rafmacalaba/data-use-mentions-extended huggingface dataset:
In what follows, the paper describes the methodology for this study and the empirical results. We then review the deployment and uptake of ICT in LAC and compute measures of relative distortions. Section 3 describes economic framework that allows us to gauge how removing distortions can lead to competition and productivity improvements in the ICT sector. Section 4 looks at the empirical links of _de jure_ and _de facto_ policies on productivity outcomes in Peru. It maps firm-level data to regulations and enforcement data at the four-digit level where markets are delineated as segments of the ICT sector. Collection of regulation and enforcement data is important for studying how regulations may improve growth and reduce market distortions.

# **2 ICT Distortions in LAC**

The uptake of computers and the internet is evidence of the importance of ICT for both work and personal activities. Computer and internet penetration rates have increased across the globe. According to the International Telecommunication Union (ITU), in 2020 the share of LAC households that own a computer ranged from a high of 68% in Uruguay to a low of 17% in Honduras (ITU (2020)). The rates are slightly higher for access to the internet (households that do not own a computer may own a cellphone with internet capabilities; see Figures 1 and 2). ICT adoption varies widely in LAC: Uruguay, Argentina, and Brazil have the highest adoption rates; Honduras, Nicaragua, and Guyana the lowest (Figure 3).

Unfortunately, the benefits of ICT are not shared between and within countries in LAC. For instance download speeds in some countries are slow relative to peers and developing countries generally (Figure 4).

Slow uptake of ICT could be the result of several factors, including the current development context and the regulatory environment of each country. The ITU’s ICT Regulatory Tracker provides comparable scores for both regulatory and competition frameworks (ITU (2020)). The Tracker traces private vs. public ownership, the degree of competition for broadband, basic and leased line services, and
_Figure 1. Population distribution across the city (two settings)_ <!-- Start of picture text --> Low income-high income configuration High income-low income configuration<br><!-- End of picture text --> Landowners supply the housing and choose how much capital K(r) to invest at each distance from the CBD to produce a housing surface H(r) , assuming that they own a quantity of land L(r) and that the housing production function is: Landowners maximize their profit function to identify the optimal amount of capital to invest at each location: with i the interest rate and ρ the depreciation rate of capital. Due to the decreasing returns to capital, landowners will invest in tall buildings only when they anticipate rents that offset the extra building cost. At the edge of the city, the anticipated profit does not exceed the agricultural rent Ra , and landowners choose to rent their land to farmers instead of building dwellings. For simplicity, we assume that agricultural rent is null.

We calibrate our idealized city on the Paris agglomeration for illustrative purposes (table 1). To calibrate the parameters governing urban densities, rents, and population, we follow Viguié, Hallegatte and Rozenberg (2014). We consider two household groups with the same number of households, where the low-income group is the bottom 50 percent of the income distribution and the high-income group is the top 50 percent. Building on survey data from the National Institute of Statistics and Economic Studies’ (INSEE) localized disposable income system, FiLoSoFi, there are 4.9 million households in the Paris agglomeration, with a median annual income of €37,232 for low-income households and €80,244 for high-income households (INSEE ‐ DGFiP ‐ Cnaf ‐ Cnav ‐ CCMSA 2015). Transportation costs include fuel and vehicle maintenance t ∗ r , proportional to distance from the city center and identical for both household groups, and an opportunity cost of time (considering an average speed of 25 kilometers per hour
category vis-à-vis unemployment. See Fougere and Kamionka (2005) for a more detailed discussion regarding this point.

As a result, the key question this paper addresses is not about how to best infer the occupational structure in some future steady state or whether or not is reasonable to assume time-invariant Markov processes in contexts of crisis. Rather, the paper explores the size and magnitude of recent occupational transitions in Colombia and assesses the extent to which previous shocks, perceptions of future risk perceptions, and social security coverage affect such transitions in stress situations.

# **3. Data**

In 2004 _,_ the Colombian think-tank Fedesarrollo, with the support of the Bogotá, Cali, and Bucaramanga chambers of commerce, converted a household survey in these three cities collected since 1999 into a rotating panel, fully renewed every four years. The 2008 phase saw a substantial increase in the sample size with the introduction of 10 additional cities (Medellín, Barranquilla, Manizales, Pasto, Pereira, Cúcuta, Ibagué, Montería, Cartagena, and Villavicencio) that form part of the ―national urban total‖ defined by DANE for the calculation of unemployment figures. The first three cities represent 73 percent of the sample, while the other 10 represent the remaining 27 percent. The survey, carried out door-to-door to all household members present, is structured in several modules capturing individual labor market and social security records; access to social protection programs; risks, shocks and coping strategies; and housing, demographic and personal information.

