rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 3. The Effect of Land Title on Investment**

The sample consists of all nonfinancial listed firms in the CSMAR database from 2008 to 2017.This table presents parameter estimates from OLS regression of investment on Titling Firm dummy variable for various specifications. The dependent variable is the averaged investment (capital expenditure divided by PP&E) between 2013 and 2017 net of various benchmark levels. That is, In column (1) and (2) the benchmark level is calculated using the average investment between 2008 and 2012. In column (3) the benchmark level is calculated using the average investment between 2010 and 2012. In column (4) the investment in 2012 is used as the benchmark level. In Column (5) the investment in 2008-2009 is used as the benchmark level. _Titling Firm_ is a dummy variable that is 1 if the firm has allocated land before the land title scheme starting from 2012 and 0 if otherwise. All variables are winsorised at the upper and lower 1-percentile. Industry FE denotes whether the industry of the listed firms (categorized according to industry categorization from CSMAR database) is considered in the specification. Exchange FE denotes whether the effect of exchange (Shanghai or Shenzhen Exchange) in which the firms are listed is considered in the specification. The _t-statistics_ are computed using standard errors that are White-corrected. *, **, *** indicate significance at the 1%, 5% and 10% level respectively.

|||∆<br>|(Investment/Total F<br>|ixed Asset)<br>||
|---|---|---|---|---|---|
||Long P|eriod|Median Period|Short Period|Using 2009|
||(1)|(2)|(3)|(4)|(5)|
|Titling Firm|0.345***|0.301***|0.317***|0.317***|0.258**|
||(3.726)|(3.137)|(3.393)|(4.174)|(1.987)|
|ROA|0.002*|0.002*|0.002|0.001|-0.003*|
||(1.817)|(1.879)|(1.367)|(0.924)|(-1.939)|
|Tobin's Q|0.007*|0.007|0.007|0.006|-0.000*|
||(1.651)|(1.603)|(1.643)|(1.641)|(-1.940)|
|Constant|-0.042**|-0.013|0.038|0.105***|0.097**|
||(-2.360)|(-0.279)|(0.868)|(2.878)|(1.979)|
|Industry FE|No|Yes|Yes|Yes|Yes|
|Exchange FE|No|Yes|Yes|Yes|Yes|
|Adj. R<sup>2</sup>|0.034|0.044|0.046|0.060|0.035|
|# of Obs.|1250|1250|1250|1250|1250|

48
Table 12: Inflation rate (it) and exchange rate (Xt) by country.

||C|hile|South|Africa|Pe|ru|Phili|ppines|
|---|---|---|---|---|---|---|---|---|
||it (%)|Xt|it (%)|Xt|it (%)|Xt|it (%)|Xt|
|1996|6,6|0,0024|9,3|0,23|11,5|0,41|7,5|0,038|
|1997|6,1|0,0024|6,2|0,23|8,6|0,40|5,6|0,035|
|1998|4,7|0,0023|9,0|0,20|7,2|0,38|9,2|0,027|
|1999|2,3|0,0020|2,2|0,18|3,5|0,34|5,9|0,029|
|2000|4,5|0,0019|7,0|0,17|3,8|0,33|4,0|0,026|
|2001|2,6|0,0016|4,6|0,14|2,0|0,32|5,3|0,023|
|2002|2,8|0,0015|13,5|0,12|0,2|0,32|2,7|0,023|
|2003|1,1|0,0018|-1,6|0,17|2,3|0,32|2,3|0,022|
|2004|2,4|0,0019|2,2|0,19|3,7|0,33|4,8|0,022|
|2005|3,7|0,0021|2,0|0,19|1,6|0,34|6,5|0,023|
|2006|2,6|0,0020|4,8|0,19|2,0|0,33|5,5|0,025|
|2007|7,8|0,0023|7,6|0,19|1,8|0,35|2,9|0,028|
|2008|7,1|0,0018|9,3|0,17|5,8|0,38|8,3|0,030|
|2009|-2,6|0,0022|6,2|0,18|2,9|0,38|4,2|0,029|
|2010|3,0|0,0025|3,3|0,20|1,5|0,40|3,8|0,032|
|2011|4,4|0,0022|6,3|0,21|3,4|0,41|4,7|0,034|
|2012|1,5|0,0024|5,8|0,20|3,6|0,44|3,0|0,035|
|2013|2,8|0,0022|5,2|0,17|2,8|0,43|2,6|0,035|
|2014|4,6|0,0020|5,3|0,16|3,4|0,42|3,6|0,034|
|2015|4,4|0,0018|5,2|0,14|3,4|0,39|0,7|0,034|
|2016|2,7|0,0019|7,1|0,13|3,6|0,37|1,3|0,032|
|2017|2,3|0,0021|4,5|0,15|3,0|0,39|2,9|0,030|
|2018|2,6|0,0018|4,4|0,15|1,5|0,38|5,2|0,030|
|2019|3,0|0,0017|4,0|0,14|2,3|0,38|2,5|0,031|
|2020|3,0|0,0018|3,1|0,13|2,0|0,36|2,6|0,032|

