rafmacalaba/data-use-mentions-extended huggingface dataset:
and non-resource), we use ICTD/UN-WIDER Government Revenue Dataset (UN-GRD). 29 The range of estimates for in literature varies widely across countries and papers. For example, while Schmitt-Grohe and Uribe (2003) set to match the volatility of the observed current-account-to-GDP ratio for Canada, Schmitt-Grohe and Uribe (2016) estimate ψψ for Argentina. We adopt as a compromise between these two seemingly extreme estimates. ψψ= 0.001 30 LTGM-NR spreadsheet since updated to constant 2015 USD. ψψ= 1 31 Initial GDP in industry ψψ= 0.1is computed as a share of total 2020 GDP. The default method is to use average value of exports in industry as a share of GDP since 2000. The following expression describes how the default initial real GDP in industry ii is computed: ii <u>�</u> 2010⁄ <u>� ,</u> 2020 � ii ii ii UUNNUUPP 2020 ii = ����������������������������<sup>1</sup> tt ii tt ~~�~~ WWDDWW pp pp x EEeeppf ttss P P i WWDDWW RRDDPP tt≥2000 tt x RRDDPP where is the real price (2010 U.S. dollars) of resource good NN RRDDPP in period taken from the World PPggPP iihP PP ssdd GGDDPP i ddi ttPPii ii i iissPP 2000 Bank Commodity Markets Outlook.ii is exports value (current U.S. dollars) of resource tt good in period pp , and is real GDP in period UUNNUUPP As ii tt is measured in current ii,tt U.S. dollars, it is deflated by WWDDWW 2010EEeeppf. ttss UUNNUUPP 32 Alternatively, we provide a measure of GDP in industry ii tt RRDDPPtt ii ii tt. derived directly from production data, EEeeppf ttssii,tt tt pp /pp 21 ii
is the equivalent of fewer than 6 years with an 8 percent return to education. Omitted variables such as parental income and ability mean that the 6 years is likely an overstatement.

15 See Battu and Bender (2020) for a survey. Another cause of the mismatch can be poor education quality, where those with a qualification are not able to perform the functions required. In this case, the cause of the engineer driving a taxi is because of he or she is not able perform the tasks of an engineer due to poor quality education.

Handel et al (2016) find that in 12 low- and middle-income countries, the overeducation/underutilization rate is 36 percent. Overeducation/underutilization rates vary across countries and can depend on how it is measured.

16 See Handel et al (2016) and Gatti et al (2013).

17 The categories here are based on "International Classification by Status in Employment, 1993 (ICSE-93)”, and also exclude workers not classifiable by status.

18 The definition of formal employment varies across countries, but generally refers to the coverage of the worker with respect to benefits like unemployment insurance, pensions, and sick or annual leave.

19 Better employment is different from “decent work” (ILO) and “Good jobs for development” (WDR 2013).

7
# 3.2. Gender Disparity in Literacy and Student Performance

Gender parity can also be measured for literacy. In 2019, the GPI for the literacy rate for the population aged 5-50 years had a value of 1.00 at the national level, as well as at the province level for areas included in this study. The gender parity in literacy is also observed in the selected districts of Banjarmasin and Banjarbaru. However, the GPI for the literacy rate in the districts of Bangkalan and Sampang is slightly lower, at 0.97 and 0.94.<sup>20</sup> The situation for the population over age 50 nationally is different, as it does not reflect the advances in education in recent decades. The gender gap in literacy for this age group is about 11 percentage points in favor of males, which is far greater than those under 50, where the gender gap in favor of males is less than one percentage point. This higher level of inequality in the older population and almost-parity in the younger population indicates that advances in equity in education have expanded literacy for girls as explained in the previous section.

<mark>Reading proficiency is another proxy for foundational learning in other subjects. A new indicator,</mark> _<mark>Learning Poverty</mark>_ <mark>, is defined as being unable to read and understand a short, age-appropriate text by age 10. The overall Learning poverty level in Indonesia is 35 percent which means 35 percent of Indonesian children at late primary age today are not proficient in reading, adjusted for the Out-of-School children. Learning poverty for girls (29.3 percent) is much lower than boys (41.4 percent), showing female advantage in reading proficiency.</mark> On student performance, girls have slightly outperformed boys in all subjects tested in the national exam ( _Ujian Nasional_ , UN) for students from 2016 until 2018 for grade 9. The UN data show the differences in exam scores between girls
- **Athey, Susan, Katy Bergstrom, Vitor Hadad, Julian C Jamison, Berk Özler, Luca Parisotto, and Julius Dohbit Sama** , _Shared decision-making: Can improved counseling increase willingness-to-pay for modern contraceptives?_ , The World Bank, 2021.

