rafmacalaba/data-use-mentions-extended huggingface dataset:
complementary (competitive) patterns of growth for nearby settlements of different (similar) size.<sup>4</sup> They also empirically reject Gibrat’s law, which underpins Zipf’s law in Gabaix (1999), finding that built-up area growth has been fastest for the settlements that were initially smallest in 1975.

> 1 The statistics on urbanization cited in this paragraph are based on data from the United Nations’ World Urbanization Prospects: 2018 Revision (https://population.un.org/wup/).

> 2 Extreme poverty is defined based on the global extreme poverty line of $2.15 a day (2017 Purchasing Power Parity exchange rates). Data on extreme poverty is from the World Bank’s World Development Indicators (https://databank.worldbank.org/source/world-development-indicators).

> 3 Until relatively recently, most empirical urban economics research has been focused on the US and other developed countries, as well as on a few large developing countries such as Brazil, China, India, and Indonesia. Exceptions to the general neglect of SSA countries include several papers by Vernon Henderson and co-authors (Henderson and Turner, 2020; Henderson, Regan, and Venables, 2021; Henderson, Nigmatulina, and Kriticos, 2021).

> 4 Henderson et al. do not explicitly distinguish between different types of settlement (i.e., cities versus towns) but instead examine settlement growth in general based on a binary classification of built-up versus non-built-up.

2
tax free bonds to finance their operations. Moreover, some of the ICS's loans were at subsidized rates, although the fraction has decreased steadily over time. In 1977, for instance, 35% of total funds intermediated by ICS was at subsidized rate. In 1990 the figure has decreased to 26.2%.

This is the background against which one has to assess our empirical results. The empirical work in this paper is based on two rich unbalanced panels of UK and Italian companies with at least five years of consecutive observations. The UK panel is constructed from the balance sheet data collected by Datastream. It includes 604 quoted companies over the period 1976-1991 and contains 5244 observations. The panel for Italy is constructed using the balance sheet data collected by Mediobanca. It includes 750 private companies, mostly unquoted, over the period 1977-1990 and it contains 6327 observations. (see Table 1 for a detailed description of the unbalanced structure of the two panels).

Throughout our empirical work we will rely on a definition of maturity, calculated as long term debt divided by the sum of long term and short term financial debt. Account payables (trade debt) is excluded from the definition of short term debt.<sup>9</sup> Short term debt is defined differently for Italy and for the UK. For the UK, it is

> 9 This decision is mainly due to data limnitations. In fact, **trade** debt is available as a separate item for the UK only for the more recent years. Account payables are, instead, available for Italy. When we include them in the definition of short term debt, the econometric results we describe below remain, on the whole, unalterd.

11
Among papers that analyze recent changes in the labor market conditions in post-communist countries, <u>Privalko (2020) evaluates the effects of age, gender, and tenure on the labor market</u> trajectories of Russian workers, finding a negative correlation between tenure and job mobility. Grotkowska, Wincenciak, and Gajderowicz (2018) estimate the long-term public sector wage premium in Poland, using job tenure as a measure of human capital. They document a positive selection into both public and private employment, even though employees earned more in the public sector, which might be an indication of efficient job markets. Generally, the more job experience a worker had, the smaller the public sector wage premium was for each additional year of tenure. Kupets (2015) studies the efficiency of job matching in Armenia, Georgia, Macedonia, and Ukraine in 2012–13. He shows that many workers with long tenure appear to be overeducated, implying that workers with long tenure may be trapped in jobs with low educational requirements because of the high tenure premium in post-communist countries.

# **3. Data and descriptive statistics**

The data from this study come primarily from labor force surveys of Albania, Bosnia and Herzegovina, Kosovo, Moldova, North Macedonia, <mark>Türkiye</mark> , and Serbia and from the Russian Longitudinal Mobility Survey (RLMS). The pooled data set consists of 4.4 million individuallevel observations across 7 countries and 26 years (1994–2020). Of these observations, about 2.3 million contain information on job tenure. Table 1 summarizes the data coverage for the sample with full data on job tenure.

To achieve intertemporal and cross-country comparability, we harmonized variables across yearly vintages of labor surveys. The years of coverage differ by country, with data from Russia spanning 25 years (1994–2020, with gaps) and data for Moldova covering just 7 years (2003–09). The period 2008–18 has the most extensive country coverage. Our analysis
Management (HRM) policies

- Impact of HRM policies on business development

- Impact of HRM policies on workplace attitude/employee satisfaction and other areas.

