rafmacalaba/datause-encoder-data huggingface dataset:
negative impact on manufacturing employment in areas where more jobs were likely to be replaced by robots. Among areas with an exposure of one robot per thousand workers, those with an initial share of replaceable jobs twice the average witnessed a decrease in the share of manufacturing jobs of about 0.31 percentage points ( 1 x 0.0988 + 1 x (-0.502) x 0.82), while the corresponding value for areas with an average share of replaceable jobs is 0.11 percentage points ( 1 x 0.0988 + 1 x (-0.502) x 0.41). The IV results show the impact of exports on manufacturing employment driven by exposure to US automation. A back of the envelope calculation suggests that the negative impacts of exposure to US automation on exports to the US (of about 3 percent), reduced manufacturing employment by an additional 0.029 ( _≈−_ 0 _._ 00997 _×_ ( _−_ 3 _._ 0) _×_ 0 _._ 82) percentage points in areas with a share of replaceable jobs twice the mean. Even in the areas impacted the most, the labor market impacts were rather small. This supports the hypothesis mentioned above about the trade effects of exposure to US automation being too small to generate sizable labor market impacts.

In contrast, exposure to US automation was accompanied by higher manufacturing employment growth in areas where the share of replaceable jobs was lower (column 1). These areas may have adjusted in a different way to the external shock. Since jobs were less likely to be replaced, firms may have coped with the increased competitive pressures by investing and becoming more productive. If capital is a complement factor to labor, this could foster the demand for workers and thereby explain the positive impacts of US automation on employment in areas where labor was initially less likely to be replaced by robots.

# **5.2.2 Impacts of US and Domestic Automation on Total Employment**

Table 14 presents the impacts of US and local automation on the ratio of wage employment to working age population. We consider employment in wage jobs, for people 12 years of age and older as reported in the published Census tabulations by the National Statistical Institute (INEGI). This is a reduced-form equation where US automation enters directly in the right-hand size. It shows that higher exposure to US automation did not have a significant 23
with Kerala receiving total rainfall of 755 mm between August 1 and 19, about 163 percent above normal. Nearly half of the rain occurred in just three days, August 15 to 17, about 700 percent above normal.<sup>3</sup> Due to the continuous rain from June onward, reservoirs were already filled to capacity at that time, so that gates of 35 dams on upstream rivers had to be opened. The heavy rainfall and the opening of the gates, as well as the geography of Kerala with a steep descent from the eastern hill districts atop the Western Ghats to low-lying coastal areas facing the Arabian Sea, caused disastrous flooding in 13 of the state’s 14 districts. The extreme rainfall also caused severe landslides, particularly in the hill districts of Idukki and Wayanad. The Indian government categorized the disaster as a level 3 calamity or “calamity of severe nature.” According to the government’s post disaster needs assessment, seven districts - Alappuzha, Ernakulam, Idukki, Kottayam, Pathanamthitha, Thrissur and Wayanad - were flooded entirely (GOK-UNDP 2018).

Due to the geography of Kerala, the areas that received the heaviest rainfall did not suffer the most from flooding. Figure A.2 in the appendix shows few flooded areas in the eastern hill region, which received the heaviest rainfall in August. Most of the flooded areas were in the low-lying coastal areas around the backwaters. Flooding in these areas was aggravated by adverse tidal conditions and sustained strong onshore winds that elevated the sea level and hampered the outflow of flood water. As a result, many of the low-lying areas were for more than two weeks.<sup>4</sup> Floods are frequent in some parts of India. Some of the most affected regions include Central India, stretching from Gujrat and Maharashtra in the West to Odisha on the east coast, and eastern states such as Bihar, West Bengal, and Assam. In regions with frequent floods, households and firms internalize them in their decisions and consequently they do not offer themselves to clearly identify the impact of a flood. In line with this, Kocornick-Mina et al. (2020) find permanent relocation of economic activity in response to large floods only

> 3Figure A.1, in the appendix, plots the daily actual and normal rainfall in Kerala from June to December 2018 using data from the Indian Meteorological Department.

> 4 See Raman (2020) on how geographical features of Kerala shaped the course of this disaster.

4
changes and actually diminish the quality of the regression (e.g., from 33% to 28% explained variation for gross savings).

