rafmacalaba/data-use-mentions-extended huggingface dataset:
rates. The results presented in <u>Table 5</u> are based on a smaller sample of observations because of the limitations of the KLEMS data set and should therefore be interpreted with caution.

# **7. Conclusions**

This paper analyzes the evolution of job tenure in Europe from 1995 to 2020. We use data from the EU-LFS to document general trends in job tenure. We then apply a series of age-period-cohort decompositions to analyze the evolution of job tenure for specific cohorts and time periods. This decomposition analysis shows that job tenure has shrunk for the younger generations, and this cannot be accounted for only by the increased prevalence of temporary jobs among the young, as job tenure has also declined for those who hold permanent contracts. To account for compositional changes in groups, we model tenure at the individual level in a second step. We estimate the probability of having short, medium, or long job tenure, conditional on a set of individual characteristics (such as age, gender, and education) and employment-related characteristics (such as occupation and firm size). The results show that after controlling for individual and employment characteristics, the probability of having a medium- or long-term job declined, and having a shortterm job increased over time.

We also assess the impact of changes in the levels of job protection, trade openness, and technological change on tenure decline. We find that stricter job protection legislation is associated with decreases in the probability of having short-term jobs, and trade openness is associated with a significant increase in that probability. We also find a positive correlation between increases in ICT use and short-term tenure, although these results are less conclusive, given the smaller sample size.

But what is the impact of these changes in job tenure and job stability on workers? For example, when we document that technological change and increased trade openness reduce job tenure, is that a good thing or a bad thing? As mentioned in the introduction, efficiency and equity arguments matter for answering this question
> On the one hand, the President’s initiative reflect an awareness at the highest levels that the continued threat of coercive structures to property rights is a major issue for future economic development. On the other hand, the creation of this group can also be regarded as recognition from the country’s top leadership that previous attempts to solve the problem of pressure on businesses have failed.

# **IV. DATA, RESEARCH DESIGN, AND HYPOTHESES**

# **A. Data**

This paper uses data from two enterprise surveys to explore regulatory uncertainty stemming from informal practice. Our main firm-level data comes from the Russian Firms in a Global Economy (RuFIGE) survey that was conducted by the Institute for Industrial and Market Studies, Higher School of Economics. The survey was conducted as part of a project aimed at assessing the comparative advantaged of Russian manufacturing firms on domestic and global markets in a comparative perspective. The survey took place in summer and autumn of 2014 and included 1,950 firms in 60 Russian regions. We choose to use the RuFIGE data set as our primary firm-level data for several reasons. First, RuFIGE is the most recent firm-level survey to provide data on investment, providing a more contemporaneous sense of how uncertainty is shaping investment decisions in Russia’s regions. Second, the RuFIGE data set also is unique in including questions about investment over a longer period (3 years), which helps to provide a more stable, less variance driven understanding of investment decisions. Finally, RuFIGE (similarly to the RR BEEPS) includes numerous questions on ownership structure, the receipt of support from the state, and receipt of public procurement contracts. These variables are likely correlated with the extent to which firms have political connections that can be used to ameliorate the effects

> 8 “The Arrest Of A Billionaire Does Not Bode Well For Russia's Economy” // http://www.businessinsider.com/thearrest-of-a-russian-billionaire-does-not-bode-well-for-russias-economy-2014-9.

> 9 http://www.kremlin.ru/
municipal solid waste in China; in 2007, over 80% of the disposed municipal solid waste was land filled, about 15% was incinerated, and about 2.6% was composted (China Statistic Yearbook, 2008).

Even though the pace of China’s solid waste management improvement is significant, China has been unable to keep up with the growing demand for waste service coverage, the environmental requirement for safe disposal systems, and the rationalization of costeffectiveness in service delivery (World Bank, 2005). Most of its landfill sites do not satisfy the national pollution control standard (Huang et al., 2006). There is also obvious regional disparity in municipal solid waste treatment capacities. The municipal solid waste disposal ratio varies significantly between the well-developed eastern coastal provinces, such as Beijing (95.73%), Jiangsu (86.7%) and Zhejiang (87.4%), and those less developed areas located in the inland, such as Heilongjiang (22.97%) and Gansu (26.32%).<sup>10</sup> There are even more difficulties in providing residential solid waste management service in rural areas in China. Ye and Qin (2008) indicate that ―the majority of local authorities at the township and the village level fail to provide the (solid waste treatment) services for their constituencies‖, and this is ―particularly true after the rural tax reform, which resulted in all rural direct taxes and fees being removed in the early 2000s to lower tax burdens on farmers.‖ They estimated that in 2005 alone, 280 million tons of garbage was produced in rural China,

