rafmacalaba/datause-encoder-data huggingface dataset:
# **Introduction**

Policy makers want to create jobs, especially in developing countries where rural residents are rapidly moving to cities. Although many studies have looked at the determinants of economic growth, we know less about policies that create jobs.<sup>2</sup> In this paper, we use firm-level data from the World Bank’s Enterprise Surveys to examine how firm characteristics, the local business environment, agglomeration and trading costs affect job growth in developing countries.

Previous studies have found that different types of firms create jobs at different rates. Many have found that small and young firms expand their workforces faster than other firms in developing countries (Ayyagari and others, 2011). Although different firms expand at different rates, policy makers have little control over size, age and ownership. We, therefore, also look at the business environment and economic agglomeration—things that policy can affect.<sup>3</sup> We focus on several areas of the business environment: inflation, infrastructure, crime and corruption, access to finance, labor regulation, and worker education and skills. The business environment can affect employment growth directly by affecting economic growth. As discussed below, if the business environment affects growth in a factor neutral way, demand for workers should increase. In developing countries, where underemployed workers in the informal economy can enter the formal sector, increased demand should result in increased employment.

Some areas of the business environment, such as labor regulation and access to finance, however, might not be factor neutral. They might encourage firms to expand employment faster or slower than they would otherwise by affecting the relative cost of labor and capital. Changes that encourage firms to replace labor with capital might, therefore, have an ambiguous effect on employment growth even if they encourage economic growth.

The final factors that we look at are agglomeration or trading costs. These include measures of the size of the local market, the national market, access to international markets, and a measure of capacity agglomeration (the share of firms that employ more than 50 employees).

> 2 See World Bank (2012c) for comprehensive surveys on job creation.

> 3 Policy can affect agglomeration in several ways. For example, governments can offer location-based subsidies to shift regional agglomeration (Kline and Moretti, 2013).

2
# Appendix A – Within PSU variation in outage reporting

Comparing SSBs within the same PSU provides an estimate of how often residents living in the same PSU will experience an outage. The results find a relatively high variance, with only 35 percent of outages reported by one box coinciding with the other in terms of timing and duration. Table 8 reports these metrics for 3 version of “agreement” between boxes: overall agreement, agreement on the timing and duration of outages, and agreement on electricity being on during a given 60-minute interval.

**Table 8: Within PSU Box Agreement**

||Overall|Agree<br>electricity<br>outage|Agree<br>electricity<br>on|
|---|---|---|---|
|Oct-16|98%|69%|94%|
|Nov-16|90%|68%|88%|
|Dec-16|76%|61%|77%|
|Jan-17|86%|41%|88%|
|Feb-17|90%|35%|90%|
|Mar-17|89%|26%|89%|
|Apr-17|86%|22%|88%|
|May-17|85%|22%|87%|
|Jun-17|86%|21%|87%|
|Jul-17|83%|19%|86%|
|Aug-17|83%|15%|87%|
|Sep-17|86%|16%|89%|
|Oct-17|87%|29%|89%|
|Nov-17|84%|25%|87%|
|Dec-17|88%|27%|89%|
|Jan-18|92%|30%|91%|
|Total|86%|35%|88%|

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Figure 20. Average monthly labor income of women and men (15+) by migration status and unconditional gains from internal migration <!-- Start of picture text --> Percent difference Migrants Non-migrants<br>14,000 100.0<br>13,186 90.0<br>12,000 12,039<br>80.0<br>10,000 10,323 70.0<br>9,060<br>60.0<br>8,000<br>39.2 50.0<br>6,000 33.5 40.0<br>27.0<br>4,000 17.1 30.0<br>20.0<br>2,000<br>10.0<br>0 0.0<br>Men Women Men Women<br>All migrants vs Non-migrants Migrants who moved for economic<br>reasons vs. non-migrants<br>Source: Own estimates based on Population and Housing Census 2020.<br>Unconditional gains from internal migration are higher for women and men who migrated from states<br>with weak labor markets. Indeed, the unconditional labor income gap for women who did and did not<br>migrate significantly varies across states of origin. Earnings gains from internal migration are higher for<br>women who emigrated from Oaxaca, Chiapas, Yucatán, and Puebla (see Figure 21). Similarly, earnings<br>gains from internal migration are higher for men who emigrated from Chiapas, Tlaxcala, Yucatán, and<br>Nayarit (Figure 22).<br>Figure 21. Average monthly labor income of women 15+ by state of origin<br>Women who migrated Women who did not migrate Percent difference<br>14,000 120<br>94.9 100<br>12,000<br>80<br>10,000<br>60<br>8,000 40<br>6,000 20<br>0<br>4,000<br>-20<br>2,000 -40<br>0 -60<br>State of origin<br>Percent difference<br>(Mexican pesos)<br>Average monthly labor income<br>Mexican pesos<br>Percent difference<br>oax chs yuc pue agc mor tla sin gro gto qro hgo qroo ver mex nay dgo slp mich jal tam col zac tab coa cdmx nl son cam bcs chh bc<br><!-- End of picture text --> Source: Own estimates based on Population and Housing Census 2020. Abbreviation codes in Annex 1.

