rafmacalaba/datause-encoder-data huggingface dataset:
In the above equation, is the standard year dummy. We impose two conditions: (1) all year effects sum up to zero, and (2) year effects are orthogonal to a time-trend, which allows us to recover 1∗ and 2∗ . As in Deaton tt (1997), this implies that time trends are attributed to age and cohort effects. We take the first age dummy and DD the first cohort dummy as being equal to zero. The result section presents graphs of the estimated age DD DD coefficients α, which capture increments to the variable attributable to the aging of the household head, relative to household heads aged 25. In the decompositions, we restrict the sample to household heads aged between 25 and 65. Effectively, α in the above equation is a vector with each element corresponding to the isolated effect of each of the ages (between 25 and 65) on the decomposition’s variable of interest.

It is important to recognize that assets are measured at the household level, but households do not have a welldefined “life cycle”. Table 5 shows that most males between the ages of 40 and 64 are heads of the households they are part of. However, this rate drops when moving up the ages, reaching 28.6% between ages 25 and 29. Households headed by a younger individuals may be a selected sample with respect to saving behavior or initial net worth, making this type of household difficult to compare with those with older heads. Therefore, it is important to keep this source of selection bias in mind when interpreting the early segments of the life cycle profiles presented in this paper. Still, households with younger vs. older heads look similar on at least three dimensions: the gender of the head, the level of education of the head, and the fraction of households that are in rural areas (Table 5).

_Table 5 Household characteristics over the life cycle_

|||||A<br>|ge group<br>|||||
|---|---|---|---|---|---|---|---|---|---|
||25-29|30-34|35-39|40-44|45-49|50-54|55-59|60-64|65+|
|% males individuals who are hh|28.6|53.6|74.2|86.3|92.0|92.5|91.9|87.2|69.0|
|head||||||||||
|% females individuals who are hh<br>heads|3.2|6.0|8.4|9.5|10.1|8.8|8.5|7.8|6.8|
|% of hh heads in age range with<br>secondary education or more|29.5|31.3|31.2|29.6|28.1|26.5|25.5|22.6|18.0|
|% of hh with heads in age range that<br>are rural|70.0|67.8|64.6|62.9|62.3|61.8|62.5|65.4|69.3|

# **6. Life cycle wealth accumulation**

## 6.1 Decomposition of average household life cycle wealth and saving profiles

Before examining wealth measures directly, we examine the life cycle profile of saving rates extracted as the residual difference between age effects in income and consumption. Equation (1) is estimated with the logarithm of household income and of household consumption as dependent variables. The difference between the two sets of coefficients on the age dummy variables captures the saving rate increments associated with increasing age by one year.

Figure 2 shows that household income starts outpacing household consumption in the early 40s, allowing the household saving rate to grow by 20 percentage points by age 55. In the 20s and 30s, income and consumption 12
numbers reached a peak due to the Delta variant, whereas other regions began to see Delta rise after June 2021.

# _2.3. Other Data_

**_CDD and HDD:_** Outside temperature is an important determinant of electricity demand, particularly in the building (residential and commercial) sector – with or without COVID (Jovanović et al., 2015; Alberini et al., 2019). Electricity generation fluctuates to balance the demand change induced by hourly, daily and seasonal temperatures. We capture the temperature using cooling degree days (CDD) and heating degree days (HDD).<sup>8</sup> For each country, data on daily temperature come from the weather stations in province capitals; we use the population of provinces as weights to calculate national-level CDDs and HDDs. The population data come from Eurostat and OECD regional databases, and the temperature data are from the NOAA Global Historical Climatology Network Daily (GHCN-D).

