rafmacalaba/datause-encoder-data huggingface dataset:
nearby urban areas (Chan 2013). However, as the unit of analysis is city in this paper, only migration across cities affect our results. In 1990, only 3 percent of the population lived in a different city five years before. The share grew to 6 percent in 2000 and remained at around 6 percent in 2005. To see whether our previous results still hold when cross city migration is taken into consideration, we calculate the sectoral employment share and school enrollment using a subsample that excludes all persons that migrated to the current city within five years.

Table 7 presents the results based on a non-migrant subsample. As expected, FDI’s impact on sectoral employment share becomes slightly smaller. The effect on school enrollment becomes larger and more significant than in Table 6. This suggests that FDI shock encourages local youth to obtain higher educational attainment while attracting more unskilled migrant workers into the city. Rising income level spurred by FDI also enables local parents to enroll their children in high schools and colleges.

|T|able 7.Robustness<br>|check: exclude <br>|cross-citymigran<br>|ts<br>||
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
||Panel A:<br>|structural transf<br>|ormation<br>|Panel B: school<br>|enrollment<br>|
|Dependent var|∆agri_emps|∆indu_emps|∆serv_emps|∆Highschool|∆College|
|FDI_shock|-2.468***|1.095*|1.077**|1.319***|0.519***|
||(0.950)|(0.640)|(0.450)|(0.352)|(0.169)|
|L.FDI_empshare|0.0111|0.0457|-0.0544|-0.0615**|0.0341|
||(0.0409)|(0.0416)|(0.0411)|(0.0311)|(0.0293)|
|Observations|607|607|607|607|607|
|R-squared|0.552|0.573|0.494|0.819|0.736|
|Prov-Year FE|Y|Y|Y|Y|Y|
|Lagged city characteristics|Y|Y|Y|Y|Y|
|SOE reform|Y|Y|Y|Y|Y|
|Tariffshocks|Y|Y|Y|Y|Y|

Note: This table tests whether the effects of FDI shock on city level structural transformation and school enrollment are driven by migrants. We excluded people that migrated from other cities within the last 5 years when calculating sectoral employment share and school enrollment. Lagged city characteristics include population, average age, share of Han ethnicity, log GDP per capita in both panels and average years of schooling in panel A. Standard errors are clustered at the city level. *** p<0.01, ** p<0.05, * p<0.1.

Nevertheless, internal migration played an important role in China’s urbanization and structural transformation. After China’s WTO accession in 2002, demand for migrant workers in manufacturing, construction, and labor-intensive services increased greatly. Many provinces, especially coastal provinces reduced regulatory barriers for migrant workers in 2003. The number of migrant workers grew more rapidly during 2003-2008. Richer provinces in coastal regions tend to have higher migrant worker shares than poorer interior provinces (Tombe and Zhu 2019). As the population census data only tracks migrant workers who have lived in the current address for at least half a year, this paper could not exclude short-term migrants in the sample. Internal migration could have a bigger effect on our estimation.

22
regime is most cost-efficient and these studies do not establish how the likelihood of bankruptcy filings varies across legal systems.

Related literature suggests that differences between countries concerning creditor rights and judicial efficacy should affect the use of courts to resolve financial distress. Harris and Raviv (1992) argue that bankruptcy law and enforceability are determinants of the variation in debt contracts features. They show that judicial differences across countries affect firm performance and corporate governance decisions, and affect the likelihood of the bankruptcy system being used to deal with financial distress. Rajan and Zingales (1995) find that contractability is enhanced by legal systems that protect creditor rights and punish management and equity holders in the case of financial distress.

The economic costs of bankruptcy have been studied in more detail for bankruptcy filings in the United States, a debtor-friendly system.<sup>2</sup> Gilson (1989) finds that after filing for bankruptcy, managers suffer large personal costs and that more than half of the managers, 52%, are fired. Gilson and Vetsuypens (1994) find that managers that survive after a bankruptcy filing receive significantly lower salaries and bonuses; on aLverage, managers receive only 35% of their previous gross income.

