rafmacalaba/datause-encoder-data huggingface dataset:
sitions (M&A).<sup>15</sup> However, there is no _a priori_ reason to believe that greenfield and M&A outward FDI flows behave fundamentally in different ways. For instance UNCTAD (2010) reports that both greenfield and M&A FDI flows have significantly dropped during the global financial crisis.<sup>16</sup> Hence, despite relying on greenfield FDI data, our results should be generalizable to the entire FDI universe.

by the _FDI Markets_ database at the country-sector level without any loss of information to obtain a proxy for FDIist.<sup>17</sup> We assume that firms engaging in greenfield FDI in a given sector primarily operate in the same sector. At the most disaggregated level, the underlying data that we use to estimate models (1) and (2) correspond to 15240 greenfield FDI projects in a new production or processing manufacturing facility made by 7126 parent companies located in 111 source (developed and developing) countries, in 13 broad manufacturing sectors during the period 2003-2010.<sup>18</sup> The largest sources of greenfield manufacturing FDI over the period 2003-2010 are OECD countries.<sup>19</sup> This pattern ought to facilitate the identification of the effect of tight credit conditions on FDI since the major OECD countries were at the epicentre of the global financial crisis.

> 15Note that deal values are frequently missing in M&A databases. For instance, in the _Zephyr_ database, deal values are missing for 60% of M&A transactions.

> 16The report puts particular emphasis on the role played by financial constraints to explain these concomitant trends.

> 17Of course, we do not deny that MNEs are heterogeneous firms. For example, within a given sector, firms are likely to vary in the credit constraints that they face. What we mean is that, given the FDI data that we have, we would not obtain different results by using unit-level data (Rabe-Hesketh and Skrondal, 2012). An analogous result can be found in the discrete choice location literature. Guimaraes et al. (2003) demonstrate that the conditional logit model and the Poisson regression model share the same log-likelihood function with purely location-specific determinants. This implies that the estimation of a Poisson regression model with a sample where data are aggregated by location will yield the same estimates as a conditional logit model applied on project-level data.

> 18Firms can invest abroad in other “functions”, e.g. logistics, sales/customer support, or retail. We prefer to exclude these FDI projects from our sample because the broad sectoral classification adopted by FDI Intelligence may lead some projects to be included in a manufacturing sector even when their purposes are only to provide support services to this sector or to facilitate the distribution and sale of its products. By focusing on FDI in a new production or processing manufacturing facility (the ‘manufacturing function’ in the _FDI Markets_ database), we strongly increase the likelihood that the parent firm truly belongs to the manufacturing industry.

> 19

8
26 The benefit of this and the Commodity Credit Corporation programs to the U.S. is the support for agricultural prices in the U.S. as well as for exports. Since U.S. agricultural prices are in fact higher than world prices, to a limited extent these programs have been successful.

# V. Conclusion

The evidence presented sheds some doubt on the effectiveness of U.S. credit programs to generate growth by increasing investment in targeted industriPs. However, since growth has never been an explicit objective of the U.S. credit policies, this may not be surprising. As discussed in Section m, there are four necessary conditions for directed credit to effectively stimulate growth. The first condition is that credit must increase borrowing in the targeted sector. Gale's work focuses on testing this condition. For the bulk of U.S. directed credit which goes toward the housing market, this first condition will not be met if the cost of government borrowing to fund the credit program raises interest rates, indicating that a large part of the housing credit has gone to inframarginal borrowers, people who would have borrowed anyway. However, as noted earlier, the allocation of credit within the housing sector may be affected and cannot be captured by Gale's simulation model. For other targeted sectors, credit allocation has increased, but the effects of each policy have been reduced by the existence of other programs.

The second condition, the link between credit and investment in the targeted sector, has only been addressed in two sectors: agriculture and education. In both cases, the studies indicate weak links between credit and productive investment. The LeBlanc and Hrubovcak study show greater increases in demand for a fixed asset, land, than for variable inputs in response to interest rate changes. The education studies also show some increase in the number of students attending college, but a significantly smaller increase than the amount of credit advanced. Thus, the
that energy input will equate its marginal revenue which includes savings in labor costs and taxes to its marginal costs which is the price. Eq. (41) is the aggregated law of motion of private capital. Eqs. (42) to (44) are the aggregated CES technologies. Eq. (45) is simply the rearranged firm’s budget constraint. Note that _T_<sup> ̃</sup> _PZ,t_ and _T_<sup> ̃</sup> _S,t_ are the Z-firms’ corporate income and payroll taxes, respectively.

