rafmacalaba/data-use-mentions-extended huggingface dataset:
# **III. DATA AND ANALYTIC FRAMEWORK**

This section begins by describing the survey designed to collect the data that have never been available before on enterprise skills. It is followed by a description of the analytic framework used to model firms’ choices of skill mix and strategies to mitigate skill deficits – the mismatch between needed and available skills – and to estimate their impacts on firm performance. In this analytic framework, firms are distinguished by whether they are high-skill or low-skill firms. The section concludes with a broad overview of the data, including simple pair-wise correlations between key skill variables and indicator measures of skills demand, and comparisons of high-skill and lowskill firms in terms of their labor productivity and job creation over the past two years.

# **Tanzania Enterprise Skills Survey**

The data needed to study the determinants and outcomes of skill shortages and strategies firms use to mitigate them are from the 2015 Tanzania Enterprise Skills Survey (TESS). Piloted by the Development Economics Vice-Presidency and Education Global Practice of the World Bank, TESS was designed to provide insights into the composition and demand for skills, and the relationship between skills (and skill constraints) and firm performance in selected economic sectors in five regions of Tanzania. The five regions include Dar es Salaam, the autonomous region of Zanzibar, and three mainland regions of Arusha, Mbeya, and Mwanza. The economic sectors are food, textiles and garments, fabricated metals, and furniture in manufacturing, and IT, hotels and restaurants, construction and transportation in non-manufacturing. Firms were classified into three sizes – small firms with 5-19 employees, medium with 20-99 employees, and large with 100 or more employees.

The survey included 424 enterprises stratified by sector, region and size, with between 40 and 76 enterprises in each sector. About half the sample (224 firms) is located in Dar es Salaam and the balance divided rather evenly across each of the remaining regions (each with about 50 firms). Overall, small firms make up 63 percent of the sample
# 3.3. Business costs of infrastructure disruptions: Utilization losses

Frequent disruptions of electricity, water or transport infrastructure often mean that firms are unable to utilize their full production capacity. Capacity utilization is a common measure of the effectiveness with which a firm converts its resources into output – and it is reported in the Enterprise Surveys. A firm that is frequently forced to halt production – for instance due to power outages, or due to input shortages caused by transport disruptions or upstream production stops – will be operating below its full capacity, i.e. have relatively lower levels of capacity utilization. This in turn will affect firm-level factor productivity and sales.

This section focusses on the capacity utilization of firms and distinguishes and assesses direct and indirect infrastructure disruptions impacts by extending the analysis to power, water and transport disruptions.

Unlike in the case of power outages, the Enterprise Surveys do not directly ask firms about the sales losses associated with water outages and transport disruptions. However, considering utilization reductions due to infrastructure disruptions in addition to self-reported sales losses makes it possible to estimate the firm-level cost of water and transport disruptions – in addition to power outages. Moreover, this estimation strategy presents two advantages. First, we are able to verify the sales loss estimates due to power outages obtained in the previous section. If the direct effect of electricity disruptions on utilization rates are estimated to be in the same order of magnitude as on sales losses, this would support the relevance and validity of the approach. It would also address the potential issue of measurements errors of self-reported sales losses due to power outages. Second, it is important to recognize that the indirect effects of infrastructure disruptions can potentially significantly exceed direct effects. For instance, even if a firm is not directly affected by infrastructure breakdowns (e.g. by experiencing a power outage onsite), it may still be forced to stop production as interruptions along the supply chain stifle input supply or output demand. Such indirect effects are unlikely to be taken into account by
suggests that a 4 percent depreciation is rather on the low side.

Column 3 includes the capital stock estimate built with disaggregated investment data originally constructed to compute the quality index for capital. This series is robustly correlated with growth. The long-run elasticities for physical and human capital are slightly higher than with the standard estimate of the capital stock. Due to the limited number of observations the regression could only be run by OLS. Thus, the coefficients are likely to be upwardly biased. Altogether, varying the assumptions about the depreciation rate moderately changes capital elasticities but does not change very much the role of human capital on growth.