The 2009 round of the survey maintains the same basic questionnaire and coverage of the 13 key metropolitan areas in the country. Some 80 percent of the 18,072 individuals surveyed in 2008 were also 6
World Bank surveys are generally done in a decentralized manner, making it difficult to synthesize the lessons that have emerged from these ambitious and often expensive efforts. Still, as the Bank continues its survey efforts, it is worth investigating what we have learned from its experiences to date. We first present a brief overview of World Bank surveys organized by research topic, including technology, incentives, market structure, transaction environment, the role of the state, and the importance of micro data in understanding macro phenomena. We note that to analyze the effects of policy intervention it is often important to collect panel data and to define industries at sufficiently disaggregated levels. Building on our "survey of surveys," we review the literature on survey design, highlighting the point that questions themselves can bias responses and provide some guidelines for recognizing the direction of this bias and minimizing it when possible.

While we address only firm surveys conducted by the World Bank, we believe that this discussion is also relevant to policy analysts outside the Bank. The World Bank has probably conducted more surveys in developing countries than any other single institution. As a result of the large number of surveys and survey topics, the Bank now possesses enormous institutional knowledge not just from the data collected, but from the survey experiences themselves. The Bank should share this knowledge with its member countries. This paper represents such an attempt.<sup>3</sup>

# **II. GENERAL DATA ISSUES AND THE BANK'S EXPERIENCE**

Rigorous policy analysis requires a great deal of data. Ideally, the data are longitudinal (i.e., firm level information collected at discrete points over time) and fairly dissagregated (i.e., industries are defined as narrowly as possible). Only with panel data can one investigate the effects of policy interventions and the process of economic growth. Consider, for example, the

> ' See Blank and Grosh (1999) for a complementary paper on household surveys. They suggest that political will,
study of US import policy in the 20th century by Robert Baldwin (1983) suggests that the United States’ use of Article XIX cycled with changes in US trade law. The US law regarding safeguards varied in the stringency of qualifying criteria over decades with the result that safeguards were never used under the 1962 US Trade Act but were far more common after reforms to the law in 1974.

> 73Data on Article XIX investigations between 1950-1959 was collected from the GATT digital archive at Stanford University and each verbal description of a product was matched to the modern HS06 product classification.

40
banks in which the government has an ownership interest, tend also to be among the least efficient. Obvious tensions therefore emerge in pursuing improvement in access, efficiency, and stability at the same time. While much has been accomplished with regard to banking reform in Kenya, there remains much left to do. Beyond maintaining macroeconomic stability and significant improvement in the contractual and informational frameworks (including the credit registry), regulatory and competition policies will play an important role. A priori, however, it is not clear that the push towards larger banks will allow Kenya to reap benefits in terms of outreach. It will be more important to focus on the ownership structure and a competitive environment for all banks to achieve the goals of Vision 2030. This would imply privatization of the remaining government-owned banks and a level regulatory playing field for all deposittaking institutions. The latter includes effective, but not overly burdensome regulation and supervision of MFIs and SACCOs. It also implies maintaining an openness to new, even nonbank, providers of financial services, which has enabled the success of M-Pesa.

35
*|0.072|
||(0.032)|(0.047)|(0.032)|(0.195)|(0.174)|(0.141)|
|=1 if Unconditional Schoolgirl|-0.139***|-0.026|-0.002|0.445**|0.338**|-0.043|
||(0.035)|(0.054)|(0.046)|(0.199)|(0.153)|(0.240)|
|p-value UCT vs. CCT|0.068|0.860|0.552|0.814|0.215|0.672|
|p-value Treatment|0.000|0.677|0.627|0.023|0.001|0.858|
|Mean in Control Group|0.372|0.313|0.369|3.967|4.052|4.134|
|Sample Size|1,963|2,013|2,045|1,967|2,018|2,047|

