rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Average Annual<br>GDP-Employment Elasticities<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>a eee<br><0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text --> <!-- Start of picture text --> Average Annual<br>GDP-Employment Elasticities<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>— Zz<br><0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text --> <!-- Start of picture text --> Average Annual<br>GDP-Employment Elasticities<br>35<br>30<br>%<br>20<br>15<br>10<br>0<br><0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text --> <!-- Start of picture text --> Growth Decomposition<br>GDP-Employment Elasticities<br>45<br>40<br>35<br>30<br>25<br>20<br>45<br>10<br>0 | ——<br><0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text -->

<!-- Start of picture text --> Growth Decomposition<br>GDP-Employment Elasticities<br>45<br>40<br>35<br>30<br>25<br>20<br>45<br>10<br>— —<br>7 <0.00|(0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text -->

<!-- Start of picture text --> Growth Decomposition<br>GDP-Employment Elasticities<br>35<br>30<br>*<br>20<br>15<br>10<br>0 |<br>| <0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75]|(0.75, 1.00] > 1.00<br><!-- End of picture text -->
younger than five years of age. Figure 3 also shows that over time the size-age gap for the 40+ year old plant has shrunk starting from 4.91 in 1983/84 to 2.62 in 2004/05. In unreported figures we find similar results when we use a weighted average across all industries in each age bin with the weights being the value added shares of each industry.

The graphs show a much faster growth rate for Indian plants than found by Hsieh and Klenow (2012) who document a flat plant lifecycle in Indian manufacturing, including in just the formal sector, using only data form the 1994/95 Census. The differences are particularly stark later in the lifecycle where we find older plants to be much larger while they find older plants to be declining in size.

# **Insert Table 3 here**

In Table 3, we use pooled OLS regressions to explore the age effects in detail. In col. 1, we regress Firm Size on age dummies and find all the age coefficients to be positive and significant suggesting that older firms are larger than younger firms. For ease of comparison with youngest firms, we add the constant (which presents the average mean size for firms younger than 5 years old) to each of the age coefficients and divide the sum by the constant. These ratios are reported in bold in square brackets below the coefficient and standard errors. Thus we see that the average 40+ year old firm is 3.775 times the size of the average firm that is younger than five years old. In col. 2, we repeat the analysis in Col. 1 using 3-digit NIC industry dummies and state dummies. While we are not allowing for time varying changes across states and industries, the dummies help us control for any time-invariant unobserved heterogeneity across states and industries. Once again we find that all the age coefficients are positive and significant. Figure 4 plots the coefficients for the age dummies and the 95% confidence bands and shows that even 18
Panel B. PPI (Percent)

||1970-2016|1970-85|1986-2000|2001-16|
|---|---|---|---|---|
|Factor||All cou|ntries||
|Global|42.1<br>(41.1, 15.3 - 61.7)|52.3<br>(45.2, 22.1 - 67.5)|5.2<br>(9.2, 2.3 - 11.2)|67.2<br>(61.4, 55.1 - 76.4)|
|Group|6.6<br>(3.2, 0.9 - 8.8)|7.3<br>(12.6, 2.2 - 18.3)|16.7<br>(22.5, 7.1 - 29.9)|3.9<br>(6.1, 1.3 - 8.1)|
|Global + Group|<br>49.9<br>(45.2,19.7 - 67.3)|<br>65.0<br>(54.8,32.9 - 81.0)|<br>24.9<br>(30.0,16.1 - 42.7)|<br>71.3<br>(63.9,54.9 - 82.1)|
|Factor||Advanced|economies||
|Global|56.1<br>(48.4, 33.9 - 63.7)|62.1<br>(50.0, 27.5 - 70.7)|4.0<br>(9.0, 2.3 - 8.3)|71.7<br>(68.3, 65.8 - 77.0)|
|Group|<br>4.5<br>(7.7, 1.1 - 9.6)|<br>7.1<br>(13.2, 3.2 - 23.1)|<br>20.7<br>(28.6, 11.9 - 45.8)|<br>5.9<br>(6.8, 1.1 - 8.4)|
|Global + Group|60.6<br>(51.6,31.2 - 68.1)|71.7<br>(58.1,38.9 - 85.1)|35.8<br>(34.5,19.1 - 52.1)|77.3<br>(69.1,66.8 - 87.8)|
|Factor||EM|DEs||
|Global|16.2<br>(28.3, 11.1 - 40.4)|33.7<br>(36.9, 12.9 - 57.8)|6.9<br>(9.8, 3.1 - 11.6)|49.4<br>(49.0, 27.6 - 63.4)|
|Group|<br>1.4<br>(4.7, 0.8 - 4.0)|<br>11.4<br>(11.6, 2.2 - 16.6)|<br>13.2<br>(11.7, 5.2 - 15.9)|<br>3.5<br>(4.8, 2.4 - 7.4)|
||22.6|50.3|20.0|57.6|
|Global + Group|(33.0,13.5 - 41.0)|(48.4,31.5 - 70.0)|(21.4,14.9 - 26.6)|(53.9,41.2 - 66.6)|

