rafmacalaba/datause-encoder-data huggingface dataset:
# **6. Conclusion**

In this paper, we have estimated the impact of COVID-19 on global inequality and poverty in 2020. Due to sparsity of income and consumption data for 2020 from traditional household surveys, we have relied on a combination of alternative data sources. In particular, we use household survey data available for 20 countries; for a further 8 countries, we use tabulated income data reported by national statistical offices; next, for 37 low- and middle-income countries, we use information from High-Frequency Phone Surveys together with pre-pandemic welfare vectors and sectoral growth rates to model the impact of the pandemic along the countries' income distribution; for a further 26 countries, we rely on data from country studies in the literature. Finally, for countries without the aforementioned data sources, we rely on per capita growth observed in national accounts.

We find that the pandemic caused the largest single-year increase in global inequality and poverty since at least 1990. On both fronts, the pandemic erases at least three years of progress. Concretely, we find that the global Gini index in 2020 increased by 0.7 point (or around 1%) compared to 2019 and that the number of people living below the international poverty line of $2.15-a-day PPP USD increased by 90 million people. The increase in poverty is driven by the countrywide economic shocks that almost all countries experienced. The increase in the Gini is driven by poorer countries facing larger economic shocks from the pandemic. Changes to inequality within countries, on the other hand, counteracted the increases to global inequality and poverty as many countries, particularly populous ones, experienced a decline in inequality. If the pandemic had hit all people within countries equally, its impact on global inequality would have been even larger. In low-income countries, this is likely due to the pandemic not hitting rural areas (where the poor predominantly live) as forcefully, while in high-income countries this is likely due to the extensive social protection programs implemented in 2020.

All our results relate to household disposable income or consumption expenditure for 2020. It is likely that with other measures of welfare, or with the same measure of welfare beyond 2020, the results would be different. Our results, for example do not speak to what happened to wealth inequality or health inequality during 2020. Our results also do not speak to what happened in 2021 and beyond, where data is even more sparce. It is likely that inequality in vaccine access and take-up between countries allowed wealthier countries to recover faster, reinforcing the increase in between-country inequality experienced in 2020. It is also likely that some wealthy countries stopped their emergency social protection programs in 2021, leading to increases in withincountry inequality.

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as a contribution to that debate. It is about the proper balance between social and non-social determinants of behavior (endowments, technology, and incentives).

The ability of so many to ignore the social determinants of behavior, in spite of the wealth of evidence for them, is testimony to the importance of two of the concepts presented here—mental models and confirmatory bias. The irrational devotion to the model of rationality provides further cause for skepticism about rationality. As this paper and the rich literature upon which it draws argue, there are alternative models of human behavior, with markedly different implications for how economic systems function. Economic research on the social determinants of preferences, perception, cognition and, thus, behavior is still young. The rapidly growing body of work suggests the fruitfulness of more realistic models of human behavior for explaining outcomes and designing policies.

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# **Table B.3. The list of selected variables in Lasso and Elastic Net models, Tanzania**

||**LASS**|**O**|**Elastic**|**Net**|
|---|---|---|---|---|
||**Non-standardized**|**Standardized**|**Non-standardized**|**Standardized**|
|Head has primary education|-0.050|-0.025|-0.049|-0.024|
|Head has secondary ordinary education|0.024|0.010|0.025|0.010|
|Head has secondary advanced education and higher<br>|0.003|0.001|0.004|0.001|
|Household size|-0.051|-0.167|-0.050|-0.164|
|Dependency Ratio|-0.023|-0.021|-0.023|-0.022|
|Gender Ratio|-0.046|-0.051|-0.046|-0.050|
|Household Head worked in unpaid apprentice in the last 12 months|0.048|0.003|0.045|0.003|
|<br>Household Head worked in farm in the last 12 months|-0.008|-0.004|-0.008|-0.004|
|Household Head employed in mining, manufacturing, construction|-0.014|-0.003|-0.012|-0.003|
|Household Head employed in retail, transportation|0.105|0.028|0.103|0.027|
|Household Head employed in information and communication|0.209|0.020|0.208|0.020|
|Household Head employed in technical, administrative, education|0.084|0.019|0.083|0.019|
|Proportion of adult males that worked for a wage, salary, or commission|-0.053|-0.025|-0.051|-0.024|
|Proportion of adult males that engaged in casual/ganyu labor in the last 12 months|-0.003|-0.001|-0.000|0.000|
|Proportion of adult males that worked in unpaid apprentice in the last 12 months|0.091|0.005|0.083|0.005|
|Proportion of adult males that worked in farm in the last 12 months|-0.004|-0.002|-0.005|-0.002|
|Proportion of adult males that worked in electricity or water supply|0.000|0.000|||
|Proportion of adult males that worked in information and communication|0.075|0.006|0.072|0.006|
|<br>Overcrowded|-0.032|-0.043|-0.033|-0.045|
|Household dwelling roof materials|-0.092|-0.030|-0.091|-0.030|
|Household dwelling floor materials|-0.042|-0.021|-0.043|-0.021|
|Burnt bricks/Concrete walls|0.007|0.003|0.007|0.003|
|Piped water/Truck water|0.075|0.038|0.075|0.038|
|Flush/VIP toilet|0.061|0.031|0.062|0.031|
|Electricity for lighting|0.072|0.035|0.072|0.035|
|Household owns a chair or sofa|-0.037|-0.018|-0.037|-0.018|
|Household owns a sewing machine|-0.072|-0.019|-0.070|-0.019|
|Household owns an electric/gas stove|0.174|0.068|0.173|0.068|
|Household owns a refrigerator/freezer|0.051|0.018|0.050|0.018|
|<br>Household owns a bicycle|-0.014|-0.006|-0.013|-0.006|
|Household owns a motor vehicle|0.030|0.010|0.031|0.010|
|Household owns a computer|0.166|0.032|0.164|0.032|
|<br>Household owns a mobile phone|0.042|0.014|0.040|0.013|
|Household owns an iron|0.010|0.005|0.010|0.005|
|Household owns an air c/fans|0.120|0.041|0.119|0.041|
|Anyone in the household owns livestock|-0.039|-0.019|-0.038|-0.019|
|Household owns cows|0.033|0.010|0.030|0.009|
|Household consumed spaghetti, macaroni|0.042|0.012|0.042|0.012|
|Household consumed onions, tomatoes, carrots|-0.138|-0.044|-0.135|-0.043|
|Household consumed sweets|-0.035|-0.008|-0.031|-0.007|

