rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Figure 4: Economic development and rates, spreads, and margins**

_Countries with higher nominal and real lending rates, spreads, and net interest margins tend to be significantly less economically developed as measured by GDP per capita._ GDP per capita by tercile of nominal lending interest rate, real lending interest rate, lending-deposit spread, or net interest margin (US Dollars) <!-- Start of picture text --> 7000<br>6000<br>5000<br>4000<br>3000<br>2000<br>1000<br>0<br>T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3<br>Nominal lending interest Real lending interest rate Lending-deposit spread Net Interest Margin<br>rate<br>GDP per capita (US Dollars)<br><!-- End of picture text --> _Note_ : Countries sorted from lowest to highest 2003-2017 average x-axis indicator. T1 and T3are terciles with the lowest and highest values, respectively. Median GDP per capita by tercile is shown.

Source: World Bank-World Development Indicators, World Bank Finstats 2019.

38
Figure 19. Resource GDP to non-resource GDP ratio in Sub-Saharan African countries, 1998-2003 vs 2014-2018 averages.

_Notes: Average resource GDP to non-resource GDP ratio in 1998-2003 compared to the average in 2014-2018. Resource GDP corresponds to the GDP coming from mining activities including mining of metals and minerals, and extraction of crude petroleum and gas. Countries above the 45-degree line increased more their resource GDP over their non-resource GDP after the boom. Chad, Gabon, and Equatorial Guinea not shown, but their ratios in 1998-2003 were -0.1, 3.3, and 1.1, respectively. Their ratios in 2014-2018 were 0.2, 0.6, and 0.4. Source: authors’ calculations using UN Statistics Division data._

**Export concentration: The boom drove increased export concentration (little progress on diversification, reversal in some cases)**

A key tenet of best practice advice to resource-rich countries is to use the opportunity created by a boom to promote a more diversified economy. This can reduce exposure to price volatility and price risk, while also moving the economy up the value chain to higher value added and shift the economy into labor-intensive export manufacturing and other non-resource sectors. Diversification is a protection against depletion of the resource base, while in the case of hydrocarbons, it may be insurance against a future global shift from fossil-fuel consumption driven by alternative energy technologies and taxation of carbon emissions (Cust, Manley, and Cecchinato 2017).

While many countries have actively pursued diversification strategies during their respective resource booms, the record of success is rather poor. For many oil-rich countries the boom was associated with increases in export concentration, including in Africa. However, some regions saw some success - notably Latin American and Southeast 30
probability that a son will work as a migrant when a parent is seriously ill. While parental illness has a statistically significant negative effect on the probability of migration, it does not completely drive the decision to remain employed in migrant destinations. Depending on dataset and methodology, Giles and Mu showed that chronic illness and infirmity led to a 15 to 26 percent reduction in the probability that a son would be employed as a migrant, and this effect was reduced if other siblings remained behind in the village. While this level of responsiveness to elder parent illness is significant, it is not absolute as parent illness only raises the probability of return migration. In addition, in the absence of children, neither market nor government currently provide significant alternatives for provision of instrumental care (Flaherty et al 2007; Flaherty, 2009; Green, 2010; Wu et al, 2009).

# **Rural-to-Urban Migration in China**

During the 1990s, China’s labor market experienced dramatic growth in the volume of rural migrants moving to urban areas for employment. Estimates using a one percent sample from the 1990 and 2000 rounds of the Population Census and the 1995 one percent population survey showed that the inter-county migrant population grew from just over 20 million in 1990 to 45 million in 1995 and 79 million by 2000 (Liang and Ma, 2004). Surveys conducted by the National Bureau of Statistics and the Ministry of Agriculture that included more detailed retrospective information on short-term migration suggest even higher levels of labor migration than evident in the census (Cai, Park and Zhao, 2008).

