rafmacalaba/datause-encoder-data huggingface dataset:
# **Section 4: Empirical Analysis of Urban Movements in Economic Activity**

This section analyzes the factors promoting or discouraging changes in urbanization from 1994 to 2005 in districts, with particular emphasis on education and infrastructure. We conduct our estimations at the district-industry level. In all regressions, we control for industry fixed effects to absorb changes in urbanization that are simply following national changes in urbanization levels for given industries. In other words, we will be considering factors that promote or discourage urbanization for a district beyond the change that would be expected based upon its industry composition. We first describe the district sample and the district traits that we consider, and then we present the estimation results.

## **_District Sample_**

Our sample focuses on district-industry cells. Our core sample contains 262 districts out of a total district count for India of 630. This sizeable decline in district count is due to the steps taken to prepare a consistent estimation setting, with the primary decline being due to lack of information in the 2001 Census on the district’s traits (e.g., education, infrastructure). This Census information was only collected for about 400 districts.

In addition to our state-level restrictions noted earlier, our regression sample also requires that the district-industry be observed in all of our manufacturing surveys so that we can observe changes over time. Our explicit criteria with respect to district size are that the district has a population of at least one million in the 2001 Census and has 50 or more establishments sampled. Given our desire to study urbanization changes, we also exclude districts that had fewer than five urban plants or five rural plants. Finally, we exclude plants that have negative value add, which accounts for 6%-7% of employment.

These restrictions are not very significant in terms of economic activity. The resulting panel accounts for over 80% of plants, employment, and output in the manufacturing sector throughout the period of study. As important, the aggregate trends for these districts mirror that of India as a whole shown in the figures.

## **_District Traits_**

Table 1 provides descriptive statistics on our districts. Beyond the starting urbanization level for a district-industry, our two most important explanatory variables are the education levels of the local labor force and the quality of local physical infrastructure. These two factors are consistently linked to India’s regional development.<sup>8</sup> Education levels can play a central role in urbanization. Many local policy makers stress developing the human capital of their

> 8 For example, Lall (2007), Amin and Mattoo (2008), Mukim (2011), and Ghani et al. (2011a).

10
necessary assumption is that the availability of a credit group by gender in a village is uncorrelated with the differenced household errors, _ε_ , conditional on _X_ .<sup>17</sup> Three more variants of household level FE method may be utilized besides the simple FEIV method. One variant includes initial conditions such as the household and community characteristics ( _X_ ) in FE regression framework, assuming that these initial factors control for the time-varying heterogeneity that correlates the errors of the borrowing and outcome equations. More specifically, following Jalan and Ravallion (1998), we rewrite model (3) as follows:

> where _X ij_ 0 is a vector of household- and village-level characteristics for the initial survey year (i.e., 1991/92).

A second variant, following Heckman (1981), is the lagged dependent variable (LDV) method applied to the above equation (10). That is, we assume that current outcome depends on lagged (say one period lag) outcome besides the credit intervention variable and other time-varying exogenous characteristics plus the initial exogenous characteristics as given in equation (10). Thus, we have the following lagged model to estimate, A third way to control for the bias due to time-varying heterogeneity is to apply a propensity score–weighted FE method. Following Hirano, Imbens and Ridder (2003), we first calculate the weight variable on the basis of the propensity score obtained from the participation equation, using the control variables obtained from the 1991/92 survey data. More specifically, the weight variable is given a value 1 for participants and _p_ /(1- _p_ ) for non-participants, where _p_ is the propensity score (probability of receiving microcredit during any time). In the second stage, the impact of microcredit

> 17 Note that the sample villages without any microcredit programs in the earlier rounds lost their control status because of proliferation of credit programs into them.

15
Caribbean, and Asia<sup>57</sup> between 2007 and 2017. The choice of these countries was mainly informed by data availability and also countries that provide the relevant weights in constructing the synthetic control.<sup>58</sup> The dataset among others provides information on the number unique of FDI projects in each country, sector, and year. I focus mainly on count of investments rather than the monetary value of the investments as these FDI projects are heterogeneous across industry, country and year. Also the number of FDI projects into a country signals the response of various investors to the business environment in the destination country. The main predictors used include lags of the number of FDI [excl. energy & construction] projects, the log of GDP (2007-2008) and total FDI (all sectors) (2009-2012), GDP growth rate (2012) and population.

# **_Results: Ghana vs Synthetic Ghana_**

The intuition behind the SCM is that if “Synthetic Ghana” is able to track the flow of FDI to Ghana in the pre-crisis era, then it provides a credible counterfactual of the flow of FDI to Ghana in the absence of the power crisis between 2013 and 2016. This section presents the main findings of the SCM. Tables A9 in the online appendix present the summary statistics of the predictors between Ghana vs. “Synthetic Ghana”, as well as the sample average during the pre-treatment period. Also, Table A10 in the online appendix shows the distribution of the weights used in constructing the “synthetic control” across the control (donor) countries. Kenya, Zambia and Zimbabwe account for the largest share with weights of 0.57, 0.24, and 0.13 respectively.