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# **Table 4.4. Change in domestic average output prices and composite good supply and domestic production**

||**Domestic Pr**|**oduction**|**Aggregate**|**Supply**|**P**|**rice**|
|---|---|---|---|---|---|---|
||**Base**<br>**(1000 Tons)**|**Change**<br>**(%)**|**Base**<br>**(1000 Tons)**|**Change**<br>**(%)**|**Base**|<br>**Change**<br>**(%)**|
|**Wheat**|19,000|6.53|20,550|-0.56|0.50|<br>4.05|
|**Barley**|8,100|1.63|7,974|-0.50|0.35|<br>3.04|
|**Maize**|2,800|32.22|4,614|2.88|0.50|<br>8.00|
|**Other grains**|758|0.19|782|-3.05|4.75|<br>5.23|
|**Pulses**|1,558|20.20|1,356|0.95|1.82|<br>7.05|
|**Tobacco**|1,529|-5.15|1,384|-3.91|1.48|<br>3.09|
|**Sugar beet**|12,623|-2.90|12,651|-3.01|0.28|<br>2.50|
|**Cotton (raw)**|2,295|5.94|2,987|-2.09|1.19|<br>0.59|
|**Oil seeds**|2,359|4.20|2,058|-2.85|0.74|<br>4.44|
|**Potatoes**|5,300|-1.59|5,179|-1.69|0.54|<br>2.63|
|**Vegetables**|24,019|-2.65|23,288|-2.64|0.69|<br>3.30|
|<br>**Fruits**|13,221|-2.59|12,096|-2.97|0.89|<br>3.73|
|**Nuts**|789|-1.96|570|-3.31|4.80|<br>4.75|
|**Other crops**|2,974|-4.06|2,581|-3.76|2.16|<br>4.76|
|<br>**Milk**|9,096|-2.43|9,126|-2.65|1.00|<br>0.58|
|**Cattle Meat**|5,243|-1.81|5,242|-1.81|1.00|<br>0.72|
|**Other Bovine Meat**|2,333|-0.61|2,305|-1.16|1.00|<br>0.52|
|**Poultry and Eggs**|3,646|-2.11|3,588|-3.08|1.00|<br>3.91|
|**Other Animal Prod.**|2,113|6.84|2,046|-4.45|1.00|<br>9.03|
|**Other Agricultural**|||||||
|<br>**Prod.**|6,213|1.64|6,347|-2.73|1.00|<br>-1.01|
|**Food Production**|31,970|-4.03|30,490|-1.69|1.00|<br>2.15|
|**Mining**|4,407|-0.92|19,962|-1.00|1.00|<br>-1.85|
|<br>**Consumer Manufac.**|101,370|-0.60|77,701|0.36|1.00|<br>-1.61|
|**Int. and Cap. Goods**|129,439|-1.19|173,759|-0.75|1.00|<br>-1.71|
|**Electricity and Gas**|26,923|-0.12|26,923|-0.12|1.00|<br>-1.24|
|<br>**Water**|9,414|-0.03|9,414|-0.03|1.00|<br>-2.25|
|**Construction**|46,351|-1.48|46,351|-1.48|1.00|<br>-1.99|
|**Private Services**|219,470|-0.34|191,560|-0.11|1.00|<br>-1.44|
|**Government Services**|69,195|-0.01|69,195|-0.01|1.00|-0.81|

_Source: Results from TACOGEM-W._ Domestic production increased significantly in almost all activities as a result of the price shocks. This increase covers the decline in imports and increase in exports. Another interesting result is the connection between agricultural trade and other sectors’ production and prices. This effect originates from the household utility maximization, which is the second main driver of the effects of the shock. Income and Urban household consumption declined roughly by 1 percent.