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**Figure 5: Chile’s Solidarity Pillar**

(in USD of December 2009) Source: SP The 2008 amendment to the pension law addresses one of the major concerns of the Chilean pension system, which was the problem of coverage for self-employed workers. The new law gradually extends mandatory coverage to self-employed workers. In the first stage, selfemployed individuals are encouraged to contribute to the pension system by establishing the payment of contributions into their default option in the annual declaration of taxable income. Suggested rates are set at 4, 7, and 10 percent of the taxable income for 2012, 2013 and 2014 respectively. During the transition period that lasts until 2014, self-employed individuals have the option of not contributing to their pension fund, but they need to request it actively. Starting in 2015, self-employed individuals will be required to contribute to the pension system at a rate of 10 percent of their taxable income (same rate as dependent workers). According to Berstein et. al. (2009) this measure is expected to increase the coverage by more than one million workers.<sup>17</sup> The fourth is a guarantee in the case of insurance company failure – if a worker’s annuity provider defaults, the government covers 100 percent of his/her annuity up to the PBS and 75 percent of the amount above this level up to a maximum of 45 UFs per month (approximately USD 1,900).

> 17 This measure does not apply to workers who as of January 1, 2002 were older than 50 and 55 years old for women and men respectively.

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# **The Margins of Labour Cost Adjustment: Survey Evidence from European Firms**

Jan Babecký, Philip Du Caju, Theodora Kosma, Martina Lawless, Julián Messina and Tairi Rõõm<sup>*</sup> _Keywords:_ labour costs, wage rigidity, firm survey, European Union _JEL codes:_ J30, C81, P5

> * Author affiliations: Jan Babecký (Czech National Bank **,** <u>jan.babecky@cnb.cz), Philip Du Caju</u> (National Bank of Belgium, <u>philip.ducaju@nbb.be), Theodora Kosma (Bank of Greece, tkosma@bankofgreece.gr), Martina Lawless (Central Bank and Financial Services Authority of Ireland,</u> martina.lawless@centralbank.ie), Julian Messina (World Bank, <u>jmessina@worldbank.org) and Tairi</u> Rõõm (Bank of Estonia **,** <u>tairi.room@epbe.ee).</u>

The work was conducted within the framework of the Wage Dynamics Network coordinated by the European Central Bank. We thank for helpful comments Giuseppe Bertola, Aleš Bulíř, Gabriel Fagan, Jaromír Gottwald, Jan Hošek, Juan Jimeno, Ana Lamo, Frank Smets, Silvia Fabiani, Juan Jimeno, an anonymous referee of the ECB WP series and all the participants of the WDN meetings, seminars at the Bank of Estonia and the Czech National Bank. The opinions expressed in this paper are solely those of the authors and do not necessarily reflect the views of their respective institutions.
individual equity deals between country pairs and sectors as tracked by ThomsonReuters‘ Platinum database. We pool equity flows for every host country across all possible origins and recent years to obtain information on the total value of M&A inflows per country and sector. We are able to match data on FDI flows with information on services policy restrictiveness for 93 destination countries and 8 services sectors.<sup>32</sup> Table 5 presents evidence on the effect of policy restrictiveness on the global pattern of M&A activity. In columns 1-3 restrictiveness is measured by the STRI score, whereas in columns 4-6 a set of key individual policy measures are included directly.<sup>33</sup> In each case we report results for a specification with sector fixed effects (columns 1 and 4), with country fixed effects (columns 2 and 5), and both sets of fixed effects (columns 3 and 6), though fixed effects are not reported to conserve space.<sup>34</sup> Whether we control directly for major determinants of FDI inflows such as country size, population density and per capita income or whether we account for unobserved country and sector heterogeneity with fixed effects, we find that restrictive policies are associated with a significantly lower value of M&A inflows.