> 8 Most Chinese municipal solid waste generation data is presented in three categories; municipal, industrial, and hazardous waste. ―Municipal waste‖ usually includes residential, institutional, commercial, street cleaning, and non-process waste from industries. In some cases, construction and demolition waste is also included and can dramatically skew the generation rate, especially in times of
(a)|+199.6|+177.9|+182.2|
|**B. NON-POOR**||||
|**Deflated expenditure per equivalent a**|**dult in household**|||
|Non-farm enterprise (b)_minus_Farm|+19751.9*|+11784.8|+13234.6|
|enterprise (a)||||
|Non-farm enterprise (b)_minus_|+16209.2|+12741.8|+13305.8|
|farming for own consumption (c)||||
|Non-farm and farm enterprises (d)<br>_minus_Farm enterprise(a)|+20906.9*|+15218.6*|+14310.7*|

Notes: the letters in parentheses refer to the outcomes explained in text. * refers to a statistically significant result at 5%. Expenditures were deflated with the price index and divided by the number of equivalent adults developed in the Poverty Profile (2002).

The results presented can be interpreted in the following way: 10932.7 is the level of deflated expenditure per adult equivalent for rural households with a non-farm enterprise _minus_ the level of expenditure for similar households which have a farm enterprise (i.e. which sold some of their harvest on the market).

It is now generally established that households diversify their economic activities in order to mitigate or reduce income variance in a risky environment. While this insures against a really bad outcome (in case a household relied on just one risky activity), it also, potentially, excludes the household from engaging in high return activities that come from specialization. We compared the poverty outcomes (using expenditure per adult equivalent) of households which are only in farming and comparable farm households with non-farm enterprises, or more diversified households. The results show that rural households which participate in non-farm self-employment are on average less poor than those which are only in farming. On
,622,089|2,484.4|100.0%|
|Combined|Economy|117,155|177,397,261|1,514.2|27.63%|
|Combined|Business|146,772|345,622,809|2,354.8|53.82%|
|Combined|First|312|661,538|2,120.3|0.10%|
|Combined|Unknown|58,092|118,446,076|2,038.9|18.45%|
|TOTAL||322,331|642,127,684|1,992.1|100.0%|

As the FY-12 data include neither flight number, airline, nor equipment flown, we simply assume the same basic flight distribution for FY12 as for CY09. Table A1.1 provides these distributions for the flights for which we have data (and, note, these data need to be scaled up by a factor of 1.055 to reach the correct overall total activity in FY12).

# **Carbon Footprint Calculations for FY12**

Table A1.2 provides calculations for the overall footprint of WBG air travel for FY-12, based on numbers in Table A1.1 scaled up with an expansion factor of 1.055 (to correct for the slightly greater flight volume in FY-12 compared to our sample period). The calculations are done for alternative assumptions about average load factors in premium classes for the flown aircraft (as these are particularly uncertain since they are not provided by airlines). Throughout we assume that the average load factor for coach class is 0.8, in accordance with reported industry averages.

26
The thick and thin bars represent the 90% and 95% confidence intervals, respectively. Standard errors are clustered at the protected area level for each regression. The text boxes display the number of observations and the mean of the dependent variable among control group protected areas. Table S3 presents these results in numeric format.

whether AP management enhances local economic well-being. Atlas AI uses daytime and nighttime optical imagery to predict asset wealth as measured in the Demographic and Health Surveys (DHS) Program [63]. After training a machine learning model on DHS data, Atlas AI predicts asset wealth for the continent of Africa. The data are produced at an annual frequency and delineated by second-level administrative divisions, spanning the years from 2003 to 2021. We filter the data to include only 9
Much of the observed decline in labor market participation in Tigray was primarily driven by the sharp falls in employment in urban areas (see Figure A1 in the Appendix). The share of households who reported participation in any economic activity in urban areas of Tigray declined by 48 percentage points from 72 percent in October 2020 to 38 percent in January 2021. In rural areas, on the other hand, employment quickly recovered after a brief dip in November 2021 to their pre-war levels by January 2021. The latter is related to the return of farm families to cultivate their land albeit at a high security risk.

