rafmacalaba/datause-encoder-data huggingface dataset:
worker effects. Given the large number of non-innovators the first decile represents in practice the first five deciles. The figure suggests that workers in the most innovative firms have 50 log points higher wages than in non-innovative firms. Thus, there is strong sorting of more capable and highly paid workers to more innovative firms that tend to pay higher wages.

Figure 2b plots the average estimated firm fixed effects for innovative and non-innovative - no PoTec workers. The average firm effects are larger for innovative than non-innovative firms by around 7 log points. This suggests that there are unexplained factors at the level of the firm - for example higher productivity, higher rents, better entrepreneurs or that already innovate - that make innovative firm pay higher wages.

The results suggest the existence of a innovation wage premium, which is robust to the inclusion of worker and firm fixed effects. However, obtaining unbiased estimates depends on the estimation of the additive form of firm and workers’ fixed effects, which requires _exogenous mobility_ . If the mobility of workers that joined innovative firms is due to match and personal specific characteristics beyond what is captured by the worker and firm fixed effects, then estimates in column (7) in Table 4 are likely to be biased. To recover unbiased estimates, Card et al. (2013) show that a sufficient condition is that the assignment of workers across firms obeys a strict exogeneity condition: We follow Card et al. (2013) and Alvarez et al. (2018) and use an event study framework to test Equation 7 using employees who transition between firms. If the variation in wage across firms is due to sorting, then workers moving across employers should not experience systemic wage changes. On the other hand, if workers moving to high-paying firms experience, on average, a wage gain, while workers moving to low-paying firms experience symmetric wage loss, then we can assume that column (7) provides a reasonable characterization of the mean wages resulting from different matching of workers and firms. We focused on workers who changed employers in year zero (2015) and held the previous job for two or more years and the new job for two or more years in order to investigate the impacts of changing jobs across high- and low-paying firms. Firms were classified based on the mean wage of coworkers. We classify the old job based on the quartile of coworkers in the last year at that job, and the new job based on the first year at that job. We end up with 16 groups based on the quartiles of mean coworker earnings at the origin and destination firms around a move between firms. Figure 3 shows movements from the first quartile to higher quartiles, as well as from the fourth quartile to lower quartiles.<sup>7</sup> The lines in red clearly show that workers moving to

> 7For the sake of clarity, Figure 3 showed only half of the possible transitions. Other transitions showed the same pattern and are available upon request.

17
dependent variable.<sup>6</sup> We add share of urban population as another explanatory variable. On the other hand, we cannot use provincial and time fixed effects due to cross-sectional data. Instead, we use regional dummies to control for specific regional effects. Again, our main data source is TUIK. Provincial development index is provided by the Turkish State Planning Organization (DPT).

# **5. Empirical Method and Results**

# **Methodology**

We use regression analysis to estimate the effect of local government decentralization on economic development and growth in Turkish provinces. The data are a panel of 1,724 observations that include 67 provinces for the years 1976 through 2001. Summary statistics of regression variables for the panel regressions are provided in Table 5. Two conventional approaches for estimating panel data are the fixed-effects and random-effects procedures. However, if the individual province fixed-effects are correlated with other exogenous variables, the random-effects estimation procedure yields inconsistent estimates. A fixed-effects model has the advantage of removing the bias from the estimation caused by a possible correlation between explanatory variables and time-invariant province specific effects.

For the cross-sectional regression analysis with the current number of 81 provinces, we also control for the spatial correlation in economic activity between contiguous provinces. Summary statistics for these regressions are shown in Table 6. First introduced by Cliff and Ord (1981) and Anselin (1988), models of spatial dependence account for any direct influence of spatial neighbors, spillover effects, and

> 6 Provincial development index is developed by the State Planning Organization. More about the index see Dincer, Ozaslan and Kavasoglu (2003).

