditures on Infrastructure . .<br>17|
|1. The Stock of Infrastructure<br>............<br>..............<br>17|
|2. Population Density<br>............<br>..<br>.................<br>25|
|3. Urbanization ..............<br>..<br>...................<br>25|
|4. Sectoral Balance .............<br>..<br>..................<br>25|
|_5._Labor Force Participation Rate .........<br>..<br>.............<br>26|
|<br>6. Level of Development<br>.............<br>................<br>27|
|<br>7. Internal Balance .............<br>..<br>..................<br>27|
|8. External Balance .............<br>..<br>..................<br>28|
|9. Size of Foreign Sector<br>.............<br>................<br>29|
|10. Terms of Trade Shocks<br>............................<br>29|
|11. Debt Obligations<br>................................<br>30|
rights for explaining performance in socialist and transitional economies is perhaps one of the most active areas for research in the past few decades (Shleifer and Vishny, 1994; Megginson and Netter 2001; Djankov and Murrell 2002; Acemoglu, Johnson and Robinson 2001; Estrin et al., 2009). However, the importance of local information for efficiency in socialist or transitional economies or the state-owned sector is rarely systematically empirically explored.

In this paper we examine the causes of decentralizing state-owned enterprises (SOEs) in China, focusing on the role of local information. The data we use is the Annual Survey of Industrial Firms (ASIF) 1998-2007, which covers nonstate firms with sales exceeding 5 million _yuan_ and _all_ SOEs. Decentralization here is defined as the oversight government of an SOE shifting from a higher to a lower level of government—that is, from the central to provincial, or from provincial to municipality (alternatively called prefecture), or from municipality to county government.<sup>3</sup> The availability of local information is captured by the distance between an SOE and its oversight government. A larger distance between the oversight government and the SOE implies that the oversight government can have less direct observations on firm performance, managerial competence, and firm competitiveness. Indeed, there is a large literature providing evidence that distance has huge consequences for firms. For instance, distance (in terms of trust level and physical distance) helps explain decentralization decision between multinational headquarters and oversea subsidiaries (Bloom, Sadun and Van Reenen, 2012); distance helps explain a headquarters’ investment allocation across plants in different locations (Giroud 2013); distance has been found to have profound implications for the lending relationship between banks and firms (Peterson and Rajan 1994, 2002; Mian, 2006; Agarwal and Hauswalkd, 2010); and

> 3 Chinese local government consists of several (from higher to lower) levels as follows: province, municipality (or prefecture), county, and township. As of 1998, there are 31 provincial-level, 331 municipal-level, and 2863 county-level governments.

3
uncorrected estimate is at 22.4 percent, and varies between 20.8 and 27.9 percent with the corrections.

The pattern of increasing and, later on, decreasing shares of inequality explained by circumstances may be explained by selection, which is likely to be larger at the secondary level. But the main result here is that learning opportunity inequality appears at early ages: a large amount of it (at least a quarter of achievement inequality) is already observed at the primary level. These inequities then build up to reach at least a third of learning inequality at the end of preparatory. Hence, tracking at the end of preparatory does not seem to explain all subsequent inequalities.

**Table 9: Opportunity Share of Inequality in National Exam Scores**

||**Primary**<br>**12-15 y.o.**|**Preparatory**<br>**14-18 y.o.**|**Secondary**<br>**17-21 y.o.**|
|---|---|---|---|
|Overall inequality|134.0|180.0|131.4|
|Inequality of opportunity: level|32.70|55.05|29.45|
|Inequality of opportunity: share|0.244|0.306|0.224|
|Overall inequality - lower correction|137.8|182.1|121.4|
|<br>Inequality of opportunity: share - lower correction|0.238|0.274|0.208|
|Overall inequality - upper correction q5|218.3|194.2|156.7|
|Inequality of opportunity: share - upper correction q5|0.291|0.352|0.279|
|Gender|0.00769|0.00809|0.0266|
|Father’s education|0.0633|0.0387|0.0286|
|Mother’s education|0.0563|0.117|0.0909|
|Wealth|0.0785|0.0851|0.0510|
|Religion|-0.000044|0.000013|0.00038|
|Birth governorate|0.0383|0.0574|0.0267|
|Total sample|1497|1525|1718|
|Non-missing score sample|2543|2118|2096|
|<br>Share of non-missing scores|0.589|0.720|0.820|
|Sample after correction|3246|3975|3719|

_Source:_ SYPE 2009 survey. _Sample:_ 12-14 y.o. primary, 14-18 y.o. preparatory, and 17-21 y.o. secondary graduates.