In terms of the main results, Figure 4 plots the number of FDI [excl. energy & construction] projects in Ghana and Synthetic Ghana before, during and after the crisis. As shown

> 57Cambodia, Ghana, Guatemala, Honduras, India, Jamaica, Kenya, Morocco, Mexico, Myanmar, Mauritius, Namibia, Nicaragua, Pakistan, Philippines, Senegal, South Africa, Uganda, Uruguay, Uzbekistan, Vietnam, Zambia, Zimbabwe

58More so non-African countries were included in the control to limit the potential violation of the stable unit treatment value assumption (SUVTA) which is likely to be violated if, for instance, the power crisis in Ghana shifted FDI into neighboring countries or other African countries with similar business environment with the pre-crisis Ghana.

42
6

**The "standard" framework of the "Three-Gap"**<sup>**modelof the World Bank3**</sup>

In the following paragraphs, a description<sup>of the WorldBank's model isgiven</sup> with the variables expressed in level tenrs.<sup>Whilethismodel canbe giveninmore</sup> compact fonn to show its properties,<sup>we optto give a descriptionof the actualworking</sup> model used by the country economists<sup>in theWorld Bank.The "standard"modelusedby</sup> many World Bank country management<sup>units (knownas theRevisedMinimum</sup> Standards Model Extended - RMSM-X)<sup>belongstoa classof "three-gap"modelswidely</sup> used by developing countries (see<sup>Bacha (1990), Iqbal(1997),Khan(,Sepehri, Moshiri</sup> and Doudongee (2000),Taylor(1990))<sup>thatis an extendedversionof the "two-gap" model</sup> introduced by Chenery and Strout (1966).<sup>It consistsof two modules:the flow of funds</sup> module that sets the accounting identities<sup>andafew behavioralequations (FOF), anda</sup> external debt module that brings<sup>ininformationof externaldebt relatedtransactions</sup> (DM). The basic version contains<sup>foureconomic agents or "sectors":Central</sup> Govenunent, the Monetary System<sup>(CentralBankand Deposit Money Banks),the</sup> "Private" Sector<sup>4</sup> ("Rest of the Economy"<sup>includinghouseholdsand private firms,non-</sup> central govermment agencies, parastatal<sup>enterprises, and non-monetaryfinancial</sup> institutions) and the Foreign Sector.

# **Equations of the "standard" model**<sup>**5**</sup>

The savings-investment gap:

> Thc "standard" three-gap model has<sup>**conic**tindcra grcatdcal of criticismfrom WorldBankeconomists,and therc had been a number</sup> of attempts to find altematives that arc<sup>capablcof handlingold issucsandncwdcmands,including povcrty-relatedwork (see,</sup> Dcvarajan **ct** al (1994), Agesior,<sup>etal (2000).</sup>

> 4The so-called 'Private Sector' should<sup>bc interpretedas a residtialsector,i.e. whateversector theuscr does not specify scparatelywill</sup> be in this 'Private Sector.'

> s For **the** symbols used. see appendix<sup>1.</sup>
45 Annex Table 1: Support to Temperate Agriculture*

||Wheat|Milk|Beef|Eggs|Pigmeat|
|---|---|---|---|---|---|
|EC|77|200|107|0|8|
|Finland|328|483|271|149|183|
|Sweden|282|307|81|138|18|
|United States|70|138|40|8|6|
|Australia|17|42|9|14|4|
|New Zealand|10|3|3|118|0|

* percentage of producer prices above world prices in 1990.

# Source: OECD

Annex Table 2: Sources of Nordic Textile hnports in 1992 (%)

||Finland|Sweden|Norway|EC|
|---|---|---|---|---|
|Estonia|2.3|1|<1|0|
|Latvia|0.3|0.3|< 1|0|
|Lithuania|0.2|0.1|< 1|0|
|Portugal|16|13|12|_5_|
|Greece|3.7|3|3|3|
|Thailand|2.6|2|1|2|
|China|10.8|12|12|_5_|
|Hong Kong|7.5|9|8|7|
|E. Europe|7|3|1|4|
|Turkey|0.9|2|1|6|
|EC|_51.60_|_50_|59|47|
|EFTA|6|6|8|3|
|Source: Comtrade|||||
γ . Our empirical analysis provides estimates of the income parameters. For the preference parameters, we choose an annual discount factor of β = . 96 and allow the degree of risk aversion γ to (separately) take values 1 and 2. These values for the preference parameters are in line with the values used in the macroeconomic literature (Cooley (1995)).