11
||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Constr**<br>**uction**|0.00|17.50|17.50|7.00|7.00|7.00|7.00|7.00|0.00|0.00|0.00|
|**Trade**|0.00|1.09|1.14|0.60|0.86|1.06|1.20|1.47|1.06|1.07|1.08|
|**Accom**<br>**modat**<br>**ion**<br>**and**|0.00|0.01|0.03|0.06|0.14|0.20|0.25|0.34|0.34|0.34|0.35|
|**Food**<br>**Servic**||||||||||||
|**es**||||||||||||
|**Transp**<br>**ortatio**|0.00|0.02|0.04|0.08|0.19|0.27|0.33|0.45|0.46|0.46|0.47|
|**n**||||||||||||
|**Bankin**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**g-**<br>**Financ**||||||||||||
|**ing**||||||||||||
|**Real**<br>**Estate**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Public**<br>**Admin**<br>**istrati**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**on**||||||||||||
|**Educat**<br>**ion**|0.00|0.01|0.01|0.03|0.07|0.10|0.13|0.17|0.17|0.17|0.17|
|**Health**|0.00|0.01|0.02|0.03|0.08|0.12|0.14|0.19|0.20|0.20|0.20|
|**Other**<br>**Servic**<br>**es**|0.00|0.02|0.05|0.10|0.25|0.36|0.44|0.60|0.60|0.61|0.62|

_Table B. 4. Annual spending associated with investments and households’ consumption under the Nicosia Tram Measure relative to the Reference Scenario by sector of economic activity for the period 2020-2030 (in million euros)_

||**2020**|**2021**|**2022**|**2023**|**2024**|**2025**|**2026**|**2027**|**2028**|**2029**|**2030**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Agricul**<br>**ture**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Forestr**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**y**||||||||||||
|**Mining**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Food**<br>**Manuf**<br>**acturin**<br>**g**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Textile**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Wood**<br>**and**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|**Paper**||||||||||||
|**Chemi**<br>**cal and**<br>**Plastic**|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|0.00|

37
|23 BREGS participation post-movie|-0.01|0.054***|5012|-0.009|0.035***|2782|-0.009|0.078***|2230|
|---|---|---|---|---|---|---|---|---|---|
|24 BREGS days post movie|-1.232|4.690***|1047|-0.586|2.842***|430|-1.704|6.011***|617|
|25 BREGS wages post movie|-72.841|406.176**|* 1047|-52.766|261.668***|430|-88.228|509.487***|617|
|26 Desired participation in R2|-0.012|0.532***|5012|-0.021|0.446***|2782|0.002|0.641***|2230|

_Note_ : Svy regressions using weights and individual outcomes. Based on R2 data only. If individual has not heard about BREGS, answers to other questions are coded as missing. *** p<0.01, ** p<0.05, * p<0.1 based on robust standard errors.

23
account for the potential intervening e↵ect of individual-level optimism or general state of mind on the day of the interview, as these could theoretically bias perceptions of public policy or directly a↵ect individuals’ propensity to trust, and therefore interfere with our results. Results are robust to the inclusion of this additional control and show that satisfaction with policy outcomes and processes is tied to individuals’ trust in institutions, independent of their perception of their own situation.

Table 6: Correlates of trust (ordered logit model): controlling for optimism bias

||(1)|(2)|
|---|---|---|
||Institutional|Institutional|
||trust|trust|
|Satisfaction with|0.247***|0.246***|
|security services|(0.0526)|(0.0522)|
|Satisfaction with|0.249***|0.245***|
|social services|(0.0854)|(0.0871)|
|Satisfaction with|0.269***|0.268***|
|water and sanitation|(0.0542)|(0.0534)|
|Satisfaction with|0.167**|0.166**|
|economic performance (Index)|(0.0709)|(0.0708)|
|Integrity perceptions|0.638***|0.635***|
|(Index)|(0.108)|(0.106)|
|Equity perceptions|0.126***|0.122***|
||(0.0313)|(0.0302)|
|Policy credibility|0.348***|0.346***|
||(0.0584)|(0.0585)|
|Positive perceptions||0.0338|
|of individual situation||(0.0290)|
|Region|X|X|
|Urban/rural|X|X|
|Gender|X|X|
|Income|X|X|
|Marital status|X|X|
|No. of HH members|X|X|
|Income|X|X|
|Education|X|X|
|Employment status|X|X|
|Observations|5,916|5,916|