We also perform the decomposition of the partial shares of scores inequality associated with the different circumstance variables. Birth governorates explain again significant shares of the inequality in exam scores: about 6 percent at the preparatory, which is rather consistent with the results obtained using TIMSS data. Family background explains the largest shares of exam scores variations. Parent’s educational attainments in particular explains, respectively, 12, 16 and 12 percent of the variation of scores at primary, preparatory, and secondary levels, respectively, while 26
with regard to future health costs, education, and pension provision seems to boost precautionary household saving. International evidence suggests such effects on precautionary saving occur. Chou, Liu, and Hammitt (2003) found that introduction of a National Health Insurance in Taiwan (China) reduced household saving by an average of 9-14 percent. Wagstaff and Pradhan (2005) found that the introduction of a health insurance scheme in Vietnam significantly increased the consumption share, especially that of the poor. For China, surveys appear to confirm that household saving is motivated by precautionary motives.

In the context of the striving to a harmonious society, and the planned associated shift from government spending on investment to consumption, the government intends to take more responsibility in these areas, including by committing to free compulsory education for children in rural areas before 2010 and expanding its contributions to and coverage of the rural health insurance scheme.

Relatively underdeveloped financial markets may also have played a role in keeping household saving high. This is largely by keeping many households credit-constrained. China’s consumer credit, as a share of GDP is at 17 percent of GDP (at end 2005) not much lower than in other countries at a similar level of development. But, consumer credit is much higher in developed countries. International experience suggests that financial market development would, by reducing the number of credit constrained people, reduce household saving (LSHS).

# **_Enterprises_**

Reasons for relatively high saving and investment by enterprises include a high share of—relatively capital-intensive—industry in GDP. Investment tends to be higher in industry than in other sectors, due to the inherent capital intensity of the processes, which also means that a higher share of total value added is distributed to capital. These relatively high enterprise earnings in industry—if retained, as is the case in China to a large extent, particularly in SOEs—are the core of enterprise saving. Empirical evidence across countries confirms a strong association between saving and investment on the one hand and the share of industry in GDP on the other hand (see section IV.2.1 and Figures 2 and 3).

Another key factor is that Chinese companies pay out relatively low dividends. In particular, for historical reasons SOEs pay only limited dividends to shareholders, and none at all to the state, their largest shareholder, although the increase in profitability in recent years has stimulated a policy discussion on the distribution of SOE profits.

The increase in enterprise saving in the last 10 years is associated with a steady rise in profits and profitability of enterprises. Profits in industry, as a share of value added, increased from 10.6 percent in 1995 to 17.3 percent in 2000 and 21.6 percent in 2005. Retained earnings, in the whole economy, rose between 2000 and 2005 by an estimated 5 percentage point of GDP to around 20 percent of GDP. In addition to cyclical factors, the increase in profitability is underscored by retrenchment and restructuring of SOEs. First, 9
In spite of this, a closer look at handling and commercial activities may be useful. Will the introduction of competition for the market be sufficient to reduce monopoly power, or should some regulatory mechanism be in place? Let us assume that an airport authority concerned with maximizing profit decides to concession a given facility or service.<sup>7</sup> It may award the concession to one or several competitive operators. For instance, it may allow only one handling agent to operate the whole airport, in which case the monopoly reproduces itself; or on the contrary, it may allow several competing agents to serve the airport. Alternatively, it may allow only one or several restaurant operators to cater the whole airport. Therefore, a regulator should also worry about these aspects of airport operations, even if they represent only a small part of airport revenues. Table 2.2 illustrates this idea.