Source: Own elaboration with data from the International Financial Statistics (IMF).

In Subsection 7.2, we use the rate of return of the project to estimate NPV target functions for each country. This rate (5.88%) is obtained through Equation B, where pqt, ct and It are shown in Table 10 and δt is obtained using Brazil’s inflation rate it in Table 11.

# **References**

Acharya, V. V., C. Parlatore, and S. Sundaresan (2022): “Financing Infrastructure in the Shadow of Expropriation,” Working Paper 30131, National Bureau of Economic Research.

Albalate, D., G. Bel, P. Bel-Piñana, and R. R. Geddes (2015): “Risk mitigation and sharing 48
# _Trends in other data sources_

Three key distinctions should be kept in mind in comparing trends from BEEPS to trends in other assessments of corruption, for 2002 to 2005. First, most other sources do not “unbundle” corruption across various functions of government, but provide only a single broadly-defined indicator. Second, broadly-defined indicators from other sources will differ from BEEPS in their inclusion of other aspects of corruption, in addition to corruption in firm-state interactions. Third, most other sources are designed primarily to compare corruption levels across countries, and only secondarily to compare corruption levels over time within countries. Although such sources are not very informative on whether corruption is improving or deteriorating for ECA or other regions, they can still be used to compare _relative_ performance. Namely, they can help answer the question of whether ECA overall is improving relative to other regions.

**Nations in Transit** (NIT) covers only the 27 transition countries in ECA; Turkey is excluded.  On the 1-7 NIT corruption scale, a 1 is the best possible rating and a 7 is the worst, with quarter-point increments allowed.  The mean rating improved from 4.85 in 2002 to 4.80 in 2005.  This small average improvement hides substantial variation

however: ratings improved for 10 countries, mostly in Eastern Europe, and deteriorated for 7 others, mostly in the former Soviet republics - although the two largest, Russia and Ukraine, show small improvements.

The **CPIA** question, “Transparency, Accountability and Corruption in the Public Sector,” is assessed on a 1-6 scale for 27 ECA countries.<sup>33</sup> in 2002 was 3.11, increasing

> 33 Slovenia was a 28th ECA country in the CPIA until it “graduated” in 2003.  It is therefore excluded from comparisons here.

31
|

45

- Hunter, Brian, current editor (issues back to 1864). The Statesman's year-book [ Statistical and historical annual ofthe states ofthe worldfor the year. ].<sup>New York:St.Martin'sPress,</sup>

International Parliamentary Union, PARLINE database website. (www.ipu.org/parline-e/parlinesearch.asp) .

- Keefer, Philip (1999). “Political institutions and crisis: The effects of political checks and balances on the dynamics of financial sector distress.” Paper prepared for the meetings of the Western Economics Association, July 1999.

- and Stephen Knack (1997). “Why don’t poor countries catch up? A cross-national test of an institutional explanation.” Economic Inquiry 35:3, 590-602 (July).

- and David Stasavage (1999). “Bureaucratic Delegation and Political Institutions: When are Independent Central Banks Irrelevant?” Mimeo, The World Bank (November).

Keesing’s record ofworld events (various years). London : Longman, (weekly).

- Lane, Jan-Erik, David H. McKay and Kenneth Newton (various issues). Political data handbook : OECD countries. New York : Oxford University Press.

- Laver, Michael and Kenneth Shepsle (1996). Making and Breaking Governments. New York: Cambridge University Press.

- London Institute of World Affairs (1947 ~ 84). The year book ofworld affairs. London : Stevens.

- North, Douglass and Barry Weingast (1989). “Constitutions and Commitment: The Evolution of Institutions Governing Public Choice in Seventeenth Century England.” Journal ofEconomic History 49:4, 808-32 (December)

- Political Parties ofAfrica and the Middle East: a Reference Guide. Longman Current Affairs Series. Detroit, 1993.