Notes: The contribution of global and group inflation factors is estimated over the three subsample periods with the dynamic factor model (two-factor model) for each of the five different inflation measures: import prices, PPI, headline, GDP deflator, and core CPI. The data set includes 38 countries (25 advanced economies and 13 EMDEs) except import prices for 21 countries (17 advanced economies and 4 EMDEs). The first argument in the first row indicates the unweighted median across countries. The first argument in the second row (in parentheses) is the mean variance share across countries. The second and third arguments in the second row (in parentheses) indicate the interquartile range (25th and 75th percentiles) of variance shares. CPI = consumer price index; EMDEs = emerging market and developing economies; GDP = gross domestic product; PPI = producer price index.

29
# and widespread use of new technologies.<sup>9</sup>

In addition to supply effects, due to the many unknowns, epidemics and pandemics can weigh on productivity through demand-side channels, by raising uncertainty, eroding consumer and business confidence, weakening investment and depressing demand (Dieppe, 2020).

Table 1: Number of episodes by type of events

||AEs|EMDEs|LICs|World|
|---|---|---|---|---|
|**Natural disasters**|**1031**|**4699**|**1098**|**5730**|
|Climate disaster|843|3054|651|3897|
|Biological disaster|50|953|369|1003|
|Geophysical disaster|138|692|78|830|
|**Wars**|**45**|**191**|**55**|**236**|
|Intra-state war|45|191|55|236|
|External war|45|68|9|113|
|**Financial crises**|**54**|**390**|**83**|**444**|
|Banking crisis|34|113|27|147|
|Currency crisis|18|208|44|226|
|Sovereign debt crisis|2|69|12|71|

Note: Natural disasters include climate, biological, and geophysical disasters (EM-DAT). Wars include intra-state and external (extra-state and inter-state) wars (COW and PRIO). Financial crises include banking crisis, currency crisis, and sovereign debt crisis (Leaven and Valencia 2018). Sample is restricted to the observations where labor productivity growth data exist for the period 19602018. For each country-year pair, the episode dummy of a specific type of event is 1 if the event occurs at least once ( _≥_ 1), 0 otherwise. The total number of episodes (in bold) for each group of events (all financials, all disasters, all wars) may include events that occur simultaneously. The events are defined in **Annex A** . AEs = advanced economies, EMDEs = emerging markets and developing economies (including low income countries), LICs = low income countries.

# **2.1.2 Stylized facts for natural disasters**

**Increase in frequency of climate and other natural disasters.** Climate disasters accounted for around 70 percent of natural disasters during 1960-2018, occurring twice as often as biological and geophysical disasters combined ( **Figure 2** ). From 1960-79 to 2000-18, there was a large increase in the number of natural disaster episodes.<sup>10</sup> Increases occurred in all three categories, but most

> 9The overall impact of a natural disaster depends partly on initial economic conditions. A disaster may be more economically damaging in periods of high employment and capacity utilization because the increase in output needed for reconstruction may not be feasible, and the increase in demand generated may induce inflation. By contrast, a disaster that occurs when the economy is depressed may cause less economic damage as the stimulus effect of reconstruction will activate unused resources (Cuaresma et al., 2008; Hallegatte and Vogt-Schilb, 2019; Skidmore and Toya, 2002).

> 10To some degree, the increase in the number of recorded events may reflect improved measurement of natural disasters, particularly for small events.

7
because of the nature of the data. Panel A uses three variables that reflect conditions around the year of birth of the child. Note that women report their birth history, in what year the child was born, and the baby’s health. She reports this information in the survey year, but retroactively, recollecting the year of birth. Therefore, we deem that the birth year mining activity is more relevant than the survey year mining activity for the outcomes in Panel A. In particular, we test if an active mine nearby in the birth year of the child influences the baby’s size at birth, infant mortality, and the number of antenatal visits. In Panel B we use the main specification of mining activity in the survey year, as these variables are more reflective of current conditions in the survey year than past conditions around the time of the birth.

