rafmacalaba/data-use-mentions-extended huggingface dataset:
the participation date and the survey date. Finally, it is likely that the effect of a reduction of subjective well-being on civil conflict does not only come from the increased propensity of ‘suffering’ civilians to participate in uprisings, but also from the fact that civilians that might not ‘take to the streets’ themselves, can support and legitimize an uprising. Therefore, we use as our dependent variable the number of events of civil resistance within a country in a year using data from the CNTS (2015). We calculate the number of such civil resistance events as the sum of strikes and demonstrations and transform it using the hyperbolic inverse sine. This transformation resembles a normal logarithmic transformation and can be interpreted as such, but it can take zeros into account.

The CNTS data were collected through New York Times newspaper articles since 1815 and are used in several other studies to measure strikes and demonstrations (e.g. Braha, 2012; Collier & Rohner, 2008; Schatzman, 2005). Demonstrations are included in the CNTS data set if they are (1) peaceful public gatherings, (2) with at least 100 participants, (3) whose primary purpose is to display or voice opposition to government policies or authority, and (4) do not have a distinctly anti-foreign nature such as anti-globalization protests. Strikes are included if (1) 1,000 or more industrial or service workers participate, (2) they involve more than one employer, and (3) they are aimed at national government policies or authority. While the average total number of demonstrations and strikes over the period of analysis (2006-2014) is 16 per country, there is considerable cross-country variation, from none in for example Finland, Bahrain and the United Arab Emirates, to 121 in the Arab Republic of Egypt, 129 in China and 209 in the United States. Any differences in the number of non-conflict events reported due to biases in the U.S. press coverage are absorbed by the country-fixed effect.

# **
management from the smaller sample of the WMS. This unique database enables us to establish a set of new stylized facts concerning the sorting of firms across management modes according to firm, regional and national characteristics.<sup>7</sup> Importantly, by working at the subnational level, we are able to purge national characteristics that may be correlated with institutions or trust.

Second, to discipline the empirical analysis, we develop a simple model of an industry equilibrium in the spirit of Melitz (2003) in which heterogeneous firms decide between family and professional management when the latter are subject to contracting frictions. Motivated by the findings in Bloom et. al. (2013), we assume that due to span-of-control limitations entrepreneurs can increase productivity by delegating managerial tasks to professional managers. The cost of this delegation is that outside managers may not act in firms’ interests, and the quality of legal institutions or enforcing social norms determine the ability of owners to recoup costs in this event. Hence, when contracting institutions are weak, fewer firms adopt the more productive professional management mode. Aggregate total factor productivity (TFP) then falls as the most productive firms remain suboptimally small, intermediate-sized firms sort into less productive family management, and the least productive firms survive due to the resulting decrease in competition.

Our framework is closely related to Burkart, Panunzi and Shleifer (2003) who develop a model where a founder chooses between his or her heir and a more productive professional manager, and characterize how this decision is shaped by the legal environment. In contrast, we incorporate heterogeneity in firm productivity within a general equilibrium setting. This allows us to characterize how these choices might affect firms differentially across the size distribution (which we find support for in the data) as well as the aggregate effects on TFP. These results are important since they imply that only the behavior of certain types of firms is distorted by weak contracting institutions through the family management channel. Our model also relates to Caselli and Gennaioli (2013) and Akcigit
improves households’ ability to smooth the idiosyncratic effects of rainfall shocks on household income and consumption. In a follow-up study focused explicitly on coping with agricultural risks associated with adverse weather shocks, Giles and Yoo (2007) finds that the ability to migrate leads to reduced precautionary saving and increased consumption.<sup>21</sup> Precautionary saving as a share of total savings is cut in half (from 30.5% to 16.6%) as the share of a village working as migrants increased from 2 to 22 percent of registered working-age laborers.