# **4. Empirical Strategy**

The monthly nature of our data allows us to use alternative empirical strategies to identify the impact of the conflict on affected households. To evaluate the impact of conflict on food security and labor market outcomes, we employ three approaches. The first approach uses a standard Difference-in-Differences (DID) strategy to identify the aggregate impacts of the war by collapsing the months preceding and following the war into pre-war and post-war-onset periods. The second approach estimates disaggregated DID model where survey round dummies are interacted with region dummy variables. Third, we use the ACLED database to generate household-level and time-varying exposure to violent conflict and estimate two-way fixed effects model. We start with the following simple DID specification: _h_ r h 1W W W 2 × W W W 3 _h_ r _h_ r (1) rr rr rr YY = αα + αα WW + αα TTWWTTW TT WW + αα XX + εε where hr stands for a measure of food insecurity and labor market outcomes associated with household _h_ living in region _r_ observed in time _t_ (pre- or post-war-onset). h stands for householdYY specific fixed effects. W W W stands for an indicator variable equal to 1 for the post-war-onset αα period, the period after November 2020, zero otherwise. We note that because the November 2020 rr WW round interviews were completed before the outbreak of the war, this round appears as pre-war. 1 capture aggregate changes
that the gap between the business-as-usual off-farm employment share and the actual off-farm employment share was 6.8 percentage points, which means that it was affecting only 11.7 percent of those that would have been employed off-farm under the business-as-usual scenario as opposed to 12.5 percent in January. China’s second quarter 2009 GDP figures showed that the growth rate had stopped declining and that growth was picking up again; similarly, rural off-farm employment was also showing the initial signs of recovery. In other words, China’s off-farm labor market was already showing signs of recovery as early as the first and second quarters of 2009, when the global financial crisis was less than six months old.

As the economic recovery proceeded, national rural household surveys show that outmigration had picked up again by the end of 2009. According to the NBS rural household survey, 145 million migrant workers were employed outside their home villages for more than six months in 2009, which was a 2.6 percent increase over 2008 (NBS 2010). Estimates based on the RCRE household survey indicate that there were 147 million migrant workers by year end, or an increase of 6.8 percent over the previous year. As evident from Figures 4 and 5, the ratio of vacancy to job seeker also rose sharply between the second and third quarters of 2009, pointing toward an increase in labor demand relative to supply.

# **5. Wage Income in the Wake of the Financial Crisis**

From annual survey data, it is apparent that incomes fell in China in early 2009 but recovered later. Descriptive evidence from the CNRS survey shows that many workers who did not lose their jobs experienced a drop in earnings. According to the CNRS, the monthly earnings of the typical unskilled worker (who worked off-farm in both 2008 and 2009) employed in 2008 and the first four months of 2009 fell by 10.5 percent in early 2009.

By the end of 2009, however, the economic recovery was evident in wages as well
Looking at a sample of developing countries, these authors find a positive relationship between financial development and poverty if financial development is measured by the ratio of M3 to GDP. Inoue and Hamori (2012) find similar results considering deposits in a panel of 28 Indian states. Kraay (2004), by contrast, studying the association between the change in absolute poverty and the ratio of M2 to GDP in a sample of developing countries, finds that financial development is not associated with a faster decline in poverty.

# **B.   Finance and Property Rights**

Financial institutions operate in settings where information is often asymmetric. On the one hand, entrepreneurs seeking financing normally have more information about their projects than their banks do. On the other, projects that may have different probabilities of success are indistinguishable from the viewpoint of a financial institution. This information asymmetry requires banks to screen applications so as to grant loans only to the most promising projects (Singh, 1992).

Stiglitz and Weiss (1981) have shown, however, that lenders cannot rely simply on increasing the interest rate. Raising the interest rate charged on loans may adversely affect the composition of the pool of borrowers. Lenders would like to be able to identify borrowers who are more likely to repay, since the expected return to lenders depends on the probability of repayment. Those who are willing to borrow at high interest rates, however, are willing to do so because if their project succeeds it will yield a high enough return to cover the high financing costs, but also because they perceive their probability of repaying the loan to be low (and hence are riskier). Under these conditions, for a given expected return, an increase in interest rates will induce low-risk projects to drop out first, leaving only the riskier ones in the pool.

More efficient exchange of information could reduce the cost of screening borrowers. Bank branches bring banks closer to their potential clients and by hiring local staff they can tap into local knowledge to discriminate between high- and
$150,000 worth of machinery and equipment assets. This data covered firm characteristics such as turnover, asset size, profits as well as the credit line, utilization and interest rate.

Credit registry-based data allow multiple banking relationships of a single borrowing firm to be tracked.<sup>28</sup> For example, the State Bank of Pakistan was in a position to produce a complete database of the 113,000 business loan accounts relationships 19962002, including information about the fluctuating size of different classes of loan, default rates, and identification of the firm and its directors, potentially allowing a merger of the information with other firm databases.<sup>29</sup>

# _Complementarities between provider and user surveys_

Among the many tasks that can be performed through provider surveys are some that overlap with user surveys at least in regard to the scale of access (even if the providers

> 28 Credit registry data is the basis also of the balance sheet and income statement information for 7 of the 11 European countries in the BACH database.  This data is built on a consistent basis for these countries and for the US and Japan, but it presented in aggregate form for 23 sectors and three size classes.

> 29 See Mian and Khwaja (2004) for a use of this dataset to study apparent patterns of political corruption in lending.

