rafmacalaba/data-use-mentions-extended huggingface dataset:
Mexico bilateral trade data. The resulting data set covers 105 tradable industries and one nontradable industry, which covers all workers who were employed in industries that could not be matched to the trade data by HS industries. Examples of the 105 tradable industries include beer, sugar, prepared vegetables, plasters, cement, and industrial chemicals. An important feature of this level of aggregation is that it is probably not sufficient to exclude products that are used as inputs for the production of final goods within a given industry category. Consequently imports in each industry include imported inputs, which are likely to be complementary with labor.

# **_3.2. Employment and Wage Data_**

The wage and employment information come from Mexico’s confidential social security records maintained by the _Instituto Mexicano del Seguro Social_ (IMSS) in Mexico City. The IMSS gathers data from all plants (establishments) on wages paid to each registered employee. We use these data to calculate total employment by industry, and we work with end-of-quarter data on employment and wages from 2007-2009. The frequency and end-of-period feature of the data have implications for interpretation and model specification, which are discussed further below. Overall, the data set covers between 3.6 and 4.2 million workers from the Mexican states that share a border with the United States.

In order to produce a data set that did not violate the confidentiality of the information, IMSS staff provided data on worker-firm pairs. That is, we received data on the employment and wage history of a person while he or she remained at the same firm. If a person moves to a new firm, the new worker-firm pair is coded with an entirely new identifier. We therefore cannot follow a person over time when she changes firms. We also do not know if two people are working in the same firm. To compensate for our inability to follow workers when they leave 9
# **_Inadequate state of physical infrastructure_**

Second, the inadequate state of physical infrastructure, another key component of Custo Brasil, constrains connectivity while hindering the increase of economies of scale, which is a crucial feature needed to reap the benefits of trade integration and to boost productivity growth. In principle, increasing the efficiency in overall public infrastructure is expected to raise productivity directly through higher infrastructure capital (measured as capital intensity) which makes private capital more productive. Recent analysis conducted by IMF (2015) - for a panel of 17 advanced economies during the 1985-2013 period - provided evidence that an increase in public investment equivalent to 1 percent of GDP is associated to an increase of labor productivity of 0.5 percent over the medium term, although primary through higher physical capital intensity. The impacts are higher the more efficient is the public spending system. In Brazil, the average rates of investment in overall infrastructure have been decreasing in the past decades, falling from an average of 5.42 percent over GDP in the 1970s and 1980s to an average of 2.15 percent in 2011-16 (Frischtak and Mourao, 2017). This sinking investment trend, explained by a reduction in public investment that was not counterbalanced by private sector investment,<sup>31</sup> brought negative impacts in terms of infrastructure adequacy: Brazil scores lower than its main export competitors on qualitative indicators of infrastructure adequacy, both in terms of overall infrastructure and transport infrastructure (Figures 21 and 22). Moreover, the strong preference for road transport over other modes combined with low percentage of paved roads<sup>32</sup> increases logistics costs. There is also evidence that inadequacy of transport infrastructure is associated with market segmentation in Brazil: evidence presented by Garcia-Escribano, Goes and Karpowicz (2015) shows a positive correlation between slower price convergence across major metropolitan areas and longer commuting times between cities in Brazil. All these factors together contribute to hamper domestic market integration, which then prevents increasing economies of scale, a
middle‐income countries. Combined with the donors’ contribution to education financing, this represents significant progress for education financing.

**Figure 1: Government expenditure on education in Low‐ and Middle‐Income countries**

<!-- Start of picture text --> Education expenditure as a share of the GDP Volume of education expenditure (constant,<br>2010)<br>2000 2005 2010 2015 2000 2005 2010 2015<br>year year<br>Source: World Development Indicators<br>4.2 1.20e+12<br>4<br>1.00e+12<br>3.8<br>8.00e+11<br>3.6<br>6.00e+11<br>Government Expenditure on Education (%GDP) 3.4<br>Government Expenditure on Education constant 2010<br>4.00e+11<br><!-- End of picture text --> Despite the progress in terms of making financial resources available for education, educational outcomes did not experience similar trends. The average primary completion rate in low‐ and middle‐income countries increased from 81 % in 2000 to 89% in 2017. Most developing countries have not yet achieved universal primary education although there is some progress in terms of access to education. More and

> 1 Miningou (2019).