Due to the existence of informal risk sharing arrangements against idiosyncratic shocks, the risk coping role of migration can be true even for households without a family member currently working as a migrant. Indeed, with a RCRE sample, Giles and Murtazashvili (2013) again using the timing of ID card distribution for identification, and show that a one percentage point increase in the share of a village workforce employed as a migrant is associated with a 3.2 percentage point decline in the probability of falling into poverty, and among the poor, the same increase in migration is associated with a 3.5 percentage point reduction in the probability of remaining poor.

A more recent paper using the RCRE panel dataset confirms the role of migration in reducing the possibility that households face consumption variability, and specifically, periods with a large drop in consumption (Kinnan et al. 2018). This reduced consumption risk allows farm households to invest in higher-risk productive activities. The paper uses a novel identification strategy to control for migrant selection: an interaction between intensity of contact with a destination province during the historical sent-down youth program and specifics of _Hukou_ reform of the destination provinces. Finally, the authors use a simple model to show that the quantitative changes in consumption and risky investments are consistent with accepted relative risk aversion parameters.

# **4.4 Recent Trends in Migrant Earnings**

Average household annual migrant earnings increased from less than 8,000 yuan in 2004 to more
# **1. Introduction**

Firm exit along with entry are crucial components of the evolution of industries both in developed and in developing countries (Caves, 1998, Tybout, 2000). Models of industry dynamics emphasizing producer heterogeneity and market selection such as Jovanovic (1982) and Ericson and Pakes (1995) suggest that in reasonably efficient markets „superior‟ firms have higher chances to survive and grow. While being innovative is a central characteristic of „superior‟ firms it also is a risky venture due to the uncertainties inherent to both the innovations themselves and their commercialization. The introduction of new products by a firm - an important type of innovation - involves high and often sunk development and production costs that may fail to bring a sufficiently high payoff to recover those costs.<sup>1</sup> Demand for these new products might not pick up or the products could be copied or replaced quickly by other new products developed by competitors. The model proposed by Ericson and Pakes (1995) illustrates the risks associated with innovation. In their model, firms engage in R&D investments which may improve their efficiency, profits, and survival but can also lead to firm exit if the outcome is not successful. Given that failed product launches are frequent, innovators might ultimately face a lower survival probability than other firms.<sup>2</sup> In this paper, we examine the relationship between product innovation and plant survival focusing specifically on the role of different types of risk for that relationship and on potential differences in performance returns for riskier types of innovation. We do so using a rich new dataset on Chilean manufacturing plants and all their products during the period 1996-2003.

Our paper makes several contributions to the empirical literature that studies the relationship between survival and observable producer characteristics, namely innovation-related variables, as a way to test the implications of industry dynamics models.<sup>3</sup> First, our dataset allows constructing objective plant-level time-varying measures of product innovation - categorical and
/day converted into 2008 Tanzanian shillings on a PPP basis.<sup>25</sup> With this absolute poverty line fixed at the same value regardless of module, we see dramatically different headcount rates depending on how consumption is measured. The benchmark personal diary records the lowest level of poverty at 47.5 percent of the population, followed by the 7-day long list (module 2) at 54.9 percent. Even though module 2 yielded mean consumption levels closest to the benchmark (Table 3 lists consumption at 3.9 percent lower and not significantly different from benchmark consumption), the fact that module 2 inequality was also higher (the estimated Gini is three points higher – Table 7) results in substantially higher poverty numbers. The 7-day subset design (module 3) reports