Panel A of table 5 shows that infants in active mine communities are less likely to be born large, and that the mother had insignificantly fewer prenatal visits. However, infant mortality decreases by 4 percentage points. Splitting the sample by gender, we note that this decrease is only statistically significant for boys at an effect size of 6.6 percentage points.

**Table 5 OLS estimates of birth outcomes, infant survival, and child health in the DHS individuallevel analysis**

|PANEL A||size at bir|th|infant mor|tality (<12|months)|ante|natal visits|
|---|---|---|---|---|---|---|---|---|
||small|average|large|all|boys|girls|# visits|at least 1|
|active*mine|0.022|0.053|-0.075*|-0.041*|-0.066**|-0.020|-0.151|-0.007|
||(0.028)|(0.041)|(0.041)|(0.022)|(0.030)|(0.035)|(0.331)|(0.028)|
|mine|-0.010|0.071**|-0.061**|0.004|0.008|0.001|0.153|0.000|
||(0.019)|(0.028)|(0.030)|(0.015)|(0.020)|(0.024)|(0.241)|(0.019)|
|active|-0.010|0.054**|-0.044|0.002|0.014|-0.012|0.012|0.002|
||(0.016)|(0.026)|(0.027)|(0.014)|(0.022)|(0.018)|(0.209)|(0.012)|
|Observations|6,771|6,771|6,771|5,356|2,718|2,638|5,704|5,704|
|R-squared|0.031|0.054|0.059|0.135|0.160|0.152|0.186|0.062|
|Mean of dep var.|0.136|0.359|0.505|0.073|0.08|0.066|5.79|0.941|
|PANEL B|in the|last 2 week|s, had:|anthropom|etrics (WH|O) in sd|has||
||fever|cough|diarrhea|ht/a|wt/a|wt/ht|health ca|rd|
|active*mine|-0.035|-0.061*|0.042|-3.532|-5.208|-0.641|0.014||
||(0.037)|(0.033)|(0.027)|(11.472)|(9.283)|(8.948)|(0.027)||
|mine|-0.002|-0.006|-0.038|-0.828|3.481|3.853|-0.006||
||(0.031)|(0.028)|(0.024)|(10.385)|(8.574)|(7.468)|(0.022)||
|active|0.023|-0.003|-0.033**|-1.904|5.265|9.433*|0.009||
||(0.020)|(0.020)|(0.016)|(5.942)|(5.304)|(5.183)|(0.012)||
|Observations|6,246|6,257|6,262|5,627|5,627|5,727|6,378||
|R-squared|0.024|0.043|0.024|0.136|0.080|0.036|0.084||
|Mean of dep var.|0.211|0.221|0.164|-101.6|-60.3|-16.7|0.913||

19
- Fernald, John G (1999). “Roads to Prosperity? Assessing the Link between Public Capital and Productivity”. _American Economic Review_ 89.3, pp. 619–638.

- Freedman, Seth M et al. (2023). _Designing Difference in Difference Studies With Staggered Treatment Adoption: Key Concepts and Practical Guidelines_ . Working Paper 31842. National Bureau of Economic Research.

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- Jaimovich, Esteban (2019). “Roadways, Input Sourcing, and Patterns of Specialization”. _European Economic Review_ 120, p. 103319.

- Jedwab, Remi and Adam Storeygard (2021). “The Average and Heterogeneous Effects of Transportation Investments: Evidence from sub-Saharan Africa 1960–2010”. _Journal of the European Economic Association_ jvab027.

- Kahn-Lang, Ariella and Kevin Lang (2020). “The Promise and Pitfalls of Differences-in-Differences: Reflections on 16 and Pregnant and Other Applications”. _Journal of Business & Economic Statistics_ 38.3, pp. 613–620.

- Kassem, Dana (2021). _Does Electrification Cause Industrial Development_ . Working Paper.

- Klein Goldewijk, Kees, Arthur Beusen, Gerard Van Drecht, and Martine De Vos (2011). “The HYDE 3.1 Spatially Explicit Database of Human-Induced Global Land-Use Change over the Past 12,000 Years”. _Global Ecology and Biogeography_ 20.1, pp. 73–86.

- Lee, Kenneth, Edward Miguel, and Catherine Wolfram (2020). “Does Household Electrification Supercharge Economic Development?” _Journal of Economic Perspectives_ 34.1, pp. 122–44.