Table 8: Regression results for the low and middle income sample, sudden hydrometeorological = <u>events (N 62)</u>

|Model|Gross savings<br>PV dConsump|Gross<br>disaster<br>savings<br>tion inper cen|Net<br>savings<br>t ofper capita|Net<br>disaster<br>savings<br>income of the|Genuine<br>savings<br> year before t|Genuine<br>disaster<br>savings<br>he event|
|---|---|---|---|---|---|---|
|Constant|-0.031|0.041|0.061*|0.120***|0.075**|0.124***|
||0.67|(1.07)|(1.79)|(4.03)|(2.01)|(3.95)|
|Savings measures|1.214***|1.014***|1.229***|0.995***|1.109***|0.974***|
||(5.33)|(4.74)|(5.05)|(4.42)|(3.96)|(3.71)|
|R Square|0.325|0.276|0.302|0.249|0.210|0.189|

Note: Significance at the * 10% level;** 5% level; *** 1% level We also analyze the effect of revising for aid by subtracting aid from consumption (see Table 9), and results do not change substantially, neither for the unadjusted nor genuine savings indicators, indicating that regular and post event aid in this sample does not lead to substantial changes in welfare post-disaster.

Table 9: Regression results for the low and middle income sample, sudden hydrometeorological = <u>events revised for aid (N 62)</u>

|Model|Gross savings|Gross<br>disaster<br>savings|Net<br>savings|Net<br>disaster<br>savings|Genuine<br>savings|Genuine<br>disaster<br>savings|
|---|---|---|---|---|---|---|
||PV dConsump|tion inper cen|t ofper capita|income of the|year before t|he event|
|Constant|-0.047|0.026|0.052|0.112***|0.067*|0.116***|
||1.02|(0.68)|(1.49)|(3.69)|(1.76)|(3.63)|
|Savings measures|1.283***|1.086***|1.269***|1.044***|1.133***|1.016***|
||(5.53)|(5.00)|(5.07)|(4.53)|(3.92)|(3.76)|
|R Square|0.338|0.295|0.300|0.255|0.204|0.191|

Note: Significance at the * 10% level;** 5% level; *** 1% level

# _Low-income group_

Finally, we turn to assessing events in low-income countries only. As expected, this produces the best results in terms of explaining consumption changes by savings measures. Table 10 reports sample information for the group of sudden hydrometeorological events.

18
of parents, number of family members living in the same household, the availability of household assets as well as gender, age, geographic location and GPA of the applicant, among others.

Second, to measure students’ ability at baseline, we use the information from the censusbased ENLACE 9th grade, measuring math and language achievement levels. From 2007 to 2013, ENLACE was administered to all students in 3rd to 9th grades and those finishing 12th grade. The test had no consequences either on graduation or on student’s GPA. The score of ENLACE is normalized to have a mean of 500 and a standard deviation of 100.

Using the CURP we were able to merge the baseline application format with the micro data from ENLACE 9th grade which was taken by applicants in May 2009. For ease of exposition and interpretation, when presenting the results, we renormalize the ENLACE results to a mean of zero and a standard deviation of 1. Table 2 shows the baseline characteristics, distinguishing between students in the treatment and the control groups. In the top panel we report the socioeconomic characteristics measured through the baseline format completed by all applicants, in the bottom panel the administrative information on 9th grade test scores. Overall, the characteristics of the treatment and control groups are well balanced in line with the randomized design of the evaluation.

Outcome variables are measured through ENLACE 12th grade administered in May 2012 to all students finishing EMS. Additionally, our evaluation sample was also merged with ENLACE 12th grade of 2013 to identify students that graduated from high school but that had a lag of one year. The participation of students in our sample, who entered EMS in August 2009, in ENLACE 12th grade of 2012 or 2013 is used as a proxy for upper secondary graduation.<sup>9</sup> The same end-of-secondary-school test is also used to measure the impact of PROBEMS scholarship on math and Spanish test scores.

> 9In what follows, when we refer to ENLACE 12th grade, which includes the information from the tests administered in 2012 and 2013. Of the total 7,321 students in our evaluation sample, 4,978 where identified in ENLACE 12th grade of 2012, a proxy for on-time graduation. An additional 262 students were identified in ENLACE 12th grade of 2013, these students also graduated but with a lag of one year. So most of the effects on outcome “graduation” are, indeed, on-time graduation. For a discussion on the reliability of this measure as a proxy for EMS graduation, see (Dustan et al. 2017), (Avitabile and de Hoyos 2018).