- **Ayehu, Atitegeb, Teketo Kassaw, and Getachew Hailu** , “Level of Young People Sexual and Reproductive Health Service Utilization and Its Associated Factors among Young People in Awabel District, Northwest Ethiopia,” _PLOS ONE_ , March 2016, _11_ (3).

- **Banerjee, Abhijit, Emily Breza, Arun G Chandrasekhar, Esther Duflo, Matthew O Jackson, and Cynthia Kinnan** , “Changes in Social Network Structure in Response to Exposure to Formal Credit Markets,” Working Paper, National Bureau of Economic Research 2020.

- **Bank, World** , “Burkina Faso: Welcome to Mamboué’s School for Husbands and Future Husbands,” 2019.

- **Barboni, Georgia, Erica Field, Rohini Pande, Natalia Rigol, Simone Schaner, and Charity Troyer Moore** , “A Tough Call: Understanding barriers to and impacts of women’s mobile phone adoption in India,” EPOD Working Paper, Harvard Kennedy School, Cambridge, MA 2018.

- **Beaman, Lori, Ariel BenYishay, Jeremy Magruder, and Ahmed Mushfiq Mobarak** , “Can network theory-based targeting increase technology adoption?,” _American Economic Review_ , 2021, _111_ (6), 1918–43.

- **Behrman, Jere R, Hans-Peter Kohler, and Susan Cotts Watkins** , “Social networks and changes in contraceptive use over time: Evidence from a longitudinal study in rural Kenya,” _Demography_ , 2002, _39_ (4), 713–738.

- **Bellows, Benjamin, Ashish Bajracharya, Carol Bulaya, and Sophie Inambwae** , “Family planning vouchers to improve delivery and uptake of contraception in low and middle income countries: A systematic review,”
followed by periods of declining relative incomes. More recently, most of the economies of Latin America have stabilized and there has been a strong upturn in growth, led by the largest economy of the region—Brazil—as will be discussed in more detail in the next section. It remains to be seen whether the recent improved growth performance in Brazil and several other Latin American economies can be sustained into the future.

> 5 For recent decades, the World Bank‘s open database provides a spreadsheet with the changes in country categories. Heckelman et al (2011) analyze empirically the determinants of countries‘ ―graduation‖ above the notional threshold for receiving World Bank lending.

> 6 Post crisis GDP per capita growth rates were roughly half the level of pre-crisis growth rates.   Malaysia‘s average growth of 6.4 percent from 1988-1997 fell to 3 percent from 1999-2010, and Thailand‘s average of 7.2 percent over 1988-1997 fell to 3.4 percent for 1999-2010.  (GDP per capita growth rates are from _World Development Indicators_ .)  On the other hand, this would still imply convergence—albeit at a slower pace--given HIC per capita growth rates of around 2 percent.

8
# **6. Conclusions**

An ongoing and large-scale civil war in Tigray has resulted in significant human suffering and economic disruptions, which are still being experienced at the time of writing. To make matters worse, besides the direct effects of the war on lives and livelihoods, what the UN calls a de facto blockade of the region has obstructed humanitarian assistance while also limiting our understanding of the breadth and consequences of the armed conflict.<sup><u>34</u></sup> One fortuitous aspect of this situation, however, has been the opportunity to repurpose a high frequency phone survey (HFPS), originally developed by the World Bank to monitor the COVID-19 situation and impacts in Ethiopia and continued to monitor household food security and economic activities amid an armed conflict. Using these survey data, in tandem with armed conflict location and events data (ACLED), we show evidence of significant disruptions to livelihoods, with negative impacts on household food security and access to food in the first few months of the war covered by the HFPS data. We find that, as a result of the conflict, moderate or severe food insecurity increased by 38 percentage points. Using the ACLED granular data on households’ exposure to violent conflict, we show that exposure to an additional battle within 20 km radius leads to 1 percentage point increase in the probability of moderate to severe food insecurity. Similarly, affected households report a 10 percentage point reduction in non-farm activities and 15 percent reduction in wage employment within two months of the start of the war. Farming, on the other hand, was notably resilient to impacts of the war, with participation in farm activities remaining relatively steady.

