rafmacalaba/data-use-mentions-extended huggingface dataset:
made. Examples of state variables may include such variables as culture, geography, institutions, and market integration.

Mundlak, Butzer, and Larson (2010) provide an in-depth discussion of panel data analysis, and in particular the dominance of the within estimator, utilizing an updated version of the data set on agricultural capital stock to extend the analysis to 2000.<sup>23</sup> The authors draw comparisons with the between estimators as well, noting that although the between estimators are biased, they still relay information valuable to understanding the underlying processes of growth. The authors explore the role of the country and time effects in accounting for the variability in productivity, and the ability of the state variables to capture some of these effects.

Using this prior work as a staging ground, we show the consequences of using an incomplete measure of capital on the estimates of production elasticities by replacing our measure of agricultural fixed capital with the FAO data on tractors. Before doing so, though, some background information on the underlying approach is required.

# **Theory**

We estimate agricultural production, _y_ , as a function of the inputs, _x_ , and state variables, _s_ , for a panel of countries, _i_ , over time, _t_ . Output and inputs are expressed in logs.

where _u0it_ ~ IID (0, σ00), _ujit_ ~ IID (0, σjj), E( _u0itujit_ ) = 0. Output and input prices are included in the set of state variables. With panel data the term _m0it_ is decomposed to a country effect and a time effect _m0it_ = _m0i + m0t_ .<sup>24</sup> The effect of the state variables is assumed to be linear; they serve as shifters in the production function. We impose the simplifying assumption of constant elasticities, so that β( _s_ )=β. This elasticity is conditional upon the set of state variables, so that a different set of state variables will yield different estimates of β. Equation (
0|Cobalt ores and con 0.6|Citrus fruits, inc|2.6|
|W/g blouses, shirts<br>Vanilla beans|3.4<br>3.1|furniture<br>Coniferous wood|0.2<br>0.2|T-shirts, singlets<br>2.4<br>Diamonds<br>2.0|Nonconiferous wood<br>Vessels,nesoi,for t|0.7<br>Fish, nesoi, with b 0.3<br>0.7<br>Flour meal &pellet 0.2|unwrought<br>2.9<br>Diam ex ind unwkd 2.4|Peas, dried shelled<br>1.9<br>Wheat(other than d 1.7|Precious metal ores 0.5<br>Vegetables mixtures 0.5|Elect appr f prtct<br>Tobacco|2.3<br>2.2|
|||||1841.||||||||
|Trade Value (US$ bill)|1599.6||687.1|7||2033<br>241.5|<br>64400|<br>1769.5|<br>4054.9||603.4|
|share of top 10|55.8||96.7|68.0||90.8<br>97.8|42.3|60.7|96.8||74.2|
|share top10 in 2003 to R|OW||90.9|69.2||92.6|44.5|83.9|86.6||93.7|

Source: Own calculations using SADC Trade Database. Data are classified at the 6-digit HS level. Tanzania data is for 2007.

11
owner notified us that the employee was no longer working, and we removed the subsidy. We believe this process and these spot checks were sufficient to root out minimize ‘phantom’ employees.

In order to determine whether the effectiveness of the wage subsidy differs with the availability of complementary inputs, we also carried out two supplementary interventions. The first was a savings intervention, in which individuals were offered a savings account in which we matched deposits made up to a specified amount. This took place before the wage subsidies started, and the goal of this intervention was to enable firm owners to build up a balance of savings, which they could then use to supplement the worker with any additional capital required to make this worker more productive. The second was a business training intervention, which also took place before the wage subsidies started. Firm owners were offered the ILO’s Improve Your Business (IYB) training, to allow for the possibility that better business practices are needed in order to be able to successfully employ additional labor. Appendix 3 describes these supplementary interventions in more detail.

# **3.3 Randomization and Balance**

After the baseline survey was conducted with those in the booster sample, we stratified firms into six strata using geographic region (Colombo, Kandy, or Galle/Matara) and sector (retail or manufacturing and services). Within each stratum we then randomly assigned 18.7% to the control group (286/1533), 16.3% (250/1533) to get the wage subsidy program alone, 19.3% (297/1533) to get the wage subsidy and the supplementary savings program, 19.3% (297/1533) to get the wage subsidy and the supplementary training program, 7.3% (112/1533) to get the supplementary savings program alone, 9.2% (141/1533) to get the supplementary training program alone, and 9.8% (150) to get the supplementary training and
# **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
0.820|0.926|0.727|0.581|0.463|0.630|
|Municipalities|969|969|969|969|969|969|969|969|969|

_Note:_ Semiurban refers to municipalities with a population between 2,500 and 15,000. City refers to municipalities with more than 15,000 inhabitants. The average income corresponds to the average per capita household income in each municipality in logarithms. The deciles of income correspond to the cut-off points of dividing in each municipality the households into ten equal groups and is expressed in logarithms. All monetary values were in real terms in August 2014. Model with fixed effects by the municipality and by year for 2005, 2010, and 2014, also using the FAIS formulated as the instrumental variable. The nonmonetary dimensions are expressed in 0–100, understanding 100 as 100% of coverage. Include observation from urban municipalities only, defined as municipalities with at least 15,000 inhabitants.