# **A.3.2 Renewable energy firms (R-firms)**

R-firms are modeled equivalently to Z-firms except that they don’t have market power, don’t have variable capital utilization, and don’t use energy input to production. Moreover, public capital can either take the form of generation capital and infrastructure. In the former case, public capital simply adds to private capital as a perfect substitute. As infrastructure, public capital constitutes an externality and enhances the marginal product of private capital. Yet, there is an exogenous satiation level of how much infrastructure can be used in production. Also, the productivity shock process in renewable energy production _VER,t_ features learning by doing. In the literature, learning-by-doing elasticity is usually estimated using costs and capacity of production. Here, we relate productivity to energy output. We assume a one-toone relationship between increase in productivity and decline in levelized cost of renewable energy, which we confirm by stochastic simulations.

Taking as given price of its output _PER,t_ , the final goods price level _PY,t_ , and the real wage rate of a homogeneous labor service _pL,t_ as well as the law of motion of capital, capital adjustment costs, the production functions, and the requirement to maintain a debt-capital ratio _λ_ , the firm _j_ chooses _{BR,t_<sup>_j, E_</sup> _R,t_<sup>_j, Lj_</sup> _R,t_<sup>_, I_</sup> _PR,t_<sup>_j, K_</sup> _PR,t_<sup>_j}_</sup> _t_<sup>_∞_</sup> =0<sup>tomaximizeitsrealbeginning-</sup> of-period value, _DR,t_<sup>_j_+</sup> _PPSR_<sup>_j_</sup> _Y,t,t_<sup>_S_</sup> _R,t_<sup>_j_</sup> _−_ 1<sup>.Evaluatingatperiod</sup><sup>_t_=0,theoptimizationproblem</sup> reads where subject to 47
2

# **1. Introduction**

Students around the world – especially in low- and middle-income countries – are not learning enough in school. In a recent international assessment, students at the 75<sup>th</sup> percentile of performance in selected countries from the Caribbean, North Africa, and Eastern Europe performed below the 25<sup>th</sup> percentile average for high-income countries (World Bank, 2018). A growing body of research demonstrates that teachers are among the most important determinants of student learning. The difference between a weak teacher and an excellent teacher has been measured at up to a full year of student learning in the United States (Hanushek and Rivkin, 2010), and large teacher effects have also been documented in Chile (MINEDUC, 2009), Ecuador (Araujo et al., 2016), Pakistan (Talance, 2015), and Uganda (Buhl-Wiggers et al., 2017). Beyond immediate improvements in student learning, teachers who raise student test scores significantly improve students' long-term outcomes – such as their probability of graduating college and adult salaries – and decrease the likelihood of teenage pregnancy (Chetty, Friedman, & Rockoff, 2014).

Furthermore, in recent reviews of the education literature, improving pedagogy so that it is more directed to individual student levels – an action that depends significantly on teachers either carrying out formative assessments or targeting instruction – and providing individualized, repeated teacher training, associated with a specific method or task were among the most recommended interventions for improving student learning (Evans and Popova, 2016; Kremer, Brannen, & Glennerster, 2013). For example, the full Early Grade Reading Assessment (EGRA) program in Liberia, which trained teachers to use an initial reading assessment and then continually assess student performance, increased students’ reading comprehension dramatically (Piper & Korda, 2011).

In-service teacher professional development is important to evaluate even beyond the
= 0.1). The figure shows the percentage of workers in each bin of the pre-pandemic and wave 3 occupations. Most workers remained in similar occupations in terms of WFH. In the case of women, 78 percent remained in the same bin —84 percent if we also consider changes to adjacent bins. The proportions are similar for men. Of those that moved from the pre-pandemic occupation, the number of cases for which the WFH index increased or decreased is similar. The latter seems counterintuitive, but it could be associated with the increase in certain essential occupations during the pandemic that are not compatible with remote work, such as delivery services.

