rafmacalaba/datause-encoder-data huggingface dataset:
world: “Give us your old, infirm, and unemployed and in return take our young, poor, and even highly talented.” The assault on globalization’s last holdout might finally be within reach.

In Section II, we spell out the relatively novel case for greater rich-to-poor mobility, and in Section III we restate the powerful case for greater poor-to-rich mobility. In Section IV we identify the policy changes needed to facilitate criss-crossing globalization. Section V lays out the elements of the proposed grand bargain.

# **II.   How Would Rich-to-Poor Mobility Help?**

The reduction in income, wealth, and opportunities across the age spectrum is now a long-term reality. Industrial countries are responding to these developments in two ways. First, there is a search for raising long term growth which is perhaps the only way of a sustained improvement in the economic situation of the entire population. But achieving this will not be easy.<sup>2</sup> The second response has been increasingly incompatible claims by different interest, even age, groups for a larger share of the national economic pie. In the United States, the call for cuts in government expenditure as a way of controlling the fiscal situation will affect retirees, the infirm as well as students. As the population ages, democracies are going to reflect to a greater extent the will of the elderly, and it is possible that they will seek to preserve their situation at the expense of the young. The cut backs in government spending on education in the UK and increases in student fees reflect to some extent this shift in political power.<sup>3</sup> Industrial countries have not fully appreciated a third way of responding to this economic crisis: increased flows of people across the age spectrum to the economically more dynamic parts of the world. As we discuss below, such flows could enhance consumption possibilities for students, the elderly and the infirm, and offer work opportunities for the skilled unemployed.

# _Retirees_

The unfunded pension liabilities of the state and local governments in the United States are estimated to range between 0.7 trillion and $4.6 trillion, depending on how assets and liabilities are valued (Congressional Budget Office, 2011; Biggs, 2012). Average inflation-adjusted household wealth has remained stagnant since 2004 according to calculations of the St. Louis

> 2 See, for example, Gordon (2012).

> 3 See, for example, Martin Wolf, “Why the world’s youth is in a revolting state of mind,” Financial Times, February, 18, 2011(available at: http://www.ft.com/intl/cms/s/0/6577ca92-3b94-11e0-a96d-00144feabdc0.html).

4
- **Dean, Andr ́es, Sebastian Fleitas, and Mariana Zerpa.** 2024. “Dynamic Incentives in Retirement Earnings-Replacement Benefits.” _Review of Economics and Statistics_ , 106(3): 762– 777.

- **De Andrade, Gustavo Henrique, Miriam Bruhn, and David McKenzie.** 2014. “A Helping Hand or the Long Arm of the Law? Experimental Evidence on What Governments Can Do to Formalize Firms.” _World Bank Economic Review_ , 30(1): 24–54.

- **de Mel, Suresh, David McKenzie, and Christopher Woodruff.** 2013. “The Demand for, and Consequences of, Formalization among Informal Firms in Sri Lanka.” _American Economic Journal: Applied Economics_ , 5(2): 122–150.

- **Elek, P ́eter, J ́anos K ̈oll ̋o, Bal ́azs Reizer, and P ́eter A. Szab ́o.** 2012. “Detecting Wage Under-Reporting Using a Double-Hurdle Model.” In _Informal Employment in Emerging and Transition Economies_ . 135–166. Emerald Group Publishing Limited.

- **Fitzpatrick, Maria D.** 2017. “Pension-Spiking, Free-Riding, and the Effects of Pension Reform on Teachers’ Earnings.” _Journal of Public Economics_ , 148: 57–74.

- **French, Eric, Attila S. Lindner, Cormac O’Dea, and Tom A. Zawisza.** 2022. “Labor Supply and the Pension-Contribution Link.” National Bureau of Economic Research Working Paper 30184.

- **Gavoille, Nicolas, and Anna Zasova.** 2023 _a_ . “Minimum Wage Spike and Income Underreporting: A Back-of-the-Envelope-Wage Analysis.” _Journal of Comparative Economics_ , 51(1): 372–402.

- **Gavoille, Nicolas, and Anna Zasova.** 2023 _b_ . “What We Pay in the Shadows: Labor Tax Evasion, Minimum Wage Hike and Employment.” _Journal of Public Economics_ , 228: 105027.

- **Gerard, Fran ̧cois, and Gustavo Gonzaga.** 2021. “Informal Labor and the Efficiency Cost of Social Programs: Evidence from Unemployment Insurance in Brazil.” _American Economic Journal: Economic Policy_ , 13(3): 167–206.

