rafmacalaba/data-use-mentions-extended huggingface dataset:
we demonstrate the importance of history and of expectations in determining outcomes. Essentially, a city can be built in a manner which makes it difficult to attract tradable production. This might be a consequence of low (and self-fulfilling) expectations or of history.

While the paper is primarily theoretical, we start by outlining three features of African cities that are illuminated by the model. The first is African cities’ failure to create jobs in internationally tradable goods or service sectors. Gollin et al. (2016) investigate this, principally at the national level, establishing the adverse effect of natural resource sectors on manufacturing employment. Natural resource dependence is only part of the story, and there are also significant regional differences. Focusing on Africa, Jones (2016) compares manufacturing shares of GDP in African and non-African economies at different stages of urbanization. In non-African economies, the manufacturing share of GDP rises from 10% to nearly 20% as the urban population share rises to 60%, above which it falls back. In Africa, the manufacturing share remains flat (or somewhat falling) at around 10% of GDP through a cross-section of urbanization rates ranging from 10% to 70%.<sup>2</sup> A more urban focus can be derived by using spatially disaggregated IPUMS data.<sup>3</sup> These are sample data collected at the individual level, with selfdeclared sector of employment. Table 1 reports the share of employment declaring to be in manufacturing in areas classified as urban. The data are presented by city (selected by size within country) and by country (in India this is urban area by state, with separate city level data unavailable). While manufacturing is not synonymous with tradable goods, the data indicate clearly the extent to which Africa is different from other regions. Simple averages across countries suggest manufacturing shares of urban employment nearly three times greater in Asia than in Africa, and increasing through time, whereas Africa’s shares have declined slightly. Accra appears as
harvest, which will minimize recall bias for crop production estimates. However, even in this case, input application may have occurred several months before and the harvest may have been conducted over several months. Adding to this, survey data are often collected over several months, often twelve or more. These data potentially suffer from recall bias since both farming decisions and data collection occur over the course of several months, leading to long and variable recall periods across households. This paper studies the extent of recall bias in agricultural statistics, using data from three national surveys conducted over a year such that the recall period has sufficient random variation across sample households. This work does not speak to concerns about the overall quality of data from smallholders. However, it does address at least one aspect of data collection – length of recall period – which could compromise data quality, and assesses whether differences in recall periods consistently affect the reported values.

Using various specifications on key input and crop production measurements, we generally find little evidence of recall bias for the average farm household. With regard to harvest estimates and fertilizer usage, we find only isolated incidents of statistically significant results for some specifications including interaction terms. Findings are similar for cash crop sales in Kenya and Rwanda and labor use in Rwanda. The most evidence of recall bias in production data is found for tobacco sales in Malawi, where, interestingly, the direction of the bias is generally positive, contradicting the initial hypothesis that the forgetting of events as time passes would lead to under-reporting and suggesting, instead, other possible sources of bias. We also find statistically significant evidence of recall bias in labor use for maize crops in Kenya and Malawi. These results represent substantial changes in terms of magnitude with regard to the mean, but they are in opposing directions, with over-reporting in Kenya and under-reporting in Malawi as recall durations are increased.

Finally, we find some evidence supporting the salience hypothesis. Hired labor usage requires payments and is not common, suggesting some salience. Yet, we
financial statements and other sources for publicly traded companies as well as data on entities’ supply chain relationships.<sup>11</sup> We use fuzzy matching methods to identify Factset entities in the Lightcast data on job open-

> 9Annual energy consumption is available only for 2020. While this value may have been impacted by the economic effects of the COVID-19 pandemic in 2020, we focus on energy intensity, i.e. energy consumption relative to sales, and since both are measured as of 2020 we believe this ratio is likely relatively constant across years.

> 10Acemoglu et al. (2020) define establishments of a firm in Lightcast (previously called Burning Glass) data as the collection of firm job vacancies in each US commuting zone. We define subnational regions as the highest-level administrative subdivision in each country, Territorial Level 2 (TL2) in OECD (2021).