> 25 An alternative to the international poverty line would be to use a nationally defined poverty line (for example, the line defined by the HBS 2006/07). In either case, we avoid defining eight separate poverty lines, each on the basis of each consumption aggregate measure, since this would introduce problems as these lines would then not be comparable, as discussed in Lanjouw and Lanjouw (2001).
Unadjusted and adjusted changes in risk associated with underlying pre-natal and postnatal risk factors for infant and early childhood stunting (0-5 months of age) <!-- Start of picture text --> Unadjusted Adjusted<br>Adequate pre-natal conditionssg: (maternal nutrition,a birth. outcomes)} 0.39-*— 0.37_<br>Adequate home care & feeding practices —_ an<br>Adequate HH food security & diversity ———- ——_—<br>Adequate post-natal maternal & child health care —_-—1.62 —_-——1.78<br>Adequate HH water, sanitation infrastructure & environment am fe<br>Female ° *<br>Single birth} +2— oe<br>Urban 7°<br>Total HH size (N) +<br>Total children under five in HH (N) ih<br>Total HH income per month (1 = 1000 PHP) (log) 2:<br>In poorest quintile of total HH income per capita —_—-—-<br>Mother has high school or tertiary education —_———<br>Mother is married oe<br>Mom wasa teenager at time of birth o—<br>Mother has a job or business a<br>1 2 1 2<br>Relative Risk (95% Cl)<br><!-- End of picture text --> Source: Author's calculations based on 2015 National Nutrition Survey (NNS). Notes: Only the relative risk estimates for statistically significant predictors are labeled.
larger scale. The specific limitations, challenges, and benefits of each of the abovementioned measurement methods are addressed in detail below.

# **_A. Self-Reported Area Estimation_**

Household surveys, particularly in developing countries, have historically relied on subjective measurements of land area, and for good reason. The marginal cost of adding one or two questions to a survey that is already being administered to a household is trivial and the exercise can be completed in a matter of minutes, with no need to visit the plot. As a result, item non-response for this item is usually negligible in existing surveys. The minimal financial investment required by this method, however, does not come without challenges, particularly in terms of data quality.

Several factors influence the accuracy of subjective farmer self-reported estimates of area, including respondent characteristics, plot characteristics, and the land registration or titling system. Land in developing countries is often passed down from generation to generation or distributed by the community, and rarely are there property rights or documentation to inform farmers of their true land area. In many cases measurement units are not standardized and determining accurate conversion factors is a timeconsuming and often ill-fated exercise. The most worrying aspect of some of the measurement error associated with self-reporting is that it may be systematic, and associated with key variables of interest.

The literature has shown the accuracy of subjective estimates to be sensitive to respondent characteristics. More educated farmers might be more numerate and more at ease at quantifying their own land area, while absentee landlords, or respondents for whom farming is only a secondary activity, may be less aware of the characteristics of their plots. Using data from Uganda, Carletto et al. (2013) analyze the determinants of the difference between farmer self-reported plot area and GPS measurements, finding that the age of the household head has a significant and positive relationship with measurement bias.

The quality of data collected through farmer self-reporting is also significantly degraded by the natural inclination of respondents to round off numbers. Distributional analysis of GPS and
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levels of significance. Firms making losses have significantly lower AU ratios (low P3). Again, the difference between the two groups of firms significantly decrease over time: The value of BL increases from -0.441 in 1980 to -0.015 in 1984, with a P4 value of 6.9%.

The increase in average financial costs between the years 1980-82 is significant, as expected. The interesting point is that the FC ratios of observations with losses do not seem to be significantly higher than those with profits: the hypothesis that all the elements of BL are zero cannot be rejected at the 10% level of significance. Notice that the t-statistics of the coefficients **EL** are also low. Hence financial costs are not important in explaining the difference in the profitability of the two groups of firms.35 The results obtained so far can be summarized as follows: In the 1980-82 period there has been a decrease in the profitability of firms in the CMB data set. Although financial costs have increased considerably during those years, that component of profitability does not explain the poorer performance of firms with losses. On the other hand, loss making observations have significantly lower GM, EBIT and AU ratios.

If this interpretatior. is correct, then it can safely be said that firms were hit by two shocks in 1980-82: the interest rate shock took place almost simultaneously with the demand shock, as reflected in the decline in gross margins. A look at the movements in leverage can now give us an idea about how firms adjusted financially to these shocks. Also, the lower levels of GM, EBIT and AU explain only the existence of the profitability gap between the two groups of observations; why the gap has increased between 1980-82 requires further explanation.