The HFPS sample is a subsample of a nationally representative sample of households drawn for an in-person survey in 2019. It covers households who reported phone ownership in the in-person survey. Due to this non-random sample and additional complication of war related nonresponse and displacements of households during the survey period, the survey is likely to include those
>11</sup>

**This model calculates wages of skilled and unskilled workers by combining firms’ demand for labor with the aggregate change in supply of these two types of workers** . In addition to the changes in rewards, the computable general equilibrium model also calculates the overall economic growth and sectoral reallocation of labor.<sup>12</sup> As already described above, changes of these variables – the Linkage Aggregate Variables (LAVs) – are used as inputs in the final step of the microsimulation.

9 We do not distinguish agricultural sector and rural regions, which may neglect the presence of rural non-farm activities. See, for example, Reardon, Stamoulis, and Pingali (2007).

10 This means that participation rates are constant. This is a conservative assumption given that the overall labor force participation rates are likely to increase as more women enter the labor force in developing countries or due to the changes in the retirement age in high income countries. The United Nations population projections are disaggregated by age groups and gender. They include net migrations per 1,000 people by country/region. We do not provide alternative migration scenarios, which can be complex and beyond the scope of the paper. However, migration can certainly be handled by the methodology, see for example, Ahmed et al. (2016).

11 The dynamic calibration allows the model to reproduce, in its baseline scenario, GDP growth rate trends that are in line with available long-term projections such as, for example, the growth projections under the Shared Socio-Economic Pathway Scenario 2 of Dellink et al. (2017). In fact, during the dynamic calibration the model targets these exogenous growth rates of GDP by endogenously determining the labor augmenting productivity factor described in the text. In the counterfactual scenarios, the real GDP growth rate is endogenized, and the uniform productivity variable is exogenized, and simulated to grow at the rate determined in the baseline scenario. The real growth rate can thus change depending on the other
.214|0.242|0.288|
|8th decile|21.7675|26.0026|20.3705|1.185|0.242|0.253|
|9th decile|29.4806|32.9604|25.2176|1.149|0.246|0.214|
|Mean|15.2130|18.6712|15.2050||||
|Gini index|0.3765|0.3143|0.2656||||

> a This table compares distributions of survey reports and usual consumption. Columns (1) considers data on _Y_<sup>_d_</sup> for all households. Columns (2) considers data on _Y_<sup>_r_</sup> for the _treatment sample_ – see note to Table 1 for definitions. Column (3) reports the estimated distribution of _Y_<sup>_∗_</sup> . Column (4) reports the estimated mean of _Y_<sup>_r_</sup> _/Y_<sup>_∗_</sup> conditional on deciles of _Y_<sup>_∗_</sup> at left. The mean of _Y_<sup>_d_</sup> _/Y_<sup>_∗_</sup> is not reported, and this is always one because of Assumption 1. Column (5) reports the Mean Squared Error (MSE) of _Y_<sup>_d_</sup> _/Y_<sup>_∗_</sup> conditional on deciles of _Y_<sup>_∗_</sup> at left. Column (6) shows the MSE of _Y_<sup>_r_</sup> _/Y_<sup>_∗_</sup> conditional on deciles of _Y_<sup>_∗_</sup> at left.

benchmark is less pronounced.

# **
Two issues raise concern about the estimates generated in the empirical analysis. First, in the ORG supplements of the CPS, missing earnings data is imputed by the Census Bureau using a hot deck procedure. However, the CPS also provides flags to identify the instances where wage information has been allocated. For the sake of consistency, all results in the paper are based on respondents who have valid, i.e. non-allocated, wage variables. Second, values at the top end of the wage distribution are top-coded in both the surveys. This is not a significant concern for hourly wage earners, as they seldom earn enough to have their income variables censored. However, to further mitigate the threat of top-coding from biasing our estimates, we follow Autor et al. (2005) and restrict our attention to respondents with income below the 90th percentile.

