rafmacalaba/data-use-mentions-extended huggingface dataset:
to control for countries wealth and the UNU-WIDER Gini to adopt an alternative measure of income inequality independent of the database we use.

As a benchmark for our analysis, we use what we could call a ‘standard’ model in happiness studies that combines cross-country and longitudinal data (see for example Alesina et al., 2004). Let _H_ = Subjective well-being; _X_ = Income; _I_ = Income inequality; _R_ = Relative income; _W_ = A measure of countries’ wealth; _C_ = A vector of control variables for individual characteristics; _T_ = A vector of country dummies; _Y_ = A vector of case of welfare studies, if information on household welfare is very homogeneous within clusters, this is an essential information to decide on the use of clusters. Therefore, in the absence of complete and reliable data information, the least risky choice would be to use both robust and cluster options while the most transparent choice would be to compare and discuss results with and without robust and cluster options. 7Data can be freely downloaded from: http://www.jdsurvey.net. We are grateful to the Values Study Group and World Values Survey Association for creating and making accessible the EUROPEAN AND WORLD VALUES SURVEYS FOUR-WAVE INTEGRATED DATA FILE, 1981-2004, (v.20060423, 2006). Aggregate File Producers: An ́alisis Sociol ́ogicos Econ ́omicos y Pol ́ıticos (ASEP) and JD Systems (JDS), Madrid, Spain/Tilburg University, Tilburg, The Netherlands. Data Files Suppliers: Analisis Sociologicos Economicos y Politicos (ASEP) and JD Systems (JDS), Madrid, Spain/Tillburg University, Tillburg, The Netherlands/ Zentralarchiv fur Empirische Sozialforschung (ZA), Cologne, Germany:) Aggregate File Distributors: An ́alisis Sociol ́ogicos Econ ́omicos y Pol ́ıticos (ASEP) and JD Systems (JDS), Madrid, Spain/Tillburg University, Tilburg, The Netherlands/Zentralarchiv fur Empirische Sozialforschung (ZA) Cologne, Germany.

> 8Wired at www.
2. Macro-micro methodologies for ex-ante evaluation of the distributional impacts of trade

**The importance of assessing the distributional gains from trade has been long recognized in economic thinking.** The origin of dynamic microsimulation can be traced back to the 1950s seminal work of Orcutt (1957) whose contributions aimed at overcoming the limitations of models available at that time. Orcutt observed that those earlier models could be used to predict the aggregate impact but could not describe the distributional impacts of policy reforms nor the effects on inequality of long-term policy options or shocks. Data availability and modeling have significantly advanced since then, yet dynamic microsimulations remain the main tool to study distributional change and to provide the unique perspective of projecting samples of population forward in time.

**Traditionally, tools for policy analysis concerned with distributional issues tend to be either exclusively micro or macro** . Microsimulation models based on household samples are used for studying reforms of a variety of policy choices, such as tax-benefit systems, impact of delivering public goods like education or health care, or changes in labor market regulations. Macro modeling, on the other hand, is typically used to measure the impact of reforms in tax, trade, finance or monetary policies on the sectoral structure of the economy, its level of employment and wages. From the aggregate information, it is possible to inform which groups in the population will benefit or lose from the reform being analyzed.

**Aggregate outcomes from macro models are insufficient to assess how individual households might be affected by the policy being analyzed** . Likewise, seemingly micro-oriented policies like a reform in cash transfers or in the pension system may have important macro effects through labor supply and savings, in turn, these macro effects of micro-oriented policies will impact wages, employment and returns to wealth affecting the income distribution within a population. Hence, the need to combine macro and micro approaches to policy analysis is obvious.