Before labor mobility restrictions were relaxed, households in remote regions of rural China faced low returns to local economic activity, reinforcing geographic poverty traps (Jalan and Ravallion, 2002). A considerable body of descriptive evidence related to the growth of 10
agricultural output, particularly the agricultural sector’s share of GDP. The Ten Year Comprehensive National Strategy (1992–2002) and the initiatives that followed, such as the Economic Salvation Program and the National Five-Year Strategic Development Plan (2007–2011), focused on supporting “large-scale mechanized agriculture projects” over “smallholder rain-fed traditional agriculture” (Ibnouf 2011, 219). This was to the detriment of female farmers who generally farmed smaller, rain-fed plots. At the same time, females engaged in agricultural activities tend to be overrepresented in unpaid jobs. According to NHBPS 2014/15, more than half of the women working in agriculture were doing this as unpaid workers (53 percent). Only 28 percent worked as own account workers, 16 percent as paid employees, and 3 percent as employers. Male farmers are more likely to be engaged in agriculture as an own account worker (42 percent), followed by paid employee (35 percent), then unpaid worker (15 percent) and then employer (9 percent).

Many of the constraints that female farmers face are similar to the constraints faced by Sudanese women entrepreneurs. A study of the role that female farmers in the western Sudan region play in food security highlights this: “[Female farmers] have limited access to credit and inputs because of gender discrimination and lack [of] collateral. Formal regulations prohibit married women to access credit without the signature of their husband” (Ibnouf 2011, 222–223). Agricultural land that is owned by potential borrowers can be used as collateral for credit. As mentioned earlier in this report, however, Sudan has two different systems of property rights—one based on a statutory legal system and one based on a customary system not regulated by the legal system—both of which include discriminatory measures against women.<sup>24</sup>

# **Informality is high, linked to poverty, migration, and low education**

Those who work in the agricultural sector are more likely
# **References**

- A.A Aibinu and G.O Jagboro. The effects of construction delays on project delivery in nigerian construction industry. _International Journal of Project Management_ , 20(8): 593–599, 2002. 8

- Belinda Archibong, Tom Moerenhout, and Evans Osabuohien. Protest matters: The effects of protests on economic redistribution. Working Paper 172, Brookings, Washington, D.C., 2022. 12

- J. Bentham. _Introduction to the Principles and Morals of Legislation (First published in 1789)_ . London: Athlon, 1970. 2

- Markus Brunnermeier and Jonathan Parker. Optimal Expectations. _American Economic Review_ , 95(4):1092–1118, 2005. 3

- Andrew Caplin and John Leahy. Psychological Expected Utility Theory and Anticipatory Feelings. _Quarterly Journal of Economics_ , 116(1):55–79, 2001. 3

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- A. Deaton. Income, aging, health and wellbeing around the world: Evidence from the gallup world poll. _Journal of Economic Perspectives_ , 22(2):53–72, 2008. 9

- Rafael Di Tella, John Haisken-De New, and Robert MacCulloch. Happiness adaptation to income and to status in an individual panel. _Journal of Economic Behavior Organization_ , 76(3):834–852, 2010. 2

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- E. Diener, R. E. Lucas, and C. Napa. Beyond the hedonic treadmill: Revising the adaptation theory of well-being. _American Psychologist_ , May-June:305
with children aged 6-24 who have graduated from high school. For spending on books and private tutoring, I consider all households with children aged 6-24. The time period is 1995-2013. I control for covariates, as well as time, province, and industry fixed effects (model 2). Heteroskedasticity-consistent standard errors accounting for clustering at the province and industry level in parentheses. P-values are calculated using wild bootstrap randomization inference (WBRI). Treated: Oil and Gas, Energy Supply industries. Synthetic control: Information (0.148), Education (0.169), and Health (0.683) industries.<sup>_∗_</sup> Significant at 10% level;<sup>_∗∗_</sup> significant at 5% level;<sup>_∗∗∗_</sup> significant at 1% level.

# **5.2 Effect on Children’s Education**

In consequence of a reduction in the family income (-15%), affected households reduced their total expenditure by 11% (Table 3). Although spending decreased for most components, it did not decrease by the same rate. As Table 3 shows, households cut spending on education by 58% (the share decreased by 5%). The reduction in education spending reflects the combination of young children not attending school and parents cutting back on school expenditures. For instance, parents may choose free public schools instead of private schools for their children. In this section, I evaluate the effects of the sanction on quantity of children’s education (enrollment rates and completed years of education) and household spending on education.