**Table 2.2. Monopoly power at airport handling and commercial activities**

|Conc|essioned|Not concessioned|
|---|---|---|
|One operator<br>YES|Several operators<br>NO|YES|

Once a regulator decides to fix prices it should also be concerned about the consequences of the measures adopted. To what extent should airport quality be affected? How could it measure and control airport performance in order to establish the degree of regulation accomplishment? As these questions are essential in any regulatory framework, they are considered in detail in sections 4 and 5.

# 2.3.- **Experiences in airport privatization**

The traditional model of airports began to be reconsidered in 1987 when the British Government decided to take the British Airport Authority (BAA) under full flotation, except for a single _Golden Share_ that was retained. Nevertheless, when other governments also chose to privatize their airports, they did not follow the same privatization path. The British case has been unique so far. Therefore, in regard to airport privatization, it should be kept in mind that a great variety of privatization forms may fit into the airport infrastructure case.

Different models of airport ownership and management can be categorized as follows:"8

# * Public ownership and public operations

- Public ownership and public operations with commercial orientation

- Regional ownership and operations

Of course, it might decide just the opposite. In such a case the exertion of monopoly right is clear. is These section relies on Kapur (1995).

14
# Size-Dependent Tax Enforcement and Compliance: Global Evidence and Aggregate Implications

Pierre Bachas, Roberto N. Fattal Jaef, and Anders Jensen<sup>_∗_</sup>

**JEL codes** : H25, H26, O23, O43, D61

> _∗_ Pierre Bachas: World Bank Research, pbachas@worldbank.org - Roberto Fattal Jaef: World Bank Research, rfattaljaef@worldbank.org - Anders Jensen: Harvard Kennedy School and NBER, anders_jensen@hks.harvard.edu We are grateful to Alan Auerbach, Tim Besley, Michael Best, Natalie Cox, Tom Cunningham, Simon Galle, Roger Gordon, Michael Keen, Henrik Kleven, Camille Landais, Florian Misch, Ted Miguel, Torsten Persson, Andres Rodriguez-Clare, Emmanuel Saez, Johannes Spinnewijn, Munir Squires, Owen Zidar and seminar participants at the LSE, the 69th IIPF Congress and the 2014 ZEW Public Finance conference for valuable comments. Bachas gratefully acknowledges financial support from the Center for Equitable Growth and the Julis-Rabinowitz Center for Public Policy and Finance. Jensen gratefully acknowledges financial support from the Peter G. Foundation Grant No.16017 and the ESRC.
||||R|est o<br>**Ta**|f<br>**bl**|<br>**e 4. Carbo**|**n Intensit**|**y by sector**|**(in tons**|**per million**|**US dollar**|<br>**s, 2004)**||Middle||||Low and||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||||Rest of||||||Rest of|||Sub-<br>||High|||
||EU27 with<br>EFTA|United<br>States|Japan<br> <br>in<br>A|high<br>come<br>nnex|<br>1|high<br>income|Brazil|China|India|Russia<br>|Rest of<br>East Asia|South<br>Asia|Rest of<br>ECA|East and<br>North<br>Africa|Saharan<br>Africa<br>|Rest of<br>LAC|income<br>countries|middle<br>income<br>countries|World<br>total|
|||||||||**Direct**|**Intensity**|||||||||||
|Agriculture|40|57|41||45|39|63|82|0|65|24|9|94|44|15|46|45|47|46|
|All energy|485|879|396|6|11|609|116|2067|1501|984|665|678|785|456|727|458|642|886|758|
|All manufacturing|18|36|21||38|36|53|127|111|158|91|127|243|152|65|47|25|107|46|
|_Energy intensive manufacturing_|43|90|53||92|98|141|326|283|313|288|735|538|384|159|126|62|279|116|
|_Other manufacturing_|7|12|6||8|8|8|35|36|31|34|20|74|50|17|13|8|31|14|
|Other industries|6|3|6||34|5|18|29|13|30|38|4|62|36|38|25|7|30|13|
|Service|21|32|16||38|39|63|82|68|171|135|89|213|107|67|73|26|97|36|
|Total|40|74|34||81|80|78|303|250|385|195|167|383|228|149|114|55|228|92|
||||||||**Total (**|**Direct plus**|**Indirect)**|**Intensity**||||||||||
|Agriculture|74|141|76|1|26|112|129|350|301|307|75|104|335|184|72|113|98|223|168|
|All energy|541|1016|433|7|35|636|186|2800|1749|1333|752|753|982|582|823|609|729|1147|928|
|All manufacturing|62|159|79|1|56|133|168|681|518|848|244|282|712|378|273|144|99|449|187|
|_Energy intensive manufacturing_|107|272|140|2|93|251|286|1163|888|1193|541|1062|1190|746|505|264|172|811|330|
|_Other manufacturing_|42|111|51||81|80|107|459|354|568|158|145|437|215|156|92|66|289|122|
|Other industries|46|69|46|1|14|77|89|561|287|381|232|218|401|179|240|110|60|342|132|
|Service|46|94|40||89|100|101|340|231|409|265|160|435|232|161|133|67|242|92|
|Total|74|153|70|1|57|155|149|772|535|767|332|281|672|380|281|199|109|479|187|

