rafmacalaba/data-use-mentions-extended huggingface dataset:
zero and hence, we reject that the data should be pooled. In the specification test, we cannot reject at the 5 percent significance level that there is significant difference between the random effect and the fixed effect estimators and hence, we cannot reject that the random effect model is misspecified.

In the Modi regression, we instrumented the endogenous variables as follows: the rate of inflation (which is included in the real rate of return of money) with the rate of growth of money plus quasi-money less the rate of growth of real GDP with the idea that prices, and hence inflation, respond to excess demand for money;27 the real interest rate with the rate of change of the real effective exchange rate, following real interest parity;<sup>28</sup> 27 Since the return on money is defined as 0 = ( --1) we transformed the rate of inflation by the l+rc rate of growth of M2 _(M 2/M 2)_ less the real growth of GDP (Y/Y), as follows: (1 +

- 1 .

28 We also tried to instrument the real interest rate using data on budget deficits assuming that countries with larger Government financing needs would have higher real interest rates and although results did not change, we had a significant loss in the number of observations. Hence, we decided not to use this variable as an instrument.
Vladimir and Gorodets, in 19931994. Then a rise began: in 1996, 5.9-6.5% of the households changed apartments. It should be noted that commission of new housing did not accelerate over that period (in Moscow the annual rate was 1.7 - 1.8%/ **a** , nd in most other Russian cities it was lower and tended to decline). It is easy to see that in the past two or three years, the increase in mobility stemmed mostly from the development of the secondary housing market.

Fig. 2.11. Household mobility in Russia (% of households by year of move into current unit) <!-- Start of picture text --> < '6.5<br>.4.9 ~~~~~~~~~~~~.<br>4.2 0 ~~~~~~~~~4Moscow<br>.6 -0-~~~~~~~Vladimir<br>O . . . . . ~ ~ ~~~~. 5.9<br>2 2.5 2<br>19 190 1991 1992 1993 1994 1995i 1996<br>Sources: The Moscow Longitudinal Household Survey, January 1996; Vladimir and Gorodets Longitudinal Household Survey,<br>December 1996. Note: Mobility rate in Moscow 1996 - own estimate (minimum).<br><!-- End of picture text -->

**ITe** analys did not coverhouseholds who rent housing This category (75% of whom occupied **their** apartments for less **than** one year at the time ofthe survey) proved inconvenient for further comparative analysis due to "hyper-mobility". According to **the** findings of surveys held in Moscow, **the** share of such households in Moscow was 0.9, 1.4, **1.8
Table A3: Differences in means

|Variable|EHPM<br>Sample|Survey-mode experiment<br>Sample|
|---|---|---|
||(Mean)|(Mean)|
||(1)|(2)|
|Age (years)|29.181|31.221|
|Female (%)|0.566|0.521|
|Oldest child age(years)|4.605|4.553|
|Observations|15,680|597|
|Education level|||
|None|0.014||
|Basic (1-9 grades, %)|0.514|0.207|
|High school (10-12 grades, %)|0.281|0.427|
|Bachelor or higher(%)|0.075|0.366|
|Observations|15,680|358|

_Notes:_ This table shows the average characteristics of the sample in the Household Survey (EHPM) that meets the eligibility criteria (column 1) and the sample in the survey-mode experiment (column 2). Panels A and B show characteristics that are available for the full survey-mode experiment sample and only for those that completed baseline, respectively.

38
the oil shock. The negative slope of the curve denotes the progressive impact of the shock. However, the poorest percentiles still lose significantly so that the equalizing effect of this result should not be overstated.

_Figure 1: Growth incidence curve due to the oil shock_ <!-- Start of picture text --> ‐4<br>‐5<br>Overall Change<br>‐6<br>‐7<br>‐8<br>‐9<br>0 10 20 30 40 50 60 70 80 90<br>Population percentiles<br>consumption)<br>Change in welfare (% change in per capita<br><!-- End of picture text --> Source: HBS data and micro‐simulation results. Note: the points represent the difference in per capita consumption measured with and without the oil shock; the line is obtained as a polynomial fit of the points.

