rafmacalaba/data-use-mentions-extended huggingface dataset:
governing the stochastic process of firm-wide productivity. Taking logs of firm-wide employment in the region where the product range is less than one, we get: where Ψ = <u>�(</u> _fρp_<sup>(</sup><sup>_η_</sup> _−_<sup>_−_</sup> 1<sup>1</sup> <u>)</u><sup>)</sup> <u>�</u> _η−η_ 1<sup>collectsparametersrelatedtothefixedcostofprovisionof</sup> products, the elasticity of substitution across varieties within a product, and the tail of the Pareto distribution of product attributes.

The moment in the data that we target to pin down the step size _h_ is the standard deviation of the distribution of employment growth rates for large firms, which in the US economy is equal to 0.25. It is easy to show that, in the stationary equilibrium : of our model, such variance is approximately equal to<sup>11</sup> Thus, for a given value of _η_ , the standard deviation of employment growth rates in the cross-section of large firms in the United States implies that _h_ = 0.25/ _η_ . We take into account that there exists a small fraction of large firms supplying all the products, for which _η_ no longer plays a role. For these, the variance of employment growth rates is driven entirely by the step size in the process for productivity, so we set _h_ = 0.25 for such group.

In terms of the probability of technological upgrading, _α_ , we set it to match properties of the right tail of the U.S. employment based size distribution. Luttmer (2010) highlights the linearity of the right tail of the US establishment and firm size distribution across employment, as well as the stationarity of the distribution over time, . using various sources of US micro-data<sup>12<
# **ABSTRACT**

Most high and middle-income countries showed symptoms of skill-biased technological change in the 1980s. India – a low income country – did not, perhaps because India's traditionally controlled economy may have limited the transfer of technologies from abroad. However the economy underwent a sharp reform and a manufacturing boom in the 1990s, raising the possibility that technology absorption may have accelerated during the past decade. We investigate the hypothesis that skill-biased technological change did in fact arrive in India in the 1990s using panel data disaggregated by industry and state from the Annual Survey of Industry (ASI). These data confirm that while the 1980s were a period of falling skills demand, the 1990s showed generally rising demand for skills, with variation across states. We find that increased output and capital-skill complementarity appear to be the best explanations of skill upgrading in the 1990s. Skill upgrading did not occur in the same set of industries in India as it did in other countries, suggesting that increased demand for skills in Indian manufacturing is _not_ due to the international diffusion of recent vintages of skill-biased technologies.

**JEL keywords:** O300 - Technological Change; J240 - Human Capital; Skills;  O530 - Economywide Country Studies: Asia ; O120 - Microeconomic Analyses of Economic Development; F160 - Trade and Labor Market Interactions.

1
# **1 Introduction**

The world population has been growing rapidly. This is particularly true in Africa where the population has multiplied manyfold since the beginning of the 20th century, and is predicted to reach four billion by the end of the 20th (UN 2013). Increases in the African population have spurred urbanization in general, and the emergence of mega-cities of several million inhabitants in many -- if not most -- African countries (Lall, Henderson and Venables 2017). How this rapid expansion of human settlements unfolds over geographical space is of paramount importance for development (e.g., Duranton 2015) and has likely deep consequences on inequality and poverty (e.g., UNDP 2017). While these structural changes create formidable challenges for policy makers, relatively little is known -- beyond the above general observations -- on the process by which urbanization is unfolding in the continent.

The purpose of this paper is to document how the spatial distribution of buildings has evolved over four decades in Ghana. To do this, we take advantage of the recently available Global Human Settlement Layer (GHSL) data from the European Commission, Joint Research Centre, which used machine learning tools to predict the presence of buildings from satellite imagery. At the heart of this study is a panel dataset of 3.8 million rectangle-shaped cells covering the entire territory of Ghana and observed in 1975, 1990, 2000, and 2014. For each of these cells, the GHSL dataset has an ML prediction of the proportion of that cell’s area that is covered by buildings. We regard the existence of buildings as indicating the presence of people and economic activity. Given that we have precise geocoordinates for each cell, we can combine this large dataset with information on roads, humidity, soil types, and proximity to borders and bodies of water.