20
of hardship is a timely and worthwhile endeavor for countries that have reached a sufficient level of development. In 2007, Vietnam introduced important reforms in this area, introducing unemployment insurance, and changing the laws that regulate pensions, sickness and maternity benefits, and work injury.

The motivation for this paper is threefold. First, given the availability of recent labor market data from newly improved datasets, the paper gives a picture of the evolution of the supply side of the labor market in Vietnam in the recent period (2007-2009), putting it 2
_al._ 2021) with effectiveness of public spending (e.g. the ability to implement large infrastructure projects) and the ability to implement policies and elicit voluntary compliance through a fair and transparent system rather than the value of taxes raised as the main mechanism. Property transactions yield more precise wealth measures than a capitalization approach (Fagereng _et al._ 2020). Registries are also key to monitor crossborder real estate investment (Alstadsæter _et al._ 2022) which substituted for cross-border financial flows (Johannesen _et al._ 2020) and, together with registries of beneficial ownership, curtailed use of real estate for money laundering (Neef _et al._ 2022).

_Data and information:_ Publicity of price data from registries allows to improve market functioning, for the public or private sector to assess impacts of actions affecting land values _ex-ante_ and put in place financing mechanisms. Making of price data public reduced price dispersion (Ben-Shahar & Golan 2019) and improved market functioning (Eerola & Lyytikainen 2015). It allows quantifying valuing the size and incidence of benefits from public goods such as road maintenance (Gertler _et al._ 2022), broadband access (Ahlfeldt _et al._ 2017), trees (Han _et al._ 2021), coastal preservation (Severen & Plantinga 2018) or losses from hazards such as contamination (Chang & Li 2021), radon exposure (Pinchbeck _et al._ 2020), proximity to nuclear plants (Coulomb & Zylberberg 2021), earthquake risk (Singh 2019), exposure to sea-level rise (Goldsmith-Pinkham _et al._ 2021), or species extinction (Frank & Sudarshan 2022) to inform public policy including interventions such as flood insurance (Garbarino _et al._ 2023). It allows to assess the differential impact of shocks such as COVID across locations (Rosenthal _et al._ 2022), effects of policies including restrictions on density (Hilber & Vermeulen 2016) or potential buyers (Lawley 2018; Hartley _et al._ 2023), or
**Table 6. Benchmarking ICT infrastructure**

||Unit|**Ethiopia**|Low-income countries|
|---|---|---|---|
|GSM coverage|% population|**9.9**|48.2|
|International bandwidth|Mbps/capita|**0.3**|5.8|
|Internet|subscribers/100 people|**0.0**|0.1|
|Landline|subscribers/100 people|**1.0**|0.8|
|Mobile phone|subscribers/100 people|**1.6**|15.1|
|**Prices**($)|**Ethiopia**|Without submarine cable|Other developing regions|
|Price of monthly mobile basket|**3.37**|11.12|9.9|
|Price of monthly fixed line basket|**2.00**|13.58|—|
|Price of 20-hour Internet package|**14.85**|67.95|11.0|
|Price of a 3-minute call to United States|**3.33**|2.59|2.0|
|Price of inter-Africa telephone calls, mean|**1.27**|0.72|n.a.|

_Source_ : Minges and others 2008. — = Data not available. n.a. = Not applicable.

16
income gains, consuming only a small share. Among participating households, we also find a 9 percent increase in per capita expenditure and a 12 percent increase in per capita food expenditure. The consumption gains are reflected in modest improvements in food diversity for young children at midline, suggesting that the program enables some mothers to act on the nutrition education that they receive during their local nutrition and health sessions.

Fifth, most of the women who accept the public works offer report maintaining control over their earnings. Only a small portion of women give their earnings to their husbands. Drawing on our unique and intensive intra-household transfers weekly survey data, we also show that the transfers that husbands make to their participating wives to manage household expenditures are substantial and remain unaffected.

Finally, we do not find that program participants sustain labor market attachment after the short-term income opportunity. Although the intervention substantially increases women’s employment for its duration and, thereby, might act as an incentive to future employment, we find that the program has neither a positive effect on women’s employment nor an impact on their willingness to search for a job or start a self-employment in the near future. However, we do observe a marginal improvement in women’s decision-making power, which we proxied by women’s self-reported perceived participation in household decisions, 9 months after the program ended.

Beyond the average effects, an important angle that we studied is whether specific subgroups benefit the most (or the least) from the intervention. To this end, we use machine learning methods to test for the extent of such heterogeneity. We find that women who were employed at baseline (and thus with a potentially higher opportunity cost for their time) are less likely to benefit from the program. The bulk of employment at baseline is accounted for by women engaged in self-employment activities who were marginally poorer than women with no employment, conditional on both having very low literacy rates.

Interestingly, the heterogeneity analysis also shows that women with more mobility constraints at baseline are