Data on household level time allocation comes from the 2003 - 2018 waves of the American Time Use Survey (ATUS). The ATUS is conducted by the U.S. Bureau of Labor Statistics (BLS) with individuals drawn from the existing CPS sample. Each wave of the ATUS survey is based on 24 hour diaries where the respondents detail their activities from the previous day. These activities are then assigned to one of the 400 ATUS’s pre-specified classification schemes. Following Aguiar et al. (2013), we segment the total time use into four broad categories. These four categories, described in detail below, are mutually exclusive and partition the total individual time endowment.

Labor time includes all the time spent working in primary and secondary jobs, and overtime, and includes time spent in commuting to and from work. However, we exclude from this the time spent actively searching for jobs.<sup>9</sup> We also exclude from our labor time the total time spent in non-market or informal work which may also generate income for the household.

Shopping time consists of the total time spent in obtaining goods and services. This comprises time spent in
at the industry level are obtained from WITS database, RRii,tt IIjj,tt ttIIOOttjj,tt

> 14 Prowess is not well suited for understanding firm entry and exit because firms are under no legal obligation to report to the data collecting agency. However, since Prowess contains only relatively large Indian firms, entry and exit is not necessarily an important margin for understanding the process of adjustment to increased openness within this subset of the manufacturing sector. In fact, very few firms exit from the sample during this period (7%) and there is no statistical difference in initial firm scope, output, TFP and R&D activity between continuing and exiting firms (Goldberg et al., 2010).

> 15 See Goldberg et al. (2010) for detailed summary statistics.

> 16 Data quality checks for the years after 2013 suggests that firms do not consistently report their product details because Schedule VI of the erstwhile Companies Act 1956 modified in February 2011 made 'Quantitative Disclosures' like 'Products Details Schedule' optional.  .

> 17 There are varying views regarding electricity, gas, water supply and construction services as services provided by manufacturing firms. For most of our analysis we included these as non-manufacturing products provided by a goods producer, however, the analysis in the annex tables illustrates robustness checks excluding these services.

> 18 Strictly speaking, servitization may be related to firm productivity in previous periods, but, given that TFP is likely to be correlated over time, we present the relationship as contemporaneous. Empirically, we also test the relationship with lags and the results remain robust.

7
competition, labor, the business environment, and performance. The main respondents are business owners and top managers of firms. The ES universe excludes extractive industries, agriculture, informal firms (i.e., firms that are not registered), fully government-owned enterprises and micro firms (i.e., firms with fewer than five full time employees). However, in addition to the standard ES, the World Bank also occasionally collects data on micro firms (registered firms with fewer than 5 employees, which are not part of the universe of inference of the standard ES. In the case of the DRC, a survey of micro firms was carried out in parallel to the standard ES.

The DRC survey was stratified according to three standard ES levels – industry, establishment size, and within country location. The stratification of industry divided the universe of firms into manufacturing, retail and other services sectors, while stratification by size divided firms into micro (1-4 employees), small (5-19), medium (20-99) and large (100+) enterprises. Finally, the within country location stratification divided the DRC into four regions: Center (Kananga and Mbuji Mayi), East (Bukavu, Butembo, Goma, Kisangani), South (Likasi, Lubumbashi), and West (Boma, Kikwit, Kinshasa, Matadi). Survey weights were calculated and are applied to all statistics discussed below. Weights ensure that estimates are inferences with a pre-determined level of precision to the universe of the ES: the non-agricultural, nonextractive formal private sector in the DRC.

The summary statistics presented in Table 2 show that the mean firm operating in the DRC has been in business for nine years and employs approximately six people. Around 4 percent of establishments in the DRC are part of a multi-establishment firm, 2 percent engage in exporting activity and 3 percent are foreign owned. Other firm characteristics of note include total female employment, as well as female participation in management and ownership: the average firm employs only one woman (although
# **1 Introduction**

There is now wide agreement that progress towards the decarbonization of the global economy will require a significant increase in the utilization of technologies that contribute to reducing CO2 emissions. While these green technologies are becoming more ubiquitous, there is still little systematic evidence on their emergence and diffusion across firms, sectors, and countries, and the economic mechanisms underlying their diffusion. In this paper, we draw on the textual analysis of patents and earnings conference calls matched with multi-country, firm-level panel data for 2012-2021 to provide systematic evidence on these issues.