We start by documenting the evolution of the distribution of land by intensity of construction. Over the period of our study, the average proportion of the country with buildings
and what is noncontributory and received from the Ministry of Labour, Employment, Veteran and Social Affairs. We used the reported values from the SILC database for all the benefits.<sup>9</sup> Assigning the value of public spending on education follows the government cost approach. Since the education system is mostly public, we calculate per beneficiary input costs by level of education as total government spending divided by the number of pupils in each level of education. This per pupil expenditure is then assigned as education benefit to each student. We use the number of students from the SILC data for tertiary education since it includes post graduate students. For the other levels of education, the number of students from the SILC is almost equal to the numbers from administrative data. For health expenditures, since access to services is near universal, we follow the cost of insurance approach and assign per capita expenditures (obtained by dividing total expenditure by the total population in Serbia) for each person in the dataset.<sup>10</sup>

> 9 For the financial social assistance, reported values are used, complemented by imputed values for some households. Validation results indicate that the number of individuals that report receiving the financial social assistance in the SILC data roughly corresponds to the number of beneficiaries in the administrative data. However, total expenditures in the survey are underestimated by about 50 percent. After careful investigation of the reported amounts, this underestimation seems to be largely due to reporting the monthly, instead of yearly as asked, amounts in the SILC data set. For those that report amounts close to the monthly benefit levels (about 35 percent of the total population that report the receipt of the benefit), we impute administrative benefit amounts. After this correction, the total value of financial social assistance from the SILC equals 86 percent of its value in administrative data.

> 10 The values for education and health benefits are scaled so that the ratio of benefit to income is the same in the administrative data (budget
standard 6-10) will follow on turning eleven, with the further prospect of continuing on to high school (standard 11-12) on reaching sixteen. In fact, some children start a year earlier, others a year or two later; and some have to repeat a class. Some children attending primary school will, therefore, be twelve or thirteen; likewise, some of those attending secondary school will have turned sixteen. To allow for all this, we have added one year to the ‘normal’ progression, so that nineteen-year olds are included.

## **4.1 The household survey**

The general position in _kharif_ 2009 was as follows. There were altogether 394 children aged six to nineteen, whose classification by age group, whether their village enjoyed an all-weather road, and whether they were attending school is set out in Table 8. All but two of the 30 villages had a primary school, and all but two of the 163 children of 18
which underlie this paper’s estimates of unit costs of implementing surveys is expanded. Moreover, costs will vary depending on the level of detail of a given survey.<sup>21</sup> In order to improve our understanding about the price of household survey data, it is important to systematically collect both ex-ante cost estimates during the survey design phase as well as ex-post actual costs for survey implementation and technical assistance.

Related to this, it is important to undertake further analyses to understand why the survey implementation cost in Africa is so high. Our current database suggests that household survey implementation in Africa costs at least USD 300 per household, which is easily five times more than the comparable figure in other regions, such as Europe and Central Asia (ECA) and Latin America and Caribbean (LAC), with more developed countries. One reason may be that the selected surveys in Africa are more complex than those implemented in ECA and LAC. If this is the case, an important question is what level of complexity we should aim to maintain. Increasing the complexity of a survey may raise survey implementation costs and exert potentially adverse effects on data quality if increased complexity is not met with commensurate resources for technical assistance. Another reason may be that hiring of qualified and well-educated enumerators in Africa may be costlier than in other regions.<sup>22</sup> While the average enumerator compensation in Africa may be lower, finding well-educated and highly qualified enumerators might be more difficult. The scarcity of qualified enumerators might in turn increase labor costs and thus survey implementation costs in Africa. If this is the case, improving training quality for enumerators and simplifying data collection via new technologies will be critical for reducing survey costs and increasing country’s ability to self-finance survey implementation.

Support to data production alone will not be sufficient to alleviate weaknesses in survey methods underlying sustainable development data. The World Bank Household Survey Strategy notes that if poor quality and limited comparability of the available survey data are considered, the crosscountry
# **V. Conclusion**

Teacher performance constitutes a critical determinant of student learning. As such, continuous contact with poor-performing teachers over several years could result in significant learning gaps. In Ecuador, evidence indicates that children in poor localities face significant risks of being taught by teachers who do not master the curricula of the class they teach. Results from teacher assessments indicate that a large majority of teachers underperform in mathematics, Basic General Education, and the natural sciences (BGE level), and in mathematics, physics, and biology (UGB level). Given the fact that many teachers in Ecuador enter the education system with important cognitive gaps, in-service teacher training could be an important tool to strengthen teacher performance.

Ecuador in recent years has made important investments in improving teacher development and quality. Since 2008, the MINEDUC has been promoting in-service teacher training through its Comprehensive <mark>System for the Development of Education Professionals</mark> (SIPROFE) program. Since 2010, the SIPROFE program has delivered in-service teacher training to about 75 percent of Ecuador’s total teacher population. Nevertheless, training programs delivered under the SIPROFE program have been supply-driven, focused on improving pedagogy, and not necessarily designed to respond to the observed teacher’s cognitive skills gaps. Moreover, their effectiveness has been largely unassessed, and program targeting has not prioritized school districts where teacher development programs are needed the most.