> 11These Factset firm-level entities are defined as _de jure_ corporate institutions (e.g., firms and affiliates).
THE CENTRAL AFRICAN REPUBLIC’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Box 2. How different are estimates from households surveys compared to government data?**

The Africa Infrastructure Country Diagnostic (AICD) uses the Joint Monitoring Data (JMP) coverage statistics as the main source of access data on water supply and sanitation and processes these using a standardized methodology to allow cross-country comparisons by technology rather than by improved or unimproved water supply or sanitation. These data might differ from those reported by governments. Whereas the JMP data are based on household surveys and therefore reported by users of the services, the government data are based on other methodologies. The CAR’s Directorate General of Water (DGH) calculates access by technology, multiplying the number of existing water or sanitation assets (functional or not functional) by a norm of how many people are served by each point. In the CAR the DGH data do not take into account population projections. The DGH estimated that by the end of 2009 there were 3,200 water points in the CAR. In 2010 the Water and Sanitation Public Expenditure Review estimated that for 2008 the access rate to safe water was 20 percent in rural areas, assuming 300 users per water point, 25 percent nonfunctioning water points, and 2.5 percent annual population growth.

Other factors underlying these potential differences include the definition of which technologies constitute improved access to water supply and sanitation, and the JMP’s use of several household surveys vis-à-vis the use of a single data point by several governments. Therefore, measures of progress toward the Millennium Development Goals (MDGs) and of the spending needed to achieve them might differ according to the data source used.

_<u>Source:</u>_ <u>AICD and World Bank 2010b.</u>

# **Challenges**

The vast majority of the population is barely on the first rung of the water and sanitation ladder with major concerns about quality of service.

Around 70 percent of the population relies
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2442 A Firms's-Eye View of Policy and<br>Fiscal Reforms in Cameroon|Bernard Gauthier<br>Isidro Soloaga<br>James Tybout|September 2000|L. Tabada<br>36896|
|WPS2443 The Politics of Economic Policy<br>Reform in Developing Countries|Richard H. Adams Jr.|September 2000|M. Coleridge-Taylor<br>33704|
|WPS2444 Seize the State, Seize the Day:<br>State Capture, Corruption, and<br>Influence in Transition|Joel S. Hellman<br>Geraint Jones<br>Daniel Kaufmann|September 2000|D. Billups<br>35818|
|WPS2445 Subsidies in Chilean Public Utilities|Pablo Serra|September 2000|G. Chenet-Smith<br>36370|
|WPS2446 Forecasting the Demand for<br>Privatized Transport: What Economic<br>Regulators Should Know, and Why|Lourdes Trujillo<br>Emile Quinet<br>Antonio Estache|September 2000|G. Chenet-Smith<br>36370|
|WPS2447 Attrition in Longitudinal Household<br>Survey Data: Some Tests for Three<br>Developing-Country Samples|Harold Alderman<br>Jere R. Behrman<br>Hans-Peter Kohler<br>John A. Maluccio<br>Susan Cotts Watkins|September 2000|P. Sader<br>33902|
|WPS2448 On "Good" Politicians and "Bad"<br>Policies: Social Cohesion,<br>Institutions, and Growth|Jo Ritzen<br>William Easterly<br>Michael Woolcock|September 2000|A. Kibutu<br>34047|
|WPS2449 Pricing Irrigation Water: A Literature<br>Survey|Robert C
⋅ The term ρ W.FDI is the spatial autoregression term. The matrix W is the spatial lag weighting matrix estimated based on the geographical contiguity between governorates and ρ is a parameter to be estimated measuring the strength and sign of the spatial relationship of FDI inflows.

# **5. Estimation results**

Table 5 presents the results of the GMM regression of regional FDI on a set of economic, institutional and geographical determinants. We note that the lagged FDI variable is positive and statistically significant in all specifications of the model confirming the high persistence effect of this variable. This result indicates that past FDI values inflows have a significant influence on current FDI activities. This finding is supported by the work of Campos and Kinoshita (2003) who point out that in presence of agglomeration effects, past FDI inflows will be a good predictor of current FDI even after controlling for other classical factors. According to the same authors, historically, FDI is found to agglomerate more often than financial investments partly because this type of investment is more of a long-term capital investment that is irreversible in the short term. The statistically significant and positive coefficient of regional GDP as a proxy for the living standard and market size of the region lends support to our hypothesis above that regions with high GDP (mainly clustered along the littoral) tend to be more attractive to foreign investors. According to recent government statistics, the size of the markets along the coastal regions is substantially larger than the rest of the country and represents about 65% of the total population of the country, with 3 out of 4 local firms being established in these regions. The proximity from key government institutions that facilitates the access to information and reduces the transaction costs for doing business, the presence of a relatively well-developed infrastructure network and the abundance of relatively well-educated labor force in the coastal regions could also explain this result. This clustering effect of FDI is not surprising. According to Walsh and Yu (2010), historically foreign firms have a tendency to gather together in specific
/sup> definition of green tasks, we find that 46 out of 428 occupations in ISCO-88 can be classified as strictly green, meaning that their strict-GTI is greater than zero.<sup>25</sup> When using the broad definition of green tasks, we count 137 occupations can be classified as broadly green; the additional 91 occupations are those that do not include any strictly green tasks but do include tasks on the broad green tasks list. See Tables A1 and A2 in the Appendix for the full lists of strictly green and broadly green occupations.