With respect to the STRI, we do not constrain the discrete index to have a linear effect on FDI inflows but rather include indicator variables for three different levels of restrictiveness, relative to the reference group of ̳open‘ economies.<sup>35</sup> Indeed, the results in columns 1 and 2 show that higher levels of restrictiveness deter investment in a significant and monotonically increasing manner, and the effect is not necessarily linear. In column 3, in which we control fully for country-specific and sector-specific sources of variation, the estimates suggest that over a 7-year period, the expected value of M&A inflow is on average about $2.2bn lower in

> less detailed than in the M&A data and often defined differently, so that including greenfield flows into the dataset would substantially limit our ability to exploit the cross-sectoral variation in restrictiveness. 32  Air transportation and maritime shipping are excluded because both services are traded primarily crossborder rather than by establishing commercial presence; in addition, in both sectors, alliances and networks dwarf mergers or acquisitions as a means of reaching foreign markets.

> 33  We also experimented with other sets of variables, but there seemed to be the strongest a priori justification for including the set shown in Table 5.

> 34 In keeping with recent advances in estimating (trade) flow variables, results in Table 5 are obtained using Poisson estimation, which addresses concerns about heterogeneity arising from log-linearizing the model and, in addition, allows us to retain zero investment flow observations.

38
alternative definitions of occupational placement, as illustrated by Figures A1 a and b, and A2 a and b.

To assess whether the strength of the negative correlation survives over time, panels a and b of Figure 3 plot the performance of the 1970s arrival over 20 years since migration. The first panel shows the progress during the 1980s versus initial occupational placement in 1980, while the second plots the performance improvement during the 1990s versus the level in 1990. The convergence continues in the second decade since arrival, but at a reduced rate. Results are robust to using the alternative indices of occupational placement (Figures A1 b and c and A2 b and c).

The question naturally arising is whether the convergence suggested by the data leads to full catch-up of countries experiencing low initial performances to the level of countries that started higher up. As Figure 4 reveals, catch-up is not likely to occur within the first ten years since arrival for either the 1985 or the 1975 immigrants. Figure A3 shows the same patterns for the 1985 arrival using alternative measures of occupational placement.

# **_3.4. EXAMINATION OF MAJOR SOURCES OF BIAS_**

The findings of the previous section may reflect a distorted picture of the real trends in immigrants’ occupational placement if there are factors that systematically and unilaterally impact on the composition of cohorts across censuses. This section aims at quantifying the effect of two important sources of bias: out-migration and usage of repeated cross-section datasets.

# **_3.4.1. Out-migration_**

Many studies have shown that out-migration is selective.<sup>30</sup> Thus, the set of individuals who decide to return to their countries of origin or leave the US for other destinations is not a random sample from the initial distribution of the migrants. If the most (least) able members of a cohort out-migrate, the improvement in occupational placement across the decade is understated (overstated). A differential selective effect of return migration by country of origin may cast serious doubts on the validity of the results obtained hitherto in the paper.

The US censuses cannot provide a clear picture of return migration, mainly because different individuals are surveyed in different years.<sup>31</sup> In addition, cohort sizes are also affected

> 30 Jasso and Rosenzweig (1988), Borjas (1989) , Borjas and Bratsberg (1996), Hu (2000), Lubotsky (2007) 31 All the detailed information available in the census databases comes from the answers to the long-form questionnaires, which are filled out by one in six persons residing in the US.  The sample changes from census to census, making it impossible to track the same individuals over time.

19
from the preferential dismantling of Nigerian tariffs. Hence, the main focus of this study is on the potential impact of Nigeria removing its tariffs on imports from the EU. Nevertheless, a discussion of the potential impacts of improved access to the EU market under an EPA in comparison to degree of access afforded by the GSP is provided in Appendix A.