Ecuador currently uses teacher and student assessments that can identify in detail the cognitive deficiencies of students and teachers. These data, however, have not been used to guide the design of in-service teacher training programs. Ecuador’s teacher assessment ( _Ser Maestro_ ) evaluates teachers on the subjects they teach in the classroom and identifies curricular areas within a teaching subject where teachers display cognitive gaps. The specificity of this information enables personalized development programs to be designed based on teachers’ actual needs. Program administrators and participating training providers could use this information to develop a more pertinent training curriculum that addresses the knowledge gaps of teachers across
level. They find that the poor are most likely to reduce savings and least likely to engage in behavioral change. Decerf, Ferreira, Mahler and Sterck (2020) provide global mortality and poverty estimates and estimate that the average number of additional years spent in poverty due to COVID-19 will be about 15 times greater than the number of lives lost. Busso, Camacho, Messina and Montenegro (2020) focus on social assistance to households in Latin America and find a substantial coverage gap in the 2nd and 3rd quintiles. Similarly, Lustig, Pabon, Sanz and Younger (2020) report evidence on the impact of lockdowns and expanded social assistance in Argentina, Brazil, Colombia, and Mexico. They conclude that impacts were worst for households in the middle of the ex-ante per capita income distribution.

At least two studies examine real-time private sector data to document impacts. Abay, Tafere and Woldemichael (2020) use Google Search data to estimate the demand for various services in 182 countries and find substantial contraction in demand for services such as retail trade, restaurants, and hotels. Meanwhile, Sampi and Jooste (2020) use Google Mobility data for nowcasting economic activity in the Latin America & the Caribbean region and find that it predicts falls in industrial production.

Several related papers consider the ability of different workers to work from home in a wide set of countries. Garrote Sanchez et al. (2020) examine the EU and find that jobs most at risk account for 30 percent of all EU employment and tend to be filled by lessskilled workers. Gottlieb, Grobovˇsek, Poschke and Saltiel (2020) find that only about 20 percent of urban workers can work from home in poorer countries, versus 37 percent in rich countries. Hatayama, Viollaz and Winkler (2020), using a different sample of 53 surveys, confirm that more developed countries have a greater share of jobs amenable to working from home. Delaporte and Pe ̃na (2020) study working from home in Latin American and Caribbean countries and estimate that the share of
production of electricity. A number of studies of electricity trading in the US before by Enron bankruptcy in late 2001 estimated that each electron ultimately delivered through a US wholesale electricity market was bought and sold in forward markets more than five times. For this reason, even if the regulator attempted to collect this forward market data from suppliers on a regular basis it would not be very useful. For example, if the regulator specified a minimum quantity of forward contract sales for each supplier it regulated, these suppliers could undertake forward contract transactions with affiliates not subject to regulatory oversight to meet these minimums. Moreover, those affiliates not subject to oversight by the regulator could then re‐construct their holding company’s desired forward contract position. Consequently, routinely collecting the forward contract positions of suppliers could cause them to render this information of little or no use to the regulator through affiliate transactions.

There is a strong argument for keeping any forward contract positions the regulator might collect confidential. As noted in Wolak (2000a), the financial forward contract holdings of a supplier are major determinants of the aggressiveness of its bids into the spot market. Only if a supplier is confident that it will produce more than its forward contract obligations will it have an incentive to bid or schedule its units to raise the market price. Suppliers recognize this incentive created by forward contracts when they bid against competitors with forward contract holdings. Consequently, public disclosure of the forward contract holdings of market participants can convey useful information about the incentives of individual suppliers to raise market prices, with no countervailing reliability or market‐ efficiency enhancing benefits.

A final aspect of the data collection portion of the regulatory process is concerned with scheduled outage coordination and forced outage declarations. A major lesson from wholesale electricity markets around the world is the impossibility of determining whether a unit that is declared out‐of‐service can actually operate. Different from the former vertically integrated regime, declaring a “sick day” for a generation unit‐‐saying that it is unable to operate when
the advantages and disadvantages of the LandScan (2012) dataset on economic activity that we use. We then try to ground-truth LandScan (2012) data using Nairobi and Kampala as test cases. Section 2 defines various measures of economic density for urbanized and rural areas, decomposing them into first and second moment components. Section 3 compares the extent of economic density and its attributes for African cities versus other cities worldwide. Section 4 looks at the relationship between different measures of economic density and income differentials across the whole spatial hierarchy. Section 5 looks specifically at cities and examines issues such as the optimal rate of spatial discount for De La Roca and Puga (2017) measures of neighbor effects; the role of the second moment aspects of economic density measures; and how important local density measures are within cities, as well as location within the city. Section 6 looks in detail at how the productivity of firms within Kampala is related to the 3
**Solicit specific topics of interest.** Motivation, perception of relevance, and other behavioral factors have also been shown to be an important factor in whether participants benefit from financial education or advice (Varcoe et al., 2005; Hung and Yoong, 2010). More work is needed in these areas in order make financial education programs more relevant and effective.

