rafmacalaba/datause-encoder-data huggingface dataset:
15 -

# V. MONTHLY EARNINGS. HOURS OF WORK. AND HOURLY WAGE

We also investigate the impact of training on monthly earnings, weekly hours of work, and hourly wage. We report two sets of results: the first focusing on overall program effects, the second on program effects by level of educational attainment of trainees. To anticipate the results, we find very significant differences in the effects of training on monthly earnings and weekly hours worked across schooling groups.

# Data and Summary Statistics

The dataset used for this analysis was constructed from the retrospective PROBECAT survey and two ENEU cohorts. The first cohort included individuals who were unemployed in the third quarter of 1990 (they are tracked for 12 months). The second cohort included individuals who became unemployed in the fourth quarter of 1990, and were not in the first cohort; for this latter coLort, only nine months of information are available. We pooled all observations reporting positive (and usable) salaries anytime over the period of the PROBECAT survey, and over the 12 (or 9) months interval in the case of the ENEU cohorts."I/ The dataset thus contains multiple observations on each individual, for every job spell experienced by trainees, and for every quarter in which ENEU cohorts are observed to be employed. The final dataset contains 1,212 trainee-observations and 1,051 control-observations for men; for the women, the corresponding sample sizes are 681 and 300 observations, respectively.

To accommodate the specific structure of this dataset, we created two kinds of control variables. The first is a variable for the number of months between the date salaries are reported and time 0, which is either the completion of training or the initial date of unemployment for the ENEU cohorts. The second is a set of quarterly dummy variables to account for inflation in salaries over the base period (pre-1991).

Means of the three outcome variables for men and women are reported in Table 10, separately for trainee and control samples. On average, male trainees report monthly earnings of 682 thousand pesos as compared to 638 thousand pesos for the controls; in other words, earnings that are about 7 percent higher than those of the control group. However, higher earnings may partly reflect inflation since trainee salaries include those reported in the II/We define usable data as positive monthly earnings less than 5 million pesos, and hours worked less than 85 hours a week.
|Land access obstacle|-0.028|0.020|0.069|0.067|
|---|---|---|---|---|
|Outage|0.082**|0.019|-0.117|0.083|
|Web Intensity|0.025|0.019|0.070|0.069|
|In Big City|0.010|0.008|-0.023|0.028|
|Capacity agglomeration<br>|0.052|0.052|0.398*|0.155|
|Inf competition<br> <br>|-0.075**|0.017|0.036|0.063|
|PM 2.5<br> <br>|-0.009<sup>×</sup>|0.005|-0.083**|0.015|
|<br>PM 2.5<sup>2</sup><br>|0.001*|0.0003|0.005**|0.001|
|<br> <br>PM 2.5<sup>3</sup>|-0.00001*|0.00001|-0.00007**|0.00002|
|<br> <br>**Other controls**|**Yes**||**Yes**||
|<br>**Industry dummies**|**Yes**||**Yes**||
|Observations|4,563||2,742||
|Adj. R squared|0.167||0.484||

> × , *, ** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticity-corrected standard errors clustered at the city level. Other controls include year dummies, missing dummy for BE variables, and base controls.

These results shed some light on how pollution correlates with productivity for African firms differently. Since the relationship appears to be cubic, the effect of pollution on employment growth and labor productivity growth differ depending on the level of pollution. At low levels of pollution, the effect on both dependent variables is negative (-0.009 and -0.083, respectively). Because the quadratic term is positive, these negative effects will have less impact as pollution levels rise, turning into positive effects. The negative cubic coefficients show that after a certain point, pollution will again have a negative effect on employment and labor productivity growth.

The effects on both dependent variables are similar. In terms of employment growth, African firms experience negative effects of pollution at extremely low levels of PM 2.5, approximately until 8 μ . At these levels, African firms may not be experiencing enough of the positive benefits from growth needed to offset the negative effects of pollution. From there, the effects /mm<sup>3</sup> of pollution increase for higher levels of PM 2.5, until approximately 26 μ , where the effects turn negative again. For labor productivity growth, the effects are negative for PM 2.5 levels /mm<sup>3</sup> below 13 μ . From that level until approximately 32 μ , the effect of pollution is positive, again, as a consequence of the positive effects of growth offsetting the negative /mm<sup>3</sup> /mm<sup>3</sup> pollution effects. After that, the effect of pollution on labor productivity growth is negative again. Pollution then has negative effects on employment growth sooner than on labor productivity growth.