It is not surprising, therefore, that managers of financially distressed firms in the United States prefer private restructuring to public bankruptcy filings. Gilson, John, and Lang (1990) study 169 financially distressed firms and find that only close to half of these firms use Chapter 11 proceedings to restructure their distressed debt. They find that firms that settle out-of-court have more intangible assets, a larger percentage of debt owed to banks, and fewer lenders. Gilson (1997), however, finds that out-of-court

> 2For example, White (1989, 1993, 1994) compare the direct transaction costs of bankruptcy under different regimes, a topic that we do not address in this paper.

_5_
Several reviews of the sector point to the key needs of (i) better integration of different data sources, including the new higher resolution data available from several providers, (ii) development of more robust algorithms that can be applied in many different settings without additional calibration, and (iii) more complete and accurate data sets of ground-based measures of crop productivity in order to rigorously establish the accuracy of remote sensing approaches (Gallego et al., 2010; Atzberger, 2013; Lobell; 2013). The MAPS remote sensing work program addresses all of these needs in a unique way. To our knowledge, no other research groups working on agriculture have comparable access to high resolution imagery from the private sector; approaches that are as scalable and potentially operational for field-scale mapping in smallholder systems; and ability to collect accurate plot-level maize yield measurement for hundreds of plots.

Specifically, in Round I, remote sensing based yield estimates were obtained based on four images acquired by the Terra Bella (formerly Skybox) satellites over the 400 km<sup>2</sup> tasking area on May 15, June 9, June 27, and July 28, 2015. These images were first geometrically corrected to ensure that plot boundaries were properly aligned with the imagery. Clouds and cloud shadows were manually masked out, and each image was radiometrically corrected to surface reflectance by a standard approach of histogram matching to Landsat images from the same locations and time of year. Reflectance values for individual bands were then used to compute two standard vegetation indices, namely (1) normalized difference vegetation index (NDVI) defined by Rouse et al. (1973) as: and (2) green chlorophyll vegetation index (GCVI) defined by Gitelson et al. (2003) as: For each image date, the mean NDVI and GCVI values for each plot were calculated based on all pixels that fell within the GPS-based plot boundaries. The crop cutting yields, based on the 20m<sup>2</sup> area covered by the combination of the 4x4m and the 2x2m subplots, were then used to calibrate an empirical model relating yield to GCVI on the first three imagery dates, namely May 15, June 9, June 27. NDVI was also tested but performed significantly worse.