Standard errors clustered by region in parentheses *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 Data source: World Bank survey on trust (2021) 20
total budget. On average, households in low-income countries spend 47 percent of their total budget on food, middle income countries spend 29 percent, and high income countries spend just 13 percent (Regmi et al. 2001). Likewise, Regmi et al. (2001) find that income elasticities for food shrink as incomes grow, suggesting that lower income countries make larger expenditure adjustments on food in response to changes in income. Both of these findings suggest that lower income economies allocate larger amounts of their budgets to subsistence goods.

Decreases in food expenditures hold within countries as they develop, as well as across income groups. As countries develop, their food expenditure shares also decrease. For example, in 1975, South Koreans spent one-third of their income on food, but by 2012 that figure decreased to 12.2 percent.<sup>11</sup> Across income groups, food expenditures also decline with income. Data from the 2011 United States’ Consumer Expenditure Survey shows that the lowest 20 percent of income earners spend 16.1 percent of their expenditures on food. This number decreases across income quintiles with the highest 20 percent of income earners spending on average 11.6 percent of their income on food.<sup>12</sup> The relationship between household housing expenditures and other types of expenditures provides additional insight into the tradeoffs households make between housing and other expenditures. Figure 6 plots housing against food expenditures by country, which presents a stark negative relationship between the two. Countries with higher percentages of expenditures allocated to food translate to lower shares of expenditures put toward housing. This indicates that increasing housing investment at the household level will be a challenge as households face the combination of limited budgets and high levels of spending on food. An examination of the relationship between housing expenditures and transportation expenditures, on the other hand shows a slight negative relationship between the two (see Figure 7).<sup>13</sup>

**Figure 6. Urban Housing and Food Expenditures (% of total expenditures)**

<!-- Start of picture text --> STP<br>NGA<br>SEN<br>TZA<br>LBR ZMB NER AGO<br>SLE MWI<br>MOZ<br>KEN<br>ETH<br>BFA<br>GHA<br>COM<br>CIV<br>UGA<br>RWA<br>CMR<br>0 .1 .2 .3<br>housing expenditures<br> country Fitted values<br>.7<br>.6<br>.5<br>food expenditures .4<br>.3<br><!-- End of picture text -->

> 11 The Economist. “Thought for Food.” March 12, 2013. http://www.economist.com/blogs/graphicdetail/2013/03/daily-chart-5. The 2012 figure comes from 2012 USDA data on consumer expenditures on food.

> 12 The United States has the lowest average spending on food as a percentage of expenditures of the 86 countries studied by the USDA. These quintile percentages were calculated from the United States’ Bureau of Labor Statistics’ Consumer Expenditure Survey, 2011.

> 13 Refer to Annex 3 for a discussion on the difference between housing expenditures and across housing shortage types.

8
issues transversal to urban and rural areas that keep some households trapped in poverty. The rural-urban distribution of the chronic poor and the poor is roughly similar.

Figure 3: Per capita consumption per day 20092015, by welfare status Figure 4: Welfare status by urban/rural residence, 2015 <!-- Start of picture text --> 8<br>7.3<br>2009 2015 Urban Rural<br>7<br>100%<br>5.8<br>6 5.3 5.4 90%<br>80% 41<br>5 47 49<br>55<br>70%<br>3.8<br>4 60%<br>50%<br>3<br>2.0 2.2 40%<br>2 1.7 30% 59<br>53 51<br>45<br>1 20%<br>10%<br>0<br>0%<br>Chronic Escapes Never Poor National Chronic Escapes Never National<br>Poor Poverty Poor Poverty Poor<br>USD 2011 PPP<br><!-- End of picture text --> _Source_ : Own elaboration based on 2009 and 2015 WMS. _Source_ : Own elaboration based on 2015 WMS.