27
incomes yields similar results as disposable incomes for those country-years for which both are available. Second, we ask whether the inverse relationship between market income share and redistribution holds for our expanded data set, using gross rather than disposable incomes.

Consistent with the earlier research, the estimations in this section focus on market incomes that include state pensions; results are entirely robust to excluding pensions from market incomes and treating pensions as a component of redistributive transfers. The regressions in Table 1 and in all subsequent analyses control for country fixed effects.

**Table 1:  Effect of market income shares on final income shares of top and bottom quintiles** (dependent variable: _sharegain)_

||(1)<br>Bottom<br>quintile|(2)<br>Top<br>quintile|(3)<br>Bottom<br>quintile|(4)<br>Top<br>quintile|(5)<br>Bottom<br>quintile|(6)<br>Top quintile|
|---|---|---|---|---|---|---|
|Dependent variables:|Change|in income|Change in|income sh|ares,_mark_|_etP togross_|
||shares g<br>_marketP_<br>income(|oing from<br>to_disposable_<br>sample A)|All s|urveys|Sample|A|
|**Market share of respective**<br>**quintile**|**-0.94**<br>**(0.000)**|**-0.42**<br>**(0.000)**|**-0.73**<br>**(0.000)**|**-0.12**<br>**(0.001)**|**-0.89**<br>**(0.000)**|**-0.30**<br>**(0.007)**|
|Log of ppp-adjusted per capita<br>income, 2005 US dollars|-3.06<br>(0.007)|0.77<br>(0.69)|-0.47<br>(0.55)|0.21<br>(0.77)|-3.68<br>(0.005)|2.49<br>(0.24)|
|Log of total population|3.47|-1.22|1.34|-3.19|2.83|-1.42|
||(0.24)|(0.69)|(0.53)|(0.16)|(0.33)|(0.61)|
|Percent of population rural|-0.021<br>(0.65)|0.00041<br>(0.99)|-0.027<br>(0.60)|-0.0029<br>(0.95)|-0.075<br>(0.11)|0.095<br>(0.15)|
|Percent of population 65 and|-0.070|-0.044|-0.22|0.098|-0.16|0.086|
|older|(0.40)|(0.73)|(0.17)|(0.48)|(0.16)|(0.38)|
|Observations|97|97|288|288|94|94|
|R-squared|0.564|0.480|0.286|0.151|0.582|0.344|
|Number of countries|32|32|52|52|32|32|

Note: All specifications are ordinary least squares, controlling for country fixed effects. Clustered, robust p-values in parentheses. _MarketP_ incomes are market incomes including pensions. _Sharegain_ is the change in income share of the respective quintile after comparing the _MarketP_ income share of the quintile with its disposable or gross income share.