_Notes:_ Regressions are OLS models with robust standard errors clustered at the EA level. All regressions are weighted to make them representative of the target population in the study EAs. Psychological distress is equal to one if the summed GHQ- 12 score is equal to three or higher, and is zero otherwise. Protein rich foods are defined as those containing animal proteins, i.e. meat, fish, and eggs. The number of days each item was consumed over the past week are summed to create the outcome variable. Baseline values of the following variables are included as controls in the regression analyses: age indicators, stratum indicators, household asset index, highest grade attended, and an indicator for never had sex. We restrict the sample to
|Observations|3709|3709|2792|1153|3709|3709|
|---|---|---|---|---|---|---|
|**C.**<br>**Upazilas not flood**|**ed in 1998, CRU based C**|**V of Rain**|||||
|CV Rain CRU|**-4.365***|**-4.569***|**-4.906***|**-12.06*****|**-9.030*****|**-11.87*****|
||(-1.739)|(-1.818)|(-1.669)|(-3.468)|(-2.648)|(-3.209)|
|Observations|1929|1929|1532|815|1929|1929|
|**D.**<br>**Upazilas flooded**|**in 1998, CRU based CV**|**of Rain**|||||
|CV Rain CRU|4.733|4.906|**12.25*****|-3.293|-1.062|3.710|
||(1.486)|(1.511)|(2.683)|(-0.754)|(-0.332)|(1.064)|
|Observations|1780|1780|1260|738|1780|1780|

Robust z-statistics in parentheses based on PSU level clusters *** p<0.01, ** p<0.05, * p<0.1 Note: The dependent
# **I. Introduction**

The literature on the effect of migration on source countries has typically focused on the direct effects of remittances on household consumption and investment. However, the emigration of labor and the growing volume of remittances open up many indirect general equilibrium issues for further research, many of which have not been fully explored. These include the labor market shock from reduced labor supply, which has considerable policy importance for developing countries.

To the best of our knowledge, Mishra (2007) is the first econometric study to model the impact of _emigration_ (i.e., a negative labor supply shock) on individual wages in a source country, building on an approach introduced by Borjas (2003) using the supply shifts in education-experience groups to assess the labor market impact of _immigration_ . Using U.S. census data to track the volume of Mexican emigration to the United States combined with Mexican census data on individuals in the Mexican labor market, this study finds that a 10 percent increase in emigration, on average, increases wages in Mexico by almost 4 percent.

Some papers predate Mishra (2007) but they focus on geographic averages (or sector averages), rather than individuals. Lucas (1987), for example, uses annual time series data from 1946 to 1978 on agricultural wage and employment and finds that mine worker emigration to South Africa has raised wages in Malawi and Mozambique.<sup>3</sup> Hanson et al (2002) finds a marginal negative impact of border enforcement on wages in cities along the U.S.-Mexican border. Robertson (2000), Chiquiar (2004), and Hanson (2004) provide evidence that those Mexican states that have greater international trade and migration links have enjoyed faster growth in average income and labor earnings. In addition, the impact of emigration on wages in Mexico has been largest in states with well-developed U.S. emigrant networks (Munshi, 2003). In yet another study, Hanson (2006)
Table 4: Analysis of classroom quality, teacher, and structural characteristics on children’s physical health & <u>well-being</u>

||DV<br>no|:Physical hea|lthand well-|being (Raw s<br>w/ teacher|core)|
|---|---|---|---|---|---|
||<br>controls|w/ controls|w/ teacher|<br> <br>& structure|IV|
||(1)|(2)|(3)|(4)|(5)|
|**Observed classroom quality**||||||
|ECERS-R|0.107**|0.073|0.079|0.083*|0.107*|
||(0.046)|(0.049)|(0.049)|(0.050)|(0.059)|
|**Teacher characteristics**||||||
|% of teachers w/ sr. secondary edu.|||0.275|0.297|0.283|
||||(0.378)|(0.381)|(0.386)|
|% of teachers w/ post secondary edu.|||0.272|0.285|0.278|
||||(0.386)|(0.390)|(0.395)|
|Mean years of teaching|||<br>-0.013*|<br>-0.014*|<br>-0.015**|
||||(0.008)|(0.008)|(0.008)|
|% of teachers w/ no ECED experience|||-0
in the Supplemental Appendix, we show results that exclude all households in Bamako from the regression specification and therefore estimate differences between urban and rural areas outside Bamako. Although these estimates suffer from limited statistical power, we find results that are qualitatively similar to our core results.