# **V. Results**

In the first step of our analysis, we use data on individual income changes from workers in different manufacturing sectors in Mexico and the methodology outlined in section II to estimate quarterly income risk parameters in each of these sectors during the time period 1987-1998. Tables II and III provide the average estimate of σε 2 and ση 2 for each year (averaged across industries) and for each industry (averaged over time) respectively. The mean value (across industries and over time) of the 2 quarterly variance of the persistent shock, σε , is estimated to be 0.0065, or 0.026 in annual variance. As expected, given the extent of measurement error in the income data (see our discussion in Section II), the estimated variances of transitory shocks are much larger in magnitude.

# _Human Capital Categories_

We separate workers into three human capital (education) categories. In the first group, we have workers with zero to six years of education. The second group consists of those workers with more than six years of education but who haven’t graduated high school. Finally, the third group consists of workers who are all high school graduates. As indicated in Table IV, In our data set, the first group comprises approximately 36 percent of the workforce, the second group comprises 46 percent and workers in the highest human capital category the remaining seventeen percent of the workforce. Table II also presents estimates (averaging over industries and time) of the quarterly variance for the different human capital categories. An indicated there, on average, workers with intermediate levels of human capital face the highest level of income risk in our data. The quarterly variance of the persistent shocks to income that they face is 0.0075. In comparison, the quarterly variance is estimated to be 16
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4 This iounterfactual scenario is needed tc decide whether alternative policies would have done better or worse, to isolate the effects of adjustment programs from other factors, and to establish causality. Sec -i, data on factor rewards and factor endowments, and socioeconomic data on the living conditions of the poor, are scarce and often of dubious quality. Many of the poor work in the informal secto-, but output and othe-, informal sector data are usually not included in official statistics. Third, analyses of adjustment programs must look at both the potential short-run costs and the long-run benefits. For many countries, the elapsed time follo'ing adjustment is still too short to measure the long-term benefits, and the uncertainty of long-run benefits may make them more difficult to model than the short-run costs.

# 2.1 Qualitative Approaches: General Eguilibium

Qualitative, general equilibrium models are valuable as a framework for thinking through the issues involved, while incorporating feedback effects. They help to identify the relevant variables in the analysis, and are useful regardless of the data situation. For example, the dependent economy model discussed below, points to the need to identify the factor intensities of the sectors whose relative price has changed.

# a. Dependent Economy Model

Most qualitative approaches use the dependent economy model to describe the theoretical effects. This is a general equilibrium, full employment model that focuses on the real exchange rate.<sup>5</sup> In its simplest form, there are two 5This model was originally derived by Salter and Swan and has been called the Australian model. A description of the trade model is Mussa (1974) which derives the important parameters for measuring the effect on factor incomes. Recent summaries in the context of adjustment are SDA (1989), Demery and Addison (1987, 1988), Edwards (1988), Corden (1986), and Knight (1976). These summaries
# **1 Introduction**

Temporary migration is part of life in many regions of the world. One of the most prominent examples is the migration of millions of mostly low-skilled workers from South and Southeast Asia to oil-rich Persian Gulf countries. Every year these workers depart with temporary employment visas and short-term contracts. Once their contracts expire, workers are no longer allowed to stay, and they return home (OECD 2008; World Bank 2018). Academic and policy research on the subject has largely focused on how these migrants increase their current income and support their families at home while they are abroad.<sup>1</sup> In this paper, we examine temporary migration from a different angle: We argue and present compelling evidence from Bangladesh that such temporary migration episodes are an integral part of workers’ savings and investment strategies implemented over a lifetime, with important ramifications on the overall economic outcomes of origin countries.

Answering questions on temporary labor migration decisions and related economic outcomes requires a dynamic framework that accounts for individuals’ intertemporal optimization decisions.<sup>2</sup> Data are needed to reveal migrants’ complete labor market trajectories over time and across borders, and to show their investment and savings behavior. Standard national surveys and administrative data sources do not provide these data, as they focus on current labor market outcomes, but do not ask detailed questions about workers’ employment and financial histories. Thus, static analyses based on these data sources are unable to identify the dynamic linkages between key decisions: whether, when, and where to migrate; how long to stay; how much to save; what employment options to pursue back at home; and whether migration provides the means to overcome credit constraints, as we argue is the case in Bangladesh.

In this paper, we identify and examine the implications of these linkages by estimating a dynamic model of decisions on emigration, return, and self-employment. We then use the estimated model to evaluate the intertwined effects that arise from key changes in migration or domestic lending policies. Specifically, we examine the effects likely to emerge as a result of a decrease in migration costs, a decrease in interest rates for self-employment loans, or the provision of more accurate information on overseas earnings to would-be migrants. Our simulations show that a reduction in migration fees, or an increase in wages earned overseas raises emigration, but decreases its duration while increasing overall savings and entrepreneurship rates in Bangladesh. We further show that core development policies, such as loan subsidies for entrepreneurs, can trigger a decrease in the extent and duration of

> 1See Mobarak, Sharif, and Shrestha (2020) for a recent study on temporary migrants to Malaysia and the evidence on the impact on their families.

