rafmacalaba/data-use-mentions-extended huggingface dataset:
481|37||603|3,037|
|Coaches: 1000 passenger-km per coach|**416**|900|862|737||4,738|596|
|Cars: 1000 ton-km per wagon|**458**|74|1020|59|804|868|925|
|Locomotive availability in %|**7**|3|35|13|40|26|—|
|**TARIFFS:**||||||||
|Average unit tariff, freight, US cents/tonne-km|**2.4**|2.0|3.3|—|2.2|2.2|—|
|Average unit tariff per passenger<br>US cents/passenger-km|**4.4**|5.8|5.5|—|3.3|5.2|—|

Source: Bullock 2009.

Derived from AICD rail operator database, downloadable from http://www.infrastructureafrica.org/aicd/tools/data — = data not available.

# Ports

## **Achievements**

Ghana has made significant progress in modernizing its ports sector and is committed to making further improvements. It is one of the few African countries in the process of adopting the landlord model, which has become the preferred institutional framework for the sector around the world. At the national level, stevedoring operations have been privatized. The Port of Tema awarded a container terminal concession in 2006, and dry-bulk handling operations are fully privatized. Further concessions are envisaged at the Port of Takoradi, where major new terminal developments are planned.

Ghana’s two major ports, at Tema and Takoradi, are large relative to other West African ports (table 4). Demand for both container and general cargo services has more than doubled over the period 19982006. Under the Ghana Gateway Program, the ports sector is being developed within an explicit
- **Petersen, Mitchell A. and Raghuram G. Rajan** , “The Benefits of Lending Relationships: Evidence from Small Business Data,” _The Journal of Finance_ , 1994, _49_ (1), 3–37.

- **and** , “Does Distance Still Matter? The Information Revolution in Small Business Lending,” _The_

- _Journal of Finance_ , 2002, _57_ (6), 2533–2570.

- **Ponticelli, Jacopo and Leonardo S. Alencar** , “ Court Enforcement, Bank Loans, and Firm Investment: Evidence from a Bankruptcy Reform in Brazil,” _Quarterly Journal of Economics_ , 03 2016, _131_ (3), 1365–1413.

- **Stango, Victor and Jonathan Zinman** , “We are All Behavioral, More or Less: A Taxonomy of Consumer Decision Making,” _Review of Economic Studies_ , 2022.

- **Stantcheva, Stefanie** , “Understanding Economic Policies: What do People Know and Learn?,” _Working paper._ , 2020.

**World Bank** , “World Development Report: Finance for an Equitable Recovery,” 2022. .

31
is vital to note, at the very outset, that crop yields are only a partial measure of productivity. Unfortunately, data constraints do not allow me to perform a multi-factor productivity (TFP) analysis. Hence, an examination of TFP trends may potentially yield different results. I return to the issue in the concluding section and reflect on how the convergence results may play out if TFPs are used as a measure of productivity.

To give some background, Bihar is India’s third most populous state and is home to more than 100 million people. It is also the nation’s poorest state with a per capita net state domestic product (NSDP) that is a third of the national average. In contrast, the situation in much better in Uttar Pradesh (UP) – India’s second-poorest state, where per capita NSDP is almost 50 percent higher (Reserve Bank of India, 2019). The state economy is still principally agrarian. More than 70 percent of the workforce derives its livelihood from agricultural activities (Census of India, 2011). Many observers believe that such an extreme dependence on agriculture together with low yields of cereal crops is the primary cause of the state’s economic woes and widespread poverty (Bansil, 2011). The low agricultural yields in Bihar are a long-standing issue and comparing the evolution of agricultural yields across neighboring states over time reveals its miserable position.