When using the entire data set of crop-cutting yields on pure stand fields (n = 235), the correlation between yields and GCVI was low, with an adjusted R<sup>2</sup> (R<sup>2</sup> adj) of 0.13 from a model using the three dates of GCVI. Based on the possibility that the area covered by the combination of the subplots may not represent the heterogeneity across the entire plot area, the model was estimated using different subsets of plots for which the standard error in the crop-cutting yields (as calculated 13
||**Tabl**|**e 7: Results fo**|**r individual i**|**ndustries**|||
|---|---|---|---|---|---|---|
|Dependent variable:|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Female workers|||||||
||**Manufac**<br>**metals (I**<br>**indu**|**ture of basic**<br>**SIC Rev. 3.1**<br>**stry: 27)**|**Manufactu**<br>**beverages (**<br>**indus**|**re of food and**<br>**ISIC Rev. 3.1**<br>**try: 15)**|**Retail sect**<br>**3.1 Ind**|**or (ISIC Rev.**<br>**ustry: 52)**|
|Maternity leave (logs)|**3.118***|**3.541***|**5.940*****|**3.709****|**2.878***|**2.341****|
||**(1.798)**|**(2.001)**|**(2.095)**|**(1.752)**|**(1.673)**|**(1.138)**|
|GDP per capita (logs)||1.687||2.120||5.701***|
|||(4.169)||(2.661)||(1.909)|
|Growth rate of GDP||0.382||0.144||0.646**|
|per capita (%, annual)||(0.526)||(0.338)||(0.267)|
|Females in adult||0.570||1.449*||2.018***|
|population (%)||(1.003)||(0.847)||(0.735)|
|Number of workers||-0.944||-1.109*||1.108*|
|(logs)||(0.960)||(0.647)||(0.603)|
|Female owner present||3.643||3.819***||5.865***|
|<br>Y:1 N:0||(2.704)||(1.443)||(1.099)|
|Top manager is female||2.399||13.778***||20.748***|
|Y:1 N:0||(3.861)||(1.871)||(1.174)|
|Age of firm (logs)||-0.374||-1.688**||0.151|
|||(1.913)||(0.762)||(0.801)|
|Labor productivity (logs)||0.752||-2.157***||-0.289|
|||(0.718)||(0.494)||(0.348)|
|Exports (% of sales)||-0.061||0.013||-0.123***|
|||(0.050)||(0.038)||(0.042)|
|Foreign ownership (%)||0.098**||-0.001||0.045**|
|||(0.047)||(0.023)||(0.019)|
|Firm offers formal training||-1.963||2.752*||0.388|
|to workers Y:1 N:0|||||||
|||(2.600)||(1.402)||(1.131)|
|Inadequately educated||-0.869||1.280**||-0.014|
|<br>workers: Obstacle severity|||||||
|(0-4 scale)|||||||
|||(0.954)||(0.611)||(0.408)|
|Firm registered when||-9.454||-0.012||1.198|
|<br>started operations Y:1 N:0|||||||
|||(6.503)||(1.821)||(1.841)|
|Firm experienced losses||<br>0.085||<br>-1.397||<br>7.513***|
|<br>due to crime Y:1 N:0|||||||
|||(3.124)||(1.454)||(1.307)|
|Access to finance: Obstacle||-1.477*||-0.469||-1.715***|
|severity (0-4 scale)|||||||

67
# **10. Conclusions**

We can summarize the main findings of our paper as follows:

- Gross minimum wage in Romania increased successively from 700 RON in January 2013 to 1,250 RON in December 2016, when the minimum-to-average gross wage ratio slightly exceeded the mean value across EU countries. As a result, the share of employees earning the minimum wage increased from 6% in 2012 to 31% in 2016. The majority of workers earning the minimum wage were employed in the private sector.

- At the same time, the share of people at risk of poverty or social exclusion decreased; moreover, empirical evidence suggests that, in general, Central and Eastern European countries that increased the ratio of minimum-to-average wage the most between 2010 and 2016 also saw a larger reduction in the share of employees at risk of poverty or social exclusion.

- The strong negative correlation between the size of the shadow economy and the gross minimum wage suggests that successive minimum wage increases may have possibly contributed to a gradual whitening of the labor market. Additional supporting evidence of this result is offered by the decline of the ratio of cash outside the banking system to M1 since 2013, when gross minimum wage growth started accelerating and the share of minimum wage workers recorded successive increases.

- The sectoral distribution of minimum wage earners suggests that during 2012–2016, on average, the economic sectors that registered the highest increases in the share of minimum wage workers (e.g. wholesale and retail trade; transport and storage; hotels and restaurants; professional, scientific and technical activities) also increased their share of employees in the economy-wide number of employees.

- The significant increases in gross minimum wage since 2013, with the adjustments being preannounced, coincided with a recovery of firms’ profits (across sectors with both aboveand below-average minimum wage workers share), while the potential negative spillover effects on unit labor costs and international competitiveness appear to have been limited.

- Using an event study framework, although difficult to be attributed one-to-one to an increase in the minimum wage, we show that a minimum wage hike could be associated with a reduction in inflationary pressures during the positive output gap period (i.e. negative unemployment rate gap shrinks or becomes positive), while having a reflationary effect during the negative output gap episode (i.e. positive unemployment rate gap shrinks or becomes

35
of the marginal returns to workers’ characteristics (e.g. experience, skills, occupation, and gender) across sector-region _k_ we interact _age_ , _agesq_ , _occup_ , _skill_ , and _gender_ with the sector-region dummies K.