38
Table 2: Main Regressions

||(1)<br>ln NTL pc<br>OLS|(2)<br>ln NTL pc<br>OLS|(3)<br>ln NTL pc<br>IV|(4)<br>ln NTL pc<br>IV|
|---|---|---|---|---|
|ln Market Access|0.0610**|0.0624**|0.188***|0.190***|
||(2.41)|(2.50)|(2.76)|(2.81)|
|ln(1+Pal.Fat._<_5km)||0.0137<br>(1.59)||0.0162*<br>(1.84)|
|ln(1+Isr.Fat._<_5km)||0.0182<br>(1.05)||0.0181<br>(1.06)|
|Year FE|Yes|Yes|Yes|Yes|
|Locality FE|Yes|Yes|Yes|Yes|
|_N_|1928|1928|1928|1928|
|adj. _R_<sup>2</sup>|0.452|0.453|0.344|0.346|

Notes: * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01. Heteroscedasticity robust _t_ -statistics clustered by locality in parentheses. _NTL pc_ measures local (top-coded) Lights emissions per capita (Source: NOAA) as described in section 4. _Market Access_ is calculated using: _MAit_ =<sup>�</sup> _j_<sup>_h_(</sup><sup>_Tijt_)</sup><sup>_Pjt_,where</sup><sup>_P_denotes</sup> destination population, _T_ denotes travel time, and _h_ ( _T_ ) = exp ( _−_ 2<sup><u>1</u></sup><sup>_T_2</sup><sup>_/θ_2)isadistancedecayfunction</sup> with _θ_ = 40 (see Section 3 for details). _Pal.Fat.<5km (Isr.Fat.<5km)_ denotes the number of Palestinian (Israeli) fatalities within a 5km radius in a given calendar year (Source: B’Tselem). In columns 3 and 4 _Market Access_ is instrumented using _Checkpoints 10-25km_ , the number of checkpoints deployed in a radius between 10 and 25km away from the locality’s center (Source: UNOCHA).

# **6.2 Robustness checks**

Robustness checks for our preferred IV estimates that control for local violence (column 4 of Table 2) are presented in Table 3. Columns 1-3 demonstrate how our estimates change when alternative distance decay parameters are used, notably _θ_ = 30, _θ_ = 60, and _θ_ = 80, respectively.<sup>19</sup> While the choice of the distance decay parameter impacts the magnitude of the coefficients, the qualitative pattern of results remains the same; market access is significantly positively correlated with lights, irrespective of the choice of the distance decay parameter. Incidentally, note that the adjusted _R_<sup>2</sup> is highest when choosing _θ_ = 40, which is our benchmark model.<sup>20</sup> Second, one may be concerned that the association between market access and lights

> 19Note that a higher distance decay parameter _θ_ implies that relatively remote locations are given more weight in the market access measure.

> 20Note that as _θ_ increases the regression coefficient associated with log market access mechanically increases as the variation in log market access shrinks. As a consequence, the impact of a one standard deviation in log market access on log NTL per capita is arguably less sensitive to the choice of distance decay parameter. Multiplying the regression coefficients reported in Table 3 obtained for different distance decay parameters with one standard deviation of the corresponding log market access measure yields estimates that range between 0 _._ 13 and 0 _._ 17. A similar degree of stability of this effect with respect to varying the distance decay parameter is obtained by Jedwab and Storeygard (2017), who examine the effect of changes in market access on local population growth.

32
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improvements is much more elusive. Table 10 provides a glimpse at this issue. It presents data on international test scores for the six developing countries that have participated in such tests and six upper-middle-income countries. Of the 11 countries which have participated in the tests in 2000 and the year 2005 only four exhibit a significant test score improvement between the two years while the rest have either experienced pronounced declines or have remained basically stagnant. While this is a small sample, it does illustrate how seemingly successful countries when judged by the standard measures of economic growth such as Spain, Mexico, Ireland, Brazil and Greece do not seem to be making any progress in increasing the quality of education. This may reflect under investment in human capital, an important intangible asset.