## **5.2.1 Effect on Enrollment and Years of Schooling**

First, I find the impact of the sanctions on the educational attainment measured by enrollment rates and years of schooling. Table 4 presents the effects on school enrollment, college attendance (any post-secondary programs), and years of schooling.
productivity among Brazilian firms showed that wages were in fact unrelated to productivity; a study in the Netherlands found a similar disconnect between wage levels and performance.<sup>92</sup> In many East Asian countries, the seniority wage-setting practice is strong, and performance-based pay practices are not implemented.<sup>93</sup> Wages that rise above productivity levels risk lowering the competitiveness of mature workers. Analysis of firm-level data in East Asian countries shows that seniority wage practices indeed are strongly and negatively correlated with hiring workers in the age group 50-64.<sup>94</sup>

**Formal regulations also matter for labor costs and may shift these above mature workers’ productivity levels** . First, social contributions increase with wages. This can contribute significantly to raising labor costs, as in some countries social insurance contributions are very high – e.g., 45 and 36 percent of wages in Argentina and Brazil, respectively.<sup>95</sup> Especially in the presence of asymmetric information, the cost of hiring older workers may therefore be higher.<sup>96</sup> If productivity does not increase in parallel with social contributions, unit labor costs for mature workers increase. A survey of UK employers suggested that pension costs were the main deterrent to hiring mature workers – more important than the limited time frame for return on investments in skills, or any perceived lack of skills.<sup>97</sup> Similarly, employment protection legislation (EPL) can increase the costs of labor adjustments, as many regulations imply higher costs with longer job tenure. Globally, the average number of weeks’ pay required as severance in the case of dismissal increases from three for workers with one year of tenure, to 21 for workers with 10 years of tenure. In Indonesia, a worker with 10 years of service receives an average severance payment equal to 22 _months_ of wages.<sup>98</sup> Strict EPL may be
we focus on IPV rather than crimes against women more generally. IPV is not only more prevalent than other types of gender-based violence, but the mechanisms underlying IPV are also likely to be different. Our study sheds light on several channels through which IPV against women may respond to increased exposure to female leaders.

Moreover, our work relates to a limited literature showing that women’s improved access to family planning services may lead to intrahousehold conflict and IPV (Ashraf et al. 2014; Anukriti 2020). More generally, we contribute to an emerging literature on

> 3While administrative data on IPV, such as police or hospital reports, represent more objective measures of IPV, the restricted access of abused women in developing countries to police stations and hospitals also raises concerns about the reliability of such data. For instance, we observe that in our data only 0.6 percent of physically abused women filed a police report and that only 0.1 percent of them visited a doctor or medical personnel.

> 4A broader literature explores the role of increased political representation for minority groups on the provision of publicly provided goods and services. For instance, greater representation of Scheduled Castes in India has been shown to increase public goods provision for these castes (Pande 2003).

3
2.1|1.9|
|Non WTO Members|11.8|12.3|22.2|26.6|26.1|36.1|1.9|2.2|2.2|2.9|
|"Non Market"|6.3|6.9|16.4|20.8|21.4|32.1|2.6|3.0|3.4|4.7|
|Other|5.5|5.4|5.8|5.8|4.7|4.0|1.0|1.1|0.8|0.7|

Explanation: R,d: Share in Investigations (Measures)/Share in World Exports _Source: WTO, IDB and Antidumping Data Base, J Miranda et. at., 1998._
(Percent of latest Enterprise Survey respondents) regulatory requirements (Average of latest<br>Enterprise Survey respondents)<br>80% 14%<br>70% 12%<br>60% 10%<br>50% 8%<br>40% 6%<br>30% 4%<br>20% 2%<br>10% 0%<br>0%<br>Cross-country averages Structural peers Aspirational peers Zambia<br>Cross-country averages Structural peers Aspirational peers Zambia<br>Source: World Bank staff calculations based on Enterprise Source: World Bank staff calculations based on Enterprise<br>Survey data and the Investment Climate Assessment 2.0 toolkit Survey data and the Investment Climate Assessment 2.0 toolkit.<br><!-- End of picture text -->