# **_Alternative Schooling Data_**

The most interesting sensitive test concerns the validity of the conclusions on the importance of human capital to growth. Column 4 uses interpolated education data from Barro and Lee (2001). Column 5 includes the interpolated time series from Cohen and Soto (2001) into the regressions. In both estimates human capital, as measured by average years of schooling, is robustly correlated with growth.

-44-
It is however not clear if net agglomeration economies in large cites can be offset by incentives and other measures to divert growth to smaller cities.

In this paper, we consider a model of a city, which consists of a demand side— what utility levels a city can pay out—and a supply side—what utilities people demand to live in a city. We estimate aspects of the demand and supply side; and then a reduced form equation that describes city sizes and their growth. For the empirical analysis, we construct a dataset of Brazilian agglomerations to examine city growth between 1970 and 2000. Much of the underlying data come from the Brazilian Bureau of Statistics (IBGE) Population Censuses of 1970, 1980, 1991, and 2000. For the estimation, we make use of GMM and spatial GMM techniques to correct for endogeneity in the presence of spatially autocorrelated errors. Our main findings are that increases in rural population supply, and improvements in inter-regional transport connectivity and education attainment of the labor force have strong impacts on city growth. Both, labor force quality improvements and base period education attainment matter significantly for growth. In terms of local characteristics, we find that local crime and violence and a higher representation of public industrial capital in the city lower city growth rates.

The rest of the paper is organized as follows. Section 2 provides the model and estimation framework of urban demand and population supply models. The models presented in this section combine traditional urban modeling with concepts from the new economic geography literature. In Section 3, we discuss findings from the empirical analysis and focus our attention on identifying main determinants of city growth. Section 4 provides results from simulations that examine if investments in secondary cites stimulate growth. Section 5 concludes.

4
between 5 and 18 years of age|(0.0080)|(0.0081)|(0.0083)|
|Number of members 65 or older|0.0105|0.0225|-0.0009|
||(0.0174)|(0.0181)|(0.0202)|
||-0.0075|-0.0086|-0.0062|
|Number of members of extended family|(0.0061)|(0.0080)|(0.0058)|
|Constant|-1.5576|-1.8440|-1.2863|
||(3.1310)|(3.1863)|(3.0895)|
|Observations|6,501|3,311|3,190|
|R-squared|0.1898|0.1898|0.1912|
|F-Test(Kleibergen-Paap)|7.679|8.046|7.203|
|Cluster by community||||
|Regional Fixed Effects||||
|*** p<0.01, ** p<0.05, * p<0.1||||

Source: CLS-WILL: first wave – household questionnaire, Instituto Geográfico Agustín Codazzi (IGAC) and Fernández (2011).

46
on decision making) at the national level of executive power – although without being explicitly discriminated against.

- _discriminated_ : Group members are subjected to active, intentional, and targeted discrimination by the state, with the intent of excluding them from political power. Such active discrimination can be either formal or informal, but always refers to the domain of public politics (excluding discrimination in the socioeconomic sphere).

- _self-exclusion_ : The special category of self-exclusion applies to groups that have excluded themselves from central state power, in the sense that they control a particular territory of the state which they have declared independent from the central government.

From this perspective it should be clear that policy changes are possible on this dimension. Discriminating policies in particular appear to be part of a political situation that can escalate into violence. Amending this could be easier than formal institutional changes. Discrimination by the ethnic group in power could, of course, be endogenously determined. One part of this is inter-ethnic trust which has received a lot of attention in recent research.

# **8.3.4 Trust and**

Figure 19 shows the share of people of people who report that "Most people can be trusted" in the World Value Survey (the alternative is "You can’t be too careful."). The blue line shows the distribution in countries that have not experienced civil conflict in the sample. The red line shows the distribution of trust in countries that have. Countries with a conflict history show drastically lower levels of trust.