Columns 3 and 4 of Table 11 complement this analysis by showing the results of a model of the change in the WFH index for those who changed occupation. The dependent variable is defined as the difference between the WFH index of the occupation in wave _w_ and the WFH index of the pre-pandemic occupation of individual _i_ in country _c_ . Again, keeping everything else constant, women were more prone to move into occupations more compatible with remote work. An increase in the WFH index is also associated with the presence of children at home, with a high educational level, and with having been a salaried worker in the pre-pandemic occupation. We also find a negative and significant partial correlation between the WFH content of the pre-pandemic job and that of the new job. Although this might seem counterintuitive, it reflects a somewhat mechanical mean reversion

# **7 Concluding Remarks**

The COVID-19 pandemic has been a huge unexpected shock on the lives of people around the world. Many have lost their jobs, suffered income reductions or had to change occupations to cope with the new situation. In this paper we explore these issues in Latin America by exploiting an unusual rich survey carried out immediately after the onset of the pandemic: the World Bank’s high-frequency phone household surveys (HFPS). In particular, by codifying the occupation variables in these surveys we are able to construct a variable for potential for work from home (WFH) and analyze the role of this factor in people’s adjustments to the shock.

Our analysis, mainly based on fixed-effects models of job losses and other labor outcomes, leads to some interesting results. First, we confirm that the impact of the COVID19 shock was (i) harder for women and (ii) strongly decreasing in the ability of doing work from home. More important, we find that the mitigating effect of WFH on the severity of the impact was especially relevant for women with children. These effects were larger in those countries/periods in which the containment measures implemented by governments against the spread of the disease were more stringent. Our results are consistent with a plausible mechanism: women with children were more likely to stay home due to school closures and the traditional intrahousehold distribution of childcare responsibilities, and thus the possibility of WFH was crucial for them to keep their jobs.

Our results provide evidence consistent with a relevant policy lesson highlighted in 17
<u>Table 9. Penalty of having children at home on time use variables</u>

|||Women|||Men||
|---|---|---|---|---|---|---|
||15-19|20-44|45-64|15-19|20-44|45-64|
|Market work|0.0211<br>[0.0342]|-0.186<br>[0.0234]***|-0.0915<br>[0.0336]***|0.163<br>[0.0480]***|-0.0171<br>[0.0372]|-0.0188<br>[0.0500]|
|Observations|19,677|111,582|31,461|18,940|98,553|28,238|
|R-squared|0.534|0.540|0.511|0.591|0.280|0.274|
|Unpaid domestic work|0.463<br>[0.0509]***|1.568<br>[0.0277]***|0.758<br>[0.0383]***|-0.0361<br>[0.0285]|0.196<br>[0.0212]***|0.193<br>[0.0285]***|
|Observations|19,658|111,387|31,440|18,892|97,600|28,068|
|R-squared|0.290|0.310|0.191|0.122|0.111|0.142|
|Personal care|-0.0837|-0.382|-0.153|-0.00496|-0.0269|0.0403|
||[0.0594]|[0.0237]***|[0.0314]***|[0.0399]|[0.0242]|[0.0330]|
|Observations|14,089|77,031|21,955|13,738|65,083|19,695|
|R-squared|0.382|0.528|0.346|0.387|0.598|0.523|
|Leisure, social and study|-0.524|-0.600|-0.280|-0.314|-0.158|-0.162|
||[0.0666]***|[0.0250]***|[0.0312]***|[0.0575]***|[0.0271]***|[0.0383]***|
|Observations|19,676|111,542|31,440|18,939|98,540|28,230|
|R-squared|0.679|0.635|0.633|0.672|0.604|0.596|

Note: Penalty on time use variables obtained from a propensity score reweighted regression (Hirano, Imbens and Ridder, 2003) where the treatment variable is an indicator of having children under 14 years of age at home. Explanatory variables include indicator variables for age groups, educational level, marital status, activity condition, area, and country fixed effects. Bangladesh and Moldova excluded from estimating sample (they do not have information in children at home).

Robust standard errors in brackets. *** significant at 1%; ** significant at 5%; * significant at 10%.