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# **Table 2: Product level regression, with interaction terms**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||Full|Homogenous|Referenced|Differentiated|Non-differentiated|
|(t-r)|1.055**|-0.439|1.307*|2.500***|0.861|
||(0.474)|(1.561)|(0.747)|(0.837)|(0.671)|
|prc_sh|-8.580***|-17.318*|-6.836|-10.758***|-8.612**|
||(2.396)|(9.931)|(4.647)|(3.869)|(4.175)|
|collpriv_sh|-15.957***|-19.535|-7.973|-15.913***|-10.044|
||(3.801)|(14.396)|(6.797)|(5.827)|(6.144)|
|for_sh|-48.065***|-49.712***|-44.589***|-45.818***|-45.800***|
||(2.919)|(13.241)|(5.533)|(4.280)|(5.159)|
|(t-r)*prc|-0.210|-0.393|-0.668|0.880|-0.617|
||(0.404)|(1.156)|(0.610)|(0.937)|(0.545)|
|(t-r)*collpriv|-0.436|0.822|-1.204|-1.469|-0.799|
||(0.609)|(2.016)|(0.861)|(1.256)|(0.799)|
|(t-r)*for|-0.600|0.340|-0.870|-1.861*|-0.543|
||(0.494)|(1.613)|(0.742)|(1.016)|(0.687)|
|HS4 fixed effects|Yes|Yes|Yes|Yes|Yes|
|Observations|7,009|522|2,011|4,176|2,533|
|R-squared|0.546|0.708|0.474|0.564|0.538|

Notes: The regressions are at HS8 level. The dependent variable is the share of indirect exports (conservative measure), ranging from 0 to 100. The export shares by trade regime and firm ownership type variables range from 0 to 1. The omitted trade regime is normal trade. The omitted firm ownership type is SOE. The last column is for non-differentiated products, including both homogenous and referenced products. *** p<0.01, ** p<0.05, * p<0.1.

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2016 (Van Ark, 2016; OECD, 2017; Eurostat, 2018). Growth in cloud use has been driven by supply-side cost reductions, as cloud providers take advantage of economies of scale in computing, with giant data centers comprised of hundreds of thousands of servers (Greenstein and Fang, 2020), and demand side pull that follows from the ability of firms to scale their digital needs flexibly on demand (DeStefano et al., 2023).<sup>3</sup> Digital services are, however, typically outside the scope of capital incentive programs. This raises a question of whether these policies distort a firm’s choice of investing in tangible IT capital versus purchasing cloud computing services. Since earlier technological vintages can be important for the path of future technology use (Jovanovic and Lach, 1989; Atkeson and Kehoe, 2007; Comin and Hobijn, 2010; Jovanovic and Yatsenko, 2012), it raises a more important question as to whether capital incentives affect the diffusion of data technologies complementary to cloud services, notably big data analytics and AI. These technologies have been identified as possible general purpose technologies, such that the answers to these questions has potential widespread implications for future employment growth, innovation and productivity (Brynjolfsson and McAfee, 2014; Goldfarb et al., 2023).

The effect of capital incentives on big data analytics and artificial intelligence (AI) depends on the extent to which firms can use their own IT capital, or rather, whether there are advantages to using cloud computing. On the one hand, if capital incentives increase investment in IT hardware to store and process data, then this may encourage the adoption of big data analytics and AI. On the other hand, if cloud computing is strongly complementary to the use of big data and AI, then capital incentive policies (by slowing cloud diffusion) may also slow adoption of these technologies. Brown et al. (2011), Goldfarb et al. (2023) and DeStefano et al. (2023) offer the argument that the ability of cloud services to be flexibly scaled up and down on demand gives reason to expect that technologies that are data intensive may be stronger complements to cloud services.

> 3Cloud services were first launched by Amazon Web Services in 2006 and cloud expenditures have grown at a rate 4.5 times faster than those on traditional IT investment since 2009 (Lesser, 2017). By 2016, it is calculated that 30% of firms used cloud across the OECD (Eurostat, 2018).

2
impact of the conflict on women. On the other hand, many more women have not been involved in the fighting and may have experienced both the expected adverse effects of war and some degree of social improvement due to the Maoist influence – whether due to genuine social change, fear of insurgent reprisal, or the unintended increase in female participation in activities traditionally reserved to men due to the men’s involvement in the fighting and migration to seek work elsewhere (Menon and Rodgers, 2011). The overall impact on gender inequality is therefore unclear.