- **Ginja, Rita, Arizo Karimi, and Pengpeng Xiao.** 2023. “Employer Responses to Family Leave Programs.” _American Economic Journal: Applied Economics_ , 15(1): 107–135.

- **Jascisens, Vitalijs, and Anna Zasova.** 2021. “Million Dollar Baby: Should Parental Benefits Depend on Wages When the Payroll Tax Evasion is Present?” SSE Riga/BICEPS Research Paper 9.

- **Kleven, Henrik.** 2022. “The Geography of Child Penalties and Gender Norms: A Pseudo-Event Study Approach.” National Bureau of Economic Research Working Paper 30176.

- **Kleven, Henrik, Camille Landais, and Gabriel Leite-Mariante.** 2024 _a_ . “The Child Penalty Atlas.” _Review of Economic Studies_ , Forthcoming.

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|**Indicator**|**Atkinson & Lugo**<br>**(2010)**|**OPHI MPI (2013)**|**MDG indicators**<br>**(2008)**|**Ethiopia WMS-HCES 2000, 2005, 2011**|**2000**|**2005**|**2011**|**Urban/**<br>**Rural**<br>**Indicator**|
|---|---|---|---|---|---|---|---|---|
|_Education of Female_<br>_School-aged Children_||||at least one girl child (age 7-15) in the<br>household is not currently attending school<br>2000, 2005: currently_registered_in school|√|√|√|U, R|
|_Institutional Birth_|||antenatal care<br>coverage;<br>proportion of<br>births attended by<br>skilled health<br>personnel|at least one child (age 0-4) in the household<br>was not born in a health facility||√|√|U|
|_Female Circumcision_||||at least one girl (age 0-14) in the household<br>underwent/will undergo female circumcision|||√|U, R|
|_Source of Information_|||mobile-cellular/<br>fixed telephone<br>subscriptions per<br>100 inhabitants|household does not own a TV, radio, or<br>phone<br>2000:phone is not specified in list of assets|√|√|√|U, R|
|_Assets_|asset deprived:<br>household does not<br>own a car, and<br>owns fewer than<br>one small asset--<br>TV, radio, phone,<br>bicycle,<br>refrigerator,<br>motorcycle|household does not<br>own a car or truck, and<br>does not own more<br>than one of the<br>following assets:<br>radio, television,<br>telephone, bicycle,<br>scooter, or refrigerator|mobile-<br>cellular/fixed<br>telephone<br>subscriptions per<br>100 inhabitants|household does not own a motorcycle, car, or<br>bajaj, and does not own a fridge, phone,<br>radio, TV, bicycle, or jewelry<br>2005: motorcycle, bajaj not in list of assets<br>2000: phone, jewelry not in list of assets|√|√|√|U, R|
|_Below Poverty Line_|||proportion of<br>population below<br>$1 (PPP) per day<br>(or below country-<br>level poverty line)|household lives below the poverty line of<br>3781 Birr per adult equivalent (using real<br>total consumption expenditure per adult)<br>2000, 2005: below the poverty line of 1075<br>Birr(in 1996prices)|√|√|√|U, R|

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_Gender Equality and Economic Development: The Role for ICTs_ 11 stay home voluntarily, so as play a larger role in their children’s upbringing. In such cases, ICTs enable women to participate in the labor force without sacrificing their presence in the home.

It was mentioned above that ICTs facilitate commerce by increasing the flow of information, reducing transactions costs, overcoming distance and thereby increasing market coverage. These benefits are especially important to micro and small enterprises (MSE), which are dominated by women entrepreneurs. There have been reports of how self-employed women in Cambodia, India and Peru have exploited numerous economic opportunities offered by e-commerce (World Bank, 2003c).

# **5. Other Determinants of Gender Inequality**

## **5.1 Determinants of Gender Inequality in Education**

### <u>Economic Development</u>

It was discussed in Section 1 that there exists a positive correlation between gender equality and economic development and this positive association was clearly illustrated in Figures 1 and 2. Several studies that presented evidence showing that improvements in gender equality in education lead to improvements in economic development were also reviewed. In this section, I argue that economic development also simultaneously exerts a positive causal effect on gender equality, which further reinforces the positive association between the two variables.