Third, educational attainment is higher among escapers than among the chronic poor. What is more, the fact that differences have widened over time suggests that acquisition of higher educational levels have gone hand in hand with escape from poverty (Table 1). The widening is explained by an increase in young adults finishing tertiary education during the period. While 20 percent of the group aged between 18 and 30 years had tertiary education in 2009, this share had increased to close to 40 percent by 2015. Differences in quality of education and the differentiation between vocational and higher education naturally lead to differences in employment opportunities among tertiary education graduates. Yet, putting these differences aside, the higher attainment levels showed by the escapers points to a gap that needs to be closed to provide greater income generation opportunities to the chronic poor.

Table 1: Educational Attainment by Welfare Status, Georgia 2009-2015

||Chronic Poor|Escapes Poverty|Never Poor|National|
|---|---|---|---|---|
|Education|||||
|Share adults|with tertiary education||||
||2009<br>10.9|20.8|43.5|30.4|
||2015<br>10.6|24.1|42.8|30.2|

Source: Own elaboration based on the 2009 and 2015 WMS.

Fourth, the chronic poor are less likely to be employed than the rest of the population. In 2015, only about a third of adults in chronically poor households were working, as opposed to close to 19
# **China: Urban Services and Governance**<sup>1</sup>

## **Hana Brixi**

> 1   The paper was developed on the basis of citizens' scorecard survey analysis and case studies, sponsored by the World Bank in partnership with the Government of China.  The survey and background research were conducted in collaboration with the Center for China Study of Tsinghua University, the Development Research Center of the State Council, and the National Bureau of Statistics.  UNICEF cosponsored and contributed to the final analysis.  Advice was received from Yang Jian of the Ministry of Education, Rao Keqin of the Ministry of Health, and Xu Zongwei of the Ministry of Construction, as well as David Dollar, Vikram Nehru, Ardo Hansson, Bert Hofman, Louis Kuijs and Barbara Nunberg of the World Bank, Yin Yin Nwe of UNICEF, and Hu Angang of Tsinghua University.  The authors of background papers include Gordon Liu and Wei Wang (health), Rong Wang and Kin Bing Wu (education), Michael Whitebread and Colin Xu (water), Yongheng Yang (questionnaire and sampling design and data analysis). Albert Park, Hubert Jenny, Phillip Keefer, Shen Mingming, Jack Langenbrunner, Magnus Lindelow, David Hipgrave, Gao Jun, Wu Yaowu, Victor Yuan, Yuan Weiliang, Zhu Xiaomeng, Dong Xiaoyan, and Guo Peiyuan provided inputs and comments.  The earlier stages of the project were managed by Mei Wang.  Mu Yan, Shi Weilin and Bai Xiaojing of UNICEF assisted in the final stages of the research.  Liu Jian translated the paper.
significant increase in the trend of large magnitude floods, with this in the face of a lack of effective flood management systems (UNDP 2006).

During the last 30 years, the total number of flood events which occurred in Asia was about 40% of all the events in the world as compared to 25% in the Americas, 17% in Africa, 14% in Europe and 4% in Oceania (IPCC AR3; CRED). The most affected country is China followed by India, Indonesia, Bangladesh, Vietnam, Thailand and Pakistan. In the last century, the number of major events occurring in Asia was the highest at 1,551, the Americas was 893, Africa was 739, Europe 473 and Oceania 117 (EM-DAT).

The question then arises as to whether the number of flood events is increasing, and whether in the future the frequency of flood events will increase causing even more devastation. In examining the trends in flood events an internationally recognized source of data is the above cited reported events database held by EM-DAT. Data from this source shows the number of reported flood events has in fact increased dramatically as shown in Figure 4 below.

**Figure 4: Number of reported flood events**

<!-- Start of picture text --> 250<br>200<br>150<br>100<br>50<br>0<br>Events 10 year moving median<br>1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010<br><!-- End of picture text --> Medium-scale events are increasing the fastest. Recent events such as the Pakistan floods and to some extent the events in New Orleans may lead us to believe that only large scale disasters are becoming more frequent and devastating. Somewhat surprisingly, the growth in reported events is stronger in medium events characterized by between 5-50 deaths, 1,500150,000 affected or 8-200 million 2003 USD of damages (Guha-Sapir et al. 2004). The number of reported large-scale events also grew but by a smaller percentage. The fact that the growth is stronger in small to medium size events may imply that the scale of impact from the reported events will not have grown quite as sharply as the number of events might suggest.