**_Fuel prices_** : Monthly fuel prices by major energy hubs (Coal, Australia; Coal, South Africa; LNG, Asia; Natural Gas, EU; Natural Gas, United States; Henry Hub Gas; Dubai Crude; Brent Crude; WTI Crude) are obtained from IMF Primary Commodity Prices. Figure A. 12 illustrates how closely the changes in fuel prices and the stringency index are linked. At the beginning of the pandemic, fuel prices fell quickly due to depressed economic activities directly and indirectly caused by the pandemic. Crude oil prices dropped by 75% from US$80/bbl. to US$20/bbl. As shown in  Figure A. 12, the first round of COVID deaths was at their peak when oil prices were at their bottom, most likely because of uncertainty about the virus and its impact on the economy. The price of oil later rebounded.

Visual inspection suggests that it is the stringency (lockdowns and other measures), rather than the number of cases, which drove the energy prices. Like crude oil, the price of coal dropped

> 8 A degree day compares the mean daily temperatures to a standard temperature of 65° Fahrenheit (18° Celsius). If the average temperature is below the standard temperature (cold weather), the temperature difference is the heating degree days (HDD), which indicates the magnitude of heating needs. On a hot day where the average temperature exceeds the standard temperature of 65 Fahrenheit (18 Celsius), we call the difference in temperature cooling degree days, which indicates the magnitude of cooling needs. The degree days measurement is designed to quantify the demand for energy needed for heating and cooling.

9
- 2 -

least managed, exchange rate. Such policies have, however, often encountered serious problems. It appears that in many<sup>cases a high real</sup> interest rate is required to induce the public to hold domestic<sup>debt.1</sup> If a fall in the interest rate is observed, then there is an<sup>immediate flight</sup> of capital, causing the interest rate to rise once again. The<sup>fact that-</sup> capital flows are highly sensitive to changes in interest<sup>and exchange</sup> rates thus greatly limits the scope of the government in carrying<sup>out</sup> stabilization measures via interest rate management.

Our aim in this paper will be to examine the implications<sup>for</sup> certain key macroeconomic variables, in particular the rate<sup>of inflation,</sup> the interest rate, and tl. rate of growth of real GNP, of reductions<sup>in</sup> public spending. We will, in addition, attempt + terive welfare implications of these reductions. The constraint on theL. Juctions, and, indeed, often their intended target, is the level of foreign<sup>exchange</sup> reserves. To what extent is stabilization of these macrovariables consistt-at with a sustainable foreign reserve position and tolerable consumer welfare? We might consider both tax increases and expenditure reductions as stabilization measures. The experience of most developing countries has been, however, that it is quite difficult to<sup>raise real tax</sup> revenues.<sup>2</sup> We will therefore focus on reductions in government spending.

- 1/ More precisely, we mean that the interest rate deflated<sup>by the expected</sup> rate of devaluation of the domestic currency must be positive and large in order to induce people to hold domestic assets.

- 2/ In addition, methodologies for optimally increasing tax revenues<sup>have</sup> been discussed in detail elsewhere. See, for example, Stern<sup>(1984),</sup> for a useful survey, while Seade (1987) gives an example<sup>of current</sup> empirical work.
working in Peru and 69% in Bolivia. In the SSA region estimated shares are as low as 8% in Madagascar and 11% in Burkina Faso and shares as high as 50% in Nigeria and 62% in Kenya.

Upper-middle-income countries (41% on average) and lower-middle-income countries (37%) had the most work stoppage. High-income countries had 26% of respondents on average stop work, followed by low-income countries at 19%. (We note that the set of countries in our data is not representative of country income groups.) In the LAC countries, respondents were also asked about whether they were planning to return to work if they stopped working. For these countries, we break down the overall share of workers who stopped working by whether they were planning to return to work in Panel (c) of Figure 1. It suggests that the majority of workers who stopped working were planning to return to work, though there is some variation across countries.