What explains this heterogeneous impact of the shock? Table 1 offers some clues. The skill and urban premia are decreasing and one may expect skilled workers and (among the unskilled) urban ones to be towards the richer tail of the distribution. This table also highlights that among the workers displaced by the shock, about 100,000 unskilled ones will have to move from better paid urban occupations to rural lower paid jobs. However, the aggregate results of this table do not inform from which part of the distribution these displaced unskilled workers are more likely to come. Finally, the lower reduction of food prices vis‐à‐vis prices of other consumption items suggests a potential regressive impact given that poorer households spend a larger share on food than richer households.<sup>13</sup> Other channels of transmission of the shock can also contribute to its uneven incidence. For example, rents in oil producing and closely related sectors (such as refineries) will be directly hit. Given the large capital stocks of the oil sector, capital returns may
innovation, the effect of technology<br>license was not statistically significant.<br><!-- End of picture text -->

# **_3.1. Managerial capabilities as a (complementary, but key) innovation input_**

Besides R&D, training activities and acquisition of technology license, managerial and organizational capabilities represent another key innovation input. As highlighted in Cirera and Maloney (2017), these capabilities are part of core competences and critical inputs for innovation as they determine the way in which knowledge is employed and accumulated, especially for firms that are far from the technological frontier. In this regard, low managerial capabilities are one of the key reasons why firms do not invest more in innovation, since they prevent firms from identifying productive opportunities, evaluating their feasibility, managing their risk, and allocating human resources effectively. Therefore, they should be understood as an innovation input that needs to be accumulated and combined with other complementary inputs to yield innovation outcomes. Recent empirical evidence shows that managerial capabilities is positively correlated with productivity and innovation. Drawing from a broad range of middle-income and developing countries, Bloom and Van Reenen (2010) show average management scores to be highly correlated with aggregate labor productivity. More recently, Cirera (2017) finds evidence that good management practices have a dual positive effect on productivity: it enables more efficient use of resources and increases the probability to innovate. Using Mexican micro data, Iacovone and Pereira-Lopez (2017) provide micro-level evidence on the complementarity between managerial practices and R&D efforts in Mexico by showing that better managerial practices increase the impact of R&D activities leading to innovation activities.

The World Management Survey (WMS) is used to assess the status of managerial practices in developing East Asia; only two developing East Asian countries are included in the survey: China and Vietnam. The WMS is a globally comparable survey of management practices that permits benchmarking countries by management practices. As per the methodology presented originally 17
the following year. If they agreed, facilitators went through the S.M.A.R.T. process (Doran, 1981) with the females, where they were asked to identify and commit to 1-3 specific strategies to follow during the next 12 months in order to achieve the goal. S.M.A.R.T. stands for Specific, Measurable, Achievable, Relevant, and Timely.

The initial goal setting activity took place in August 2017. This activity took about 90 minutes and was done one-on-one with a trained enumerator. Of the 865 females randomly selected from the baseline survey to receive invitations to participate in the goal setting activity 789 participated (91%).<sup>7</sup> Of the 789 participants, 113 females (14.3%) 7Of the 174 females who did not participate, only 2 refused. The rest were either unavailable at the 10
To add to the complexity, while health is commonly thought to worsen during recessions, findings have surprisingly been mixed. Ruhm (2000, 2015a) are among the most prominent studies on this topic. They showed that recessions are actually good for health: health improves when the economy temporarily deteriorates. Ruhm (2000) used panel data for 50 states in the U.S. and D.C. and found consistently that most causes of deaths are actually pro-cyclical. He provided four reasons for this pro-cyclicality, which we will go into in detail in the literature review. That trend, however, has attenuated over time (Ruhm, 2015a).

This paper overcomes the identification difficulty by using a Bartik instrument. As will be clearer in the identification strategy section, the Bartik instrument captures _a county’s tradable job losses that are only driven by declines in U.S. tradable aggregate demand_ , and not by county-specific issues. Since there are more than 3,000 counties in the U.S, the U.S.’s tradable aggregate demand is largely exogenous to a county, that is, it is little affected by county-specific fundamentals. This implies that reverse causality and county-specific omitted variable problems, such as productivity shocks, are not likely at play.