2
_Table 3. Impact of Change in Exports on Skill Premium_

||(1)|(2)|(3)|
|---|---|---|---|
|Skill*∆Export Index|-.023***<br>|-.023***<br>||
||(.0067)<br>|(.0080)<br>||
|∆Export Index|.027**|.022**||
||(.012)|(.0089)||
|Skill*∆Export US Index|||-.045***<br>|
||||(.014)|
|∆Export US Index|||.032**<br>|
||||(.014)|
|Skill status|.29***|.21***|.22***|
||(.033)|(.033)|(.034)|
|∆Skilled/unskilled status|.080<br>|.023<br>|.023<br>|
||(.065)|(.068)|(.068)|
|∆Marital status|-.12|-.12*|-.12*|
||(.070)|(.064)|(.064)|
|Initial wage in log|-.33***|-.41***|-.41***|
||(.022)|(.029)|(.029)|
|Female||-.12***<br>|-.12***<br>|
|||(.026)|(.026)|
|Minority||-.016|-.018|
||
4 however, the continued decline of public investment followed naturally from the government's new economic strategy, which was oriented toward creating a more private sector-based, market-friendly economy.

**Table 1: Mexico** -- **Gross Fixed Investment**

|(con|stant prices, as|% of GDP)||
|---|---|---|---|
|**Years**|**Total**|**Private**|**Public**|
|1960-1964|16.8|12.6|4.2|
|1965-1970|19.2|13.1|6.1|
|1970-1977|21.2|13.5|7.7|
|1978-1981|23.8|13.3|10.5|
|1982-1987|17.7|11.0|6.7|
|1988-1994|17.7|13.8|3.9|
|1995-1996|15.4|12.0|3.4|

Source: INEGI, Sistema de Cuentas Nacionales, Serie 1960-93 and Serie 1988-96. World Bank, "Mexico After the Oil Boom" (Report # 6659-ME), 1987, for data in 1960-70.

The role of public investment under the new economic strategy is very different from what it had been before. To start, the decline in public investment is a natural consequence of the privatization of many state enterprises. Also, the new strategy sees the private sector as taking the lead in overall economic investment decisions and limits the state to a facilitating role in providing public infrastructure that complements private initiatives and to the correction of market failures. The latter calls for a refocusing of public investment in sectors considered to have important externalities (education, health, environment) and whose services are traditionally under-provided by the private sector. To permit a better prioritization of the public investment program in this sense, an "Investment Unit
platform were only known by 10 percent or fewer of the firms. Only one provider had a brand recognized by at least half of the firms in the program. This shows that most small firms lack information about most business service providers in the market.

# **2.2 How easy is it to find information about business service providers and how do they signal quality?**

To better understand what the marketplace for business service providers looks like, we conducted a survey of providers in Lagos and Abuja between November 2017 and June 2018. A key challenge for conducting such a survey is that there was no master list, website, or database of service providers. This points to the challenge facing small firms who wish to use one of these services: it is difficult to know exactly which companies provide these services. We constructed a sample frame by using multiple methods: we began with the set of providers that had been selected for the GEM program, then contacted the professional industry associations (Association of National Accountants of Nigeria; Institute of Chartered Accountants of Nigeria; the National Institute of Marketing of Nigeria) to get a list of their members, and supplemented this with web searches, snowballing, and word of mouth. This resulted in a survey of 271 service providers in total, of which 39 were providers for the GEM program.<sup>2</sup> By specialization, we have 68 human resource specialists, 102 accounting firms, and 101 marketing firms.