**Be creative with the delivery format of financial education programs.** Some evaluations suggest that information might be best conveyed through interactive or experiential program designs, such as actually opening up a bank account, or through computer or video games.  A number of interventions convey information via a dramatic or fictional medium, such as radio or television soap operas (e.g. Spader et al., 2009). Other formats include formal courses, seminars and workshops, individual counseling, and DVD instruction. The optimal format, of course, would also depend on the type of information being delivered, and the target audience. Individuals of different geographic, socioeconomic, and cultural backgrounds may find certain formats more engaging or informative than others. More research should be done in this area as well.

**Implement complementary interventions to achieve objectives** . With regard to increasing financial access and take-up of financial services, such as savings accounts, financial education may not be as beneficial as other interventions, such as subsidies (Cole et al. 2011). Some have also argued that consumer protection measures may be more effective than improving individual financial literacy in preventing fraud. Other studies indicate that behavioral factors, such as impatience, may play a bigger role in financial outcomes than financial illiteracy (Hastings and Mitchell, 2011).

**Consider supply-side failures.** Financial education and other consumer side interventions to increase take-up may be misguided in cases where the products are fundamentally flawed. In this case, stronger regulation and consumer protection measures would be required. For instance, the FinScope survey in Kenya indicates that some individuals may mistrust insurance products
efficiency include excessive regulation, the prevalence of organized crime, and the general condition of the country’s infrastructure. The authors also emphasize that some level of regulation increases port efficiency, but excessive regulation can be damaging.

The positive impact of port efficiency on trade is confirmed by several other studies. By using port efficiency measures from a survey of Latin American port terminals in 1999 and conducting a principal component analysis, Sanchez et al (2003) show that trade cost decreases by 0.06 percent because of a 1 percent increase in port efficiency. Similarly, Wilmsmeier et al (2006) show that a 1 percent increase in port efficiency leads to a 0.38 percent reduction in trade costs in 16 Latin American countries in 2002. Herrera Dappe et al (2017) find that the more efficient the port, the lower the maritime transport costs in the Indian and Western Pacific Oceans. An efficient port sector would reduce countries’ average maritime transport costs by up to 14 percent and increase exports by up to 2.2 percent.

**Table 15: Survey of selected papers on the development impact of ports**

|**Author**|**Level of**<br>**Analysis**|**Country/**<br>**Region**|**Indicator of**<br>**Development**|**Main Findings**|
|---|---|---|---|---|
|Clark et al|County|Global|International|Improving port efficiency from the 25th to the 75th percentile reduces shipping|
|(2004) *+|||Trade|costs by 12%. Bad ports are equivalent to being 60% further away from<br>markets.|

42
Yi =a+P-gi ***+(e,-P-U,)** (4) We identify the model with the assumption is that the observable determinants of governance included in x; are uncorrelated with the composite error term in Equation (4). In particular, these determinants of govemance must be uncorrelated with both the other determinants of income excluded from our simple specification (captured by ej), and with the measurement error in governance (captured by uj), i.e. E[x1 * e, ] = 0 and E[x, u;] = O. Under this assumption, the elements of x1 are valid instruments for measured governance gj* in Equation (4). Given such instruments, we can address the problems of omitted variables bias, simultaneity bias, and attenuation bias using twostage least squares (2SLS).

We follow Hall and Jones (1999) in using the fraction of the population speaking English, and the fraction of the population speaking a major European language (English, French, German, Portuguese or Spanish) as instruments. As Hall and Jones (1999) argue, a major feature of world history in the past several centuries has been the spread of Western European influence across countries. Together with this influence came an institutional and cultural background that in certain countries was conducive to the development of strong institutions and good govemance. Since the extent of this influence varies widely across countries and may safely be thought of as exogenous to economic outcomes centuries later, proxies for this influence such as the linguistic variables are good candidates for instruments.13

# Data

Our data are drawn from readily-available sources. The six governance indicators have already been discussed in the previous sections, and refer to the period 1997-98. We measure per capita income as the average over all available years 13 Hall and Jones (1999) also use distance from the equator as a proxy for the strength of past Westem European influences. We found this not to be a good instrument inour