# **3 Data: The I2D2**

We use the International Income Distribution Database (I2D2) developed at the World Bank. The I2D2 is a global harmonized household survey database, covering 120 countries. Data are collected from more than 1,000 surveys, and harmonized in order to be used for quantitative analysis. For the purpose of this study, we had to select surveys for which some key information on worker’s gender, age, education, occupation, industry affiliation and location of residence (administrative region and rural/urban area) is non-missing. We also selected individuals working as paid employees between the age of 15 and 65. There are 10 occupations in the database: Senior officials, professionals, technicians, clerks, service and market sales workers, skilled agricultural workers, craft workers, machine operators, elementary occupations workers, and military. We define two broad categories of occupation: a managerial-type occupation (senior officials, professionals, and technicians) and a non-managerial occupation comprised of the remaining occupations. Similarly, we define 17 age categories by grouping individuals in a three years of age interval. For instance, individuals between 15 and 17 (included) are given the age of 16; individuals between 18 and 20 (included) are given the age of 19 and so on. The reason for this is that we need to reduce the dimensionality of worker characteristics for the selection equation to be able to perform appropriately.

Sectors are defined in 10 categories: agriculture, mining, manufacturing, public utilities, construction, commerce, transport and communications, financial and business services, public administration, and other services. Because some sectors have a very small size in some surveys, we aggregate them into 7 categories by merging agriculture with mining, public utilities with construction, and public administration with other services. Unlike sectors, the definition of regions as a geographical unit is not harmonized across surveys. Most of the countries in our final data set consist of on average 6 regions. Our final data set is a collection of 234 worker-level surveys covering 47 (almost exclusively) developing countries over the period 1981-2012. Table 1 provides the full list of surveys.

Our identification strategy relies on the age of workers. If older workers are less likely to move, i.e. they face higher mobility costs, then we should observe larger wage differences across sectors and/or regions for them than for younger workers. This implies a positive correlation between the age of workers and the variance of wages within this age group. To investigate this, we estimate the following equation: 8
patterns (Acemoglu et al. 2001; Rodrik et al. 2004). Several papers that examine a particular dimension of the investment climate using aggregate data have found significant results (e.g. the effect of labor regulations (Botero et al, 2004; Heckman and Pages, 2004), regulations of entry (Djankov et al, 2002, Klapper et al. 2006.) or creditor rights (Levine, 2005)).

However, there are a number of shortcomings within this literature. Looking only at a single dimension carries the risk of omitted variable bias. Also, these aggregate indicators do not allow one to explore the rich diversity of experiences within a single country. Too often these measures either rely on the responses of a small number of experts or are based only on perception surveys. This paper fits in the growing literature that instead uses enterprise-level data and disaggregated measures of the investment climate. It uses quantitative measures of the investment climate based on the actual experiences of the full range of enterprises.

Looking at the entire cross-country dataset, Aterido et al. (forthcoming) find a significant role for multiple dimensions of the investment climate on enterprise growth, addressing many of the methodological concerns discussed above. Those results suggest strong composition effects; a weak investment climate shifts downward the size distribution of enterprises. It also found significant non-linearities on the impact of investment climate conditions by size of enterprise. However, it did not explore how these relationships might vary across regions or focus on conditions within SSA.

The collection of new enterprise-level datasets in SSA, of which the ES surveys are a significant contribution,<sup>8</sup> has enabled new research into the determinants of enterprise performance within SSA. Eifert et al. (2007) show how much higher indirect costs of production are for SSA enterprises compared to enterprises in Asia. Higher costs for transportation, electricity, water, security, and marketing and accounting services can more

> 8 A significant source of such data are the Regional Program on Enterprise Development (RPED) surveys carried out by the World Bank since the early 1990s (see Biggs et al. 1999).  They were a precursor to the current Enterprise Surveys that have expanded the objective indicators collected as well as being fielded in countries around the world.