Table 1 summarizes key data from these provider surveys. The first few rows provide information on the characteristics of business service providers in these markets. Most of these providers have been in operation for some time, with a median of 9 years and mean of 11.5 years. They have an average of 10.9 professionals working for them (directors, managers, accounting experts, marketing experts, and human resource experts), with a 10-90 range of 3 to 18 professionals. 38 percent have more than one branch. The
according to some economic and **geographical** variables, which consequently form the list as a **geographical serpentine.**

- c) <u>Poverty map: this is the design proposed in the present paper; PSUs are</u> firstly stratified in the 24 governorates, and then they are further stratified (implicitly) with systematic sampling by ordering the lists of PSUs by average per capita consumption expenditure estimated with the poverty mapping method presented in Section 3.

On the basis of the new information from the current population in Tunisia, and the design effect of 2.19 estimated on the basis of the EBCNV 2015, Table 5 reports the expected sample sizes to obtain maximum percentage errors reported in the left column. The expected design effects corresponding to scenarios a) and c) have been estimated using equation (3) in Section 2, where Y is the mean per capita consumption expenditure from the poverty mapping in equation (6) in Section 3.

First of all, we can observe that in order to obtain a percentage error of about 10% a very high sample size is not needed when good designs with implicit stratification are taken into account (such as the classical “Serpentine”). However, this can be verified when very complex methods of variance estimation, usually based on replication techniques, are implemented. The recent JRR version, proposed by Verma and Betti (2011), and implemented to estimate the design effect 2.19 of the “Serpentine” design, is much more easily implemented by National Statistical Offices.

**<u>Table 5. Sample sizes in function of expected errors and type of design</u>**

|||Explicit<br>stratification|Serpentine|Poverty map|
|---|---|---|---|---|
||**deft**|**3.71**|**2.19**|*
Figure 8 _Extreme weather conditions in rural communes by climate zone in 2010, 2012 and 2014_

## _b. Maximum temperature_

## _a. Maximum rain_

<!-- Start of picture text --> dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>c. Minimum rain d. Minimum temperature<br>dry_cold dry_hot wet_cold wet_hot dry_cold dry_hot wet_cold wet_hot<br>2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014 2010 2012 2014<br>40<br>1,500<br>35<br>1,000<br>rainfall in mm<br>500 temperature in C 30<br>0 25<br>100 30<br>25<br>50<br>rainfall in mm<br>temperature in C 20<br>0 15<br><!-- End of picture text -->

## _c. Minimum rain_

Notes: Box plot shows the distribution of commune observations for each climate zone. The boxes illustrate the 25 to 75 percentile with the median value represented by the line in the box. The whiskers indicate the lowest and highest adjacent value. Maximum rainfall is measured by the rainfall level in the wettest month. Minimum rain is measured by the rain level in the driest month. Maximum temperature is measured by the mean of maximum temperature in the hottest month. Minimum temperature is measured by the minimum temperature in the coldest month. Source: Author’s calculation based on VHLSS 2010, 2012 & 2014 and CRU data.

# **4.5 Weather events**

Variables describing the occurrence of weather events, such droughts, floods and storms allow to measure the impact of weather shocks and natural disasters. A recent study for Vietnam uses monitored data on riverine floods and cyclones to identify natural disasters
before 2021) limits precision in some specifications, the estimated effects on service provision in the administrative data remain consistently positive and large, ranging from 0.28 to 0.68 s.d. increases in the overall index of public services. We also report the fuzzy RD estimate, which yields a similar result with slightly larger magnitude. Columns 2–4 present analogous robustness checks for citizens’ perceptions of service access and quality as well as village governments taking action to improve services. We again find consistent evidence that turnovers improve perceived service access, quality, and village-level investments on service provision.

## **5.3 Downstream Effects on Attitudes**

Despite the improvements in service provision observed in both administrative and survey data, turnovers do not improve citizen satisfaction or trust in the village government. Appendix Table A.23 shows that this finding holds in the full sample (panel A) and across subsamples of citizens who are more or less socially distant from the village government (panels B through D). Consistent with bureaucrats’ reports (see Table 3), citizens overall indicate more frequent interactions with bureaucrats after a turnover (column 1, though not statistically significant). However, they do not express greater satisfaction with the village government (column 2) or higher trust in it (column 3). Panels B through D show that less socially distant individuals are driving the increase in bureaucrat–citizen interactions after a turnover, while changes in trust towards the village government do not vary substantially between citizens at varying degrees of social distance.