After a long period of stagnation, the eastern states of UP and West Bengal (WB) embarked on a path of agricultural yield expansion that began in the early 1980s. In contrast, the yield gain in Bihar was much weaker and short-lived even though coming off a low base (Pandey and Pal, 2000; Kishore, 2004). For instance, Bihar and UP had similar rice yields in 1980, but the latter produced 50 percent more output per hectare by the turn of the century (Kishore, 2004). Moreover, the rich endowment of agronomic resources makes the prevalence of low yields even more a cause
The existing transfer program is poorly targeted, so that many non-poor families receive transfers and many poor families do not receive benefits. The government of Ecuador is in the process of redesigning the _Bono_ , so that families in the bottom two quintiles of the nation’s wealth distribution will be added to the program roster and those in the top three quintiles will be removed. In addition, to receive transfers families with pre-school children will be required to take them for regular health center visits, and families with older children will be required to enroll them in school. The planned evaluation will focus on new entrants to the social transfer program who have pre-school but no school-age children (and so will be bound by the requirement to take pre-school children to health clinics); as a consequence, these groups where over-sampled. Of the children in the sample, 82 percent are in the bottom two quintiles of the wealth distribution; 98 percent have no school-age siblings; and none received social transfers in the six months prior to the survey. The sample therefore consists primarily of children from young, poor families.<sup>5</sup> Because of these selection criteria, our sample of families are poorer, on average, than families with young children in Ecuador as a whole, and poorer than families in the parishes included in this study. Table 1 shows means of selected characteristics for households in our sample, and for households from two other data sources: The 1999 _Encuesta de Condiciones de Vida_ (ECV), the most recent nationwide multi-purpose household survey conducted in Ecuador, and the 2001 Population Census. We selected households with at least one pre-school child in the ECV and the Census, and calculated nation-wide averages using the ECV, and averages for households in the same parishes included in our sample using the Census.<sup>6</sup> A comparison of columns 1 and 2 indicates that, relative to the national average, households in our sample tend to be worse off: They have fewer rooms, and
569 observations), or WHZ was below -5 or above +5 standard deviations (1,321 observations). Our final sample has about 10,000 children age 0 to 35 months living in rural areas.

The summary statistics can be compared with the international reference group, which has an expected mean Z-score of 0 for all normalized growth indices. In general, Z-scores 2 standard deviations below the reference are associated with severe growth retardation in the case of HAZ, and acute malnutrition for WHZ. The distributions of HAZ and WHZ for children below 36 months of age of both rounds of the Nigeria DHS are displayed in Figure 3. The distribution of height-for-age Z-scores is highly skewed to the left implying that growth retardation is very prevalent among young children of rural areas. The average child is 1.5 standard deviations shorter than the international reference mean, with the two frames might have used a different classification of EAs as rural or urban. However, our results are not qualitatively different if we only use the (larger) 2008 DHS.

14
construction, and can only avail of the subsidy once the toilet is fully constructed and verified as such by local district authorities.<sup>6</sup> Importantly, households can only avail of the subsidy once. SBM provides no financial support to households who want to repair or upgrade a toilet.

From an economic perspective, a renumeration post verification model is a solution to the imperfect commitment problem which exists in this setting. If the Government provided funds prior to construction, it has no commitment device to ensure that the money is indeed spent on toilet construction. However, such a model has a clear limitation: poor households might either not be able to access funds to construct a toilet, particularly in the presence of credit market imperfections; or might be unable to afford to construct a toilet at all. This weakness has been noted by a number of practitioners (e.g. Rama Mohan, 2017), with micro-credit proposed as a potential mechanism through which liquidity constrained households could obtain so-called bridge funding.

SBM officially defines households to be eligible for SBM subsidies if at the time of the SBM baseline survey in 2012-2013 (conducted by communities and verified by district and state officials) they were recorded (a) not to have a toilet, and (b) to be either Below Poverty Line (BPL) or to belong to any of the following marginalised Above Poverty Line (APL) groups (SBM, 2017): (i) Scheduled Castes/Scheduled Tribes (SC/ST), (ii) Persons with disability, (iii) Widow/old age pensioners, (iv) Landless labourers with homestead, (v) Small farmers, (vi) Marginal farmers, and (vii) Female headed households.