Using demand-driven tradable job losses as an instrument for total job losses in a county, we examine the impacts of unemployment on different types of mortality. We find that when countycharacteristics control variables are included, unemployment has statistically insignificant impacts on mortality. In other words, aggregate mortality in a county is not significantly affected by the county’s job losses, _ceteris paribus_ . This is consistent with Ruhm (2015a), where he found that mortality is becoming acyclical in recent years.

When disaggregating mortality by types, we find that most types of mortality have statistically insignificant relationship with unemployment. The only exceptions are Alzheimer’s, poisoning, 3
In Figure 3.3 we provide a pictorial view of the war victimization based on household responses. We plot the average responses at the department level; darker shades imply a higher average rate of victimization experience for the inhabitants in a department. It is evident that the civil war had an adverse effect on the livelihood of the entire population in Côte d‟Ivoire; however the impact was more prevalent in the Middle and the Northwest of the country. Overall, between 30 to 50 percent of the respondents experienced declines in their income. The incidence of war victimization was more prominent in the departments located near the UN-peace keeping line and to the West where the civil war was more intense. Nearly 30 percent of the respondents had to hide during the war in the Northwestern departments. The conflict in the mid-West of the country is also marked by high levels of internal displacement. The adverse effect of the war on jobs and land is prevalent throughout the country. However, the people in the mid-West reported to have experienced loss of livestock and non-land assets.

Finally, we turn to the education system in Côte d‟Ivoire. It mostly follows the centralized French education system, where the government plays a key role in curriculum development, coordination and allocation of resources and the organization of national examinations through the ministries of Education, Vocational Education and Higher Education. The _Certificat d’etude primaries elementaires_ (CEPE) is awarded after completing six years of primary education, which is followed by seven years of secondary schooling. In the final year of secondary school students earn a baccalaureate degree. Universities, technical and vocational trainings are part of the higher education system in Côte d‟Ivoire (Sany, 2010). As is evident from the ENV-2008 data, in the sub-population consisting of individuals aged 12 and above, about 35 percent earned a CEPE whereas only 10 percent completed the baccalaureate degree. However, almost 40 percent from the same group of people did not complete the CEPE. The average years of education stands a little above
# **I. Introduction**

The presence of financial constraints and its effects on firms’ investment decisions have received intense interest in the corporate finance literature. There is considerable evidence that financing constraints are an impediment to the investment and growth of firms (Hubbard 1998, Stein 2003). While the majority of the research results have been derived from the experience of developed economies that have many institutional similarities, less work has been done to further our knowledge about financial constraints in developing countries that have different institutional structures. Firms in developing countries tend to face more severe financial constraints than those in developed countries, and their owners typically name financial constraints as one of their primary obstacles to investment (Dethier et al. 2011). Moreover, the governments of these countries tend to play a larger role in directing financial resources than in developed countries (Ayyagari et al. 2012).

Credit allocation in China has been characterized by government intervention and has been biased towards state-owned enterprises (Brandt and Li 2003; Huang, 2003: Bai et al. 2006; Li et al. 2008; Cull, Xu and Zhu 2009; Gordon and Li 2011). Insufficient financial support imposes severe difficulties in the development of firms that lack government connections.<sup>2</sup> We use firm-level data to understand whether and how firms with differential government connections are financially constrained in China and how that affects their investment patterns. We compare not only state-owned enterprises (SOEs) to non-state firms (both foreign and domestic), but also examine whether CEOs’ governmental connections explain financial constraints within the subset of non-state firms.

> 2 Dollar and Wei (2007) also provide evidence that distorted capital allocation had led to persistent dispersion in returns to capital across sectors and geographic areas. Farrell and Lund (2006) report that by 2006 the private sector had produced more than half of China’s GDP but received only 27% of total loans.

2
household table (see Appendix section S6 for details about the item categorization). The lower part of the table shows the value of incoming resources. The mean tax rate is 14.4% (s.d. = 26.5%).<sup>22</sup> It is clear both that redistribution is widespread, and that there is significant heterogeneity between households in the implied social tax rate.