The survey data were analyzed from a gender perspective to create an overview of the SME environment in Myanmar, assess the barriers and challenges faced by entrepreneurs and their businesses, and identify firm-level characteristics leading to the use of good business practices. The analysis also investigates the adoption of gender and family-friendly policies both as an outcome and as a determinant of business success. This document brings together the analysis of the MGES with other contextual information about the gender dimensions of PSD in Myanmar.

6
.112)|(0.028)|(0.021)|
|Rural|0.003|-0.064|-0.036|0.003|
||(0.002)|(0.095)|(0.026)|(0.016)|
|Oil prices|0.004***<br>(0.000)||||
|Diplomatic exchange||-0.704***<br>(0.197)|||
|Confict|||0.232***<br>(0.048)||
|Illegal status||||-0.594***<br>(0.070)|
|Legal status||||0.130***<br>(0.044)|
|Observations|14,103|1,940|1,849|8,269|
|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.=2.|282; Illegal mig|.=1.672; Legal|mig.=2.395||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Data comes from the 2018 ELMPS survey. 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. 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
**Table  5. Scenario Ranking in terms of  Effectiveness for Each Aid Receipting Countries or Regions***

|_Region_|_Trade_<br>_Balance_|_Welfare_|_Income Per Capita_|
|---|---|---|---|
|Middle East|TA & CB|TAA|TAA|
|Central America|TA & CB|TAA|TAA|
|South America|TA & CB|TAA|TAA|
|South Asia|TA & CB|TA & CB|TAA|
|Southeast Asia|TA & CB|TAA|TAA|
|China|IR|TAA|TAA|
|North Africa|TA & CB|TA & CB|TAA|
|Sub-Saharan||||
|Africa|TA & CB|TA & CB|TAA|
|Rest of the World|TA & CB|TAA|IR|

Source: Modeling results based on OECD.STAT and GTAP data base Note * TA & CB stands for technical assistance and capacity building, TAA stands for trade, Adjustment assistance, IR stands for institutional reform

**Figure 6. International income inequality index (change with respect to benchmark scenario)**

<!-- Start of picture text --> 0.3<br>0.2<br>0.1<br>0<br>-0.1<br>-0.2<br>-0.3<br>-0.4<br>-0.5<br>-0.6<br>Atkinson index Coefficient of variation<br>Trade adjustment assistance<br>Institutional reform<br>Technical assistance & capacity building<br>Economic infrastructure<br><!-- End of picture text -->

**Source: Authors' Modeling Results**

**21**
25 their monthly electricity bill to avoid the outages experienced in the month one year before the survey.

However, when we divide the WTP amounts by the number of kWh lost due to unscheduled outages and load shedding, our preferred estimates of the resulting VoLL (5 – 15 NR/kWh, or ¢4.7 – ¢14/kWh) are low when compared with the marginal cost of adding capacity or delivering electricity when the demand is high. Imports from India have a marginal cost of approximately 30 cents per kWh during the times of highest demand. The highest marginal cost for the limited oil-based generation in Nepal is 20-30 cents/kWh. The cost of spinning reserve shortfall is likewise estimated at about 20 cents/kWh.<sup>18</sup> Each of these alternate estimates of the marginal cost of bringing additional electricity to the grid exceeds the VoLL.

It is even more striking that our estimates of the VOLL, based on data collected in 2017, is no larger than the figures obtained by Karki et al. (2008) in a survey in 2008. Furthermore, the respondents that we call "attentive" (because they appear to recollect the number of days with outage from the prior year correctly) are the ones that announce most often that they are not willing to pay at all to be rid of outages.

Economic theory and common sense suggest that the value of avoiding the loss of one kWh should be higher when the outage is unannounced, and we find evidence of this when we use the full sample of responses. But when attention is restrictive to the "attentive" respondents, it appears that the VoLL from unscheduled and scheduled outages is the same. Given the many daily hours without electricity before October 2016, it is likely that the notions of "intensive" and "extensive" margin (corresponding to one more kWh lost in scheduled and unscheduled outages, respectively) get blurred for such "attentive" respondents.