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26
unskilled male labor in urban areas, with smaller declines in the case of females. The institutions index also moved against the urban unskilled. The story of wage gains by the rural unskilled relative to their urban counterparts is, therefore, one of changing patterns of industrial employment rather than one of changing relative supplies (as was the case within the rural sector).

# **IV. State‐level SDI Analysis**

We now compute changes in mean log wages, ∆����, ∆����, and ∆���� separately for each major state or group of adjacent states.<sup>5</sup> Our “data set”, therefore, consists of 448 = 2 x 2 x 8 x 14 observations for 2 decadal intervals, 2 sectors (rural/urban), 8 demographic groups, and 14 states. Note that in treating a state as, for all intents and purposes, a distinct labor market, we are assuming that changes in, say, labor supply within a given state are not driven by inter‐state migration. This assumption seems reasonable as a first approximation given India’s historically low mobility <u>(see Hnatkovska and Lahiri, 2013).</u>

**Figure 7:  Bivariate relationship between state/group wage changes and supply shifts**

> 5 State groups consist of Chhattisgarh with Madhya Pradesh (called Madhya Pradesh); Uttaranchal with UP (called Uttar Pradesh); Jharkhand with Bihar (called Bihar); Seven Sisters in the Northeast with Sikkim (called seven sisters); Goa, D & N Havelli, D& Diu with Maharashtra; A&N Island with West Bengal (called West Bengal); Lakshadweep with Kerala (called Kerala) and Pondicherry with Tamil Nadu (called Tamil Nadu).  Finally, Haryana, Punjab, Himachal Pradesh, Delhi, and Chandigarh and combined into Northern states.

12
Bayona-Sáez R&D Eureka grants achieve competitive gains as a first GMM-Arellano Bond Estimators, with random subsample and Garciafor control group. Marco (2010) Eureka Program research project will have a positive effect on performance in participating firms.

BascavusogluInvestments in innovation promoted through tax Moreau and incentives, matching grantsand reimbursable loan Colakoglu schemes from National Systems of Innovation. Analysis of (2010) evaluating the determinants of Turkish SMEs’ innovative capabilities Knowledge production function (Griliches, 1979), whichmodels the "functional relationship between the inputs of the knowledge production and its output that is economically useful new technological knowledge". The patents do not play any explicit economic role in Griliches’ model. They are just an indicator of innovative activity. OLS model used corrected for heteroskedasticity and clustering FE. Non-experimental.

Garcia and Austria incentives are only granted for eligible Mohnen expenditures, i.e. those that are considered as valuable to (2010) the economy Cerulli and Fondo per le Agevolazioni della Ricerca (FAR) managed Poti (2010) by the Italian Ministry of Research that is one of the two main pillars over which national R&D and innovation supporting policies are based. Contains both bottom-up and top-down measures as well as basic and more applied research projects.

Structural model explaining the determinants of various sources of government support and their effects on R&D and innovation output. Government support, R&D and innovative sales are all three endogenous. Estimation made through asymptotic least squares using 2 stages (probit and tobit). Non-experimental.

Measuring the presence or absence of additionality. Structural model identifies the optimal level of R&D investment as the point in which marginal rate of returns (MRR) and marginal capital costs (MCC) associated to R&D investments. Econometric methodology used to evaluate the input (R&D outlay) and output (patents) additionality of the FAR fund is based on the literature on “program evaluation”. Models estimated with OLS pooled sample. For the output additionality equation a Poisson regression was used. Non-experimental Simon (2010) Assess R&D policies at the macro and manufacturing Simple growth model, using pooled OLS. Nonlevels experimental.

45
<!-- Start of picture text --> 70<br>60<br>so<br>—+— European Union (28)<br>“ —— United States<br>=<br>328 40 —+———SsChinaSwitzerlan &Hand Hong Kong<br>a}2 —— United Arab Emirates<br>s2s 30 —SaudiKorea, ArabiaRep.<br>——Japan<br>20 Singapore<br>India<br>:0 ES== ~ : ————— a<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text -->
**Figure 3: Number of Active Coffee Exporting Firms**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br>Source: Uganda Coffee Development Authority<br><!-- End of picture text -->

**Figure 4: Herfindahl Concentration Index of Coffee Exporters**

<!-- Start of picture text --> 12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003<br><!-- End of picture text -->

**Source: Authorʹs Calculations based on  Uganda Coffee Development Authority data**

**_— 23 —_**
variable assumed to be distributed identically and independently across individuals according to the standard normal distribution. We estimate equation (14) by ordinary least squares.