**Several approaches have been proposed to integrate macro-micro frameworks that allow for the simultaneous study of the distributional and
Supplementary Appendix S2

# **BANGLADESH SURVEY OF FORMAL AND INFORMAL JOBS**

# **Conducted by RAND, USA and BIGD, BRAC University**

# **IDENTIFICATION OF THE SAMPLE AREA**

|**Sample Area Particulars**|**Name**<br>**Code No.**|
|---|---|
|Zila||
|Upazila/Thana||
|Union/Ward||
|Mouza/Moholla||
|PSU Number||
|Questionnaire Number||
|InterviewerName||
|Supervisor Name||
|Name of Person EnteringData||
|Date of Interview||

# **ENUMERATOR: PROVIDE INTRODUCTION / READ INFORMED CONSENT**

Will you be interested to participate in the second part of the survey, today?

1. Yes I would like to participate today

2. Yes I would like to participate another day: Date and Time 3. No, I would not like to participate Reason: ....................................................................................................................................... .........................................................
affects productivity by inducing adjustments in the use and price of factors of production, through changes in the demand structure and by altering the allocation of resources between high and low productivity firms. For example, Bloom et al. (2020a) estimate a reduction in productivity in the United Kingdom, primarily due to an increase in the cost of intermediate inputs in the short-run associated with measure to contain COVID-19 and lower R&D investment and managerial time in the medium-run, although they also document a partially offsetting positive shock of reallocation of resources from low to high productivity firms. Andrews et al. (2021) find a positive reallocation effect for three OECD countries, mainly driven by technology readiness to face the shock and the additional investments in technology. Statistical agencies, at least in developed countries such as the United States are already witnessing a decline in productivity. Multifactor productivity decreased in the United States by 1.7% (U.S. Bureau of Labor Statistics, 2021).

On the flip side, ex-ante firm exits may be contingent on initial firm productivity, as noted in Borino et al. (2021) who use survey data from 31 countries, including a few in Africa. More productive firms may be more prepared to offset the shock and are more agile in adjusting (for e.g., adjustments in operations model, work from home, innovation in products and so on) to minimize the negative impact of the lockdown restrictions, physical distancing norms or the disruptions in supply-chains.

One channel, as suggested in Andrews et al. (2021) is the fact that initial productivity may reflect technological and management capabilities that allow a faster and more effective firm response. Combining information on technology sophistication pre-COVID-19 and the BPS data for firms from Brazil, Senegal, and Vietnam, Cirera et al. (2021a) show that firms using more sophisticated technologies before the pandemic were more likely to increase the use of digital technologies in response to COVID-19 and achieved better sales performance, compared to firms that were similar
23 no price observation, other items in the same food group (closest nutritional substitutes) do have observations. Second, where the price for a market food item is missing while the item was reported as usually available in the market (17% of all observed market/food item combinations in the detailed follow up study by Kaiyatsa et al. (2021)), and that the pattern of missing prices was not explained by the food item, time, or market. Third, the fact that individual diets are feasible in most markets and months provides further confidence. While each of these observations does not exclude the possibility of erroneously missing prices leading to an erroneous non-solution of the least cost diet problem, together they suggest that no-solutions of the least cost diet problem mostly correspond to realistic infeasibility in that market/month.

If the model can converge on a solution, we calculate the total cost of the diet multiplying the quantities of each food obtained through the linear programming results with the prevailing prices in that market and month. To compute the total household diet cost at the lower bound, we solve the linear programming for each individual and then add their diet costs together to get the household total. The cost under household sharing is solved as a single problem per household and month where the diet solution must meet the shared household nutrient requirements and total energy budget.

We convert all costs into 2011 US$ PPP, smoothing the annual conversion factors provided by the World Bank’s International Comparison Project over our monthly time series using the Denton method (World Bank 2015; Denton 1971; International Monetary Fund 2018). We calculate monthly food and total expenditure based on one month of annualized expenditure calculated following Deaton and Zaidi (2002) and the method of poverty calculation in Malawi (National Statistical Office (NSO) [Malawi] and World Bank Poverty and Equity Global Practice
for one year had higher levels of total factor productivity and return on assets. Using administrative data, the authors also show that, five years after the treatment, those firms that had received consultancy services employed significantly more workers; showing that the impacts of improved management practices endure.