8
and payment systems to facilitate tax transactions, as well as the imposition of penalties in the face of non-compliance. While policy variables such as tax instruments and tax rates are undoubtedly crucial to the performance of a tax system, this paper focuses on inputs to tax administration. To a large extent, lower-income countries have adopted tax instruments and rates that resemble those in higher-income countries. That low tax revenues persist among the former underscores the value of examining gaps in administrative capacity.

# **Figure 1: GDP per capita and Tax/GDP, 1990 to 2019**

Data source: UNU-WIDER Government Revenue Dataset (2022) and UNU-WIDER World Income Inequality Database (2022).

Notes: This figure displays the relationship between changes in tax revenues as a share of GDP (y-axis) and GDP per capita (x-axis) for low-income and lower-middle-income countries by country income group. Each measure is calculated as the difference between values calculated separately for the periods 1990 to 2000 and 2010 to 2019, divided by the value for the base period (1990 to 2000). We truncate the second period in 2019 to omit the period of the COVID-19 pandemic. We pool annual observations by country within each period as measures for individual years are frequently missing due to intermittent collection of these measures. Values within each period are calculated as the mean of annual observations for each nation within that period. The tax revenues to GDP measure corresponds to tax revenues as a share of GDP within a given year, excluding grants and social security. The income categories are drawn from the World Bank country classifications by income level, as applied in the UNU-WIDER datasets. Low-income countries are displayed as green circles, and lower-middle income countries are displayed as blue triangles. The dashed blue line of best fit is estimated using all displayed observations and has slope 0.04 and robust standard error 0.16.

3
Remittances affect economy-wide resource allocation. At the macro level, inflation, exchange, and interest rates are determined by the amount of money coming into the country in the form of remittances (for example, Djajic 1986). Distributional implications of remittances affect the country’s rate of economic growth. At the household level, remittances provide a means of achieving consumption smoothing (Yang and Choi 2005) and mutual insurance (Stark and Lucas 1988), as well as alleviating liquidity constraints (Taylor and Rozelee 2003). Household decisions about the labor market activities of household members, investments in human and physical capital, fertility, and migration also depend on the amount of remittances the household receives (Rapoport and Docquier 2004).

While a large body of literature on international migration exists, the empirical research of the impacts of work-related migration and remittances on poverty and inequality is limited. A macro-level study of 74 low- and middle-income countries by Adams and Page (2003) find that remittances have strong poverty-reducing impact. Adams (1989, 1991) presents micro-evidence on the importance of remittances for poverty reduction in rural Egypt, while Adams (2005) summarizes the results of microlevel analysis in several countries, finding that poverty reduction in Bangladesh, Ghana, and Uganda could be attributed to the effects of remittances. Gustafsson and Makonnen (1993) report that completely removing remittances in Lesotho would raise the poverty rate from 52 to 63 percent, and Barham and Boucher (1998), in examining the net effects of migration and remittances on income distribution in Nicaragua, find that migration and remittances increase average household income and income inequality when compared with the no-migration counterfactuals. Yang, Park, and Wang (2005) studied the effects of migration and remittances on poverty in China, finding that without migration and remittances the aggregate poverty rate would increase from 14.4 to 15.4 percent. Other recent papers by McKenzie and Rapoport (2005) and McKenzie et al. (2006) estimate the overall impact of remittances on income distribution in Mexico taking into account their direct and indirect effects on receiving households and the spillover effects on neighboring communities.

The growing numbers of domestic and international migrants who secure work and send remittances back home have a profound effect on many socioeconomic, 3
# **Figure 2 Estimated Revenue Capacities of Five UAs in India**

# **(Rs, 2004-05)**

<!-- Start of picture text --> 30,000<br>25,000<br>20,000<br>15,000<br>10,000<br>5,000<br>0<br>Non Central Central City Central City Non Central Central City Non Central Central City Non Central<br>City City City City<br>Kolkata Delhi Pune Hyderabad Chennai<br>M illio n s<br><!-- End of picture text --> 31