Several studies have presented evidence to this effect. Dollar and Gatti (1999) find empirical evidence indicating that increases in per capita income lead to reductions in gender inequality. They used focuses on four different types of measures of gender inequality: (1) access and achievement in education (2) improvement in health (3) indexes of legal and economic equality of women in society and marriage (4) measures of women’s empowerment (representation in parliament, right to vote). Easterly (1997) estimates fixed effects panel regression in which the gender variable is the female to male secondary school enrollment ratio and the only right-hand side variable is per capita income. He shows that there is a positive relationship between income and gender equality. His work establishes that the correlation between income and gender equality in secondary education is not simply a cross sectional association, but in fact true for individual countries as they develop.
Policy Research Working Paper 8875

# **Abstract**

This paper uses plant-level, panel data from the Ethiopian manufacturing census to estimate the effects of demand-side and supply-side factors on industrywide aggregate productivity. The paper focuses on the effects of three factors: (1) local market size, (2) the value of transportation costs that firms incur in selling to customers outside their market, and (3) licensing fees needed to enter the market. Identification is based on a model of production under monopolistic competition, which enables interpreting the estimated coefficients of a reduced form, dynamic productivity equation.

The paper analyzes 11 industries in Ethiopia over 2000 to 2010. Several interesting results emerge. In the most parsimonious specification, the estimated coefficients are consistent with all three predictions of the model—but only for one industry: cinder blocks. In this industry, the expansion of the local market boosts industrywide total factor revenue productivity, while increases in transport costs and licensing fees reduce it. The picture is somewhat mixed in the other 10 industries but broadly consistent with the predictions of the model.

This paper is a product of the Office of the Chief Economist, Africa Region and the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jonesp@newpaltz.edu; elartey@worldbank.org; and azeufack@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
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<!-- Start of picture text --> Estimated GDP Actual GDP<br>29.5<br>29<br>28.5<br>28<br>27.5<br>27<br>26.5<br>199219931994199519961997199819992000200120022003200420052006200720082009201020112012<br>ln(GDP)<br><!-- End of picture text --> Source: WDI (2014) and NGDC (2014). Authors’ calculations.

Since 2006, Rwanda is divided into 30 districts, which are the main service-delivery units. All household surveys are representative at the district level, but there is no information on districtlevel economic growth. Estimating district-level growth based on nightlights is however complicated by the low intensity of lights: Four districts did not emit any observable lights at all during 2000-2011, and ten districts only have nonzero observations towards the end of the 20002011 period. As a result, we can only estimate nighlights-based GDP growth for 16 districts.

Figure 8 shows estimated average GDP growth for the 16 districts with sufficient lighting. According to the estimates, Kamonyi, Nyanza, and Musanze districts grew fastest between 2000 and 2012, with annual growth rates of 9.9, 8.3, and 7.1 percent, respectively. Rubavu, Karongi, Gicumbi, Nyamagabe, and Rwamagana also grew at more than 5 percent per year. Within Kigali city, Gasabo district grew at 4.8 percent per year, Kicukiro at 3.8 percent, and Nyarugenge at 3.3 percent. The two slowest growing districts (among those with data) were Huye (3.1 percent) and Nyarugenge (3.3 percent).

15
equation (18) and replacing ∆UNILIBt with a common term for the 1990-1992 period (REFt) during which the liberalization took place step by step (column 2). In this version, β1 becomes significant at the 10% level and β2 at the 5%. REFt has a negative and significant (at the 1% level) coefficient capturing the common downward trend. In column 3, I further allow for a common constant term on top of REFt recognizing the small changes in the other years, and both β1 and β2 become significant at the 5% level. These results indicate that due to political economy, the extent of liberalization is smaller for the sectors with a smaller reduction or a higher increase in their productivity as compared to similar sectors. Note that in all differenced results, β1 and β2 are statistically identical, which is predicted by the model. In levels, this may not occur because of Although the theoretical section involves protection through tariffs, I repeat the specifications in Table 5 and Table 6 with the effective rates of protection (ERP) in order to see whether the results hold with a different measure of protection. Effective rates are based on value added and essentially take into account the effect of tariffs on the inputs as well. ERP data are provided by the National Planning Department of Colombia (DNP) and I am limited by their computations since I do not have the detailed data to calculate them myself. As can be observed from Figure 1 and Table 4, the effective rates are higher than the regular tariff rates but otherwise display a similar trend. I exclude the three sectors<sup>44</sup> that exhibit negative ERP (in levels not logs), because it is hard to argue that these sectors are indeed protected. In Table 7, I repeat the specifications from Table 5 and the results appear to be totally consistent. The only difference is that the significance levels for the main variables are lower, and the constant term becomes insignificant in columns 2 and 3. The same arguments apply to the figures in Table 8 which are the replicas of the estimates from Table 6 with ERP. The results are again qualitatively similar but less significant.