Discussions concerning the economic impact of a potential EPA with the EU have to date tended to center on the preferential trade liberalization that ACP countries will have to implement. According to economic theory, the overall welfare impact of such discriminatory trade reform is ambiguous so that careful empirical analysis is required to identify potential outcomes and highlight policy options for ameliorating negative effects. While on the one hand, the reduction of tariffs against a particular trading partner may lead to lower import prices<sup>3</sup> and higher consumption, the resulting discrimination amongst trading partners may shift demand away from more efficient non-preferential to less efficient preferential producers, resulting in a more than proportional loss of tariff revenues. In many countries discussions concerning an EPA have taken place in the absence of such an empirical assessment that is crafted to the economic reality of the countries concerned. This paper presents analysis to address this issue using a partial equilibrium model of trade that takes key features of Nigeria’s trade regime into account.

This study updates and builds on previous analyses by using more recent and relevant trade and protection data, especially with respect to Nigeria’s applied tariff by taking into account exemptions granted.<sup>4</sup> The aim is not to produce a precise quantification of the likely impact of the policy changes, but rather to provide orders of magnitude of the different short-term effects and the chain of causation producing them. Moreover, the paper also discusses two important characteristics of the Nigerian economy that influence trade outcomes: (i) the pervasiveness of non-tariff barriers, including outright import bans, and (ii) the prevalence of unrecorded informal trade (smuggling).

> 3 A dominant supplier from a preferential partner may be able to maintain the local price after the agreement and increase its profits, capturing for itself the forgone tariff revenues.

> 4  For instance, Busse and Grossmann (2004) and Karingi et. al (2005) use partial equilibrium models to evaluate the EPA impacts.  Raihan et al (2007) and Sandrey et al (2007) use a multi-region computable general equilibrium model.  Enterplan (2005) uses both a partial equilibrium model and a dynamic computable general equilibrium model.

3
Transportation analysis is among the most promising applications for CDR models. Social and economic characteristics are only implicitly recorded in the CDR data, and thus strong assumptions and complex modeling are required to infer them. Spatial behavior, on the other hand, is explicitly recorded by the locations of cellular antennae. For this reason, purely spatial analysis tends to be simpler and more robust than other forms of CDR analysis.<sup>21</sup> Because of the relative simplicity of the analysis, some MNOs and third-party consultants are beginning to offer standard transportation data products to local and national governments.

## **_Disaster Preparedness and Response_**

Large population movements often occur in the wake of natural disasters, and these movements can render pre-disaster surveys and censuses obsolete. In order to provide humanitarian assistance and restore basic services in disaster-affected areas, governments and organizations require revised population data that can be collected swiftly and at a modest cost. This was the case in Haiti after the 2010 earthquake, which prompted several researchers to examine the potential of CDR data to quickly produce high-frequency tracking information on short-term population displacement. This method was found to correspond closely with other estimates of population mobility. Moreover, it was found that that detailed historical data on predisaster population mobility could be used to predict residents’ responses to the earthquake, allowing agencies to better prepare for future disasters.<sup>22</sup> Similar techniques have since been applied successfully during and after other natural disasters.<sup>23</sup>

# **Conclusion**

The dramatic expansion of mobile phone use in developing countries in recent years has yielded a rich and largely untapped source of information about the characteristics of communities and regions. CDR-based research methods have the potential to provide detailed and reliable estimates of poverty rates in real time and at a far lower cost than traditional surveys. These methods have especially promising applications in developing countries such as Guatemala, which suffer from high rates of poverty and inequality, and where limited fiscal and budgetary resources both complicate the task of collecting data and accentuate the importance of precisely targeting public spending.

> 21 For example, Angelakis et al. (2013) used CDR data to examine transport patterns in Côte d’Ivoire. They found that various travel matrices, including routes and travel times, could be calculated from that data at both the national and city level.

> 22 Flowminder (2016a).

> 23 See for example Moumny et al. (2013) and Flowminder (2016b).

**20**
**Table A3. Official poverty rates and spatial price indexes in Ghana**

||||Officialprice i|ndex|
|---|---|---|---|---|
||Povertyrate|Total|Food|Non-food|
|National|23.41|0.96|0.94|1.01|
|Urban|7.76|0.97|0.96|1.02|
|Rural|39.52|0.95|0.93|1|
|Western|21.10|1.02|1|1.04|
|Central|13.77|0.98|0.94|1.03|
|Greater Accra|2.47|1.02|1.02|1.04|
|Volta|37.27|0.99|0.94|1.08|
|Eastern|12.56|0.95|0.94|0.96|
|Ashanti|11.60|0.95|0.9|1.03|
|Brong Ahafo|26.81|0.94|0.91|0.97|
|Northern|61.08|0.97|0.98|0.96|
|Upper East|54.83|0.86|0.8|0.93|
|Upper West|70.86|0.92|0.9|0.96|

_Source:_ Authors’ calculations based on GLSS7.