- Politicat Parties ofEastern Europe, Russia, and the Successor States: a Reference Guide: Longman Current Affairs Series. Harlow, UK, 1994.

- Pritchett, Lant and Jonah Gelbach (1997). “More for the Poor is Less for the Poor: The Politics of Targeting.” The World Bank, Policy Research Working Paper, No. 1799.

- Rasmusen, Eric and Mark J. Ramseyer (1994). “Cheap bribes and the corruption ban: a coordination game among rational legislators.” Public Choice 78:305-27 (March).

- Roubini, Nouriel and Jeffrey Sachs (1989). “Political and Economic Determinants of Budget Deficits in the Industrial Democracies.” European Economic Review 33, 903-938.

Schady, Norbert (1998). “Seeking Votes: The Political Economy of Expenditures by the
we find no positive effect of Minas Fácil Expresso may be that some firms do not want to register with all three levels of government. Before the new system unified municipal, state, and federal procedures, firms had the option of registering with only one or two of these authorities, thereby evading fees and taxes associated with the others. Taking away this flexibility may make some firms less likely to register.

# **5.3 Impact on Municipal Tax Collections from Firms**

One motivation for governments to attempt to bring firms into the formal sector is to broaden its tax base. We are unaware of any study which has looked empirically at this issue, and the administrative data available in Brazil offers the opportunity. Given that the flow of newly registered firms is likely to result in only a small change in the total stock of registered firms in a municipality, we should not expect to see large changes in tax revenues unless a reform leads to a dramatic change in the number of firms registering, which the above analysis shows was not the case. Nevertheless, estimating the treatment impact offers a check on this, as well as illustrating how the choice of a control group matters a lot more when there are pre-treatment trends.

Figure 4 plots the time path of tax revenues received over time. Unlike registrations which show no visible trend, we see business tax revenues have been trending upwards since January 2010 in the municipalities which later received the Minas Fácil Expresso offices. In contrast, average tax revenues in the full sample of untreated municipalities have not been growing as fast in level terms. We again use propensity score matching to obtain a more similar control group, this time matching on the time path of pre-reform business tax receipts instead of registration rates. Figure 4 also plots the time path of the nearest neighbor-matched municipalities, which are lower in levels, but exhibit a similar time path to the treated municipalities.

Table 4 reports our treatment estimates for the impact of introducing the Minas Fácil Expresso offices on municipal tax revenues. The first two columns, which use the full set of municipalities or those in the common support, would suggest that treatment led to a large increase in municipal business tax revenues. However, this reflects the large gap which opened up in revenues between treatment and control municipalities in 2010, after revenues had been similar in the two groups in 2008 and 2009 – that is, it reflects a pre-existing trend. Columns 3 and 4 of Table 4, which use more similar control groups, find instead no significant impact on tax revenues, which is consistent with what we would expect given the relatively small change in registration rates.

13
Policy Research Working Paper 5451

# **Abstract**

The paper analyzes the impact of the recent global crisis in the context of the previous two decades’ growth and capital flows. Growth decomposition exercises show that Egyptian growth is driven mostly by capital accumulation. To estimate the share of labor in national income, the analysis adjusts the national accounts statistics to include the compensation of self-employed and non-paid family workers. Still, the share of labor, about 30 percent, is significantly lower than previously estimated. The authors estimate the output costs of the current crisis by comparing the output trajectory that would have prevailed without the crisis with the observed and revised gross domestic product projections for the medium term. The fall in private investment was the main driver of the output cost. Even if private investment recovers its pre-crisis levels, there is a permanent loss in gross domestic product per capita of about 2 percent with respect to the scenario without the crisis. The paper shows how the shock to investment is magnified due to the capital-intensive nature of the Egyptian economy: if the economy had the traditionally-used share of labor in income (40 percent), the output loss would have been reduced by half.