∞ Fiscal revenue items in the logarithm of the average monthly per capita value three previous years. ‡ Occupied per inhabitant, employees of the economic units or indirect workers, reported in the economic census of the circa year. ‡ ‡ Productivity by the total of occupied and active of the economic units of each municipality. Standard error in parentheses. *** p < .01 ** p < .05 * p < .1 37
6 In previous research (Constantinescu, Mattoo and Ruta, 2015), we use an Error Correction Model (ECM) to investigate more formally the behavior of world trade and income. Specifically, we estimate the following regression equation using annual data for the period 1970-2014 for the world economy: where m is world trade in volume terms, y is real world GDP, t is year, β is the short-run trade elasticity, -  is the speed of adjustment, and -  /  is the long-run trade elasticity.

The regression results for this specification are reproduced in Table 1. The long 1990s were indeed different, with a long-run elasticity of world trade to GDP of 2.2. In the 2000s, this elasticity reverted back to 1.3, the level prevailing in the period 1970-1985. For each percentage growth of world income, trade was growing by 2.2 percent in the long 1990s but only by 1.3 percent in the period 1970-1985 and in the 2000s.<sup>2</sup> Formal tests also confirm that there was a statistically significant structural break between the long 1990s and the preceding and subsequent periods.

> 2 Reported estimations are based on world GDP growth aggregated using PPP rates. Estimation using market exchange rates yields elasticities of 2.51 in the 1990s and 1.87 in the 2000s. The elasticity in the 1990s is higher than the one in 2000s, and the difference between the two estimates is statistically significant, though smaller than in the case when PPP weights are used.
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Four inputs are considered: labor ( _L_ ), electricity ( _E_ ), intermediate and/or final goods ( _F_ ) and others ( _M_ ). The firm’s productivity may also depend on the quality of available infrastructure services ( _Z_ ) and other firm characteristics ( _X_ ). Based on their locations, firm productivity is also affected by transport connectivity, _V_ .

12. Following the traditional industrial organization literature (e.g., Nerlove, 1963; Christensen and Greene, 1976), the translog cost function is considered: 1 1 0 2 y + 2 h h h + yy 1 kk wwkk kk kk wwkkww kk ln cc= ββ + ββ ln yy+ ββ ln yyln yy+ ∑ββ+ 2 h ln wwh ∑∑ββh + ln ww ln ww + ∑ββkk yywwh kk ln yyln wwh kk + ∑ββhkk+zzkk ln zzkk ∑∑ββkk zzkkzz ln zzkk ln zz + ∑ββkk yyzzkk ln yyln zzkk(2) kk wwkkzz kk kk xxkk kk kk vvkk kk ii ∑∑ββ ln ww ln zz ∑ββ ln xx + ∑ββ ln vv + ββ tt+ εε 13. Output is measured by total sales in Mozambique meticais. Ideally, this should be measured by some variable in real terms, but this is only the usable common proxy for output in the BEEPS data.<sup>3</sup> Along with firm-specific characteristics, the sector-specific dummy variables are also included to control for unobserved heterogeneity across sectors: agrobusiness, light manufacturing, metal and chemical, and services.

14. Three infrastructure services are examined: electricity, water supply and transport. For power, the level of service quality is measured by the average duration of power outages in hours ( _ZE_ ). The quality of water services is measured by the number of days without sufficient water ( _ZW_ ). The quality of transport infrastructure is not directly measured by available data in the data set, but it can be represented by the level of inventory
Madrigal (2008), Perry et al. (2007), Camacho et al. (2009), Maloney (2004, 1999), Maloney and Bosch (2006), Maloney, Goni and Bosch (2007), Kugler and Kugler (2009), Mondragón et al. (2010), have shown that provisions of social protection, notably non-wage costs, incentivize informality. Only a few authors, such as Kugler and Kugler (2009), Mondragón-Velez, Peña and Wills (2010) and Camacho, Conover and Hoyos (2009), conduct econometric analysis of the occupational choice in Colombia, typically as an aggregated indicator in the economy or as a decision within firms and across sectors. To the best of our knowledge, no study analyses these effects either as an individual worker decision or within the context of transition or mobility trends.