# **c. Decomposition approach**

We estimate models (13) and (14) for the first and final year, and simulate the impact of occupational changes by substituting the estimated parameters for one year with the parameters of the other year. We then use this hypothetical income to calculate a series of distributional statistics and compare them against those estimated using the actual income data.

 **C.1** Accounting for the impact of occupational changes To carry out this simulation, we assign the estimated coefficients of equation (13) in year t′ to the household survey in year t . To allow individuals to change occupations in the simulation, we need the � residual terms ε� of the multinomial logit in equation (13), which are unobserved. Following Inchauste et al. (2014) and Train and Wilson (2008), we draw the residuals from an extreme value distribution in a way that is consistent with observed choices. The simulated earnings for individual i are given by: The difference between this simulated distribution and the earnings distribution observed in year _t_ is accounted by the change in the occupational structure, which in turn is due to changes in structural parameters ( Φ ) of the occupation model. This is the main focus of the decomposition in this paper. Note that the occupation structure can also shift because the exogenous characteristics change. However, while the impact of shifting characteristics can be used to decompose the distribution of occupations, one cannot separate the _direct_ impact of shifting characteristics on earnings from the _indirect_ impact of shifting characteristics on occupations and from these on earnings. This simultaneous direct and indirect impacts of shifting characteristics are thus jointly accounted in step C.2 below as follows.

 **C.2** Accounting for the impact of changes in relevant exogenous characteristics In order to study the role played by changes in individual and household characteristics which are _exogenous_ to the occupational choice model (such as education, gender and age structure of the population) we perform a reweighting exercise as the one proposed by Bourguignon et al. (2008). First of all, we split exogenous characteristics ( Z� for the occupational choice model and X� for the earnings equation) into a group of relevant, common characteristics ( W� : education, gender and age ), which are 12
ANGOLA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE to 22 percent over the same year (table 13), particularly in urban areas. Use of traditional latrines also increased from 16 to 21 percent between 2001 and 2007, mostly in rural areas, where 2.2 percent of the population has been gaining access to this form of sanitation each year (figure 16b).

**Figure 16. Angola has made important progress in the sanitation sector but not in the water supply sector**

Population gaining access per year, 1998–2006 <!-- Start of picture text --> a. Water<br><!-- End of picture text --> <!-- Start of picture text --> b. Sanitation<br><!-- End of picture text --> <!-- Start of picture text --> 7 7<br>6 6<br>5 5<br>4.7 4.7<br>4 4<br>3 2.9 3 2.9<br>2 2<br>1 0.9 1 0.9<br>0 0<br>-1 National Urban Rural -1 National Urban Rural<br>-2 -2<br>-3 -3<br>-4 -4<br>Piped Water Standposts Septic tanks Improved Latrines<br>Wells/Boreholes Surface Water Traditional Latrines Open Defecation<br>Annualized Growth Annualized Growth<br>Population gaining access per year (%) Population gaining access per year (%)<br><!-- End of picture text --> _Source:_ WHO Joint Monitoring Program 2010, from the 2001 Multiple Indicator Survey and 2007 Malaria Indicator Survey.

# **Challenges**

Angola still has the highest rate of diarrheal disease in the world, with 114 years of life lost to diarrheal diseases for every 1,000 Angolans. In 2006 a cholera epidemic hit Luanda, affecting 23,000 people and causing almost 300 deaths (LUPP 2007). Contaminated water, inadequate storm-water drainage, deficient operation of the limited sewer system, and high reliance on open defecation have resulted in high—and steadily increasing—rates of water- and excreta-related diseases (USAID 2009). The situation is particularly bad in periurban informal settings and in refugee camps, where more than a million internally displaced persons still reside.

Angola’s urban population is growing at an increasingly fast rate (4.7 percent per year), and the current piped and stand-pipe water supply and management system cannot keep up with the demand for domestic water consumption. This gap is increasingly being filled by private water vendors selling largely untreated water. The supply of water from tankers rose from 10 percent of the population in 2001 to 37 percent in 2007 (figure 17). Indeed, in periurban areas of Luanda, 70 percent of residents purchased their water from water vendors. This water is extremely expensive, with prices varying from $4/m<sup>3</sup> close to the distributional area of the water tank to $20 in more distant areas (Cain, Daly, and Robson 2002), leading to a significant increase in the number of small-scale operators (figure 17). Most of the water comes from the ANGOMENHA filling station and is untreated, posing serious health risks. Tanker drivers who buy 29