38
|1998-2002|47%|
|---|---|
|2003-2007|57%|
|2008-2012|38%|

Table 3 presents the population coverage for each five-year time period. We find that for the initial surveys from 1993 to 1997, the population coverage of low- and middle-income countries in our analysis was 18%. However, we see population coverage increase over the years to 47%, 57%, and 38% for the periods 1998-2002, 2003-2007, and 2008-2012, respectively. This shows that population coverage improved, likely because the number of surveys across countries increased over the years. While we may not have sufficient data for precise aggregation at this point, as more surveys are conducted in the future, we will have greater population coverage and greater precision in future analysis.

<u>Table 4: Percentage of regional population covered in the malnutrition aggregations</u>

|Region:|1995|2000|2005|2010|
|---|---|---|---|---|
|East Asia and Pacific|0%|1%|5%|7%|
|Europe and Central Asia|40%|31%|57%|16%|
|Latin America & Caribbean|54%|22%|19%|20%|
|MENA|26%|26%|57%|43%|
|South Asia|12%|85%|85%|26%|
|Africa|32%|65%|86%|86%|

Similarly, in Table 4 we present the population coverage for the regional aggregations in Figure 3. As we can see, while South Asia and Africa are well-represented in several of the timewindows, the coverage for the other regions are generally well below 50%. This demonstrates that the regional coverage estimates need to be interpreted with caution. However, it is important to note that the purpose of this exercise is not to create regional aggregates with sufficient coverage.

It is instead to show how these new indicators and new estimates can be used for analysis.

22
place to assist poor households in sending their children to school (for instance, text book cost exemptions). From the perspective of many poor households, it was found that the school tuition fees themselves are not the main problem - it is the many contributions and supplementary costs which make the financial burden unmanageable. Similar statements about the need to reduce the financial burden of educating young children were repeated in every site, demonstrating that this problem is not simply a localized issue.

- The Ho Chi Minh City report states that: _"the official school fee is just token, but other costs and contributions are considerable, such as uniforms, textbooks, facilities, computer costs, extra classes, and even unreasonable e.g. contribution to flood victims. Required contributions can hurt and humiliate poor people. One woman reported that she was shouted at by the headmaster when she submitted a request for reduction of some contributions for her child"._

- In Lao Cai, one women's group gives an account of some of the additional costs associated with education: _"(a) for kindergarten - tuition fees, payment for toys, and cost of a meal if this is not included in the tuition fee; (b) for primary school - contribution for school building, text books, uniforms, photographs for pupil's cards; (c) for junior secondary school - contribution for school building, guards, bicycle park, school yard, and tuition fees."_

- In Vinh Long, one mother said that because she was not able to find work, she could not pay the contributions: _"My children cried and did not dare to go to school"._ Another mother reported that: _"the school did not allow my son to go to school or take examination unless I made the payment",_ which she could not so he dropped out. Commune officials appeared reluctant to admit the existence of the school maintenance fee burden, one saying only that _"sometimes, people have to contribute to school maintenance",_ and another that _"local people don't have to make contributions to maintenance of the schools"._

# **Identifying the main policy messages and recommendations**

Through this type of iterative analysis an assessment can be made of the overall priority attached to the proposed policy actions in order to identify the main policy messages and recommendations emerging from the consultations. An example of the priority given to the proposed policy actions on developing social infrastructure from Vinh Long Province is given in Table 5 (see also Box 1 & Box 4 for the original policy actions and research questions). This priority ranking also includes policy actions proposed by the local participants but not included in the draft poverty reduction strategy.

37
**Table 8: Asian Free Trade Area: Macro and Trade Performance (Experiment 1A: No Productivity Linkages)**

|||Percenta|ee change from|base||
|---|---|---|---|---|---|
||Real GDP|Real<br>Absorption|Real<br>exchange<br>rate|Terms of<br>trade|Export/<br>output<br>share|
|United States|0.03|0.04|2.29|0.03|0.18|
|Japan|0.88|0.74|7.29|-1.33|0.47|
|China|0.07|0.13|2.55|0.07|0.54|
|ASEAN 4|0.56|0.36|3.09|-0.40|1.36|
|Asian NIEs|2.61|2.44|8.51|-0.33|1.01|
|European Union|0.0|0.05|0.0|0.41|-0.0|

|||Billion US$||Percent|age change fr|om base:|
|---|---|---|---|---|---|---|
||Trade<br>expansion|Trade<br>creation|Trade<br>diversion|Trade<br>expansion|Trade<br>creation|Trade<br>diversion|
|United States|15.22|32.22|-17.00|2.64|19.70|-4.12|
|Japan|26.31|30.27|-3.96|6.46|13.09|-2.25|
|China|5.38|13.03|-7.65|3.79|16.97|-11.77|
|ASEAN 4|8.41|5.89|2.52|6.12|6.35|5.65|
|Asian NIEs|14.73|14.00|0.73|5.76|8.98|0.73|
|_Total, FTA members_|70.04|95.41|-25.37||||
|European Union|-0.18|-9.20|9.02|-0.02|-4.32|1.74|