7
<!-- Start of picture text --> (c) Distribution of Extended Agricultural (d) Distribution of Restricted Agricultural<br>Spending (%) Spending (%)<br>100%<br>100%<br>90%<br>90%<br>80% 80%<br>70% 70%<br>60% 60%<br>50% 50%<br>40% 40%<br>30% 30%<br>20% 20%<br>10% 10%<br>0% 0%<br>Current Investment Current Investment<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> Note: In Figure 3b, 3c and 3d, ―restricted‖ spending refers to narrowly defined agricultural spending (including spending in core public goods, such as research, extension, irrigation), and ―extended‖ refers to rural development spending broadly defined (including expenditures in productive infrastructure, such as roads, rural electrification, etc.). The detailed definition is provided in Annex 2. ― _Prefectura_ ‖ refers to the departmental government, currently called ̳Gobernaciones‘.

Source: World Bank APER (2010).

**Figure 4. Per Capita Agricultural Spending by Department (constant 2005 prices)**

<!-- Start of picture text --> Chuquisaca La Paz Cochabamba Oruro Potosi Tarija Santa Cruz Beni Pando<br>2003 2007<br>0<br><!-- End of picture text --> Source: Authors from World Bank (2010) APER database.

The increasing trend of per capita agricultural public spending, however, is not uniform across departments (Figure 4). This is partly due to transfer mechanisms currently in Bolivia. Central government transfers constitute almost the sole source of income for departmental governments and the largest source of income for municipal governments. The discretionary use of transfers is 8
detail the contribution of small firms to employment, productivity, and job creation in our data. In Section IV, we present growth regressions and sensitivity analysis and place our results in the context of existing literature. Section V concludes.

# **II. Data and Variable Construction**

In this section, we describe the survey dataset and define the various variables used to describe the relative importance of small firms in different countries. We use the World Bank Enterprise Surveys (ES)<sup>5</sup> that are an on-going initiative of the World Bank to benchmark the investment climate in different countries across the world and to analyze firm behavior and performance. The Enterprise Surveys survey from the universe of eligible firms obtained from the country’s statistical office<sup>6</sup> using stratified random sampling with replacement to generate a sample representative of the whole non-agricultural private economy (so fully government owned firms are excluded from the sampling universe) in the country. The surveys are stratified according to three criteria: _Sector of activity_ (population of industries include manufacturing sectors, construction, services, transport, storage, communications, and computer and related activities), _Firm size_ (the strata include small firms (5-19 employees),<sup>7</sup> medium firms (20-99

> 5 The ES surveys and their precursor, the World Business Environment Survey have  been used to investigate a series of questions in developmental economics including the relation between property rights and contracting institutions (e.g. Acemoglu and Johnson, 2005; Ayyagari, Demirguc-Kunt, and Maksimovic, 2008a),  investment climate and business environment obstacles to growth (e.g. Beck, Demirguc-Kunt, and Maksimovic, 2005; Ayyagari, Demirguc-Kunt, and Maksimovic, 2008b), firm financing patterns (e.g. Beck, Demirguc-Kunt, andMaksimovic, 2008; Cull and Xu, 2005, Ayyagari, Demirguc-Kunt, and Maksimovic, 2010) and dispute resolution via courts (e.g. Djankov, La Porta, Lopez-de-Silanes, and Shleifer, 2003).

> 6 The master list of firms is sometimes obtained from other government agencies such as tax or business licensing authorities. In some cases, the sampling universe is generated from lists maintained by the Chamber of Commerce and business associations or marketing databases where registration is voluntary. In a few cases, the sample frame is created via block enumeration.

> 7 We have a few firms in the sample that report having less than 5 employees. All our results are robust to dropping these firms. Our results are also robust to dropping firms that started out as informal enterprises and were formally registered after starting operations.

6