These findings are taken up by Rohner et al. (2013b) who argue that this pattern could be explained by a three-way relationship between interethnic trust, trade and conflict. Low levels of trust lead to low levels of economic interactions which, in turn, makes conflict more likely. Conflict, on the other hand, destroys trust. The incidence of conflict can be reduced by policies abating cultural barriers, fostering inter-ethnic trade and human capital, and shifting beliefs. In this view peacekeeping forces
2017; Chakraborty et al. 2017; Liu and Mu 2017). The principal focus is transactional, on the implicit assumption that agents have common perceptions of the problems but different priorities for addressing them.

This paper attempts to contribute by addressing another aspect of heterogeneity that has received less attention in the literature: the role played by differences in perception. Within localities, collective action may be easier if elite and non-elite households have similar perceptions of critical environmental problems. At the same time, extensive research indicates that regional governance is dominated by economic and educational elites who communicate across village lines (Khan 2008; Ghertner 2011; Oyono 2004; Lucas 2016; Piabuo et al. 2018). By implication, regional environmental governance is likely to be more effective if elites in different localities have common perceptions of the critical problems. Several other possibilities emerge from this line of inquiry, depending on comparative conditions at the village and regional levels. In the weakest scenario, environmental governance is hindered by perceptual heterogeneity within and across villages. Local governance may be strengthened if village elite and non-elite households share common perceptions, while regional governance may dominate if village-level perceptions differ but elites share perceptions across villages.

We conducted a survey of 600 households in the Indian Sundarbans, which is threatened by several environmental factors related to climate change, and explored the impacts of location and socioeconomic status on patterns of perceptions of environmental change. Our findings have three noteworthy features. First, for the broad Middle group, we find a positive, highly-significant correlation of perceptions across all localities.<sup>6</sup> Common locality adds a highly-significant

> 6 These are the constant terms in the regressions.

12
regulation of product markets helps make economies more resilient to shocks. They show that more competition-friendly PMR can reduce structural unemployment while raising turnover. Such regulations also seem to decrease the persistence of unemployment.

A common element of all research presented above is that more pro-competitive PMR are associated with increases in aggregate employment. Some papers reassess this finding by estimating local projections based on different data, i.e. not the OECD PMR indicators. Duval and Furceri (2018) rely on a narrative database on labor and product market reforms in 26 advanced economies since 1970 to construct regulatory shocks. They find that past product market deregulation shocks increased GDP by about 1.5 percent on average after 5 years. This positive impact becomes statistically significant only after 3 years and fully materializes after about 7 years at 2.25 percent GDP growth. They find no significant effects on employment and labor productivity. In addition, the authors find that the effects of product market reforms do not depend significantly on the business cycle conditions, which stands in stark contrast to their own findings for labor market reforms.

Using the same narrative reform data, Bouis et al. (2020) shed light on the differential effects of PMR deregulation in the short versus long run. Using local projection methods, they find that output effects become significantly positive only 3 to 4 years after the reform, as prices start dropping. They do not find evidence of any transitory cost, apart from a short-lived drop in Single Market Program.

16
>US$1,000 to a few million dollars. For larger<br>investments, commercial lenders’ loan terms are often<br>difficult to meet: monthly interest rates can range from<br>1 percent to 3 percent, for a period of a year to two<br>years. Non-commercial lenders may charge 5 percent<br>per month interest rates.<br>•<br>Financing instruments include: co-financing grants,<br>output-based subsidies, soft loans, guarantees for debt<br>financing, subsidies, and equity contributions from<br>different sources (beneficiaries, investors, etc). For<br>user-based systems, contributions to capital investment<br>may also be made in the form of cash, labor or local<br>materials.|

# **4.6 Pricing, Sales and Earnings**

Data on pricing, sales and earnings was not provided consistently and information was generally limited to prices of goods (e.g. solar panels) and water and electricity services (see Table 10). More information on prices was available for water supply than for electricity (water data from 93 points of reference in 47 countries was collected and analyzed, as presented in Chart 2 below) Data on profits was scarce, and the reliability of this, particularly for informal SPSPs, was difficult to ascertain. The most accurate information on sales was available from tankers and kiosks as it was easier to track sales for these over a given period through observation.