35 This result changes when outliers are not omitted. See Appendix 2.
|(0.000)<br>(0.005)|
|Observations|147,977<br>145,628|
|R-squared|0.351<br>0.331|
|Controls at student level|YES<br>YES|
|School fixed effects|YES<br>YES|

Notes: 1) Placebo: estimates using data from 2016 and 2017, when the ENTRE program did not exist. Experiment: estimates using data from 2017 and 2018 (year of the intervention) and the preferred specification in Table 3. 2) Standard errors clustered at schoolyear level in parenthesis; 3) *** p<0.01, ** p<0.05, * p<0.1. 4) The dependent variable is a dummy variable that is equal to 1 if student _s_ in school _j_ dropped out school between year t and year t+1. 5) _ENTREj_ represents the interaction _Entrejt_ x _Postt_ where _Entrejt_ : (1= Student _s_ attends a school _j_ in year _t_ that is in the treatment group) and _Postt_ : (1 = year 2017 in Placebo; = year 2018 in Experiment; 0 otherwise); _ENTRE ij_ with i=1,2,3 captures the same interaction as _ENTREj_ but using assignment to the treatment arm _i_ instead of assignment to the general treatment group. 6) All specifications in panel (i) include the corresponding _Postt_ and _Entrejt_ dummies, while those in panel (ii) include the _Postt_ and _Entre ijt_ dummies; 7) Student characteristics used as control: age dummies, gender and risk (probability of dropout estimated in the Early Warning System).

25
percent of this list were intermediate goods and 24 percent were consumer goods. Several products were dropped from the earlier list, including bedsheets, gloves, and smartwatches. China responded with its own finalized list of $60 billion of US exports, with tariffs ranging from 5-10 percent, down from the originally presented 5-25 percent. Both sets of tariffs then went into action on the 24th September.

Finally on the 1st December 2018, the US and China agreed to halt the next increase in tariffs that was due in January while they negotiate a solution. At the end of 2018, the US faced retaliation on 8 percent of its total exports whilst covering approximately 12 percent of its total imports with tariffs, largely on intermediate inputs and capital goods.

# **3. Data**

## **3.1 Online job adverts**

This paper uses data collected by Burning Glass Technologies (BGT), a company that scrapes what they identify to be the quasi-universe of US online job postings on a daily basis. BGT estimate that these postings are sourced from approximately 40,000 online job boards and company websites. This data has now been widely used to study US labor markets (e.g. recent examples include Acemoglu et al. (2022) and Bloom et al. (2021)).

Hershbein and Kahn (2018) provide a detailed analysis of the industry-occupation mix of vacancies in BGT relative to other detailed US data sources, such as JOLTS, and how this mix has changed over time. They find that the BGT postings are disproportionately concentrated in occupations and industries that typically require higher skill levels, but that the distributions are relatively stable across time and the aggregate and industry trends in the quantity of vacancies track official sources reasonably closely. Therefore, while online job adverts do not provide a complete picture of the entire labor market, they can provide a useful barometer of labor market demand.

BGT classify the job adverts along a range of dimensions; this paper makes use of the classification by
,000 women in Egypt and 70 per 100,000 women in Bali. Bali still has the higher rate but the difference is much smaller. The difference between the two measures of maternal mortality reflects differences in the number of pregnancies in the two populations, which in turn largely reflects the use of family planning. In this case, about 50 percent of women were using contraception in Bali versus 25 percent in Menoufia, Egypt.