How does the existence of social taxes affect a household’s incentives to purchase in

> 21It is not possible to determine whether an item was sold or given away. This could be problematic if many households re-sell consumer items. That does not appear to be the case. Other households in the study villages—not the households that filled out consumption diaries—were randomly selected to participate in a survey experiment dealing with measurement of labor supply. These households were able to indicate their sectors of work, with varying degrees of detail. The share of respondents that indicated “buying and selling” as their primary sector of work ranged from 5–8% across survey modules. Hence, even if every purchase made by such households was for the purpose of re-selling—which is surely not the case—for the vast majority of disposal events it is still reasonable to assume that items “sold or given away” are in fact “given away.”

> 22A small number of estimated tax rates are over 100%. All results are robust to winsorizing by replacing rates above 60% with 60%.

28
7 ( _BHv_ 2) _xy ∈/ EM_ = 0 _._ 01 _∗_ ( _BHv_ 1) _xy ∈/ EM ⇒_ ( _BHv_ 2) _xy ∈/ EM >_ 100 _m_ (3) Next, for BH pixels showing a height between 50-100 m without an EM point in their vicinity (meaning here, EM is still supposed to be reasonably complete), we assume that BH is overestimated because otherwise EM should indicate the presence of a high-rise. In this case, the BHv1 value is reduced by 90% of the difference between the current BHv2 value and a fixed virtual height of 50 m (see eq. (4)). The 50 m height is used because it represents the empirically determined completeness threshold for tall buildings in EM (see Appx. Section B): ( _BHv_ 2) _xy ∈/ EM_ = ( _BHv_ 1) _xy ∈/ EM_ +0 _._ 01(50 _−BHv_ 1) _xy ∈/ EM ⇒_ 50 _m <_ ( _BHv_ 2) _xy ∈/ EM >_ 100 _m_ (4) In order to generate the new building volume product BVv2 (m<sup>3</sup> ), the BHv2 building heights are merged with the building areas given by BAv1. See eq. (5).

The improvements in BH achieved by integrating EM are documented by a comprehensive global validation campaign (see Appx. Section A for details). The WSF3Dv2 data can be accessed free and open at: https://geoservice.dlr.de/web/maps/eoc:wsf3d. Likewise, the replication files for the analysis and the country- and city-level data sets can be found here: https://doi.org/10.7910/DVN/TQ1AS5.

In Figure 1 below, this improvement can be observed in Manhattan by comparing the BH (building height in m) layer of the original WSF3Dv1 (top-right panel B) and the EM-improved WSF3Dv2 (bottom-left
# **Table A.3.8: Results of simulation 1 reported for the full sample with LAYS data and the subsample with PISA data**

|Changes in|Optimistic|Intermediate|Pessimistic|
|---|---|---|---|
|**Full sample (157 countries)**||||
|Learning-Adjusted Years of Schooling (LAYS)|-0.3|-0.6|-0.9|
|Per student average earning loss in annual terms ($)|-355|-872|-1,408|
|Per student average lifetime earning loss at present value ($)|-6,472|-15,901|-25,680|
|Aggregate economic cost of foregone earnings at present value ($)|-4.3 T|-10.0 T|-15.9 T|
|Aggregate economic cost as a share of total spending on basic<br>education|6.7%|15.5%|24.7%|
|**PISA subsample (92 countries)**||||
|Learning-Adjusted Years of Schooling (LAYS)|-0.3|-0.6|-1.0|
|Per student average earning loss in annual terms ($)|-497|-1,269|-2,072|
|Per student average lifetime earning loss at present value ($)|-9,061|-23,156|-37,806|
|Aggregate economic cost of foregone earnings at present value ($)|-3.9 T|-9.3 T|-14.9 T|
|Aggregate economic cost as a share of total spending on basic||||
|education|6.5%|15.2%|24.3%|

Source: Authors’ calculation. Simulation 1 results based on
.957|1.491|4.475|0.999|

Notes: The table shows the RMSE of dynamic forecasts over 2014-2016 obtained with model estimates using data for 1970-2013 under alternative specifications of cross-sectional dependence. Specifications including a common factor use an AR(1) model to predict its future values.

# **5 Conclusion**

Output growth displays substantial comovement across countries. Existing empirical literature has modeled the cross-sectional dependence of growth as reflecting either – localized linkages across countries or regions, or pervasive common shocks i.e., weak and strong cross-sectional dependence, respectively. In this paper we have brought both perspectives together by assessing the international comovement of GDP growth in a setting that allows for both spatial dependence and latent common factors, using annual GDP growth data over the years 1970–2016 for 117 advanced and developing countries.