Overall, these null effects on attitudes—despite significant gains in service provision—suggest that citizens may not immediately recognize or attribute these improvements to the village government, limiting short-run gains in trust or satisfaction. Improvements in service provision may be misattributed to other actors, such as higher levels of government or foreign donors (Cruz and Schneider, 2017; Guiteras and Mobarak, 2015), or trust may adjust only slowly to new information (Khan et al.
The resulting indexes for labor earnings in the informal sectors of Cote d'Ivoire and Senegal show there was no deflationary trend over the 1980s and early 1990s. These indexes are represented in Figures 2 and 3 respectively.6 In C6te d'Ivoire, labor earnings in the informal sector increased roughly at the same pace as labor costs in the formal sector, at least until the end of the 1980s. In Senegal, they increased at a much faster pace. Figures 2 and 3 also show that both labor earnings in the informal sector and labor costs in the formal sector increased far more than the SMIG.

The plots in Figures 2 and 3 do not imply that formal sector wages are flexible downwards though. They only suggest that downward rigidity, if it does exist, did not play a role in the series of events leading to the 1994 devaluation of the CFA Franc. In particular, they reject the hypothesis that labor market policies and institutions were the obstacle preventing wages in the formal sector to adjust to a more unfavorable international context. But the downward rigidity of wages, if there is such a thing in CFA countries, could still represent a serious problem when facing truly deflationary pressures. The fact that these pressures did not materialize in the 1980s and early 1990s does not rule them out for the future.

There is, however, some evidence that formal sector wages are flexible downwards, at least in M6te d'Ivoire. The reason why this flexibility may not be apparent is because analyses based on averages tend to neglect composition effects. In a study using data collected at the plant level, Levy and Newman 6 The represented series are unweighted averages of the indexes for hair-cuts and laundries. Each of these two, in turn, is an unweighted average of the indexes for all of the items available in the European consumption bundle of the two countries. In Cote d'Ivoire, the price index for hair cuts is based on items 41011 and 41012, whereas the price index for laundries is based on items
import price �a<sup>t</sup> nn<sup>< 0</sup> �, then the implied import demand is inelastic, so that an increase in import price induces a less than proportionate decrease in import quantities. Finally, if the share of import in GDP increases with import prices<sup>�</sup> a<sup>t</sup> nn<sup>> 0�,thentheimpliedimportdemandmustbeelastic</sup> such that an increase in the price of import induces a more than proportionate decrease in import quantity.<sup>8</sup>

# 3 Empirical Strategy

With data on output shares, unit values and factor endowments, Equation (10) is the basis of our estimation of import elasticities. In principle, we could fi rst estimate the own price effects, a<sup>t</sup> nn<sup>, for</sup> every good according to Equation (10) , and apply Equation (11) to derive the implied estimated elasticities, since the own price elasticity is a linear function of own partial effects. There are, however, at least three problems with the estimation of the elasticities using (10). First, there are literally thousands of goods traded among the countries in any given year. Moreover, there is also a large number of non-traded commodities that compete for scarce factor endowments and contribute to GDP in each country. Thus the number of explanatory variables in Equation (10) could easily exhausts our degrees of freedom or introduce serious collinearity problems. Second, even after solving this fi rst problem, we could also run out of degrees of freedom given the short time span of trade data available at the six digit of the HS —which only started being used in the late 1980s. Finally, there is nothing so far to ensure that the estimated elasticities satisfy second order conditions of GDP maximization, i.e., there are negative. We tackle these three problems
any given day that contain country _c_ in its location tags.