24 The strict definition of green tasks is measured by the total number of strictly defined green tasks in a 4-digit occupation divided by the total number of tasks in that occupation. A job is classified as green if it has a green task intensity (GTI) greater than zero, meaning it has at least one strictly defined green task.

25 Our original list had 47 strictly green occupations, including ISCO code 5169 (protective services not elsewhere classified) with a GTI of 10 percent. We chose to shift this occupation to the list of broadly defined green occupations. While this occupation contains jobs such as park rangers and wardens that would be classified as green (park rangers are classified as green in the DHET (2013) OFO skills list), the majority of the workers in this occupation are security officers whose tasks cannot be classified as strictly green. Because this occupation accounts for a very large share of jobs (over 500,000 employees), removing it from the strictly green occupations list reduced the share of strictly green employment in South Africa from 9 percent to 5.5 percent.

11
applications processed and checks delivered (van Ginneken, 1999). In Thailand, village elders identify the rural poor, mostly widows without children. This system may work well in some villages but one can imagine personal favoritism playing a role in others.

In a less traditional model, the process may be computerized. Based on data from household surveys, poor communities and their residents may be identified and given stipends. This method is being used in the Progresa program in Mexico, which targets poor rural villages and families using an econometric model that aggregates several socio-economic variables. The econometric model is structured to give heavy weight to families with children and to provide an incentive for these children to attend school. But old people also benefit-and the relative weights given to school-attending children versus the old becomes a political issue. This is approach is worth serious consideration in other countries.

Still a third method of means-testing involves self-selection: offer assistance in a form for which mainly low income people will apply. Self-selection can be achieved by requiring queues for receipt of cash benefits, a cost which high income people are less likely to be willing to incur, or by offering in-kind benefits such as free lunches that only low income people would accept. In many countries soup kitchens and/or dormitories accommodate the poorest old, often run by NGO's but financed by governments. NGO's may provide the service at lower cost and in a less bureaucratic way than the government, particularly when small scale ventures are involved. The advantage of self-selection is that it avoids the transactions costs and possible mis-identification of an application process. The disadvantage is that the benefit and quality level may not be optimal, and many old people may be unable to stand in queues to partake. At this stage, we don't really know which of these three methods entails higher transactions costs or greater exclusion of needy old people.

_Old versus young candidates for social assistance._ This raises a final issue concerning means-tested programs-should different criteria apply to old versus young people who
The unexpected time and localized nature of the violence make the 2002 Gujarat riots a suitable setting to study the effects of communal violence. We will carefully discuss our choice of treatment and control groups in the following section and show evidence of parallel trend below, an essential identifying assumption for difference-in-difference.

We check for differential trends in women’s age at marriage and the rate of child marriages by estimating the following event-study equation: where _Y eard_ are year-specific dummies and the omitted year is the year 2002; _βt_ represent the coefficients on interactions between the year dummies and the _Riots_ dummy. Another advantage of estimating the difference-in-differences coefficients by year is that it enables us to investigate how women’s marital outcomes evolved over time after the riots.

# **5.3 Choice of Treatment and Control Groups**

As discussed in section 5.1, we take Gujarat as the treated state and the rest of India or neighboring states or a synthetic amalgamation of states as the control. In principle, one can think of a within Gujarat analysis where we analyze whether women’s marriage market outcomes are correlated with the intensity of riots. There are two major issues with such an analysis: The first is the high mobility of women across districts within the same state, specifically for marriage. According to the 2001 Indian Census data, while the leading cause of migration for men is work, it is marriage for women: 70% of all women who migrate do so for marriage (Kone et al., 2018). In table 2, we see that, among those women who migrate for marriage, more than 70% cross district borders within their states; for Gujarat, this number is more than 80%. Since the available data only records the residence district of the women after marriage, a within Gujarat district-level analysis will be unable to account for the spillover from treated to control districts and vice versa.