In addition to stopping work altogether, we examine measures of reduced payment. Figure 2 shows the share of respondents reporting partial or no payments for work performed among wage employees. This question on partial or no payments for work performed is available mostly in countries in the LAC region. The share reporting partial or no payments in this region ranges from 17% in Chile to 30% in Peru. This indicates that in addition to stopping work, reductions in pay due to reduced economic activity was an important challenge to workers. The workers nominally kept their jobs but were not receiving the full payment for the work performed, either possibly due to some furlough type of arrangements or employers delaying or reducing the pay in response to the crisis. Workers may also have adjusted working hours, but with the HFPS data we cannot measure reduced working hours directly.

The large disruption in the labor market is also apparent from the high share of workers changing jobs during the pandemic (Figure 3). Where data are available, job changing ranged from 2% to 21% in the SSA region and 4% to 14% in the LAC region. This could be an indication that some of the jobs that workers changed from were affected by the pandemic 18
|Sand|Topsoil sand fraction from Harmonized World Soil<br>Database|% wt.|0-94|
|---|---|---|---|
|Clay|Topsoil clay fraction from Harmonized World Soil<br>Database|% wt.|0-50|
|Gravel|Topsoil gravel fraction from Harmonized World Soil<br>Database|% wt.|0-32|
|Carbon|Topsoil Organic Carbon from Harmonized World Soil<br>Database|% wt.|0-35|
|pH|Topsoil pH (H2O) from Harmonized World Soil<br>Database|-log(H+)|3-8|
|Lime|Topsoil Calcium Carbonate from Harmonized World<br>Soil Database|% weight|0-11|
|Assets|The current value of all durable goods such as<br>machinery above a value of 50 USD that a household<br>possesses excluding buildings andland.|1000<br>USD ppp|1-129|
|AgLand|The totalagricultural land that ahousehold owns.|hectares|0-612|
|Education|The average years of education of all household<br>members aged between 16 and 60 years.|Years|0-27|

27
31 faster than demand (due largely to education policies of the previous decade), there was a sharp drop in the real wage of skilled labor. These factors, together with the removal of quantitative restrictions whose quota rents had gone to wealthier Moroccans, helped to reduce income inequality.

Much the sane pattern developed in Cote d'Ivoire with the brunt of adjustment falling on urban workers who lost their jobs in the private and parastatal sectors. If one assumes that the unemployed urban workers received some support from those that kept their jobs, then the per capita incomes or urlian workers fell the most. On the other hand, government employees (the better-off socioeconomic group) managed to retain their jobs although their real wages fell. In rural areas, the little evidence available from household surveys suggests an increase in per capita expenditures for non-food items. Lambert, Schneider, and Suwa also suggest that the inicrease in food prices relative to nonagricultural products must have helped sustain rural income levels.

T'hus both countries managed to reduce the rural-urban income gap during adjustment, largely by improving agricultural incentives. Adjustment measures contributed in two ways: devaluation of the real exchange rate, which stimulated agricultural exports, a particularly significant effect in - Morocco, and liberalization of agricultural markets reduction of price controls on food, reduction of taxation of agricultural exports, the abolishment of public monopolies in fertilizers and agricultural exports in Morocco, and the commercialization of rice in Cote d'Ivoire.9 Unfortunately, these positive effects of adjustment on supply and on the rural-urban primary income gap were not matched by trends in social expenditures, especially in COte d'Ivoire, which had to rely heavily on cuts in public expenditures. In C6te d'Ivoire, per capita expenditures on education fell, and primary school enrollment grew slower than primary school population. Not only social expenditures, but transfers to the poor were cut as well, including subsidies on items that constituted a significant proportion of the consumption of the poor, such as rice, electricity, water. Morocco also
- Size: do larger firms have greater incentives to invest in energy efficiency?

- Energy in total production costs: do energy-intensive firms (i.e., firms with higher share of energy costs in total production costs) have greater incentives to invest in energy efficiency?

- Ownership: are private firms more energy efficient than public firms?

- Employment: are labor-intensive firms less energy efficient than capital-intensive ones?

- Financing: have high upfront capital costs hindered the adoption of energy efficiency technologies?