In the next section, I provide specification tests and some sensitiviy analysis for the main estimations I covered. Then, in Section 6, I discuss how accounting for the endogeneity of tariff policy, as implied by my theoretical and empirical results, may improve the estimates of the effect of trade reform on productivity.

> 44The excluded sectors are: a) ISIC 3122, manufacture of prepared animal feeds; b) ISIC 3512, manufacture of fertilizers and pesticides; c) ISIC 3822, manufacture of agricultural machinery and equipment.

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15 The claims cover all states and regions in the QSEM panel with the exception of Chin State and Mandalay Region. The land confiscation claims include claims against the military: a village in Shan State that has separate claims against three different military bases bordering the village; claims against business interests with ties to the military: as per several villages in Kachin State and a village in Rakhine State where wives of military officers attempt to seize five hundred acres of communal land; and claims relating to infrastructure investments: such as claims surrounding the gas pipeline in Rakhine State, a railroad built to support a steel factory and oil interests in Magway Region, as per the example above, and land confiscation by a company to build a road in Ayeyarwardy Region. Across a number of these cases, villagers perceive local officials including village administrators or tract administrators and township officials, particularly from the land records department, as playing a facilitating role in the land acquisition process.

22
in the world. Recent data indicate that the LFP rate for Iranian women hovers around 14%, which is less than one-third of the global average of 46%. In fact, it is even smaller than the regional average of 19% for MENA (World Bank, 2022). On the other hand, the country has been under international sanctions for an extended time (Salehi-Isfahani, 2023). If the pandemic affected all sectors in the economy, as it did in richer countries, women were likely affected more severely. But if the pandemic effects were muted—for example, because the U.S. sanctions worked as a buffer against international trade shocks—women were likely less affected. Whether, and how much, these effects translate into reduced employment or labor participation of women are therefore empirical issues which we investigate in this paper.

In addition, after China, the Islamic Republic of Iran was the epicenter of the COVID-19 pandemic in the first months of its global spread starting from early 2020, when protection measures such as vaccines were unavailable. A better understanding about whether, and to what extent, the early waves of the pandemic could have negative impacts on female LFP can be useful for policy makers in the fight against future pandemics.

We find that female LFP decreased during the pandemic years by around 1 percentage point in 2021 and 2022. When controlling for excess mortality rates, the decline could be between 3.9 and 8.7 percentage points, with the larger impacts occurring in late 2021 and early 2022. Compared to a modest female LFP that hovers around 21.5 percent for the 5-year period before the pandemic, these decreases translate into those of 5 percent and 18-40 percent decreases. We also find that individuals with more education were more likely to work. Compared to married individuals, divorcees were more likely to work, while those that who were widowed or never married were less likely to work.

3
**Table S11. Income loss due to the COVID-19 pandemic, by income source and urban/rural residence**

||Farm income<br>reduced|Business<br>income<br>reduced|Wage<br>income<br>reduced|Remittances<br>reduced|Other income<br>sources<br>reduced|
|---|---|---|---|---|---|
|Urban|-0.019|0.024|-0.016|-0.016|-0.137***|
||(0.025)|(0.022)|(0.035)|(0.045)|(0.046)|
|Ethiopia|0.001|0.009|-0.004|-0.083*|-0.017|
||(0.024)|(0.025)|(0.042)|(0.045)|(0.054)|
|Nigeria|-0.132***|0.072***|0.076*|0.029|0.157**|
||(0.025)|(0.025)|(0.041)|(0.040)|(0.074)|
|Uganda|-0.314***|0.011|-0.234***|-0.256***|-0.281***|
||(0.028)|(0.034)|(0.045)|(0.056)|(0.055)|
|Observations|5,624|3,849|3,447|1,687|1,574|
|R-Squared<br>_Note_: Each col|0.081<br>umn reports results|0.005<br>from a single|0.056<br>regression of|0.038<br>a binary variable|0.091<br>equal to 1 if the|

_Note_ : Each column reports results from a single regression of a binary variable equal to 1 if the household lost income from that source and 0 otherwise. Reporting loss of income from a given source is conditional on receiving income from that source prior to the COVID-19 pandemic. The binary variable is then regressed on indicators for each country, with Malawi as the base case, along with an indicator equal to 1 if the household was located in an urban area, as opposed to a rural area. Data are only from the first phone survey round in each country. Robust standard errors are reported in parentheses (*** p<0.01, ** p<0.05, * p<0.10).

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