# **B.** **<u>Asset prices and related variables</u>**

We explore the impact of news-sentiment measures on stock market returns. Equity returns are computed using the country’s main stock market index. Returns are computed in local currency to isolate the effect of sentiment on the local stock market from its effect on the exchange rate. Daily world returns, which are used as a global control, are computed using the Dow Jones World Index. The final coverage, along with the list of indexes and sources used to compute equity returns is reported in the Appendix.

To proxy for market liquidity, we also collect daily equity trading volumes reported by local stock exchanges. Following Campbell, Grossman, and Wang (1993) and Tetlock (2007), we compute the detrended log of daily trading volume, using the rolling average of the past 60

> 6 This article’s main location tag is Argentina and one of the topic tags is “Economic News”.
# **3 Empirical Strategy**

This evaluation of the CADENA program focuses on the effect of insurance payments on ex-post production decisions and other coping mechanisms. To identify this effect, we limit our sample to municipalities that were insured through drought index insurance policies between the years of 2005 and 2013. Using weather data provided by CONAGUA, we match policies to their corresponding weather stations and calculate the total precipitation recorded at that station within each of the three phases designated in the policy. We then subtract the policy-specific precipitation threshold from the realized precipitation to obtain deviations from the threshold for each of the three phases, _Xmcsti_ = _precipmcsti − thresholdmcsti_ , where _m_ indexes the municipality, _c_ the crop, _s_ the weather station, _t_ the year, and _i_ the phase. A policy results in payment if precipitation at the corresponding weather station falls below the pre-determined threshold in any of the three phases.

In an ideal setting, we would observe insurance payments and all relevant outcomes for precisely the area covered by the policy. The running variable in this setting would be the minimum deviation from the threshold at a given weather station over the three phases of the policy: _Xmcst_ = min _i∈{_ 1 _,_ 2 _,_ 3 _}{Xmcsti}_ . However, we only observe payment and outcome variables at the municipality level, so we collapse the observations to the municipality-crop-year level and define the running variable as the minimum deviation from the threshold over all of the weather stations associated with a given municipality, _Xmct_ = min _s∈S_ ( _m_ ) _{Xmcst}_ . Given the data limitations, we have to account for the fact that there is substantial heterogeneity in the intensity of the treatment among municipalities receiving payment because some municipalities may have received payouts on 25% of their insured area and others may have received payment on 100%. In addition, there exists variation in terms of the percentage of agricultural land in the municipality that is insured via CADENA, since the policy is limited
# **Introduction**

Sustained economic growth is always accompanied by urbanization. But in Africa, urbanization occurred without growth. Is the implication that Africa's urbanization process was distorted? Or rather that urbanization is not always accompanied by sustained growth. And that in economic downturns, the poor and the migrants don't necessarily flock back to rural areas.

The initial motivation for this paper was to understand why once again, Africa appeared to be "different." To do so, we investigate the determinants of urbanization across countries, over the last 40 years. Most analysis of this kind focus on individual's decision to migrate. Here, we rely on macro data and cross-country comparisons instead. This paper then is about the determinants of urbanization everywhere. It is not limited to Africa, although it does focus on trying to answer the puzzle raised by the phenomenon of urbanization without growth in Africa.

Why this interest in urbanization at a macro level? A central hypothesis of the paper is that individuals move (with varying degrees of ease) in response to economic incentives and follow economic opportunities. If location incentives are distorted, so presumably is the growth process. Distorted location incentives may also result in higher than necessary social and environmental costs, that are not offset by the increase in productivity and wealth that usually accompanies urbanization.

An additional objective of the paper is to show that urbanization is part and parcel of the structural changes that accompany economic development. And that there is very little point in trying to stem it. Where governments have tried, they have generally failed (China, Soviet Union, Vietnam), creating substantial pent-up demand that led to brutal adjustments (colonial Africa, Trujillo's Dominican Republic) and generally harmed the poorest (Nyerere's Tanzania.) Understanding the dynamics of urbanization can help policy makers mitigate its costs rather than worsen them. In many cases, slums are created and remain locus of environmental and human disasters for decades because of the myth that slums dwellers are there