_Note:_ Price index is calculated by the population weighted average of monthly (from October 2016 to September 2017) index values. Because of this, Greater Accra’s value is not equal to 1.

**Table A4. Comparison of food price indexes: Laspeyres, Paasche, and Fisher**

||Laspeyres<br>(1)|Paasche<br>(2)|Fisher<br>(3)|
|---|---|---|---|
|Western Urban|104.9|101.4|103.2|
|Western Rural|96.5|92.4|94.4|
|Central Urban|110.3|105.4|107.8|
|Central Rural|107.4|103.9|105.6|
|Greater Accra Urban|109.9|100.2|105.0|
|Greater Accra Rural|114.6|104.0|109.1|
|Volta Urban|99.3|97.0|98.1|
|Volta Rural|102.5|98.4|100.5|
|Eastern Urban|102.8|102.1|102.4|
|Eastern Rural|104.6|100.9|102.7|
|Ashanti Urban|115.1|112.4|113.7|
|Ashanti Rural|105.9|102.3|104.1|
|Brong Ahafo Urban|92.2|90.5|91.4|
|Brong Ahafo Rural|93.5|89.1|91.3|
|Northern Urban|105.4|70.2|86.0|
|Northern Rural|105.5|70.6|86.3|
|Upper East Urban|130.0|100.3|114.2|
|Upper East Rural|116.4|93.8|104.5|
|Upper West Urban|104.7|87.5|95.7|
|Upper West Rural|108.2|78.6|92.2|
|Ghana|106.5|95.1|100.0|

_Source:_ Authors’ calculations based on GLSS7 market price survey.

29
could then fall into a poverty trap.<sup>20</sup> For this reason, poor households will prefer to smooth their assets instead of smoothing their consumption.<sup>21</sup> An asset-smoothing behaviour might be a desirable strategy for households to avoid falling into poverty traps. As pointed out by Zimmerman and Carter (2003) who build on Dercon (1998)’s approach by incorporating the role of endogenous asset price risks, portfolio strategies can bifurcate between rich and poor households. In this setting, poor agents respond to shocks by using consumption to buffer assets when they get close to a critical asset threshold.<sup>22</sup>

# **_Econometric Framework_**

Let us quantify vulnerability to poverty by considering the probability to be poor in the future that is having predicted future income or assets below a pre-defined threshold, conditional on household characteristics and exogenous shocks. This probability can be stated as follows: where _ait_  1 is household _i_ welfare (using per capita asset index as a proxy) at time _t+1_ ,<sup>_x_</sup> _it_ and<sup>_x_</sup> _it_  1 are vectors of household characteristics at time _t_ and _t+1_ respectively that are not used in the definition of cohort _c_ , and _z_ is a given threshold. This probability is modelled using pseudo panel data. Indeed, in the absence of panel data, repeated cross-section data can be grouped together by age cohort, education, and geographic groups in order to implement the methodology. So, the welfare index can be modelled in logarithm as follows:<sup>23</sup> where superscript _c_ denotes cohort group. It is assumed that the residual term  _itc_ can be decomposed into an individual specific effect  _ic_ and an error term  _itc_ as follows:

> 20 Zimmerman and Carter (2003) and Carter and Barrett (2006), among others, have analyzed the existence of poverty traps when households are involved in various asset accumulation dynamics.

> 21 Note that if households are able to diversify their portfolio of assets into risky and safe assets, then in presence of credit constraints they will choose to lower the proportion of risky assets held in order to smooth income over time (Morduch, 1994).

> 22 The empirical evidence concerning the existence of such asset-poverty traps and thresholds are mixed with some authors finding evidence of its existence: see, for instance, Lybbert et al. (2004), Adato et al. (2006), Barret et al. (2006) or Carter et al. (2007). Carter and May (1999, 2001) also provide evidence of poverty traps although they are differently theoretically grounded.

> 23 Bourguignon and Goh (2004) proposed a similar method for assessing vulnerability to poverty, although relying on earning dynamics.

14