Notes: _Real GDP_ (C + I + **G** + **E-M)** provides a production-based measure of economic activity. _Real Absorption_ (C _+_ I _+_ G) provides a welfare measure based on economywide real final demand by households, government, and investmnent. Changes in this measure equal the _equivalent variation_ for the economy, with changes in government consumption and investment valued according to private consumer's preferences. _Export/output share_ indicates the change in the aggregate export/output ratio for the economy. _Trade expansion_ is the increase in total exports for each region.

_Trade creation_ is the increase in exports to _members of the PTA;_ the trade creation figure for the European Union indicates the change in EU exports to FTA members.

_Trade diversion_ is the increase in exports to _non-PTA members,_ comprised of EU and rest of world.

is nearly four times larger than the decline of trade with regions outside the FTA, with the net expansion on the order of $70 billion. While this criteria is also satisfied for each member of the FTA, the balance between trade creation and diversion varies significantly across regions: for the US, trade diverted represents around one half of trade created, while in Japan, the proportion is only around ten percent. For both ASEAN4 and the Asian NIEs, there is no diversion: exports to non-FTA members actually rise.

36 _Lewis, Robinson, and Wang_
Table A13: IV regression: The effect of migration on yields

||(1)<br>Wheat|(2)<br>Rice|(3)<br>Sorghum|(4)<br>P millet|(5)<br>Maize|(6)<br>F millet|(7)<br>Barley|(8)<br>Chickpea|(9)<br>Pigeonpea|
|---|---|---|---|---|---|---|---|---|---|
|migration|-0.0461***|0.0454***|0.00150|0.00361|0.0392***|-0.0148|0.00201|-0.00680|-0.0132|
||(0.0113)|(0.00993)|(0.00903)|(0.0154)|(0.0109)|(0.0114)|(0.0115)|(0.00704)|(0.00992)|
|FDR q-values|[0.001]|[0.001]|[0.510]|[0.510]|[0.001]|[0.267]|[0.510]|[0.402]|[0.267]|
|observations|838|919|412|365|533|195|286|509|510|
|mean dep var.|7.45|7.26|6.41|6.43|7.20|6.68|7.38|6.53|6.35|
||(10)<br>Groundnut|(11)<br>Sesame|(12)<br>R/M|(13)<br>Castor|(14)<br>Linseed|(15)<br>Sun|(16)<br>Soya|(17)<br>Sugar|(18)<br>Cotton|
|migration|0.0186|0.0371**|-0.0767***|-0.0426|0.0101|0.0183*|-0.00656|-0.0165|-0.0200|
||(0.0168)|(0.0167)|(0.0229)|(0.0318)|(0.00915)|(0.00993)|(0.0123)|(0.0106)|(0.0140)|
|FDR q-values|[0.332]|[0.079]|[0.004]|[0.267]|[0.332]|[0.167]|[0.462]|[0.262]|[0.267]|
|observations|467|511|462|231|259|259|227|490|286|
|mean dep var.|6.68|5.48|6.34|6.10|5.97|6.67|6.55|8.34|5.20|

Note: This table uses the shift-share instrumental variable regression to present the relation between the yields of all major crops (in natural log in kg/ha) and the number of high-skilled male out-migrants between the ages of 20-64 years (as a percentage of the total male out-migrants between the ages of 20-64 years). P millet refers to pearl millet. F millet refers to finger millet and R/M refers to rapeseed and mustard seed. The population controls include total population, sex ratio, population density and share of high-skilled males. Remaining controls include the percentage of adverse rain events and the percentage of adverse temperature events. Robust standard errors are reported in simple parenthesis. FDR-adjusted standard errors following Benjamini, Krieger and Yekutieli (2006) and using the code of Anderson (2008) are reported in block parenthesis. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.