In this paper, I exploit within- and between- district variation in the intensity of violence, as measured by the number of fatalities, but also school destructions, and abductions by Maoists, to shed light on ways in which the conflict affected two key areas of decision in a woman’s life, namely education and family formation. More specifically, I estimate the impact of conflict intensity on educational attainment for men and women and on the probability of marriage (for women). Violent civil conflict may affect education, and may do so unevenly for men and women for a variety of reasons. In addition, the functioning of the marriage market may be affected by conflict either directly, or indirectly through changes in schooling.

Educational attainment is generally expected to be adversely affected by exposure to violent civil conflict. Direct youth enrollment in the military, limited mobility, and the destruction of schools may all negatively affect the ability of children to attend school. Increased poverty may pressurize parents into taking children out of school (to avoid direct costs), and putting them to work (to avoid opportunity costs). Political instability and reduced life expectancy may decrease the expected returns to education and, in turn, reduce investments in human capital. Last but not least, in the case of Nepal, parents may have been deterred from sending their children to school out of fear of having them abducted by the insurgents (Human Rights Watch, 2004). Quoting figures from the Informal Sector Service (INSEC), UNESCO (2010) reports that between 2002 and 2006, the Maoists destroyed 79 schools, one university and 13 district education offices, while 10,621 teachers and 21,998 students were abducted (p.8). According to additional data provided by INSEC, the total number of abductions by Maoist forces during the whole conflict totaled over 85,000. While most abductees were seemingly returned safe and sound after a few days of intensive indoctrination (Macours, 2011), a number of youths joined the Maoists fighters (in 2003, an estimated 30% of Maoists forces were aged 14-18) and between 1996 and 2006, 145 teachers and 344 students were killed and many more wounded (UNESCO, 2010).

The adverse effect on education could be higher for women if a conflict-related decrease in income affects investment in female human capital disproportionately or if there are overwhelming concerns about physical violence (including sexual violence) leading the 3
Figure A1: Survey period in context–contd.

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**Table 3: FDI Inflows to China $US Milios**

,

||Contracted|Utilized|
|---|---|---|
|-1982|6,010|1,166|
|1983|1,732|636|
|1984|2,651|1,258|
|1985|5,932|1,661|
|1986|2,834|1,874|
|1987|3,709|2,314|
|1988|5,297|3,194|
|1989|5,600|3,392|
|1990|6,596|3,487|
|1991|11,977|4,366|
|1992|58,124|11,007|
|1993|111,436|27,515|
|1994|82,680|33,767|
|1995|91,282|37,521|

_Source: China Statistical Yearbook, 1996_ Chinese statistical authorities categorize FDI projects into five different groups: (i) "equity joint ventures"; (ii) "wholly foreign-owned enterprises"; (iii) "cooperative operations"; (iv) "joint development"; and (v) "other foreign investment" (including imported inputs for processing and assembly). Recent values of the first four categories are in Table 4. Although the equity joint venture has been the most popular form since the mid-1980s, constituting about half of the foreign capital directly invested, growth of wholly foreign-owned enterprises has been the most rapid form in recent years. Joint development ventures pertain mainly to off-shore petroleum exploration. This form of FDI was extremely popular in the early 1980s, but its use has been declining in recent years.

**Table 4: Types of FDI in China** $US **Millions**

||1989<br>Value'|1990<br>Value|1991<br>Value|1992<br>Value|1993<br>Value|1994<br>Value|1995<br>Value|
|---|---|---|---|---|---|---|---|
|Equity Joint Ventures|2.659|2,704|6,080|29,129|55,174|40,194|39,741|
|Cooperative Operations|1,083|1,254|2,138|13,256|25,500|20,300|17,825|
|Wholly Foreign-owned Enterprises|1,654|2,444|3,667|15,696|30.457|21,949|33,657|
|Joint Development|204|194<br>||<br>92|43|30|24|8|

_Source: China Statistical Yearbooks_ * Contracted amounts.

4
Despite their important role the international experience also shows that setting technology-transfer offices is costly, as specialized expertise and infrastructure is needed (WIPO 2011) and revenues from commercialization activities represent only a fraction of TTO’s budget on average (AUTM, 2010).

Finally, the literature of financing of early stage innovation deals with funding needs beyond internal resources due to the uncertainty, length and scale of the innovation process. Information asymmetries and the risk associated with innovation tend to generate a “funding gap” for available inventions that require further private investment to be commercialized. This underinvestment in innovation is greatest for earlystage technological development, the phase where the commercial viability of inventions is validated (Branscomb and Auerswald 2003).