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# **Table 4: Single Equation Probit Results**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|**Dependent Variable**|<br>**Fall in**<br>**output**<br>**(t+2)**|<br>**Crisis**<br>**in t and**<br>**fall in**<br>**output**<br>**(t+2)**|<br>**Crisis**<br>**in t and**<br>**fall in**<br>**output**<br>**(t+2)**|<br>**Crisis in t**<br>**and fall in**<br>**output**<br>**(t+2)**|
|Fall in output (t+1)|**0.176**||**0.016**||
||(9.26)**||(1.91)*||
|Fall in output<br>(concurrent)|-0.011||||
||(0.60)||||
|Ratio of external<br>liabilities to external<br>assets (t-1)|**0.016**|**0.005**|**0.004**||
||(2.80)**|(1.77)*|(1.60)*||
|Share of domestic<br>credit to the banking<br>sector in GDP||**-0.013**|**-0.013**|**-0.014**|
|||(2.84)**|(2.84)**|(3.43)**|
|Change in current<br>account surplus (t-1)||**-0.0001**|**-0.0001**|**-0.0001**|
|||(1.85)*|(1.87)*|(1.83)*|
|Increase in capital<br>flight (t-1)||**0.00002**|**0.00002**|**0.00002**|
|||(6.04)**|(6.24)**|(9.37)**|
|Change in ratio of<br>M2 to non-gold<br>reserves||**0.019**|**0.017**|**0.019**|
|||(2.89)**|(2.68)**|(3.48)**|
|Ratio of external<br>liabilities to reserve<br>assets (t-1)||||**0.005**|
|||||(2.28)*|
|Overall Fit|<1%|<1%|<1%|<1%|
|Observations|2936|2253|2253|2657|

Robust z statistics in parentheses

*significant at 10%;** significant at 5%; *** significant at 1% † Without country-specific weights
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Figure 19: Uncertainty about sales and probability innovating <!-- Start of picture text --> (a) Investment digital (b) Innovation<br>.3 .25<br>.25 .2<br>.2 .15<br>.15 .1<br>0 20 40 60 0 20 40 60<br>Uncertainty about sales Uncertainty about sales<br>Std. Deviations Std. Deviations<br>Probability of investing in digital platforms<br>Probability of creating a new product/service<br><!-- End of picture text --> Notes: Panel (a) is the bin scatter of the probability of investing in digital platforms controlling for size, sector, severity of the crisis and wave. On average, an increase in 1 standard deviation in uncertainty is associated with 0.15 percentage point decrease in the probability of investing in digital platforms (p-value: 0.000) Panel (b) repeats the same for product innovation. Because we have this information only for wave 2, it does not control for wave. On average, an increase in 1 standard deviation in uncertainty is associated with 0.07 percentage point decrease in the probability of innovating (p-value: 0.020)

# **4.6 Stylized fact 6: As the COVID-19 pandemic evolved, governments have expanded the support for businesses.**

Relying on the first wave of BPS data covering more than 120,000 firms in 60 countries, Cirera et al. (2021c) document the wide range of policy support measures, the reach of these policies, the alignment of measures with firm needs, and their targeting and effectiveness. Despite the plethora of measures launched around the globe, they show that only one in four firms received any type of public support at the time we conducted the surveys. These results show stark differences by income levels: the probability of receiving some public support is 11% in low-income countries, 15% in lower-middle-income countries, 30% in upper-middle-income countries, and 53% in high-income countries. They show that policy reach has been limited, especially for the more vulnerable firms and countries. Using data from the second wave, we provide three novel findings.

_First_ , through time the probability of accessing public support increased significantly from 19% to 32% for an average firm in 38 countries surveyed in the second wave (Figure 20, panel a). _Second_ , there is significant heterogeneity in access associated with country-level characteristics. Firms in countries with more severe initial mobility drop witnessed a significant increase in the probability of accessing support in wave 2 (Figure 20, panel b). Firms in higher income countries and those with lower indebtedness (within their income-group) have a higher probability of accessing support in both waves (Figure 20, panels c and d). Firms in highly indebted countries are also at a disadvantage. These findings suggest that countries with more public resources significantly responded to the COVID-19 pandemic by providing a wider coverage to businesses.

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- Alvaredo, Facundo and Londo ̃no V ́elez, Juliana (2013). High incomes and personal taxation in a developing economy: Colombia 1993-2013. Working Paper 12. Commitment to Equity-CEQ.

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- Diaz-Bazan, Tania (2014). Measuring inequality from top to bottom. Working Paper.

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26
replacement of identity rules by a system of impersonal rule. Societies based on identity rules place few constraints on the power of the elite vis-`a-vis the rest of society but grant some rule of law within elites. Most importantly, unconstrained power makes it difficult for rulers to credibly commit to rule-based dealings with less powerful groups. This is precisely how we think ethnic heterogeneity enters the picture. It reflects the presence of other identity groups, such as the Bemba miners in Zambia, with whom the ruling elite needs to coordinate (Bluhm and Thomsson, 2015).