> 18 Dr. Debabrata Chattopadhyay, head of the
# **6. References**

Acemoglu, Daron, Davide Ticchi, and Andrea Vindigni. "Persistence of Civil War." NBER Working Paper No. 15378. 2009.

- ———. "A Theory of Military Dictatorship." _American Economic Journal: Macroeconomics_ 2, no. 1 (2010): 1-42.

Banks, Arthur S. "Cross-National Time-Series Data Archive." Databanks International.

Jerusalem, Israel. see http://www.databanksinternational.com, 2010.

Basedau, Matthias, and Jann Lay. "Resource Curse or Rentier Peace? The Ambiguous Effects of Oil Wealth and Oil Dependence on Violent Conflict." _Journal of Peace Research_ 46, no. 6 (2009): 757-76.

Beck, Nathaniel, Katz, Jonathan N., and Richard Tucker. ―Taking Time Seriously in Binary Time-Series Cross-Section Analysis." _American Journal of Political Scienc_ e 42, no. 4 (1998):1260-1288.

Belkin, Aaron, and Evan Schofer. "Toward a Structural Understanding of Coup Risk." _Journal of Conflict Resolution_ 47, no. 5 (2003): 594-620.

Bueno de Mesquita, Bruce, Alastair Smith, Randolph M. Siverson, and James D. Morrow. _The_ _Logic of Political Survival_ . Cambridge: MIT Press. 2003.

Bueno de Mesquita, Bruce and Alastair Smith. "Leader Survival, Revolutions and the Nature of Government Finance" _American Journal of Political Science_ 54, no. 4 (2010): 936-50.

———. "On Economic Causes of Civil War." _Oxford Economic Papers_ 50 (1998): 563-73.

- ———. "Resource Rents, Governance, and Conflict." _The Journal of Conflict Resolution_ 49, no. 4 (2005): 625-33.

———. " Military Spending and the Risks of Coups d’Etat?" WP Oxford University (2007).

27
students despite expected constraints unless their expectations fall very short of their aspirations.

These non-linear patterns are aligned with theoretical and empirical studies that explore the differences between aspirations and current situations. Foundational theory of aspirations in economics argues that aspirations motivate individuals up to a point after which aspirations become too far off one’s initial situation and are no longer motivating, coining the term “aspirations gap” to refer to the difference between someone’s aspirations and his current situation (Ray 2006, Genicot and Ray 2017). Panel data for Indian adolescents confirm that educational outcomes at age 19 correlate positively with the size of their aspirations gap of at age 12—the difference between the wages corresponding to their aspired job and education, and the wages corresponding to their parent’s occupation—up to a point corresponding to about seven times the average parents’ wage, twice the average aspiration gap, and one and a half times the average aspired level (Ross 2019). Similar nonlinear relationships were 4
*observed**<br>**period**|
|---|---|---|---|---|
|**Observations**|69,712|61,112|10,787|4,172|
|**Individuals**|31,588|22,988|5,955|1,364|
|_Source: Authors’_|_calculations base_|_d on LFS._|||

# **4. Stylized facts**

Reservation wages in North Macedonia follow a fairly typical normal-like distribution ( **Figure 1** ). However, compared to market wages, the reservation wage distribution is considerably narrower and bulged. Still, when both are compared in average and median terms, then no significant differences are evident ( **Table 2** ). This initial observation is against the widespread perception that reservation wages in North Macedonia are frequently set high so as being an obstacle to accepting a job.

5
change in aggregate productivity from period _t −_ 1 to _t_ can be decomposed as follows: The first and second terms on the right-hand-side represent changes in within-firm productivity, and the covariance between productivity and market share, of firms that survive from _t −_ 1 to _t_ . The third term represents the contribution of firms that enter in period _t_ , weighted by the market share of entrants, _SE,t_ ; note that their contribution will only be positive if the productivity of entrants is higher than the productivity of surviving firms in period _t_ . Similarly, the last term represents the contribution of firms that exit in period _t −_ 1, weighted by the market share of exiters, _SX,t−_ 1, and will only be positive if the productivity of exiters is lower than the productivity of surviving firms in period _t −_ 1. It is also important to note that the productivity of a firm that enters in, for example, 1990, is associated with entrants when calculating TFP growth from 1989 to 1990, but with survivors when calculating TFP growth from 1990 to 1991. We use this method to calculate the change in TFP between period _t −_ 1 and period _t_ , then add the change in TFP to the existing level of TFP in period _t −_ 1. TFP is normalized to be zero in 1985. In this analysis, we do not use the sampling multipliers.