We will refer to BPL households and these vulnerable APL groups jointly as _Vulnerable Groups_ (VGs). SBM’s administrative data (see Section 3.2) suggest that overall in rural India, 63% of households were recorded as not having a toilet at 2012-13
Appendix Table S1.5 shows the values of the FGT indices (headcount, poverty gap and squared poverty gap) obtained with both surveys and poverty lines. Not much difference is found between the two surveys. PSF finds more people under the nutrition threshold (16.7% versus 12.5%), but less under the basic needs one (42.8% versus 45%). Using the Datt and Ravallion decomposition framework (Datt and Ravallion 1992), we can decompose the difference in the poverty measures from the 2 surveys into its “growth” (difference in mean consumption) and “redistribution” (difference in inequality) components, and a residual. The fact that mean consumption is higher in the PSF data should lead to a decrease in poverty relative to ESPS, but the higher inequality plays in the opposite direction. Which of these components plays most is not a priori obvious. It happens that in our context, these two opposite biases happen to nearly exactly compensate each other.

# **3 Intra-Household Inequalities**

# 3.1 How Important are Intra-Household Inequalities?

Table 3 reports various measures of inequality, first on total consumption and then on food and non-food consumption separately. The top panel of the table shows inequality statistics, computed across individuals, assigning them per capita _household_ consumption, while the bottom part displays those obtained using per capita _cell_ consumption.

As expected, the Gini index of inequality in the distribution of per capita household food expenditures is much lower than that of non-food spending, reaching 39.2% vs. 62.7%.

Using per capita cell consumption rather than per capita household consumption, inequality levels are revised upward.<sup>16</sup> Most of the difference comes from the inequality of nonfood consumption, while inequality in food consumption is only mildly affected. The Gini of 15
region of Nigeria, subject to intrinsic model robustness, which may lead to imputation of egregious price data outputs, especially over a long timeframe, characterized by multiple seasons. Note that the period was ostensibly defined by a market-disruptive shock of calamitous proportions, the COVID-19 pandemic, which triggered unanticipated episodic price behavior. The market price effects during this period were duly reflected in the trend of crowdsourced data, as previously reported (Adewopo, et al., 2021), and the strong correlation with AI-estimate prices suggest that the underlying algorithm effectively modeled both the periodic shifts and the indicative monthly changes in prices.

It is noteworthy that the inherent price signals in crowdsourced data became more observable as multiple daily submissions were averaged into daily, weekly, and monthly values. This is attributable to the tendency of averaged price values to coalesce around the modal values, thereby minimizing the effect of outliers and amplifying the signal-to-noise ratio. The emergence of observable price signal from the plume of crowdsourced datapoints can be valuable for various purposes in food system analysis and decision-support. At the basic level, the detectability of price signals addresses a major need to track seasonal price dynamics, and can support the development of agile analytical systems that links commodity prices to ancillary socio-economic and ecological factors. Unequivocally, the temporal granularity of commodity price monitoring systems should match the relevant temporal scale of target market-focused policies or food systems intervention. Therefore, the highfrequency datapoints proffer the possibility to aggregate price data at the required timescale for data-driven insights and decision-making in fragile national or regional contexts.

The monthly AI-based estimates are currently generated across 36 countries, with comparable survey data available from different organizations to potentially scale up such approaches in other countries. The strong correlation between AI-estimated prices and crowdsourced data, triangulated against enumerator-led data, over an extended timeframe 12
Gini rises by around 30% after the replacement of top incomes.

# Reweighting and replacement

The third solution to missing top incomes that uses household and administrative data is a combination of the previous two methods. Blanchet et al. (2022) focus on reweighting to solve the under capturing of the top tail in household surveys, but they also use replacement. The authors highlight that in previous work using replacement of household survey top incomes with admin data (including Alvaredo (2011) for the US, Burkhauser et al. (2018) and Alvaredo and Londono Velez (2013), all discussed in the previous section), the point in the distribution where survey data information is replaced with administrative data is chosen arbitrarily (e.g. the top 1%). The novel contribution of the authors is to endogenously determine the optimal merging point between household surveys and tax data. Having done so, the authors use survey calibration methods to ensure that the numbers of individuals in each tax bracket in the surveys and the tax data match.