In terms of the long‐term impacts of management practices on productivity, recent analysis by Giorcelli (forthcoming) confirms what was found by Bloom et al., 2018 and Bruhn et al., 2018—that the positive impacts of improved management practices are long lasting. Studying Italian firms that received funding for management training trips to the US and more technologically‐advanced machinery as part of the _Marshall Plan_ , the author finds that both interventions (management training and improved machinery) led to increases in productivity, but that the impacts of management training lasted longer. Furthermore, Giorcelli shows that while in the short run, larger firms benefitted relatively more from management training, over time, firms with fewer than 50 employees benefitted more.

4
**Figure 52: Chinese Real Industrial Sector Profits**

<!-- Start of picture text --> 3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>RMB, billions<br><!-- End of picture text --> <!-- Start of picture text --> Sources: CEIC; Organization for Economic Co-operation and Development national accounts data;<br>UBS; and World Bank national accounts data.<br>Note: Industrial sector coverage is of enterprises with annual revenue above 5 million RMB.<br><!-- End of picture text --> 107. This analysis suggests that without addressing the root causes of income disparity and corporate savings in China, consumption will be hard to increase, and excess savings and the large current account surplus would be hard to eliminate. In order to tackle its current account surplus, China needs to undertake a range of macroeconomic and microeconomic policy reforms.<sup>84</sup> 108. **Corporate governance** should be strengthened and dividend policies reviewed with a view to lowering retained earnings by enterprises. Requiring state-owned enterprises to issue dividends would reduce their retained earnings and possibly also increase households‘ consumption.

109. **Financial sector reform:** With a view to improving access by SMEs to credit, more competition needs to be introduced in the banking system, possibly by strengthening banking supervision, imposing a minimum share of large banks‘ loans granted to SMEs, and liberalizing the entry of small local banks. At this point, large corporations have access to loans at subsidized rates, but do not pay dividends.

110. **Royalty rates for coal companies and gas and other minerals** : Earnings are extremely high, but royalty levels are low. Therefore, royalties on state-owned and private enterprises should be increased.

> 84 Kuijs (2006).

69
this information from the Agricultural Census of 1998. We estimated percentage of agricultural land under LDO leases (including permits and grants). The geographic information including travel time from surveyed DSDs to major urban centers with population of 100,000 or more are drawn from the Geographical Information System (GIS) database. The travel time is estimated using the existing road network and allowing different travel speed on different types of roads.

The most important variable for our instrumental variables analysis is the historical district level malaria prevalence (endemicity) rate. The data on historical malaria prevalence are taken from Newman (1965). The measure for malaria prevalence used in this paper is called Gabaldon’s endemicity index (see column 2 in Table 4, P.34, Newman, 1965). This index is based on the estimates of enlarged spleens in children due to malaria, and is a good indicator of the degree to which malaria is high and permanent in a district. The HIES 2002 has complete employment, education, age, gender, ethnicity and religion. Summary statistics of all variables used in the analysis are provided in appendix Table A.1.

In both farming and non-farm activities, a considerable share of those employed are self employed. In the empirical analysis, we thus make a distinction among five different types of employment. The main categories of employment considered are self-employment in agriculture (farming), wage employment in agriculture, self-employment in non-agriculture, wage employment in the manufacturing sector and wage employment in services. The analysis of HIES shows that among 8,948 adult males in the labor force, 14 percent are farmers, 18 percent are agricultural wage laborers, 17 percent employed in manufacturing, 37 percent in services and 14 percent as self-employed in non-agriculture (Table A.1).