# 7. Conclusion

Aggregate labor productivity of formal firms weakened between 2014 and 2021, driven by secular within-firm declines in the non-mining industry and service sectors. By contrast, labor productivity grew in agriculture and remained flat in mining over the same period. Real wage trends for formal workers have mostly mirrored labor productivity dynamics, declining 40-50 percent across non-agriculture sectors but growing slightly in agriculture, largely driven by within-firm shifts rather than between-firm or between-sector dynamics. The declines in labor productivity and wages reflect business environment challenges related to access to finance and electricity, as well as burdensome formal compliance 24
**Figure 13:  Dwell Time,** **<u>by Annual Volume of Imports, 2011</u>**

<!-- Start of picture text --> Manufacturing Assembling Domestic Wholesaler<br>Transit Wholesaler Small & Medium Retailer Other<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Kenya Nigeria South Africa Uganda Zambia<br>Source: Authors based on firm surveys.<br><!-- End of picture text -->

# **Volume of importations**

Contrary to the common belief that the volume of imports is an important determinant of cargo dwell time, in SSA the volume of imports is not correlated with dwell time (Figure 14). Hence, shippers that import medium volumes seem to have significant longer dwell time – about one day longer – compared to shippers that import very low, low and large volumes (t(212.253) = 1.669; p = 0.048). And shippers importing large volumes seem to have significant shorter dwell time – approximately one day shorter – than all the other shippers (t(268.041) = 2.218; p = 0.013).More than the size of the company, the type of company and its business model are what matter the most.

**<u>Figure 14: Cargo Dwell Time, by Annual Volume of Imports, 2011</u>**

<!-- Start of picture text --> 12<br>9.80 9.99<br>10 8.76<br>8.16<br>8<br>6<br>4<br>2<br>0<br>Very Low (< 100 Tons) Low (>=100 and <=500) Medium (>= 500 and Large
# **Appendix 3:**

The World Bank Latinamnerican and Caribbean Regional Database contains data on annual real GDP from 1965-98 for Ecuador. Since this data base could have been. alternatively used for our cointegration analysis, we also perform a cointegration analysis using this data base. This allows us to contrast the results of a cointegration analysis based on annual data with the results of a cointegration analysis based on quarterly data. Since cointegration is a long-run property, the frequency of the data should not matter and we would expect to find similar results.

The results of the cointegration analysis with annual sectoral GDP data from the World Bank Latinamerican and Caribbean Regional Database indicate one cointegration relationship and thus confirm the results of our cointegration analysis based on the quarterly data base of the Banco del Ecuador.

**Table A6:**

|Null|Alternative|Lag: 3|95%|90%|
|---|---|---|---|---|
|Hypothesis|Hypothesis|With Constant|Critical<br>Value|Critical Value|
|**xtace**test|||||
|r 5 0|r>0|38.49*|35.10|31.88|
|r S I|r> 1|14.66|20.17|17.79|
|r < 2|r>2|4.83|9.10|7.50|

_Note:_ Estimates are based on a VAR specification with a constant in the cointegration space and a laglength of 3. This model specification was found to be sufficient to produce random errors _Source:_ Authors' calculations.

26
cannot always be certain of this since sometimes the source was ambiguous about this difference.<sup>23</sup>

# **3.4 Data on the covariates**

The available data limit our ability to test the Relative Prices Hypothesis. The <u>International Comparison Program provides estimates from each ICP round of the prices of</u> different consumption categories, including health services, education services and the aggregate category of food and non-alcoholic beverages. With this information, the relative price of health and education to food and non-alcoholic beverages can be easily calculated.<sup>24</sup> However, the ICP rounds are not annual: they are only available for years 2005, 2011 and 2017.<sup>25</sup> The health and education price relatives are highly correlated (r=0.92 for 2005, r=0.90 for 2011 and r=0.82 for 2017), so we show results with them entering separately as well as jointly.

In testing Governance 1, thanks to the Word Bank’s WGI, we have regular measures of the responsiveness of governments to their citizens, namely their “voice and accountability” (VA) index. As defined on the WGI website, VA “... captures perceptions of the extent to which a country’s citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media.” The index is a composite of indicators on democracy, electoral processes, accountability of public officials, rights, reliability of government budget documents, transparency in policy making, the freedoms allowed for citizens, and citizens’ trust in their government. The index comes normalized to have mean zero and a standard deviation of unity in every year.