- Split incentives: are rented spaces where landlords pay energy bills less energy efficient than self-owned spaces?

- Knowledge: is lack of knowledge about energy efficient technologies one of the key barriers?

- Technical barriers: are there any technical barriers preventing scaling-up of energy efficiency measures?

- Existing rules/regulations: have existing rules and regulations helped improve energy efficiency?

- Firm’s bureaucracy: have a convoluted and complex internal decision process slowed down adoption of energy efficiency measures?

The study employed a sample survey of 500 commercial/service and industrial establishments throughout the country done in 2012. The data collected were then used in a cumulative Logit model to estimate the importance of the various barriers to the adoption of energy efficiency measures in Ukraine. Our analysis shows financial barriers (e.g., high upfront costs or high costs of financing) are the key factors impeding firms’ investments in energy efficiency measures in Ukraine. Knowledge and technical barriers follow this. We find mixed results regarding split incentives, whereby the building is rented and/or jointly owned. Contrary to general intuition, the study does not find evidence to support the energy-intensity hypothesis that assumes energy-intensive firms are more likely to adopt energy efficient technologies compared to non-energy-intensive firms. Instead, firms with higher revenue per unit of energy consumption tend to invest more on energy efficiency improvements. The separation of industrial and commercial firms allow us to introduce heterogeneity among sectors that lead us to 4
|Indonesia Power Transmission Development Project|Muchsin Chasani AbdulQadir|
|---|---|
|Indonesia Power Transmission Development Project|Muchsin Chasani AbdulQadir|
|Indonesia Power Transmission Development Project|Muchsin Chasani AbdulQadir|
|Indonesia Power Transmission Development Project|Muchsin Chasani AbdulQadir|
|Indonesia Power Transmission Development Project|Muchsin Chasani AbdulQadir|
|PL - GIS - GREEN INVESTMENT SCHEME|Nathalie Weier Johnson|
|PL - GIS - GREEN INVESTMENT SCHEME|Nathalie Weier Johnson|
|PL - GIS - GREEN INVESTMENT SCHEME|Nathalie Weier Johnson|
|PL - GIS - GREEN INVESTMENT SCHEME|Nathalie Weier Johnson|
|PL - GIS - GREEN INVESTMENT SCHEME|Nathalie Weier Johnson|
|PL - GIS - GREEN INVESTMENT SCHEME|Nathalie Weier Johnson|
|AF-CZ GIS/AAU TRADE|Nina Rinnerberger|
|HaJia Railway|Hua Tan|
|HaJia Railway|Hua Tan|
|HaJia Railway|Hua Tan|
|HaJia Railway|Hua Tan|
|HaJia Railway|Hua Tan|
|HaJia Railway|Hua Tan|
|HaJia Railway|Hua Tan|
|HaJia Railway|Hua Tan|
|HaJia Railway|Hua Tan|
|HaJia Railway|Hua Tan|
|HaJia Railway|Hua Tan|
|HaJia Railway|Hua Tan|
|DR StrengtheningGender Statistics|Juan Carlos Parra Osorio|
|DR StrengtheningGender Statistics|Juan Carlos Parra Osorio|
|DR StrengtheningGender Statistics|Juan Carlos Parra Osorio|
|DR StrengtheningGender Statistics|Juan Carlos Parra Osorio|
|DR StrengtheningGender Statistics|Juan Carlos Parra Osorio|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
|DJ-Rural CommunityDevelopment and Water Mobilization Project|(Mohamed Medouar|
# **Contents**