This paper—a product of the Poverty Reduction and Economic Management, Middle East and North Africa Region —is part of a larger effort in the department to analyze the drivers of growth and examine the growth prospects of the region’s countries in the medium term. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at sherrera@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
||(4.48)***|(4.50)***|(1.24)|(4.45)***|(5.32)***|
|---|---|---|---|---|---|
|Mother’s height|0.021|0.022|0.022|0.022|0.024|
||(2.87)***|(3.03)***|(3.11)***|(2.90)***|(4.25)***|
|Father’s height missing|-0.002|-0.037|0.095|-0.040|-0.093|
||(0.03)|(0.46)|(0.83)|(0.49)|(1.27)|
|Mother’s height missing|0.048|0.077|-0.055|0.074|0.128|
||(0.40)|(0.63)|(0.58)|(0.60)|(1.30)|
|Mother’s years of education|0.023|0.021|0.002|0.022|0.017|
||(1.88)*|(1.97)*|(0.12)|(2.07)**|(1.35)|
|Father’s years of education|0.016|0.010|0.019|0.012|0.018|
||(1.08)|(0.73)|(1.29)|(0.86)|(1.51)|
|Constant||||||
|Observations|3006|2922|1362|2922|2922|
|R-squared|0.17|0.18|0.08|0.18||

**Notes:  See table 4.** Column (5): the total number of different individuals observed between 1 and 4 times is 1214.

24
The document is organized as follows. Section 2 presents the background and summarizes some of the significant work in this area, and begins by describing the evolution of individual earnings inequality using information from the National Urban Employment Survey (ENEU). Although educational attainment has an impact not only on income but also on other outcomes that are important for an individual’s well-being but are not necessarily measured in monetary terms, this paper does not consider the nonmonetary impacts of education. Sub-section 2.2 analyzes the evolution of educational attainment, while Sub-section 2.3 relates changes in the distribution of education to changes in earnings inequality. Section 3 presents the paper’s methodology and Section 4 examines the evolution and structure of the rates of returns to education by means of ordinary least squares and quantile regressions. The last section offers concluding remarks.

# 2. BACKGROUND AND LITERATURE REVIEW

Earnings contribute to most of the overall inequality, being responsible for almost half of inequality at the national level. These figures clearly may be affected by the underreporting of capital gains, but understanding the mechanisms that produce earnings inequality represents a large step toward understanding the behavior of total inequality. As long as labor is the main, if not the only, asset of the poor, a better knowledge of earnings inequality is a valuable input for the assessment of poverty and welfare issues. The ENEU household survey is used to examine the behavior of earnings inequality because it is extremely rich in household characteristics (see Annex 1). The population under analysis is urban individuals between 16 and 65 years old working 20 hours a week or more (seasonal workers are excluded). The two highest observations were dropped from the sample given the clear evidence of outliers in some years. Table 2.1 shows that the Gini coefficient rose from 0.395 in 1988 to 0.464 in 1996, ending with a slight overall increase to 0.396 in 2002. Another index, the R10/20, which is the ratio of the income share accruing to the richest 10 per cent to that accruing to the poorest 20 per cent, 4
represent the actual range of temperatures and consumption levels in different Bolivian municipalities, so that the magnitude of climate impacts can be seen in the appropriate perspective. A 95% confidence interval on the temperature-consumption relationship is also indicated in the graph.

**Figure 1: Estimated short-term relationship between temperature and consumption levels in Bolivia**

<!-- Start of picture text --> 2245<br>1745<br>1245<br>745<br>245<br>3 5 7 9 11 13 15 17 19 21 23 25 27<br>Average annual temperature (oC)<br>Consumption per capita (PPA-$/Year)<br><!-- End of picture text --> The estimated relationship indicates that Bolivians do considerably better in hot areas than in cold areas, even when controlling for other factors such as education attainment and urbanization levels. Inhabitants in the hottest regions are able to consume almost twice as much as inhabitants in the coldest regions. The slope is decreasing with temperature, suggesting that already hot areas would benefit only little in terms of consumption from additional increases in temperature, whereas presently cold areas would benefit more.

Having established that temperature has an important effect on consumption possibilities in Bolivia, we will now proceed to test whether there have been any significant changes in temperatures in Bolivia during the last 6 decades.

# **4. Recent climate change in Bolivia**

In this section we will analyze climate data from the 18 meteorological stations of highest quality in Bolivia from May 1948 to May 2008 to test whether there are any significant trends, and whether these trends differ between regions.

8
out of 8.9 percentage points), while other factors explain the other half. Similarly, a firm’s import status explains around 40 percent of the difference in the female labor share (1.9 out of 4.7 percentage points).

Table 2 focuses on the mean differences between global and non‐global firms. The female labor share premium is 4.0 percentage points for GVC participants compared to non‐participants, wheras the difference in the female labor share is only 1.5 percentage points on average between foreign‐owned and domestically‐owned firms. In other words, GVC participation explains roughly a third (4.0 out of 12.2 percentage points) of the difference in the female labor share relative to non‐participants that was shown in Figure 1, while other factors explain two‐thirds. FDI explains only around 14 percent (1.5 out of 10.9 percentage points).