The second issue is the widespread prevalence of riots across Gujarat. According to contemporary news articles (Outlook India
.32|-0.32|-0.25|-0.24|-0.26|
|Russian Federation|-0.68|-0.61|-0.58|-0.54|-0.52|-0.51|-0.49|-0.46|-0.45|-0.41|
|South Africa|-0.36|-0.26|-0.24|-0.31|-0.34|-0.17|-0.24|-0.21|-0.13|-0.22|
|Ukraine|-0.59|-0.51|-0.52|-0.46|-0.44|-0.43|-0.42|-0.41|-0.36|-0.33|

_Source:_ Estimates based on national socioeconomic surveys. _Note:_ In most cases, a multiple time cross-section model with time fixed effects is used. Demographic controls include the age, education, and gender of the household head, the share of individuals by age-group in each household, and urban status. Deciles have been created using per capita household expenditure.

Figure 2. Price Elasticity of Tobacco Consumption, Medium-Bound Estimate, by Decile <!-- Start of picture text --> 0.00<br>1 2 3 4 5 6 7 8 9 10<br>-0.20<br>-0.40<br>-0.60<br>-0.80<br>-1.00<br>Chile Ukraine Moldova<br>-1.20 South Africa Bangladesh Indonesia<br>Russian Federation Bosnia and Herzegovina<br><!-- End of picture text --> _Source:_ Estimates based on national socioeconomic surveys. _Note:_ In most cases,
commodities are expected to be among the first commodity types that might shift their mode of transport to faster modes. This is because the time value of these commodities is typically higher than low-value bulk commodities.

Moreover, Avetisyan (2018) studied the impact of a global carbon tax of 27.3 USD/ton CO2 applied to transport sectors of all modes (i.e. industry sectors which provide transportation services) on modal choices of shippers globally. The study concluded that under a scenario, based on trade data from 2011, where a global carbon tax is applied to all transport modes, the transport of high-value products (e.g. microchips and seeds) would shift to maritime transport from air transport, while the transport for bulky products (e.g. paddy rice, wheat, and cereal grains) would shift to maritime transport from other (land-based) transport modes (rail, road).

**Table 1:** Impact of 100% increase in maritime transport costs and 65% speed reduction on global modal share of international freight transport by 2030 (source: (Halim et al., 2018))

||**Baseline**<br>**2030 (Mt)**|**Share**<br>**(%)**|**100% increase**<br>**in maritime**<br>**transport cost**<br>**and 65% speed**<br>**reduction(Mt)**|**Share**<br>**(%)**|**Difference**<br>**in share (%)**|**Difference in**<br>**weights (Mt)**|
|---|---|---|---|---|---|---|
|**
each firm we impute the volume of electricity and natural gas that it consumes by multiplying its output, as recorded in our data, by the average level of energy-intensity of the sector in which the firm operates. Based on this inferred consumption, we assign firms to Eurostat end-user price bands for electricity and natural gas. For model estimation, all prices are represented as indices, based on real 2007 values.

Using information in our data set we can account for the level of utilization of fuel-using capital stocks ( _Ui,j,t_ ). For each firm in the data we observe its fuel inventories at the beginning and at the end of each year.<sup>5</sup> Based on these data we calculate an average annual fuel utilization rate. This is given by taking the total fuel consumption in that period - which is the sum of the value of opening stocks plus fuel purchases minus closing stocks, and dividing this by the total value of fuel available for consumption - the sum of opening stocks plus purchases.

# **3.2 Calculation and disaggregation of fuel-using capital stocks**

The census asks firms for information on capital acquisitions by type of capital. Capital acquisitions data are disaggregated as follows: acquisitions of computer equipment; computer software; plant machinery and equipment; motor vehicles; building and construction work; buildings purchased; land purchased; capitalized R&D, and “other”. In our analysis we focus on the plant machinery and equipment component of capital, where substitution between different types of fuel-using stocks is technologically feasible. Capital stocks are calculated using the perpetual inventory method and based on capital acquisitions and disposals, as recorded in the data. The starting stocks are calculated based on the CSO’s industry-level breakdown for the previous year and then subsequently disaggregated to the firm level using each firm’s share of fuel use in total industry-level fuel use. A detailed description of how this variable is created, including information on depreciation rates and assumed assets lives, is provided by
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- Dell, M., Jones, B. F., & Olken, B. A. (2014). What do we learn from the weather? The new climate-economy literature. _Journal of Economic Literature_ , _52_ (3), 740–98.

- Deryugina, T., & Hsiang, S. M. (2014). _Does the environment still matter? Daily temperature and income in the United States_ (No. w20750). National Bureau of Economic Research.

- Deschênes, O., & Greenstone, M. (2007). The economic impacts of climate change: evidence from agricultural output and random fluctuations in weather. _American Economic Review_ , _97_ (1), 354–385.

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- Deschênes, O., & Greenstone, M. (2011)