# **II. Factors Affecting Technology Commercialization**

## **II.1. Research Base of the Innovation System**

Notwithstanding substantial investments in R&D, Russia has been experiencing a decline in scientific output and employment. In the last few years, Russia has invested US$ 10 billion–US$ 14 billion annually in R&D (Ministry of Science and Technology 2009). This large absolute value surpasses the R&D expenditures in more **Figure 1: Total researchers per thousand total employment** innovative but smaller 10 countries such as Israel, US$ 8 billion, or Finland, OECD EU-27 US$ 9 billion. Yet, this falls 9 short of the R&D EU-15 Russia expenditures in larger advanced economies (e.g., the 8 United Kingdom, US$ 50 billion, or the Republic of Korea, 7 US$ 31 billion), as well as the expenditures in other BRIC countries (e.g., China, 6 US$ 49 billion) and it is less than 5 percent of the top country in absolute R&D 5 expenditures (the United States, US$ 369 billion). Russia’s 2008 R&D 4 expenditures as a share of GDP (1.03 percent) is certainly higher than some Source: OECD Science and Technology Indicators 2011. Eastern European countries (e.g., Poland, 0.6 percent or Romania, 0.54 percent) and comparable to some BRICs (Brazil 1.02 percent or China 1.4 percent). Yet, it is less than half the EU Lisbon Agenda target of 3 percent and much lower than that of economies at the technological frontier (e.g., the United States 2.7 percent, Sweden 3.7 percent, and Israel 4.7 percent).

In parallel to declining scientific performance, the proportion of researchers in the workforce has been diminishing. While in the mid-1990s, the proportion of researchers in overall employment in Russia was 1.5 times the OECD average – almost twice the share in EU-27 countries and more than double the EU15 country ratio – in 2008 it had become lower than in both (Figure 1). At the same time, Russia’s 7
about 15% of the country’s population. As can be seen from Figure 1, there is a large disparity in temporal changes in the number of refugees hosted even within these major refugee-hosting regions. Considering the refugee-to-population ratio in Figure 2, the highest increase is recorded in the Gambella region, reaching roughly 60% in 2014 from about 6% in 2009, followed, respectively, by the Benishangul Gumuz and Somali regions.<sup>9</sup> Despite achieving one of the fastest rates of economic growth globally in the past decade, Ethiopia remains as one of the poorest countries, with average annual income per capita of just US$772 as of 2018. Poverty is pervasive, as 27 million people, close to one-third of the population, live on less than US$1.90 a day as of 2015 (World Bank, 2018). As a result, improving welfare and reducing the number of people living in poverty are among the top policy priorities in the country.

Given the increasing instability in the region, which remained as the major cause of forced displacement, and the open-door policy toward refugees and the pervasive poverty in the country, understanding the well-being implications of hosting refugees in Ethiopia is important. The evidence could support policy making that aims at easing refugee integration, a relevant precondition for them to rebuild their lives, without overburdening the host economies as envisioned in the Global Compact on Refugees (GCR).

# **3. Data Sources**

I use data from two sources: (1) the Ethiopian Socioeconomic Survey (ESS) data from the Central Statistical Agency of Ethiopia (CSA) and the World Bank, and (2) data on refugees in Ethiopia from the United Nations High Commissioner for Refugees (UNHCR).

## **3.1. ESS Data**

The individual- and household-level information comes from the ESS data. The ESS is a three round panel survey, at the individual and household levels. The first round (ESS1) was collected in 2011/12 and contains 3,969 sample households from 333 rural and small urban enumeration areas (EAs)<sup>10</sup> in 10 of the 11 administrative regions of Ethiopia.<sup>11</sup> Of these sample households,

> 9Figures 1 and 2 shall only serve as an exposition to the existence of a large spatial difference in within-location (within-village/EA) temporal changes in refugee intensity. They shall not be interpreted as implying the location of refugee settlement sites located in any one region are closer to all points (sample EAs) in that region compared with the points (sample EAs) in other regions. As an example, some of the largest refugee-hosting camps that are located in the Somali region are closer to a large part of the Oromia region than more than half of the Somali region.

> 10EA is a sub-village geographic unit with 150-200 households. On average, CSA subdivides each village into about 4 EAs. Despite this, I use the word village in the paper to refer to EA.

> 11Small urban centers have a population of fewer than 10,000 persons in the 2007 PHC (CSA and World Bank, 2015).

8
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