# **Robustness**

Our main conclusions are unaffected by a variety of robustness checks, such as dropping influential observations, varying the national accounts data, and the inclusion of policy variables. We only present a selection of the most insightful perturbations here. A comprehensive battery of robustness checks can be found in Supplementary Appendix S7.

Table 4 takes the estimated breaks and data as given and focuses on unobserved heterogeneity and influential observations. Column (1) adds country-level random effects (frailties) and income-level fixed effects on top of region fixed effects. Column (2) drops former Soviet countries. Both perturbations do not affect our findings. Column (3) takes a more radical step and drops all of Africa. The estimated effect sizes are smaller and we lose precision on the interaction term. This loss of significance is hardly surprising. The African continent is home to some of the most ethnically diverse countries with the weakest institutions. Africa drives our results but also provides much of the variation needed to estimate the overall effect.<sup>19</sup> Column (4) deletes the censored spells. Now the estimated effect of political institutions is even smaller, and the interaction term becomes insignificant. A look into the underlying data reveals what is going on. Countries with completed spells

> 19We have also taken a less radical approach and interacted our three variables of interest with an Africa dummy. In every case, the added interactions are individually and jointly insignificant at the 5% level. We take this as further evidence that our results can be generalized to settings outside of Africa, such as Asia, where institutions are stronger and linguistic diversity is comparable. We thank an anonymous referee for pointing us towards this test.

27
existing monitoring initiatives in the next section, we will then turn to some of the potentially important focus areas spurred on by new and potentially WTO-inconsistent policies brought on by the 2008–2009 global economic crisis.

# **_A brief history of monitoring before and during the global economic crisis_**

While the last section highlighted the theory behind public sector monitoring of export market access interests of developing countries, we do not mean to suggest that public monitoring does not yet exist. This section reviews both the state of monitoring prior to 2009 and the new monitoring initiatives and developments that took place in 2009, spurred on by the global economic crisis. Many of the developments of 2009 in particular have resulted in a tremendous amount of new and useful information. However, this information still needs to be analysed by technical experts and repackaged to be made useful to exporters in developing countries.

Prior to 2009, most of the public monitoring that actually took place was done by the WTO. Through its performance of national Trade Policy Reviews of member economies, and by serving as a repository for government notification of changes in trade and trade-related policies, the WTO both laid the groundwork for other groups to monitor and provided most of the global monitoring efforts itself.<sup>12</sup> However, in the context of the firm-level model presented in the last section, there are two main concerns with the WTO 12 See Bown and Hoekman (2008) for a critique of the WTO’s current Trade Policy Review process. Bown (2009a, chapter 8) details other pre-crisis efforts to provide information beyond that being generated by the WTO Secretariat, including the Global Subsidies Initiative and the Global Antidumping Database that is described in more detail below.

13
Morgan 1997). Salehyan (2009) is interested in analyzing endogenous regional conflict clusters formed by the intrinsic links between civil wars and international conflicts. The bridge between the two types of mutually constituted and mutually reinforcing wars is provided by transnational rebel sanctuaries, which generate international hostilities and empower rebels in their struggles against their own state (Salehyan 2009, 50).

Our distinction between a civil war and a regional war complex is superficially similar to that made by PRIO/Uppsala between “internal armed conflict” and “internationalized internal armed conflict.” Our concepts and those of PRIO/Uppsala share a recognition of the importance of external involvement as a factor that has the capacity to alter thinking about these experiences of large-scale violence. Our concept differs in practice from the concept implemented by PRIO/Uppsala because we consider much more of the African violence to be part of regional war complexes, as argued below in our discussion of table 1. By contrast, users of these data, such as MGB, lump both of these categories of conflict together under the heading “civil war.” In Africa, the episodes that are popularly termed civil war and that are coded as such in the econometric studies are often not predominantly internal conflicts. The Nigerian Civil War over Biafra’s attempted secession from the Federation is an exception to most so-called civil wars in Africa in many ways and thereby provides an instructive case (coded entirely and appropriately by Xs in table 1). Its inception was an entirely domestic affair. Following political troubles in the Western Region, two bloody coups and pogroms against Ibos outside the Eastern Region and their flight home, the Ibo-dominated Eastern Region attempted to secede as Biafra from the spring of 1967. The war involved fighting by massed armies commanded by officers, many of whom had been former colleagues in the federal army, just as the opposing officers had in the American Civil War, and with every other attribute of structured combat. Biafra sustained 20
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