To use this method, we have to assign every firm in our panel to the category of survivor, entrant, or exiter in every year. Given the nature of our panel data, this requires two relatively strong assumptions. First, to address the fact that we do not directly calculate TFP for 1995-1997, we impute missing values for TFP and output for each series that bridges these years using linear interpolation. We perform a similar linear interpolation of TFP and output for individual firms for which we have bridged over another year.<sup>13</sup> Second, we have to make some
education attainment of people living in Haiti in 2005. Consequently, the attainment profiles may differ from those of that of the original cohort. The fact that the less educated probably have lower life expectancy, and thus lower likelihood of surviving through 2005, means that the education levels shown here are probably biased upwards relative to those of the original cohort, particularly for the oldest generations. This would suggest that the increase in education attainments that has taken place over time is even greater than what this analysis suggests.

The effect of migration is harder to assess. The population of Haitians living in the United States was 420,000 in 2000, according to U.S. Census data, and very rough estimates

> 9 The wealth index was constructed using the approach of Filmer and Prichett (2001). The wealth index was generated using the pooled dataset with all three survey years. The coefficients of the wealth index can be found in Table A.4 in the appendix.

> 10 The figures for Colombia and Rwanda are based on calculations from the 2005 DHS surveys.

7
the contract, i.e. the amount of effort exerted, will always be higher under operator-ownership of the land than under tenant-landlord relationship.<sup>3 8</sup> This would imply that redistribution of land from the landlord to the tenant - or any other measure (e.g. increased off-farm opportunities) that would increase the tenant's reservation utility - would be associated with an increase in aggregate productivity. Still, while such a redistribution could increase the aggregate utility of both parties (thus making compensation of the landlord a theoretical possibility), a voluntary market-based transfer of land from the landlord to the farmer is not feasible. The intuition is simple - since a credit-based land purchase does not enhance the tenants' wealth, the limited liability constraint will still be applicable and the debt overhang incurred by the cultivator to purchase the land will reduce the incentive to apply effort instead of just defaulting on the loan. However, a non-market transfer of land from landlords to farmers could be associated with an increase in overall productivity as well as aggregate welfare.

Insights on the relationship between liquid assets and contractual parameters are provided by Laffont and Matoussi (1994). Their results suggest that direct dependence of the optimal choice of contract on the working capital available to both landlord and tenant may account for the coexistence of a variety of contracts in the same environment among parties with roughly similar risk aversion but different endowments of working capital.<sup>39</sup> The positive relation between the crop share and the tenant's working capital endowment which theory would lead one to expect even with perfect monitoring of effort is indeed observed in the Tunisian data used. Output is shown to increases significantly with tenants' wealth for all contract types as well as for share contracts but tenant wealth has no effect if only fixed rent contracts are considered. Similarly, the wealth of the landlord has, as expected, a negative effect on the tenant's share and a positive effect on production under the share contract
# **<u>Table 21. Second Stage Regressions Formal Labor Markets: Household Head and Spouse</u>**

||**Worked for a sal**<br>**months (1 if he/**<br>**sal**|**ary in the past 12**<br>**she worked for a**<br>**ary)**|**Tried to find a job in**<br>**(1 if he/she tri**|**the past 12 months**<br>**ed to find a job)**|
|---|---|---|---|---|
||**OLS**|**2SLS**|**OLS**|**2SLS**|
|Violent Shock|0.0237|0.1641***|0.0044|0.0555|
||(0.0156)|(0.0600)|(0.0148)|(0.0542)|
|Rural District Price Index|0.0073*|0.0090|-0.0026|-0.0020|
||(0.0041)|(0.0064)|(0.0034)|(0.0041)|
|Litk I Sh f Ttl Ailtl I|0.0481**|0.0580***|-0.0251|-0.0215|
|vesoc ncome as are o oa grcuura ncome|(0.0190)|(0.0210)|(0.0157)|(0.0158)|
|_Dummy_Participation in Goverment Program Familias in Acción (1|-0.0196|-0.0235*|0.0161|0.0146|
|if has acces to the
# 2 Empirical analysis