Whilst the calibration solves non-sampling error problems, Blanchet et al. (2022) note that the method still leaves small samples at the top of the distribution, which can, as discussed above, result in large 19
# **I. INTRODUCTION**

A major focus for Tanzanian policy makers is how to attain middle-income status by 2025<sup>2</sup> through greater economic diversification to reduce poverty and to ensure good quality jobs for the approximately 800,000 young people who enter the labor market each year. Creation of new jobs of good quality will require more rapid economic and productivity growth and, on the demand side, growth in employment from expansion of existing firms and the creation and growth of new enterprises. The skills deficit in the labor force is acknowledged to be a constraint in Tanzania for both existing and new firms, given the low levels of educational attainment and technical skills of the population. However, there is little data on the specific skills shortages that firms face, the strategies they use to address these shortages, and the way these skill deficits impact their operations.

The most important source of systematic information on the demand for skills in African companies comes from Enterprise Surveys fielded by the World Bank.<sup>3</sup> The Enterprise Survey contains information on firm characteristics and performance but only limited data on workforce skills; where it shines is in detailed assessments by firms’ managers of different elements of the business environment, rankings of obstacles, and their overall impact on their operations. Many elements – power outages, corruption, infrastructure, access to finance and changes in regulatory or fiscal regimes – directly constrain or introduce uncertainty into firms’ operations, and as such loom large in rankings of major business environment constraints. Hence, rather counterintuitively, the results seem to show that availability of skills is not a major constraint for firms in Africa.

This study uses a unique survey of Tanzanian businesses to investigate the demand for skills in formal-sector firms,<sup>4</sup> how they respond to skill constraints, as well as the effects on productivity of their strategies to meet skills deficits. The motivation for this study was to inform the policy dialogue on developing higher level skills
|
|_Northern_|-16.1|-18.9|14.9|5|3|0|5|
|_Sub-Saharan_|-30.7|-32.1|5.7|2|26|7|39|
|Americas|-22.5|-20.0|7.7|4|14|2|23|
|Asia|-24.5|-16.6|4.0|1|18|3|43|
|Europe|-13.1|-9.9|3.0|2|16|1|35|
|_Total_|-23.5|-18.9|9.4|3|77|13|145|

_Notes_ : Depth is defined as the percent decrease in GDP per capita at the trough relative to GDP per capita before the slump (not log difference). Mean and median duration are expressed in years. As a result of some spells being censored, both mean duration and depth are underestimated. The number of countries refers to countries with more than one million inhabitants and more than 20 observations of GDP per capita in a particular income group or region. _Source_ : Authors’ analysis based on data from the Penn World Tables version 9.0 (series _rgdpna/pop_ ).

et al., 1993, Rodrik, 1999, Pritchett, 2000, Jones and Olken, 2008). Several collapses follow the post-communist transitions of 1989–1990. We find no slumps starting in the period of the early and mid-2000s, but a handful of slumps begin in 2008.<sup>9</sup> Note that the global recession of 2008–2009 is still a bit too close to the end of the sample to permit reliable

> 9Supplementary Appendix S4 reports the annual frequency of starting dates for both samples.

13
HIPCs and Fragile countries are the groups most affected by shocks, both in the size and length of breaches, while Oil Exporters are less affected, except for the shock on exports growth (B2).

Overall, LICs on average are more vulnerable to suffer a downgrade in their risk ratings because of shocks affecting their solvency rather than liquidity, a result consistent with their historical – albeit decreasing – access to concessional financing.