Maine et al (1986) used World Fertility Survey data to estimate the proportion of maternal deaths that would be prevented if all women saying they want no more children and currently not using effective contraception, were able with family planning to avoid all future births. The median proportion of deaths averted for twenty-six developing countries was 29 percent, with a range from 5 percent in Ivory Coast to 62 percent in Bangladesh. The median percentage reductions were as follows: 17 percent for 8 African countries; 35 percent for 10 Asian countries; and 33 percent for 8 Latin American countries. Two points deserve emphasis here. First, Maine et al estimate the proportion of deaths averted. The absolute number of lives saved obviously depends on the maternal mortality ratio. For many developing countries, especially those in South Asia and sub-Saharan Africa, the ratio is 400 - 1,000 maternal deaths per 100,000 live births, a level up to 100 times as high as that prevailing in Northern Europe and North
model assumes) as student numbers fall (e.g. see Sondergaard et al. 2012). For the countries with the need to hire more teachers, the government may face challenging in hiring as many additional teachers as our simulations suggest (because of limited government finance, lack of interested candidates, etc.).

On the other hand, our model may be _overestimating_ the importance of the stock. We assumed that the teacher attrition rate is constant for the coming decades in each country. However, counterparts in several Pacific Islands countries report that many of their teachers are leaving the profession, migrating to other countries. Data from one Pacific Island country shows a higher teacher attrition (up to nearly 10 percent) right after the onset of COVID-19, and thereafter, a decline to below 2 percent, which is within the assumption we used in this analysis. Another country presented a relatively modest increase (below 4 percent) after the onset of COVID-19. However, unfortunately, many of the counterparts were unable to quantify how many teachers were leaving the teaching profession annually. If, indeed, attrition rates in Pacific Island countries are higher than assumed (and on an increasing trend), these countries would need to hire more teachers, which, in turn, would imply that their stock of teachers already in the system would be smaller.

The reality is that the higher the rate of attrition, the higher the importance of recruiting and training new teachers (to replace those teachers that leave the system).

We recommend that countries try to fill this knowledge gap, either by analyzing teacher payroll data (to identify the rate at which teachers are leaving the profession) or through surveys.

# V. Conclusions

This paper presents a simple approach to estimate the stock and flow of primary school teachers in the future across 22 middle-income countries in the East Asia and Pacific region. It uses selected indicators already in place to monitor progress against the SDG4 target – the net enrollment rate, the pupil-to-teacher ratio, and the teacher
World Bank, 2019).<sup>10</sup> In the analysis that follows, the BRSS indicator is the index of overall restrictions on banking activities. Following Barth, Caprio, and Levine (2013), it is

> 9 See Claessens et al. (2018), p. 31, Box A.

> 10 For more details on the survey itself and the BRSS dataset, see: <u>https://www.worldbank.org/en/research/brief/BRSS.</u>

9
# **3.2 Comparing service quality across sectors**

As we have discussed, effective access provides useful insights into the level of functional service coverage, and whether investments are yielding effective outcomes. Yet, even this more rigorous standard fails to provide much information on service quality.<sup>6</sup> While a private piped connection may serve as a household’s primary source, its value to users can be diminished if water supply is intermittent and/or not potable. Information on the quality of service is important for refining investment priorities.

The baseline survey included questions on service quality for most sectors. We used these responses to develop a ‘quality-adjusted’ access variable that records what we deem is the minimum ‘desirable’ level of service in each sector. Notably this includes a 24-hour service requirement for the water and electricity sectors. Though this is a reasonable expectation in most contexts, it may be unrealistic where resources are highly constrained. Because quality is a subjective measure and requires trade-offs with affordability, the desirable threshold for each indicator in any particular setting will need to be determined in consultation with local groups, neighborhoods and context. Table 3.1 above records the threshold indicators we developed across five services - electricity, water, toilets, sewage disposal, and access roads. For the purposes of this analysis, we have defined indicators for quality-adjusted access as follows: _Quality-adjusted access indicators:_

- Electricity: %HH with in-house electricity who also use it as a primary source of lighting, and 24hour supply and rare outages

- Water: %HH with in-dwelling or in-compound (shared or private) piped water who use it as their primary source and have 24/7 supply

- Toilet: %HH with non-public toilet facility (shared or not), but shared with fewer than 10 people

- Sewage Disposal: %HH with connection to public sewer or septic/soak pit that is emptied by truck (non-manual)

- Access Roads: %HH with ‘surfaced’ access roads