This paper analyzes the magnitude, direction and composition of labor transitions in Colombia between 2008 and 2009, using recently available data from the latest two rounds of Fedesarrollo’s Social Longitudinal Survey, FSLS (Fedesarrollo 2008, 2009), a household survey panel data representative of the 13 main metropolitan areas of the country. The paper also provides evidence on the personal and professional characteristics of those who transition across occupations and jobs, including their personal circumstances, motivations, preferences and exposure to and strategies against risks. Finally, the paper explores econometrically how these factors contribute to observed labor transitions. Even though the analysis does not establish causal links between the crisis and labor transitions, results confirm that between 2008 and 2009 there were large and asymmetric transitions among occupations in Colombia. Asymmetric transitions mask different mobility patterns across occupations: formal salaried workers may first try to move to the informal sector rather than transitioning into unemployment or out of the labor force, while an informal self-employed worker may more likely move into unemployment and out of the labor force. This result may be picking up not only period-specific labor dynamics but also a more structural phenomenon whereby workers voluntarily opt into salaried or self-employed occupations
World Bank’s Word Development Report 2020. Section 6 concludes.

Each section shares the same structure. At the beginning we provide a brief overview of the measures therein discussed. Then, we show how to compute those measures in `icio` , providing also some examples and a list of several relevant questions that could be addressed, with the related `icio` ’s syntax. In addition, in the Appendices we present the related conceptual frameworks.

# **2 Inter-Country Input-Output tables**

Input-Output (IO) models were developed by Leontief (1936) to represent and analyze production and consumption relationships within an economy. The related statistical tools, the IO tables, indicate the monetary amount of inputs of each sector necessary to produce the total output of a given industry and, in turn, how this output is used as final consumption (or investment) or as intermediate inputs for other productions. National IO tables distinguish only between domestic and foreign inputs; on the output side, exports represent one of the possible ‘final’ uses 3
summarizes survey evidence from health workers in Africa who similarly indicate professional advancement, better facilities to work in, and safety for their families as prime motives for migrating, in addition to the higher incomes they can earn.

The evidence is consistent with the high-skilled wanting to be in places in which they can live well, without fear of violence or instability, and in which they have sufficient opportunities to advance their careers and work with like-minded people. It is thus not surprising that India, with a large population, rapidly growing economy, and high-tech centers, sees brain drain now as much less of a problem that small, remote or politically unstable countries do.

# **Question 6: Does Brain Gain Exist?**

A series of theoretical papers over the past 15 years, summarized in Docquier and Rapoport (2011), have shown that it is possible that high-skilled emigration can lead to a rise in human capital levels in the home country. The basic idea of such ―brain gain‖ theories is that decisions of individuals to invest in education react to the prospect of future migration, and that not all those who choose to increase their education because of the chance they may migrate actually end up migrating. Providing more opportunities to work in the U.S. for doctors and nurses from poor countries may then not deplete supplies of medical workers at home, but could in principle raise them by increasing the incentives for people in poor countries to train as doctors and nurses.

In the last few years, some case study microeconomic evidence has started to emerge on whether such a mechanism operates in practice. The key challenge for empirical work is identifying a plausible source of exogenous variation in either migration opportunities or returns to skill abroad, which can then be used to examine the responsiveness of human capital acquisitions. Shrestha (2010) uses a change in the educational requirements of British Gurkha Army recruiters in 1993, which increased the returns to education abroad for Gurkha men in Nepal, to show that this led to increases in
inverse propensity score approach, and (iv) PSW (Lee 2006) with non-PSW adjustments (maxentropy, raking and/or post-stratification).

Second, we conduct the experiment above using a sample of Ethiopia HFPS7, which was collected based on the ESS4 sample. The subsample is subject to both sampling and nonresponse biases. Using this subsample, we examine whether reweighting procedures and the SWIFT poverty projections combined can reduce sampling and nonresponse biases in poverty estimates.<sup>8</sup>

# _III.1 Results from experiments with Rwanda EICV5_

Phone ownership is 65.8 percent in Rwanda (9,589 out of 14,574 households own a mobile phone, according to EICV5). As shown in Table 2, the phone owner subsample is significantly wealthier in terms of asset ownership and housing conditions. For example, bicycle ownership among phone owners is 18 percent compared to 13 percent in the full national sample. However, the average household size, which is usually negatively correlated with household welfare, is about 6.8 percent higher in the phone owner subsample than in the full sample (Table 2). While these two effects offset each other, the actual poverty rate of the phone subsample (29.3 percent) is significantly lower than the national average (38.2 percent), indicating that the phone owner subsample is wealthier than the entire population (Figure 2).