# **5 Results**

In this section we report three sets of results. First, we report descriptive results which come directly from the COVID-19 phone survey, targeted at understanding the responses to and socio-economic effects of the coronavirus pandemic. While these results do not specifically quantify the effect of the coronavirus pandemic on food security, they do provide insight of how individuals in Mali perceive the effect of the pandemic in their own lives. Second, we report estimation results based on variations of the regression specification in equation (1). These results provide some of the first estimates of the effect of the coronavirus pandemic in low- and middle-income countries. As we have previously discussed, we see these results as estimates of the lower bound of the effect of the pandemic on food security. Third, we discuss and implement several robustness tests.

11
between 1990 and 1995 of per capita GDP in 1987 US dollars adjusted for purchasing power parity.14 For 152 countries, we obtain this data directly from World Bank estimates of PPP-adjusted per capita GDP as reported in the World Development Indicators. For a further 10 countries we have data on per capita GDP converted to dollars using market exchange rates in the World Development Indicators, and we use a regression of the log-level of per capita GDP at PPP on the log-level of per capita GDP at market exchange rates to crudely impute PPP adjustments for these countries. Finally, for the remaining 16 countries for which neither measure is available, we use data for per capita incomes at PPP in 1997 from the 1998 CIA Factbook (CIA (1998)). We obtain data on infant mortallity and adult literacy primarily from the World Development Indicators, supplemented for a few countries from the CIA Factbook. Our data on linguistic variables are based on the data reported by Hall and Jones for their sample of 151 countries, and are extended to our larger sample of 178 countries using Grimes (1996).

# Results

Our empirical results show a strong positive causal relationship from improved govemance to better development outcomes. In Table 2, we present the results for per capita incomes. The rows of this table report our results for each of the six governance aggregates when entered separately in Equation (4). In the first two columns we report the 2SLS estimates of P and the associated standard errors, for each of these indicators The most striking feature of these results is that the magnitudes of the estimated coefficients are very large. To see this, recall that our choice of units for governance implies that the standard deviation of governance across countries is equal to one. Therefore, the coefficient on govemance can be interpreted as the 100x(eP-1)-percent increase in per capita incomes due to a one-standard deviation improvement in govemance. The estimated coefficients in the top panel of Table 2 (corresponding to the largest possible sample of countries for each indicator) indicate
1%|15.9%|0.3%|
|Avg after 10 yea|rs (f)<br>7.1%|7.1%|35.1%|14.3%|7.9%|
|Avg after 14 yea|rs(f)<br>6.8%|7.0%|30.4%|12.7%|7.4%|

- (a) Interest rates for 90-365 day deposits; deflated by the UF (Unidad de Fomento-the economic index that reflects inflation variations according to the CPI (IPC)).

- (b) Real annual average intemal rate of return for fixed income instruments traded on the Chilean stock exchange.

(c) IFC Global Index; From 1994 forward, Index constitutes 60% to 75% of the total capitalization of the local stock exchange. This index takes into account the effect of dividend reinvestment.

- (d) Real pension fund index without adjustments for fees and commissions.

- (e) Annual Intemal Rate of Retum reflects the IRR up to the corresponding year.

- (f) Simple average is taken for the CD rate (a); Fixed Income Real IRR (b); IFCG Total (c); and Pension Fund Retums (d). IRR returns reflect average returns for an affiliate over the period 1982-1995.

Sources: _Bolsa Comercio de Santiago;_ IFC Emerging Markets Database; "Informe Econ6mico y Financiero," Banco Central de Chile.
**Figure 3: Impact of winning the lottery on migration**

<!-- Start of picture text --> 76.4<br>70.0<br>57.1<br>29.1<br>22.9<br>18.6<br>14.7<br>1 0. 4<br>0.0<br>Migrated G2G intermediated Currently abroad<br>T1 T2 Control<br>100<br>80<br>60<br>Percent<br>40<br>20<br>0<br><!-- End of picture text --> Source: Authors’ estimates from the survey data collected for this study.

Note: Figure shows the impact of winning the lottery on migration. The bar shows the migration rates and the vertical lines denote 95 percent confidence interval.