24
.02|0.26||0.49|0.57|
|**Brazil**|0.08|0.43|0.51|0.03|0.28|0.39|0.51||0.11|
|**USA**|0.30|0.05|0.05|0.06|0.02|0.10|0.46|0.66|0.56|
|**Germany**|0.06|0.02|0.01|0.02|0.01|0.13|0.79|0.33|0.70|

Source: Own calculation based on trade data from UN COMTRADE for the year 2001. A 5 digit level of disaggregation is used for this exercise.

# **3.3. Business Cycle Synchronization and Trade**

Empirical evidence on trade integration and business cycle synchronization is somewhat mixed. While Frankel and Rose (1998), Choe (2001), Calderon, Chong and Stein (2002) and Calderon (2003) find that a higher trade intensity tends to increase business cycle synchronization, Shin and Wang (2003) find that increasing trade itself does not necessarily lead to more synchronized business cycles, evidence for East Asia suggests that only the expansion of intra-industry trade had such an effect. However, Garnier (2003) find only weak or no relations between intraindustry trade and business cycle synchronization for 16 industrialized countries and conclude that intra-industry trade at most only partially explains business cycle transmission; the low respectively. The adjusted Grubel Llyod index makes an adjustment for trade imbalances.

11
who reported they worked on any household plot during the season (a “person”).<sup>15</sup> By this definition, then, any specific person-plot hours could total zero.

Panel A of table 3 reports the results at the person-plot level. Hours per day, exaggerated by roughly 11 percent, are more accurately reported in recall than are other aspects of time use such as hours, days, or weeks.<sup>16</sup> By contrast, total weeks in recall are higher than in weekly visits by 128 percent, and total days are higher by 179–223 percent. The cumulative effect of the exaggerated days and weeks in the recall modules results in a striking divergence in the time spent 14 The recall NPS households were asked to report the number of days spent performing each of four agricultural activities. They did not provide the specific days on which these activities occurred; so, we do not know if reporting one day in weeding and one day in planting was, in fact, two separate days of work, or a single day in which both of these two activities were performed. To compute total time in hours for the recall NPS group, we chose to compute an upper bound for the number of days by assuming that each activity-day reported was sequential or mutually exclusive, that is, that people did not perform more than one activity on the same day. This choice is supported by the similarity between this measure and the days data reported by recall ALT households. It is also supported by the activity patterns of the weekly surveyed households, where we find evidence that agricultural workers overwhelmingly tend to pursue one agricultural activity in a given workday. The typical length of an agricultural workday (roughly four or five hours) as reported across the other three arms of the study is similar to the mean hours per activity in the recall NPS survey, further supporting this interpretation.

> 15 Of the 3,707 individuals ages 10 and older in the 854 households
local shocks. we plot the distribution of flexibility measures as well as number of states each chain is in across stores by store type in Appendix Figure O11. Both drug and merchandise stores belong to a few large chains that price rigidly, whereas a large amount of grocery stores belong to chains that price flexibly or chains that are located only in a few states. Over 50 grocery chains are in the sample whereas both drug and merchandise stores come from around five retail chains each.<sup>66</sup> This implies most grocery stores are engaging in local pricing, whereas drug and merchandise stores are not.