> 2See Dustmann and G ̈orlach (2016) for a review of the literature.

2
**Figure 8: Business Creation and the Informal Sector, by Country, Average 2003-2005**

<!-- Start of picture text --> Entry rate Density rate (percent)<br>20% 15<br>15%<br>10<br>10%<br>5<br>5%<br>0% 0<br>0 20 40 60 0 20 40 60<br>Informal Economy (percentage of GNP) Informal economy (percentage of GNP)<br><!-- End of picture text --> Source: World Bank Group Entrepreneurship Database and World Bank (2006).

# **_6.4 Business Creation and Governance_**

In order to study the relation between Governance and entrepreneurship, we use the average of the six Kaufmann, et al. (2006) governance indices: Voice and Accountability, Political Stability, Government Effectiveness, Regulatory Quality, Rule of Law and Control of Corruption. The data show a strong and significant relationship between entry rates and good governance (Figure 9).

**Figure 9: Entry rates and Kaufmann et al Governance Indicators, by Country, Average 2003-2005**

<!-- Start of picture text --> Entry rate(percent) Density rate(percent)<br>20 25<br>16 20<br>12 15<br>8 10<br>4 5<br>0 0<br>-2 -1 0 1 2 -2 -1 0 1 2<br>Governance (Kraay indicators average) Governance (Kraay indicators average)<br><!-- End of picture text --> Source: World Bank Group Entrepreneurship Database and World Bank (2006).

This result suggests that a stable business environment should be expected to foster private sector development and growth. The case of Peru shows the sensitivity of new firm registrations to political changes (Figure 10).

21
The remainder of the paper is organized as follows. Sections 2 and 3 introduce our data and methodology, respectively. The main empirical observations derived from our database of Twitter data on COVID vaccination are presented in Section 4. In Section 5 we contrast our Twitter-based estimates of vaccination hesitancy to estimates obtained from more conventional survey data and rationalize the divergence. Finally, Section 6 concludes.

# **2. Data**

Between December 2020 and April 2021, we used Twitter’s open API to download tweets featuring the most prominent COVID hashtag in Arabic: كورونا#. As our interest is in COVID vaccination attitudes in the region, we filtered the database for vocabulary related to vaccination. Specifically, we compiled a list of 24 vaccination-words in Arabic (shown in Table 1) and identified the sub-sample of tweets that feature at least one of these words.

|**English**|**Arabic**|
|---|---|
|Vaccine|لقاح|
|Vaccine|مصل<br>|
|Treatment|علج|
|Immunization against|تحصين ضد<br>|
|Doctors Syndicate|نقابة<br>أطباء|
|Sinopharm|سينوفارم|
|Vaccination<br>|التلقيح<br>|
|Possible side effects|والعراض الجانبية<br>المحتملة|
|Clinical trials|التجارب<br>السريرية|
|Immunity|المناعة<br>|
|Medicines|أدوية|
|G42 UAE company|شركة<br>G42الماراتية|
|G42 UAE Healthcare Company|شركة<br>G42الماراتية للرعاية<br>الصحية|
|Chinese vaccine|اللقاح الصيني|
|Pfizer vaccine|لقاح فايزر|
|Sputnik vaccine|لقاحسبوتنيك|

Table 1: Vaccination vocabulary Our data is organized into two sub-periods. The first spans from mid-December through to mid-February and contains approximately 730,000 tweets and 230,000 user accounts engaging in the vaccine discourse. Approximately 4000 influencers accounted for 95% of the discourse. The second period runs from late February to early April and contains approximately 500,000 tweets and 190,000 user accounts. About 5100 of these influencers accounted for 95% of the overall discourse.

Twitter shares a select number of tweet and user account attributes, including tweet content, number of likes, number of retweets, user Twitter handle, number of followers. Attributes such as location (i.e. country of residence, nationality), whether the user is an 3
# **8. Conclusions**

Clearly, it is worth having estimates of the costs of adjustments to GG policies, both from a policy perspective and from an academic perspective. Some of the early approaches used to compute trade adjustment costs (TAC) could in principle be used to compute crude measures of green growth adjustment costs (GGAC). However, implementing a sophisticated dynamic general equilibrium model is also feasible. The discussion in Section 5 suggests ways to do this. Much more work is needed to fine tune the details, conceptually but more importantly empirically. But this effort is feasible and is also likely to generate evidence of significant value to assess Green Growth policies comprehensively.

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