# **5 Empirical Results**

The empirical analysis of structural change focuses on two different dependent variables: one representing the broader choice of agriculture versus non-agriculture, and the other making a distinction among five different types of employment choices (
The paper begins in Section 2 with a review of existing empirical work in two critical dimensions: domain and specification. _Domain_ refers to the type of globalization (products, capital, or labor) and the type of inequality (global inequality between all individuals in the world, inequality between the countries, within-country inequality between individuals, or wage inequality between wage earners) under examination. It also refers to the locus of the study and whether it is a single-country or a multi-country study. _Specification_ is of two types with variables being measured either in levels or in first differences. This subsection also deals with the important issue of interaction between policy variables and initial conditions. In Section 3, we present a simple, yet comprehensive, model of the equilibrium relationship between wage inequality and trade policy. Four categories of wages are examined: for skilled and unskilled labor; and for labor in protected and unprotected sectors. The model allows us to explore the ways in which trade liberalization affects the skill premium, the protection premium, and the union premium. In Section 4 we discuss the variables on wage inequality and trade and labor liberalization that we use in the empirical part. The empirical model in Section 5 is estimated in two different ways that draw on two large sets of data on wage inequality. One is a database of Occupational Wages around the World (OWW) covering the period from 1983 to 1999 and more than 150 countries. The data were collected from the ILO sources (the _October Inquiry_ ), and then put together and analyzed by Richard Freeman and Remco Oostendorp (2000).<sup>2</sup> The other is a database of inter-industrial wage differences created by James Galbraith and associates. This University of Texas Inequality Project (UTIP) database covers on average about 90 countries annually over the period 1975-99.<sup>3</sup>

> 2 The _Occupational Wages around the World_ (OWW) database in available at <u>www.nber.com.</u>

>
.S., Bala, S.K., Islam, G.M.T., 2020. Changes in climate extremes over Bangladesh at 1.5 °C, 2 °C, and 4 °C of global warming with high-resolution regional climate modeling. Theoretical and Applied Climatology 140, 1451–1466. https://doi.org/10.1007/s00704-020-03164-w

- Kirschbaum, D.B., Adler, R., Hong, Y., Hill, S., Lerner-Lam, A., 2010. A global landslide catalog for hazard applications: Method, results, and limitations. Natural Hazards 52, 561– 575. https://doi.org/10.1007/s11069-009-9401-4

- Lee, S., Ryu, J.H., Won, J.S., Park, H.J., 2004. Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Engineering Geology 71, 289–302. https://doi.org/10.1016/S0013-7952(03)00142-X

- Oommen, T., Baise, L.G., Vogel, R., 2010. Validation and Application of Empirical Liquefaction Models. Journal of Geotechnical and Geoenvironmental Engineering 136, 1618–1633. https://doi.org/10.1061/(asce)gt.1943-5606.0000395

- Rabby, Y.W., Li, Y., 2019. An integrated approach to map landslides in Chittagong Hilly Areas, Bangladesh, using Google Earth and field mapping. Landslides 16, 633–645. https://doi.org/10.1007/s10346-018-1107-9

- Reddy, C.S., Pasha, S.V., Jha, C.S., Diwakar, P.G., Dadhwal, V.K., 2016. Development of national database on long-term deforestation (1930-2014) in Bangladesh. Global and Planetary Change 139, 173
### and storms.

Theoretical literature on the economic effects of natural disasters has been gradually evolving. Marto et al. (2018) develop a model that explores the macroeconomic impact of a major natural disaster. Their study focuses on debt sustainability concerns that arise from the need to fully rebuild public infrastructure over the medium term and compares policies, such as building adaptation infrastructure and fiscal buffers, with the post-disaster support provided by donors. Burns et al. (2021) develop a macrostructural model for Jamaica that embeds climate damages from natural disasters and considers different risk management strategies to address them. When it comes to DSGE models, Keen and Pakko (2011) develop a model to investigate the appropriate monetary policy response to natural disasters like Hurricane Katrina. Fern ́andez-Villaverde and Levintal (2018) compare different solution methods for computing the equilibrium of DSGE models with rare disasters, given large nonlinearities that could be triggered by these events. Strulik and Trimborn (2019) examine the welfare effects in the aftermath of natural disasters. Similarly, Cantelmo et al. (2019) study the macroeconomic outcomes and welfare implications in disaster-prone countries and policies to mitigate them. Most of the papers focus on the channel in which disasters destroy durable goods or productive capital. Gallic and Vermandel (2020), on the other hand, build and estimate a DSGE model with a weather-dependent agriculture sector to investigate the adverse weather implications in New Zealand.