The dependent variable in all specifications is _sharegain_ : the difference between a quintile’s share of market and either disposable or gross income. The key question is whether this difference falls as the quintile’s share of market income ( _marketP_ , including pension income), the right hand 12
# **1 Introduction**

Firm takeovers where one firm absorbs another firm are common phenomena in markets worldwide. Such consolidations can not only impact product competition, prices, and consumer welfare but can also be consequential for workers (Autor, Katz, Patterson and Reenen, 2020; Azar, Marinescu and Steinbaum, 2022; Benmelech, Bergman and Kim, 2022; Rinz, 2022). Understanding the shortand long-term impacts of consolidations on worker outcomes is important for effective regulation of consolidations and the design of labor market, social insurance, and safety net policies.

This paper studies how takeovers affect the labor market outcomes of workers at consolidating firms by using administrative data on takeovers in the Netherlands from 2011 to 2015. Leveraging rich individual-level information on workers at firms involved in takeovers, we find evidence of heightened job loss post-takeover for workers at acquired firms, leading to long-term negative impacts on workers’ labor market outcomes, such as earnings and total income. These negative effects are pervasive across many types of takeovers. Characterizing the types of workers affected, we find that “over-placed” workers who have higher-than-expected pay prior to the takeover and “duplicative” workers with skills that are likely already present at the acquirer are harmed the most. These results are consistent with _labor restructuring_ , where consolidating firms restructure their workforce in order to decrease labor cost and thereby increase efficiency post-takeover.

We first document the short- and long-term impacts of takeovers on incumbent workers at the firms targeted by takeovers (“target firms”). Our analysis matches target firms to control firms that have not participated in any consolidation activity during our study period and then characterizes differential worker outcomes using an event study framework. We find that takeovers can have a large and long-lasting impact on workers at target firms. While workers who are present at treated and control target firms four years prior to the takeover have similar earnings and employment trajectories prior to the takeover, our event study estimates indicate that these workers fare quite differently post-takeover. There is substantial job loss among the workers of target firms: in the four years after a takeover, workers at a target firm are 8.5% less likely to be retained at the consolidated firm compared to workers in the control firm. This lower retention rate translates into income loss: workers at target firms have e1,069.51 (2.6%) lower labor income and e789.37 (1.9%) lower total income, which includes earnings at any firm and income from various government benefits (e.g., 1
Table A.12: Robustness checks, controlling for unemployment rates at destination. Estimating the effect of migrants’ legal status on hourly wages after return, unconditional on return

||(1)<br>Migrate|(2)<br>Illegal|(3)<br>Return|(4)<br>Log of<br>hourly wage|
|---|---|---|---|---|
|Age|0.012***|-0.071**|-0.011|-0.001|
||(0.001)|(0.034)|(0.007)|(0.005)|
|Age squared|-0.000***|0.001*|0.000**|0.000**|
||(0.000)|(0.000)|(0.000)|(0.000)|
|No education|0.019***|0.483***|-0.066***|-0.445***|
||(0.004)|(0.117)|(0.024)|(0.026)|
|Primary or preparatory|0.032***|0.478***|0.022|-0.420***|
||(0.004)|(0.126)|(0.024)|(0.025)|
|Secondary|0.017***|0.359***|0.013|-0.285***|
||(0.003)|(0.102)|(0.019)|(0.020)|
|Rural|0.009***|0.259***|0.011|-0.028*|
||(0.003)|(0.082)|(0.017)|(0.016)|
|Oil prices|0.013***<br>(0.000)||||
|Diplomatic exchange||-0.285***<br>(0.103)|||
|Confict|||0.028*<br>(0.016)||
|Unemployment at destination|||0.007***<br>(0.001)||
|Illegal status||||-0.237***<br>(0.052)|
|Legal status||||0.207***<br>(0.044)|
|Observations|14,879|1,955|1,690|8,054|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted log wage: Non-mig.=1.|515; Illegal mig|.=1.323; Legal|mig.=1.797||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. Equation (3) additionally controls for the unemployment rate at destination at the time of return migration (for return migrants) and at the time of the survey in 2012 (for current migrants). For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use diplomatic exchange between Egypt and the countries of destination of Egyptian migrants at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange neither in Egypt, nor in the country of destination and is equal one if there is evidence of diplomatic exchange either in Egypt or in the country of destination at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, unconditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row.

59