Three types of weights were calculated to adjust for the sampling bias in the phone owner sample of Rwanda EICV5, namely, PSW (Lee), PSW (Inverse), and PSW & non-PSW. The first subfigure in Figure 2 compares the poverty rates of the phone owner sample by applying the abovementioned weights to the actual consumption data. The poverty rates among the phone owners applying original weights, PSW (Lee) weights, PSW (Inverse) weights, and PSW & non-PSW weights are 29.3%, 31.4%, 32.0%, and 31.9%, respectively. While
Figure 22: Balance of Payments accounts (percent of GDP) <!-- Start of picture text --> 9%<br>7%<br>FDI Portfolio Other Change in reserves Financial account<br>5%<br>3%<br>1%<br>-1%<br>-3%<br>-5%<br>-7%<br>-9%<br>Source: Authors’ elaboration using data from the Central Bank of Turkey<br>Notes: A negative value represents incoming investment or reduction in reserves.<br>Figure 23: Non-FDI financing of the CAB (percent of GDP)<br>4.0%<br>2.0%<br>0.0%<br>-2.0%<br>-4.0%<br>-6.0%<br>CAB<br>-8.0%<br>Non-FDI CA financing<br>-10.0%<br> Source: Authors’ elaboration using data from the Central Bank of Turkey<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> 17
# **4.3 Linking sentiment to economic activity**

While the supply and demand sentiment measures provide the relative size of supply and demand shocks over time and their sectoral differences, their impacts on economic activity are not observed. We use a local projection model to trace out the impact of sentiment as measured in the earning call transcripts. The benefit of such approach is we do not need to place onerous restrictions as in the case of VAR models. The model controls for the alternate sentiment measure, consumer inflation, exchange rates, oil prices, and policy interest rates.

Figure 5 plots the impulse response functions from demand and supply sentiment shocks. Both supply and demand sentiment shocks are statistically significant drivers of output. On average, the impact from a 1 unit positive shock to demand is larger than the equivalent supply shock–by about three-quarters–and leads to a 1.25 percent increase in output. One possible reason for why supply sentiment has a weaker impact in comparison to the demand shock is that the worsening sentiment might be transmitted at different degrees to the real economic activity. For example, supply chain difficulties might be addressed by existing inventories and/or alternate supply channels for some firms. Even if the initial disruption is a shock to supply, intersectoral linkages might exacerbate the impact of demand shocks, resulting in significant reductions in aggregate demand (Guerrieri, Lorenzoni, Straub, and Werning, 2020). While the larger impact of demand on output may be contrary to the perspective of real business cycle models (see, for example, Kydland and Prescott, 1982) of the importance of supply shocks, it fits into models of hysteresis and other possible sources of demand shocks (see, for example, Cerra et al., 2020).

A demand sentiment shock does not lead to a statistically significant increase in inflation beyond the first period, with the increase in inflation small (0.1 percentage point) relative to the shift in output. This implies a contemporaneous Phillips curve slope of
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1981).<sup>7</sup> In the region, IMF (2020) applies the fixed-effects method to estimate the impact of transport infrastructure under Nurly Zhol on firm revenues and profits in Kazakhstan. However, the fixed-effects model may lead to biased estimates if there are any time-variant individual effects that are correlated with the residuals.

25. The generalized method-of-moments (GMM) estimator is more efficient than the fixedeffects estimator. In this paper, the system GMM using instruments from both levels and differences (e.g., Roodman, 2009). The traditional difference GMM approach (Arellano and Bond, 1991) where only differences lagged differences are used as instruments tends to poorly perform particularly when the time series persistence is high in a dependent variable. The system GMM can generate more instruments under the assumption that their changes are uncollated with the individual fixed effects and better control for the endogeneity and autocorrelation issues simultaneously. The reliability and validity of instruments will be examined by ex post statistical tests, such as the Hansen overidentifying restriction tests.

26. Assuming that agglomeration economies are influenced by transport connectivity and other factors, the following dynamic panel model is considered: where _Nit_ is the number of firms registered at location _i_ at year _t_ . Our unit of analysis is region or district. The level of spatial disaggregation is different across countries (see **Table 1** above). All time-invariant characteristics that are specific to location _i_ , such as local culture, language and politics, are assumed to be captured by the location-specific fixed-effects, _ui_ , and removed by the first difference transformation of the dynamic panel model. The systematic differences in definition and coverage of firm data across countries are also expected to be controlled by _ui_ .

27. To deal with the persistence of agglomeration economies, the lagged dependent variables are included on the right-hand side. As shown above, our dependent variable exhibits strong

> 7 As will be shown below, the dynamic panel model estimator is more suitable to our data