# **I Firm Dynamics and Market Structure**

To investigate the impact of SNAP-benefit changes on firm dynamics and market structure, we use two additional data sets. First, we use FNS data on a yearly panel of retail stores that participate in SNAP from 2006 to 2015. For each store, we observe the years in which it is contained in the sample, that is, registered with the FNS to sell to SNAP consumers. We also observe the exact authorization date, the name and address of the store including the county it is located in, and the store type. To focus on the grocery industry, we restrict our sample to stores that are classified as grocery stores, supermarkets, or superstores. We then construct five county-level outcomes of interest from the data: (1) authorization rate, the number of newly authorized stores per month divided by the total number of stores, (2) entry rate, the number of new stores in the sample each year divided by the total number of stores, (3) exit rate, the number of stores that exit the sample the next year divided by the total number of stores the previous year, (4) reallocation rate, the sum of entry and exit rates, and (5) log count per population, the log of the total number of stores per population. Second, we use County Business Patterns (CBP
References Allison, P. D. (1999). Comparing logit and probit coefficients across groups. Sociological Methods and Research 28.2, 186-208. Arrow, K. (1950). A difficulty in the concept of social welfare. Journal of Political Economy 58, 328-346. Bartholomew, D. (1982). Stochastic Models for Social Processes. New York: John Wiley. Baulch, B. and J. Hoddinott (2000). Economic mobility and poverty dynamics in developing countries. Journal of Development Studies 36.6, 1-24. Blanchflower, D. and A. Oswald (2000). Well-being over time in Britain and the USA. National Bureau of Economic Research Working Paper 7487. Brickman, P. and D. Campbell (1971). Hedonic relativism and planning the good society. In M. Apley (Ed.), Adaptation-Level Theory: A Symposium, pp. 287-302. New York: Academic Press. Browning, M. (1992). Children and household economic behavior. Journal of Economic Literature (30), 1434-75. Clark, A. and A. Oswald (2002). A simple statistical method for measuring how life events affect happiness. University of Warick, mimeo. Conlisk, J. (1990). Ranking mobility matrices. Journal of Mathematical Socology 15, 173-91. Deaton, A. (1992). Understanding Consumption. Oxford: Clarendon Press. Deaton, A. (1997). The Analysis of Household Surveys. Washington, DC: World Bank. Diener, E., E. Suh, R. Lucas, and H. Smith (1999). Subjective Well-Being: Three decades of progress. Psychological Bulletin 125, 276-302. Easterlin, R. (1974). Does economic growth improve the human lot? Some empirical evidence. In P. David and M. Rider (Eds.), Nations and Households in Economic Growth. Essays in Honor of Moses Abramovitz. New York: Academic Press. Frey, B. and
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-2 0 2 4 6<br>Estimated RTA Effect<br>MENA Morocco<br>.8<br>.6<br>.4<br>RTA Effect Frequency<br>.2<br>0<br><!-- End of picture text -->

**<mark>Notes:</mark>** <mark>The kernel density graph shows the distribution of RTA-trade relationships from separate PPMLHDFE-estimated equations for each RTA registered with the World Trade Organization.  The solid vertical lines represent Morocco’s three agreements: EFTA – Morocco, Türkiye – Morocco, and United States – Morocco. The dashed vertical lines represent 35 agreements that include MENA members Algeria, Israel, Jordan, Lebanon, Oman, the Palestinian Authority, Türkiye, Tunisia, and Syria. Estimates calculated using data from UN Comtrade database, the World Integrated Trade Solutions (WITS), and the World trade Organization (WTO).</mark>

As a labor-abundant country, the effects of trade agreements may be larger for laborintensive goods. To explore this hypothesis, we consider two labor-intensive industries: apparel and furniture. We replace total trade in equation one with bilateral apparel or furniture trade and then repeat the same procedure to estimate the agreement-specific changes in trade and compare them to the overall agreement average.

Figure 3.3 shows the baseline gravity model results for trade in apparel. Again, three main results emerge from Figure 3.3. First, the results are similar in direction, with the GSTP the only RTA with a negative coefficient. That is, Morocco’s agreements are associated with more apparel trade. The second main result is that the individual agreements have larger estimated changes in trade than the “average” agreements. The “average” estimates for the effect of trade agreements on apparel trade are much smaller than those for total trade and have a higher standard deviation because trade agreements often have restrictive