|1. Growing but Unhappy: Making Sense of the Puzzle............................................................................... 3|
|---|
|2. Conceptual Framework for the Analysis of Perceptions of Mobility....................................................... 5|
|3. Individual and National Level Data........................................................................................................ 8|
|4. Perceptions of Economic Mobility and Their Correlates......................................................................... 9|
|a)<br>What are the perceptions of economic mobility observed in ECA?................................................ 9|
|b)<br>What is the socio-demographic profile of those with perceptions of downward mobility?............ 12|
|c)<br>What variables are correlated with perceptions? A multivariate analysis........................................ 14|
|d)<br>Are perceptions similar across sub-regions?.................................................................................. 20|
|5. Conclusions.......................................................................................................................................... 21|
|References................................................................................................................................................ 23|
|Appendices.............................................................................................................................................. 27|
|A1. Sample Characteristics................................................................................................................... 27|
|A2. Regression methodology................................................................................................................ 33|
|A3. Robustness checks......................................................................................................................... 38|
|A4. Perceived Movements by Individual Characteristics: Heads and Other Member............................ 42|

2 | P a g e
# **ANNEX. Definitions and Sources of Data**

# **1.Definitions**

# **Patents**

_USPTO_ : Property right granted by the Government of the United States of America to an inventor “to exclude others from making, using, offering for sale, or selling the invention throughout the United States or importing the invention into the United States” for a limited time in exchange for public disclosure of the invention when the patent is granted.

_EPO_ : Legal title granting its holder the exclusive right to make use of an invention for a limited area and time by stopping others from, amongst other things, making, using or selling it without authorization. A European patent can be obtained by filing a single application in one of the official languages of the European Patent Office (English, French or German) in a unitary procedure before the EPO and is valid in as many of the contracting states as the applicant cares to designate. A European patent affords the same rights in the designated contracting states as a national patent granted in any of theses states.

The following definitions were obtained from UNESCO’s Statistical Yearbooks:

# **Research and experimental development (R&D)**

In general R&D is defined as any creative systematic activity undertaken in order to increase the stock of knowledge, including knowledge of man, culture, and society, and the use of this knowledge to devise new applications. It includes fundamental research, applied research, and experimental development.

_Fundamental Research_ : Any activity directed towards the increase of scientific knowledge or discovery of new fields of investigation, without any specific practical objective.

26
<!-- Start of picture text --> 0 5000 10000 15000 20000<br>benefits received from EGPP<br>250<br>200<br>150<br>Frequency<br>100<br>50<br>0<br><!-- End of picture text --> Note: Kernel density of EGPP benefits received over the past 12 months. Source: EGPP sample.

At any rate, given the substantial size of the benefits, EGPP participation is expected to have greater implications on the economic behavior of participating households than experienced in other social assistance programs. Additional incomes would change the pattern of consumption and risk-coping mechanisms, while the employment opportunities would influence households’ decision on labor supply.

# **4. Empirical Strategy**

## **4.1. Identification**

To measure the effect that participation in EGPP has on the outcomes of interest including consumption for poor households, taking household and locality characteristics into consideration, we consider the following regression model, where R��� denotes an outcome (e.g., the monthly consumption) of household �, in a _Union_ � at time �, H is a vector of the household characteristics, �� and �� are _Union_ (time invariant) and time 18
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# **Contents (Appendix)**

|**A FAT survey**|**48**|
|---|---|
|A.1 Sampling frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>48|
|A.2 Implementation and quality control . . . . . . . . . . . . . . . . . . .|. . . .<br>56|
|A.3 Survey Weight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>57|
|**B Details on the technology measures**|**58**|
|B.1 Sector-specifc business functions<br>. . . . . . . . . . . . . . . . . . . .|. . . .<br>58|
|B.2 Hybrid technology structures. . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>64|
|B.3 EXT index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>67|
|B.4 Regional-level analysis . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . .<br>68|
|**C Additional results on cross-frm diferences in technology**|**70**|
|**D Robustness to using indices based on logarithmic transformations**|**of tech-**|
|**nology sophistication rankings**|**77**|
|**E Additional results on within-frm technology**|**84**|
|**F Additional results on technology and productivity**|**92**|
|**G Detailed acknowledgments**|**106**|

47