## 2.1 General Considerations

Empirical investigation of the e¤ect of emigration on institutions in a cross-section or a panel setting raises a di¢ cult trade-o¤. In a cross-sectional dimension, it is possible to use better data both for migration and institutional quality. In particular, for migration, it is possible to use the Docquier and Marfouk (2006) data set, which considers international migration by educational attainment. This data set describes the emigration of skilled workers to the OECD for 195 source countries in 1990 and 2000. For institutional quality, the World Bank Governance data by Kaufmann, Kray and

> 9The KKM (2005) data set starts in the late 1990s and is therefore not long enough to allow for panel data analysis. Similarly, the Docquier and Marfouk (2006) dataset o¤ers estimates of emigration rates by skill levels for 1990 and 2000 only.

6
A recent paper by DellaVigna and Gentzkow (2019) shows that prices of the same UPC do not differ much across retail chains in the US within the same week, using the Nielsen price data. In our data and time period, we find that there is substantial variation in prices of the same UPC within the same chain and quarter across markets.<sup>6</sup> These differences could be explained by several factors such as differences in timeline (DellaVigna and Gentzkow (2019) looked at prices within the same week while we look at quarterly prices); wide variations across different categories of products such as near uniform pricing in yogurt or very different pricing for razors or cigarettes; or different time and locations in our sample. We further show examples of such wide fluctuations in the UPC prices of goods for certain categories of products in Online Appendix A.

# **3.3 Labor Market Data**

To measure the impact of a new plant entry on incumbent households, we draw on two representative household data sources: the May Outgoing Rotation Group (ORG) supplement, and the American Time Use Survey (ATUS). We describe these data briefly here.

The March Annual demographics file of the CPS offers the longest data series on household labor force participation and earnings in the United States, providing measures of annual earnings and wage rate, weeks worked, and hours worked per week for over 50 years. However, as explained in Autor et al. (2005) and Lemieux (2006), wage distributions calculated using the March CPS estimates are less precise compared to the May CPS Outgoing Rotation Group (ORG) counterpart, which provides a real point-in-time estimate of the hourly wage measure.<sup>7</sup> We thus use the May ORG files from 2003 to 2018 for this analysis.<sup>8</sup>

> 6Consistent with DellaVigna and Gentzkow (2019), the mean SD in prices is small, slightly more than 25 cents, but there
databases employs a different definition of SOEs with varying degrees of state ownership thresholds, and some of them follow countries’ official SOE definition, which varies significantly and complicates comparability even further. For example, the survey-based exercise led by the OECD quoted above collects information on the corporate entities recognized as SOEs by the respective national laws

> 54  https://www.oecd.org/publications/the-size-and-sectoral-distribution-of-state-owned-enterprises-9789264280663-en.htm

> 55 Given the low coverage for some countries such as Albania and Kosovo many were not included in the exercise. Annex 4 (IMF, 2019) indicates that the IMF database does not include neither SOEs in natural monopolies nor countries with less than 30 SOEs and limit the assessment to those with total assets over USD 100,000.

> 56 (IMF, 2020). https://www.elibrary.imf.org/view/books/089/28929-9781513537511-en/ch03.xml

35
|
|Distance-discounted commercial bank branches (regional)|0.54**<br>(0.22)|||0.51***<br>(0.18)||
|Distance-discounted commercial bank branches (global)||3.21***<br>(0.86)|||1.76<br>(1.11)|
|Observations<br>1,122|1,122|1,122|1,033|1,033|1,035|
|R-squared<br>0.99|0.99|0.99|0.99|0.99|0.99|
|Kleibergen-Paap rk LM statistic<br>16.63***<br> <br>|18.66***<br>|13.86***<br>|4.61**|4.47***|2.78*|
|Cragg-Donald Wald F statistic<br>18.83|23.62|13.78|4.7|5.11|2.52|

Notes: Standard errors clustered at the country level in parentheses, *** p<0.01, ** p<0.05, * p<0.1. Here common controls and constant terms are omitted for brevity. All regressions include both country fixed effects and year fixed effects. The dependent variable at the second stage is DGE-based output informality (in percent of GDP). “High (low) trade openness” are countries where trade flow (i.e., imports plus exports) as a share of GDP is above (below) median. Commercial bank branches are per 100,000 adults. GDP per capita is in constant 2010 U.S. dollars. See sections 2 and 3 for detailed data descriptions and empirical methods. Data are between 2004 and 2018