36
Table S1.3 Average Wage and Size of the Formal Workforce in Household Surveys versus Administrative Data

|_a. Br_|_azil_|||||
|---|---|---|---|---|---|
|Year|Number of frms|Number of f|ormal workers|Average|log real wage (ppp)|
||RAIS|RAIS|PNAD|RAIS|PNAD|
|1995|1,007,491|15,208,033|18,353,257|6.128|6.170|
|1996|1,082,197|15,793,866|18,481,332|6.127|6.174|
|1997|1,222,684|16,947,898|20,382,370|6.137|6.092|
|1998|1,281,059|17,160,371|20,889,209|6.135|6.101|
|1999|1,348,541|17,661,290|20,949,785|6.105|6.051|
|2001|1,495,028|19,545,908|27,124,772|6.106|6.052|
|2002|1,580,489|20,462,491|25,108,709|6.097|6.015|
|2003|1,635,272|21,203,348|26,102,820|6.081|5.984|
|2004|1,718,258|22,959,016|27,737,267|6.086|5.992|
|2005|1,980,565|24,316,954|29,212,251|6.084|6.018|
|2006|2,
36 years of civil strife–, the government embarked on an ambitious construction program that increased the number of pre-primary schools from about 5,300 to 11,500 between 1998 and 2005.<sup>3</sup> We combine longitudinal school-level administrative data from 1995-2008 with Population Census data at the community level to estimate the impact of opening a preprimary in a rural community on primary school enrollment and progression by the age of 12. Precise geographic information makes it possible to construct a panel of rural communities that had one primary school during the period of analysis, but no pre-primary school when the construction program began.

Our identification strategy exploits variation over time in the construction of preprimaries across communities and the fact that some cohorts therein had access to a preprimary while others did not. By focusing on rural communities with only one primary school and examining various cohorts over time, we circumvent the potential problem that pre-primary access may affect the composition of students attending primary education. The final selection of beneficiary communities was decided by the central government following an _ad hoc_ procedure with no strict guidelines. Still, we apply trimming and propensity score re-weighting in order to obtain a comparison group with similar preintervention characteristics to the beneficiary communities.

The research design we adopt is closely related to Berlinski et al. (2009), though it differs in a number of ways. First, we exploit variation in access to pre-primaries using a finer level of aggregation. That is, while Berlinski et al. (2009) estimate effects using variation in access to pre-primary education at the municipality level, we estimate effects using variation at the community level. Hence, we count with sharper variation in the variable of interest (a community either has a pre-primary or not). Second, Berlinski et al. (2009) examine effects on test scores of children in third grade of primary school (who are about 8 years old). In contrast, we assess effects on adequate primary school progression for children that are 12 years old. It is well
from selling to the government divided by the firm’s total stock of capital. Regression (16) is estimated with firms obtaining at least one procurement project over 2000-13 using annual data. Standard errors are clustered at the firm level;<sup>_a_</sup> indicates significance at the 1% level,<sup>_b_</sup> at the 5% level, and<sup>_c_</sup> at the 10% level.

## **Table 2.** Change in Leverage and Procurement

_β_ 2 = 0 _._ 191. For the model to generate these regression coefficients and a credit-to-capital ratio of 0.55 we need _φp_ = 0 _._ 10, _φg_ = 0 _._ 15, and _φa_ = 0 _._ 64. Hence, firms can pledge 10% of their annual earnings from selling to the private sector, 15% of their annual earnings from selling to the government, and 64% of their capital stock. Using equation (10), these numbers translate into _φp_ = 0 _._ 27, _φg_ = 0 _._ 41, and _φa_ = 2 _._ 81, which mean that firms can increase their capital by 27% of their annual earnings in the private sector and by 41% of their annual earnings from selling to the government.<sup>22</sup> This is the result of a multiplier effect: firms can borrow against their revenues, allowing them to buy more capital, which can be partly collateralized to obtain further credit. This is an important interaction and captures how earnings-based constraints affect a firm’s ability to grow also depends on the value of _φa_ .

# **5.3 Block #3: participation and size of procurement**

There are four parameters driving the size and composition of procurement: _Yg_ , _mg_ , _η_ 0, and _η_ 1. The parameters _Yg_ and _mg_ are policy parameters governing the relative size of procurement in