**Table 2: Female labor share premium of global vs. non‐global firms**

|Dependent<br>variable:||**_GVC pa_**|**_rticipant, gvc_**|||**_Foreign ow_**|**_nership, FD_**|**_I_**|
|---|---|---|---|---|---|---|---|---|
|_fem_shisrt_|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|ln_Yisrt_||0.1780**|0.2588***|0.3449***||0.2878***|0.4292***|0.5157***|
|||(0.014)|(0.009)|(0.001)||(0.000)|(0.000)|(0.000)|
|ln_kisrt_|||‐0.0565|‐0.1713|||‐0.0020|‐0.1138|
||||(0.626)|(0.198)|||(0.987)|(0.394)|
|ln_tfpisrt_||||‐1.5388**<br>(0.012)||||‐1.5336**<br>(0.011)|
|**_globalisrt_**|**3.7144*****|**3.4534*****|**4.0120*****|**3.9658*****|**2.2865*****|**1.7237*****|**1.4961****|**1.5119****|
||**(0.000)**|**(0.000)**|**(0.000)**|**(0.000)**|**(0.000)**|**(0.002)**|**(0.035)**|**(0.044)**|
|_constant_|17.7215**|‐0.5901|1.6322|68.8774***|23.3275***|46.8038***|51.3708***|50.4568***|
||(0.014)|(0.636)|(0.850)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Obs.|29,075|24,154|15,935|14,485|29,060|24,143|15,926|14,476|
|R‐squared|0.52|0.54|0.54|0.54|0.52|0.54|0.54|0.54|

Note: p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). All regressions include country‐sector, subnational region and year fixed effects.

# **4.2 Production versus Non‐Production Workers**

In this section, we test the hypothesis (see section 3.1) that the overall findings are driven by production workers. Figure 9 shows female labor share premium for production and non‐production workers separately. The latter include managers, administration and sales. The results suggest that the female labor share premium is much higher for production‐workers compared to non‐production workers. The female labor share premium is twice as high for production workers when comparing exporting with non‐exporting firms (5.2 versus 2.7 percentage points) and more than twice as high for GVC participants relative to non‐participants (4.5 versus 1.9 percentage points). The female labor share premium between production and non‐production workers is much smaller when comparing importing with non‐importing firms, and disappears for FDI relative to non‐FDI firms. For the underlying regression results, see Appendix 4.

18
roles in the household; shared decision-making; and positive parenting. In later sessions, couples were encouraged to become ‘agents of change’ by supporting IPV survivors and modeling genderequitable relationships in their communities. Each session included take-home activities for practice and reflection. The full curriculum is detailed in table A1. Participants received a nominal stipend of RWF 2,500 (approximately USD 2) per session to cover attendance costs, an amount likely exceeding participation costs given low formal employment in rural areas. An independent survey company monitored attendance during each session.

# **2.3 A Conceptual Framework of Identity and Intimate Partner Violence**

We present a framework for understanding how gender identity can influence IPV and why programs targeting gender norms may inadvertently increase IPV. Traditional economic models of intimate partner violence emphasize bargaining power and outside options, focusing on how men use violence either instrumentally (to extract resources or coerce women to stay) or expressively due to inherent preferences (Bloch and Rao, 2002; Tauchen et al., 1991). However, the identity model suggests that individuals derive utility not only from material outcomes, but also from conforming to social categories and their associated prescriptions. When these identities are threatened — whether through one’s own actions or others’ behavior — individuals may engage in destructive behaviors to reaffirm their sense of self (Akerlof and Kranton, 2000). This framework helps explain how policy changes that challenge traditional gender roles could affect violence even without changing relative economic resources between spouses: either reducing it by successfully shifting identities and associated prescriptions, or provoking backlash and spurring an IPV increase.

Akerlof and Kranton (2000) define identity as one’s sense of self and belonging to social categories, which directly enters utility functions and influences economic decisions. Their model yields three implications particularly relevant for understanding IPV prevention efforts. First, identity can explain seemingly detrimental behaviors that bolster a diminished self-image. For instance, men may use violence to reaffirm masculine identity when it feels threatened. Second, identity creates externalities: one person’s behavior change may affect others’ identities and provoke responses, potentially generating cascading effects throughout a community. For example, a man taking on housework, childcare, or spending leisure time with his wife instead of at bars may threaten other men’s (and women’s) identities. This externality may then create further externalities if commu8