The contribution of this paper is twofold. First, it provides evidence of the fiscal implications of natural disasters in a broad sample of countries using different specifications and the disaster measure that is based on physical intensity. As pointed out in Felbermayr and Groeschl (2014), EM-DAT and similar databases comprise disaster intensity measures that are a function of economic development. Reported monetary damages of disasters are higher in richer economies, while human costs are usually lower. As a consequence, this is likely to lead to biased estimates. Second, the paper provides a theoretical explanation of the obtained results by looking at the
# **III. The role of initial conditions, growth, policy, and other factors**

**_Initial conditions count in MDG performance, but subsequent growth and policy also matter greatly--or more._** In most cases, countries that are doing better (those on or close to the target) exhibited favorable starting conditions around 1990 (the reference year). A higher per capita GDP in 1990 is generally associated with better MDG performance (figure 3).

**Figure 3. MDG performance is stronger in countries with good initial conditions**

<!-- Start of picture text --> MDG performance and Initial income conditions MDG performance and Initial institutional<br>7000 conditions<br>6000<br>3.60<br>5000 3.40<br>4000 3.20<br>3.00<br>3000 2.80<br>2000 2.60<br>10000 on targetclose to target 2.402.202.00 on target<br>far from target close to target<br>far from target<br>Source: Authors’ calculations based on data from the World Development Indicators database.<br>Note: A country is ―close to the target‖ if its distance to getting on target (that is, its gap of trajectory) is smaller than the average<br>gap of all lagging countries. Otherwise, it is ―far from the target‖ (that is, its distance is greater than the average gap).<br>GDP per capita, 1990, CPIA index, 1996<br>PPP (constant 2005 international $)<br><!-- End of picture text --> Although there is no perfect indicator of the overall quality of policy and institutions in developing countries, the World Bank’s annual Country Policy and Institutional Assessment (CPIA) provides a broadly consistent framework for assessing country performance on 16 items grouped in four
Page 5 meant to proxy for similar school, household or community characteristics and that appear highly correlated.

14. Part II presents indicators drawn from the school stakeholder survey in Albania that have been previously associated with school performance in other countries. The second part of the report starts by motivating the indicators selected from the survey with a review of the empirical literature that uses the “ _education production function_ ” as its analytical foundation. Disparities in key “inputs” and “outputs” across schools are then presented, and with the important caveats above about the limitations of what can be concluded from retrospective data, statistically significant associations are discussed. Students‟ performance on final year exams, repetition rates, and dropout rates are examined in relation to indicators of school resources (physical and human); teacher effort; parent and community characteristics that shape the context in which students learn; as well as “top down” and “bottom up” accountability structures.

15. Part III delves deeper into the role of local participatory governance structures and parents‟ demand for accountability in school performance **.** As discussed previously, better school performance can depend on parents holding teachers accountable through participatory structures. But even where these structures are in place, they are vulnerable to lack of parent capacity and capture by better informed teachers and school principals. Parent involvement depends on awareness of these institutions, and _their willingness_ to actively participate to try and keep teachers accountable. For this reason, parents‟ demand for accountability is critical. However, it is difficult to measure parent demand for accountability with only _subjective data_ on perceptions about problems in schools, _attitudinal data_ about how things ought to be changed, or _aspirational data_ about their willingness to do something about it. Techniques from experimental economics can overcome these limitations by drawing robust measures of demand for accountability that corroborate the subjective, attitudinal, and aspirational data from surveys. In Part III, the results from lab-type, controlled field experiments conducted with parents and teachers, are combined with survey data to