The evidence provided above, while certainly not conclusive, seems to be consistent with the idea that a large number of countries appear to excessively focus in the accumulation of tangible assets to the detriment of intangible assets, particularly human capital and natural capital. Past distortions affecting the allocation of government spending as well as other public policies that tend to bias the incentives in favor of tangible assets materialize over time in a progressively more unbalanced asset composition of the economy. Many economies may reach a tangible to intangible asset ratio that is so distorted that becomes an obstacle to economic growth. López and Islam (2008) have in fact provided a vast volume of econometric evidence showing that these public spending biases are indeed costly. The higher the share of spending in public goods the higher is economic growth over the long run. This confirms the notion that, on average, countries overspend in private goods and under-spend in public goods. One can conclude that if the reason why growth decelerates in countries that overspend in promoting tangible assets to the detriment of intangible ones is the asset ratio distortion that such policies incubate, natural disasters that affect mostly the tangible assets may in fact contribute to reduce such distortion and hence allow for faster economic growth over the long run.

# **III. THE MODEL**

In Section I we have shown that the empirical evidence on the effects of disasters is in certain respects conflictive (i.e., facts 1 and 2 in Section I) while it is far more consistent in other aspects (i.e., facts 3 and 4). The structure of the model presented below reflects the later group of stylized 11
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By converting the supply chain disruptions into their impact on trade capacity, the GSCSI offers a tangible representation of the scale of the global supply chain challenges faced over the 2020-2022 period and beyond. This supply chain stress metric can serve as a valuable tool for tracking, analyzing, and contextualizing the significant operational disruptions that have reverberated through global maritime and logistics networks in recent years. It underlines that shipping lines and terminal operators must contend with a notable unpredictable variability, in addition to more predictable seasonal and economic cycles.

# **The COVID-19 Pandemic and the 2021-22 Supply Chain Disruption**

The 2021-22 supply chain crisis resulted from two trends: First, containerized trade rebounded rapidly from the initial pandemic drop, overshooting pre-pandemic levels by late 2020. This surge was driven by a shift in consumer spending toward goods needed to accommodate sudden changes in lifestyles (e.g., workfrom-home) in high-income countries. While the shipping industry aimed to meet growing demand, supply was hampered by pandemic-induced port closure (notably in Asia), and operational constraints at other ports that caused multi-week vessel queues (especially on the US West Coast).

By mid-2021, severe pier and yard congestion had become prevalent among the world's major port gateways (Map 1), exacerbating the availability of containers and straining shipping lines’ capacity to move containerized cargo. This widespread congestion resulted from delays concentrating on a limited number of key international gateway ports.

Ship delays were amplified in the first half of 2021 by repositioning containers to where they were needed for exports (notably East Asia). At the start of the pandemic, shipping lines often cut capacity through canceled (blank) sailings. When demand bounced back rapidly, the industry struggles to promptly reassign capacity, and reposition containers, leading to bottlenecks. Even after restoring capacity, overloaded networks suffering slower operations due to high demand and landside congestion can face perceived capacity shortages, triggering soaring freight rates and container shortages.

Map 1. Delayed capacity at World’s Major Port Gateways (August 2021) 8
_Decentralization: The Way Forward for Rural Development?_ agents, or a level of service that is wholly inadequate excessive travel times to reach school, infrequent visits by extension agents.

A special issue of the Journal of Development Studies (Varshney 1993) reconsidered the question of urban bias. Contributing authors argued that there are conditions under which urban bias might be eroded, and several country examples were cited, e.g., an increase in the variety of political institutions offers the possibility for rural interests to be represented under competitive party systems (India, Costa Rica); technical change through sustained growth in agriculture may begin to make the rural sector more powerful, as its contribution to economic growth expands (C6te d'Ivoire, Cuba, Costa Rica, China, India); ethnic, religious and other class identities may cut across the rural-urban divide (South Korea, Taiwan, India).

It is clear that under certain specific conditions, particular elements of urban bias may decline, and rural forces may even be successful in securing a redirection of resources to rural areas, e.g., in Indonesia where a significant number of farmers have benefited from public resource allocations. Nevertheless, as Lipton (1993) points out in a rejoinder article, specific examples taken out of context do not prove that the rural sector has ceased being discriminated against, and if a wider definition of urban bias is considered there is clear evidence of the tendency for urban bias to continue to exist.

# **Excessive Agricultural Taxation**

Incentive policies have been analyzed in a five-volume series, summarized in Schiff and Vald6s (1992). They examine the impact of trade and pricing interventions on agricultural incentives in eighteen developing countries between 1960-85. They found that discrimination-in the form of overvalued exchange rates, industrial protection and direct intervention in the agriculture sector-adversely affected both agricultural growth and overall economic development.

Schiff and Vald6s present a detailed exposition of the particular types of policies utilized by governments to discriminate against agriculture. This analysis leads to the following conclusions: _4_