# **4.3 Assessing Debt Vulnerabilities of SSA Oil Exporting Countries after the 2014 Oil Price Shock**

This section sheds some light on the SSA oil exporting countries’ debt vulnerabilities resulting from the oil price shock of late 2014, which directly affects their debt repayment capacity. The exercise illustrates how the different debt burden indicators – and the risk of debt distress – would change in each of the four oil exporting countries after the shock, if they were to substitute the lower exports and government revenues with new government (PPG MLT) debt, so that no adjustment is forced on the economy – no reduction in imports, government expenditures or changes in the exchange rate occurs. The magnitude of the changes in the debt burden indicators under such, albeit extreme, assumptions illustrates how exposed oil exporting countries are to oil price shocks or, alternatively, to what extent an adjustment to the above variables is warranted if a risk classification downgrade is to be avoided.

In order to evaluate how significantly the oil price shock can affect the countries’ debt situation, we make new projections for the five debt burden indicators using the latest available DSAs and a new oil price series that incorporates the drop of late 2014 and an assumed slow recovery thereafter. We begin by comparing the oil price projections made by the US Department of Energy in April of 2014, with those made by the World Bank Prospects Group in January of 2015. The comparison shows a drop of 45 percent in the oil price in 2015 (year average) and a recovery thereafter of 7 percent yearly until 2023. We use the projected drop
Table 3: The effects of 10 FDI projects on wages by skills and gender (% change)

||Me|n|W|omen|
|---|---|---|---|---|
||Skilled|Unskilled|Skilled|Unskilled|
|Brazil|0.00|1.01|0.00|1.01|
|Colombia|1.01|4.08|1.01|3.05|
|Ethiopia|-11.31|8.33|-1.98|55|
|Mexico|0.00|3.05|1.01|5.13|
|Philippines|4.08|10.52|5.13|13.88|
|South Africa|-1.00|2.02|0.00|6.18|
|Vietnam|4.08|4.08|2.02|4.08|
|From Eq. 2|_e_<sup>10(</sup><sup>_λ_+</sup><sup>_η_+</sup><sup>_ω_+</sup><sup>_ν_)</sup>|_e_<sup>10(</sup><sup>_λ_+</sup><sup>_ω_)</sup>|_e_<sup>10(</sup><sup>_λ_+</sup><sup>_η_)</sup>|_e_<sup>10</sup><sup>_λ_</sup>|

Note: The numbers are percentage increases based on estimates from equation 2.

tariffs reduce the skill premium.

# **4 Conclusion**

In this paper we examined the effect of FDI on wage skill premia in seven emerging economies. We matched information on FDI penetration from a project-level FDI data set with data on
1982 downturn (Leslie et al. 1988). More recently, this effect was also present during the Latin American economic crisis of the mid 1990s in urban Argentina (Cerutti 2000). The only exception to this pattern in Latin America comes from Humphrey’s (1996) analysis of female labor force participation rates in Sao Paulo, Brazil during the debt crisis of the 1980s. Using data from Argentina, Pessino and Gill (1997) apply different measures of the business cycle and estimate the impact on women’s and men’s labor force participation. They find that all women’s labor force participation is countercyclical while among men, labor force participation is countercyclical only for those aged 20-49. Analysis of household survey data from Mexico’s Peso crisis of the mid-1990s also shows evidence of a female added worker effect. Skoufias and Parker (2006) find that during the Peso crisis wives were 14 percent more likely to enter the labor force as a result of husband’s transition to unemployment. Parker and Skoufias (2006) analyze the impact of the male household head’s unemployment on the wife’s probability of entering the labor force during the economic boom or recovery period in Mexico and compare the impact to that obtained during a recession. They find that husband’s unemployment increases the wife’s probability of entering the labor force, during both economic crisis and economic prosperity, but this effect is larger during the crisis.

There is also some evidence of rising female labor force participation in some countries during the East Asian crisis of 1997. As male unemployment increased, female labor force participation rose in the Philippines (Lim 2000) and Indonesia (Smith, Thomas, Frankenberg, Beegle, and Teruel 2002). Using household level data from Demographic Health Survey from 66 countries and across 21 years (1985-2006), Bhalotra and Umana (2009) show that globally, on average, a 10 percent drop in country 8