In the paper’s empirical setting, the dynamics of growth reflect the action of global common factors as well as spatial effects accruing through the growth of economically neighboring countries. Estimation employs a bias-corrected quasi-maximum likelihood procedure recently developed by Shi and Lee (2017), alternatively considering all 117 sample countries, or a subsample of 21 advanced economies. To capture the interactions among countries, we employ two alternative spatial weight matrices – one based on bilateral trade, and another based on geographic distance. To determine the number of latent common factors driving GDP growth across the world, we use a variety of information criteria. On the whole, they indicate the presence of a single factor for both country samples considered.

Under the two alternative specifications of the spatial weight matrix and for the 29
today accounts for some 5 billion people. In 1870, the gap between the richest countries (Australia and Great Britain) and the poorest (Nepal and Ghana) was 8 to 1; in 2007, it is 31 to 1 (United States and Norway vs. Nepal, North Korea and Ghana).

Moreover, the very logic of widening income disparities alluded to before shows that the effect of the number of countries cannot be decisive. Because the logic is that some people, living in specific geographical locations, earn today approximately the same—very low--level of income as their ancestors did more than a century and a half ago, while other people, at other locations, live at income levels that are manifold greater than in the past and some 100 times above the subsistence. Thus, the ratio between top and bottom must rise, almost regardless of how we decide to ―slice‖ the population of the world (that is, in how many countries they live).<sup>15</sup> We obtain the same conclusion if we compare real wages in rich and poor countries. Table 1 shows wages for three occupations in five selected countries (or rather their major cities): two rich (London and New York) and three poor (Beijing, Delhi and Nairobi). The data refer to after-tax nominal wages which are then deflated by the food cost of living in the same cities to obtain real food wages. The advantage of these data, collected for 74 cities and 14 occupations and published by Union de Banques Suisses, UBS (2009), is their almost full comparability. In effect, the wage data refer to net wages earned by the same, very narrowly defined, professions (see Notes to Table 1); they are adjusted for the effective number of hours worked, and are geographically limited to large cities for which UBS publication also calculates a food cost of living index.

In Table 1, I have selected three occupations with increasing level of skills
> The baseline survey includes a set of five questions about caregivers’ beliefs regarding early childhood development, in particular on stimulation and the home environment. The baseline questions about beliefs are similar to those used at follow-up to capture attitudes toward early

> 25Also recall that the negative leader effect is observed for children from households that do not receive text messages, as mentioned above.

> 26For example, several households said they had learned they could give eggs to children, while before they thought this would hurt them. Other examples include parents stopping to give coffee to kids, abandoning harsh disciplining practices, or letting girls plays with toys traditionally considered to be for boys.

21
<!-- Start of picture text --> Freetown (distance) |<br>Share of Christians |<br>Health Centre (distance) |<br>Secondary School (distance) |<br>Market (distance) |<br>Political Stronghold |<br>Village Size |<br>Number of NGOs |<br>Kailahun (distance) |<br>Average Poverty Score<br>-.2 -.1 ie) 1 2 3<br><!-- End of picture text -->

<!-- Start of picture text --> HH purifies water (any method)<br>Children vaccinated |<br>Children born in hospital |<br>Resp. had child that died |<br>Children born in hospital |<br>Dwelling has hand-washing soap |<br>Children have health card |<br>Resp. away more than 1m over past year |<br>Received death certificate for most recend decesead |<br>Primary Schooling |<br>Correct answer to Malaria knowledge question |<br>Correct answer to diarrhea question |<br>Children are vaccinated |<br>Resp. away more than 1m over past year |<br>Dwelling has hand-washing place |<br>Partner had antenal check-ups |<br>Secondary Schooling |<br>No schooling |<br>Received birth certificate for most recent child |<br>Children born in hospital |<br>HH owns livestock |<br>Resp. has access to condoms |<br>Correct answer to diarrhea question |<br>Resp. had child that died<br>-.2 -.1 0 Al<br>[_] DHS 2013 HH ME DHS 2013 Men<br>MEE «DHS 2013 Women [EE NPS 2007<br><!-- End of picture text -->