<mark>We consider two other measures of accountability. The first is the electoral democracy</mark> index (also known as the polyarchy index) produced by the Varieties of Democracy
it is likely that government relief programs have contributed to this. Total budgets of relief packages for households and businesses equaled 23 percent of GDP in 2020 and 15 percent in 2021 (World Bank, 2021). For example, a World Bank survey indicated that about 75 percent of households with monthly income below RM 4,000 had received cash transfers between June and November 2021 (World Bank, 2021). Even so, this leaves an important share of the eligible population without assistance.

> 17 For comparison, the same graph based on the highest poverty line (RM 618 under scenario 3) is given in the Appendix.

11
of the heights-income relation, hence 65% and 10% of the volume-income relation (25% for areas). In contrast, at the country level and including rural areas, we found 60% for high-rises, 5% for low-rises, and 35% for areas.

**C6. Classifying regions into 4 groups for primate & secondary cities & rural areas**

For each subregion, Appx. Fig. C.7-C.9 show the (locality) population-weighted average residuals when only considering large cities (defined as cities in the top 15 of each country), small and medium-sized cities (cities not in the top 15 of each country) or rural areas & towns (localities _<_ 50,000 c. 2015). Comparing the three figures, one can see how some regions exhibit similar patterns for each type of locality. The coefficient of correlation between the pro-height residuals based on large cities and the same residuals but based on small and medium-sized cities or rural areas & towns is _∼_ 0.7-0.9. Correlation is less high but still positive, at _∼_ 0.45-0.6, for the pro-highrises residuals. Thus, regions tend to be similarly “specialised” across all localities.

# **REFERENCES**

> Chen, Jiandong, Ming Gao, Shulei Cheng, Wenxuan Hou, Malin Song, Xin Liu, and Yu Liu, “Global 1 km _×_ 1 km gridded revised real gross domestic product and electricity consumption during 1992–2019 based on calibrated nighttime light data,” _Scientific Data_ , 2022, _9_ (1), 202.

> Combes, Pierre-Philippe and Laurent Gobillon, “The empirics of agglomeration economies,” in “Handbook of regional and urban economics,” Vol. 5, Elsevier, 2015, pp. 247–348. Esch, Thomas, Elisabeth Brzoska, Stefan Dech, Benjamin Leutner, Daniela Palacios-Lopez, Annekatrin Metz-Marconcini, Mattia Marconcini, Achim Roth, and Julian Zeidler, “World Settlement Footprint 3D-A
Income data have been adjusted for spatial price differences. We constructed a spatial price index that reflects prices of basic goods, services and housing at the state level, separately for rural and urban areas within the state (see Table A in the Appendix). The index for the years 2004-2016 was derived from the household-specific poverty lines that the Department of Statistics Malaysia calculates for each survey year, which depend on the location and demographic composition of the surveyed household.<sup>15</sup> For 2019 and 2022, we extrapolated the index based on the CPI of the corresponding area.

As explanatory variables in our income model, the method requires we only use variables that are time-invariant. We use three such categories. First, we employ dummy variables for the birth cohort of the household head, using five-year bins. The first bin starts in 1940; the cohort 1965-1969 is the omitted dummy, since it has members in every survey year. Second, we use six dummy variables for the highest level of education enjoyed by the household head. Those with primary education or less form the base category. Third, we use dummy variables for the ethnicity of the household head. The four categories are Bumiputera, Chinese, Indian and Other; Bumiputera is set as the base category. Fourth, we use a dummy variable for the gender of the household head.

In addition, we use two location dummies that, strictly speaking, are not time-invariant. These are one dummy for urban or rural location and another for being located in either Peninsular Malaysia or East Malaysia. This restricts the interpretation of results to households that did not migrate from one such classification to another. Table B in the Appendix provides the OLS regression results of our income models for each survey year, based on the subsample of households with heads in the age range of 25–60.

# 3. Results

We discuss the results of our analysis of economic mobility in three parts. First, we focus on absolute mobility at the lower end of