We begin by establishing new stylized facts about the emergence and dissemination of green technologies over the last two decades using textual analysis of patents. We identify a set of business and technical-relevant keywords for technologies aimed at protecting the environment and making a positive impact on climate change mentioned in the World Intellectual Property Organization (WIPO) green patents database. This approach allows us to identify in a systematic way green technologies. The analysis of patent data for the period 2000-2021 reveals that green innovation was primarily driven by new technologies from the construction, energy, and transportation sectors, including technologies such as biofuels, geothermal energy, LED technology, drones, and hybrid/electric vehicles. We then provide evidence on patterns of adoption and diffusion of these green technologies across firms and countries, drawing on the textual analysis of earnings conference calls transcripts (ECTs). We follow a growing literature seeking to infer firm-level technology adoption using general-text classification methods for identifying business-relevant keywords associated with specific technologies; and by counting the number of times these words are mentioned in conference calls that publicly listed firms host with financial analysts. This approach has been increasingly used by researchers to measure firm uncertainty (Hassan, Hollander, van Lent, and Tahoun, 2019), firm sentiment (Hassan, Hollander, van Lent, and Tahoun, 2020), and the diffusion of disruptive technologies (Bloom, Hassan, Kalyani, Lerner, and Tahoun, 2021). The rationale behind it is that earnings calls
MINIMUM WAGES IN CAMBODIA 6 <!-- Start of picture text --> All Female<br>2007 2009 2011 2013 2015 2007 2009 2011 2013 2015<br>Year<br>Labor-force participation Share of LF in wage emp<br>Share of wage employment in garments & footwear<br>All Female<br>2007 2009 2011 2013 2015 2007 2009 2011 2013 2015<br>Year<br>Monthly, garments & footwear Monthly, other private<br>Hourly, garments (right) Hourly, private (right)<br>1<br>.8<br>.6<br>.4<br>Participation<br>.2<br>0<br>13.5 8.2<br>8<br>13<br>7.8<br>12.5 7.6<br>Log(real wages)<br>7.4<br>12<br><!-- End of picture text --> Figure 2. Trends in participation rates (top panel) and wages (bottom panel) for male and female workers _Source_ : Author’s calculations from Cambodia SES 2007-2015. _Note_ : Vertical lines represent times of minimum wage hikes. Sample restricted to the working age population. Sample for wage figures (bottom panel) further restricted to those who work at least 20 hours in the week preceding the survey and have wage-work as their primary employment.
3 geneity in tastes, population, productivity, and crop specialization. It incorporates several structural features and policies that characterize the economy of Sri Lanka and are common across developing economies: land inequality, quantitative restrictions (QRs) on fertilizer imports (as opposed to ad valorem trade costs), a tax-funded fertilizer subsidy program, and non-homothetic preferences that generate a negative relationship between income and the agricultural expenditure share. These features play a key role in shaping the welfare effects of fertilizer access restrictions.<sup>9</sup> The model delivers a closed-form solution for the elasticity of crop yields with respect to fertilizer use as a function of changes in the prices of inputs and outputs, clarifying the distinction between PE adjustments—in which only fertilizer prices change—and GE adjustments—in which wages and output prices also change.

In the third step, we estimate the model’s parameters using a range of empirical strategies and a rich set of novel data sources. Household survey data is used to estimate the income elasticity of food’s expenditure share (Engel elasticity), a policy-relevant parameter widely used to analyze food security concerns in developing countries (Baquedano et al., 2021), but hard to estimate causally. We deploy an instrumental variables (IV) approach relying on unexpected income shocks (lottery winnings and disaster relief). Survey data on crop prices and household expenditures enables us to estimate the elasticity of substitution _across crops_ through another IV approach that leverages the effect of regional crop suitability, exogenously determined by regional geography and measured by the Global Agro-Ecological Zones (FAO-GAEZ) project, on local crop prices. Technology parameters in agricultural production functions are recovered from input cost shares computed from national Input-Output (IO) tables and data on fertilizer requirements and subsidy rates. The (regionspecific) parameters that govern land distributions are estimated using survey data on landholding sizes. Unobserved variables (e.g. productivities and taste shifters) are backed out via model “inversion” by solving the equilibrium system of equations while