rafmacalaba/datause-encoder-data huggingface dataset:
26 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo / LCR Flagship Report on Cities and Productivity from the Emissions Database for Global Atmospheric Research (EDGAR)<sup>7</sup> of the JRC. Finally, we used OpenStreetMap data to account for a number of urban amenities like hospitals, universities, among others. ( © OpenStreetMap contributors<sup>8</sup> ). Table 5 presents a complete list of all control variables.

# **5 Empirical results**

# _5.1 Strategy 1: Lagged model_

Table 6 presents the correlations between the seven variables that we use to describe the three dimensions of urban form, and Table 7 presents a series of 10 alternative specifications for equation (2).<sup>9</sup> The main equation is estimated using OLS and assuming intra-group correlation; i.e., that the residuals are correlated within geographical clusters, but uncorrelated between clusters. We cluster at the country level and have a total of 32 countries in the sample.

Even though the three indexes used to describe the shape of the urban extent seek to measure different aspects of urban form (roundness, smoothness and fullness), the correlations in Table 6 show that there is very high positive correlation between _exchange_ index_ and _perimeter_ index_ (0.74). We also found high correlation between the variables _intersection_ density_ and _street_ density_ . Both variables measure the level of connectivity within a city and therefore have a very high correlation of 0.95. For this reason, we test alternative specifications, as shown in Table 7 where columns 1 to 5 use _intersection_ density_ and columns 6 to 10 use _street_ density_ . Finally, there is a high and negative correlation between _fullness_ and _sprawl_ (-0.71); for this reason we use them separately.

> 7 http://edgar.jrc.ec.europa.eu/gallery.php?release=v42&substance=PM10&sector= TOTALS

> 8 http://www.openstreetmap.org/copyright

> 9 Table A1 shows the descriptive statistics of the variables.
Table 2: FDI and Growth: Cross-country Regressions starting in 1990s

||(1)<br>1990-2009|(2)<br>1990-2009|(3)<br>1990-2009|(4)<br>1995-2014|(5)<br>1995-2014|(6)<br>1995-2014|
|---|---|---|---|---|---|---|
|FDI|-0.008|0.058|0.116|0.017|0.188***|0.177***|
||(0.011)|(0.094)|(0.116)|(0.025)|(0.034)|(0.035)|
|FDI _×_ Pr. Cr.||-0.098<br>(0.131)|||-0.249***<br>(0.046)||
|FDI _×_ School.|||-0.182<br>(0.162)|||-0.268***<br>(0.056)|
|_GDPt−_1|-0.672**|-0.631**|-0.647**|-1.008***|-0.818***|-0.911***|
||(0.326)|(0.306)|(0.316)|(0.263)|(0.232)|(0.243)|
|Pr. Cr.|2.355|2.797|2.489*|1.536|2.945**|1.774|
||(1.458)|(1.691)|(1.441)|(1.310)|(1.324)|(1.243)|
|School.|0.486|0.344|0.813|1.340**|0.879|2.188***|
||(0.809)|(0.772)|(0.968)|(0.653)|(0.543)|(0.621)|
|Inf.|-0.119|-0.110|-0.107|0.120|0.219|0.200|
||(0.193)|(0.184)|(0.179)|(0.255)|(0.219)|(0.213)|
|Trade|-0.160|-0.345|-0.485|-0.116|-0.667|-0.581|
||(0.494)|(0.630)|(0.630)|(0.470)|(0.468)|(0.441)|
|Govt. Cons.|-0.027|-0.058|-0.022|-0.578|-0.677|-0.640|
||(0.612)|(0.604)|(0.579)|(0.540)|(0.504)|(0.484)|
|Instit.|0.333|0.374|0.413|0.333**|0.397***|0.428***|
||(0.243)|(0.286)|(0.293)|(0.164)|(0.143)|(0.135)|
|SSA|-1.752***|-1.745***|-1.781***|-0.900*|-1.059**|-1.089**|
||(0.593)|(0.605)|(0.603)|(0.472)|(0.467)|(0.459)|
|B. M. P.|1.342|1.818|2.400|5.066|5.088|5.315|
||(11.095)|(11.587)|(11.711)|(6.588)|(6.181)|(6.109)|
|Constant|5.321|5.046|4.848|5.946|5.848|5.802|
||(7.523)|(7.763)|(7.778)|(4.544)|(4.021)|(3.904)|
|N. Obs.|81|81|81|96|96|96|
|_R_<sup>2</sup>|0.394|0.401|0.413|0.298|0.408|0.401|

**Notes:** This table reports a set of cross-country regressions where the dependent variable is the average growth rate of real annual GDP per capita and the explanatory variables are: net FDI inflows as a percentage of GDP; credit to the private sector by deposit money banks as a percentage of GDP (this variables is scaled by 100); the log of average years of secondary schooling in adult population; the log of initial GDP per capita; the log inflation; the log of export plus import over GDP; the log of government expenditure over GDP; the ICRG investment risk index; the log of the black market premium; and a dummy that takes value one for countries located in SubSaharan Africa. Columns (1)-(3) focus on developing and emerging economies for which we have data starting in 1970. Columns (4)-(6) focus on developing and emerging economies for which we have data starting in 1975. Robust standard errors are in parentheses. Significance levels are denoted as: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

10
These studies suggest that policy shocks that improve the operation of the banking system in an economy enhanced competition in the non-financial sector, especially by easing barriers to the entry of new firms.

# **3.2.3 Impact on microenterprises**

To draw sharp inferences about finance and firm performance, researchers have also used randomized experiments and other survey-based methods for identifying causal effects. Cotler and Woodruff (2007) assess the impact of very small firms, micro-enterprises, receiving credit. Using the pipe-matching methodology, they evaluate the impact of credit on microenterprises that receive credit and those that were selected to receive it in the future by a microfinance lender in Mexico. They find a positive and significant effect only for the smallest retailers, but a negative effect on larger retailers’ sales and profits.<sup>12</sup> Thus, finance can exert a particularly positive impact on very small enterprises.

Randomized experiments provide stronger insights on the connection between finance and firm performance. Specifically, De Mel et al. (2008) and McKenzie and Woodruff (2006, 2008) give funds to small retail firms in Sri Lanka and Mexico, providing an exogenous shock to capital. They show that this shock generated large increases in profits, indicating returns to capital that are substantially higher than market interest rates.13 McKenzie and Woodruff (2008) also show that increases in returns were especially high among the most constrained enterprises in Mexico, indicating that capital constraints are an important source of inefficiency. But rigorous empirical evidence based on credible control-treatment evaluations remains scant, and the same researchers find much weaker effects on returns to capital for female microentrepreneurs in a randomized controlled trial in Sri-Lanka. Furthermore, the studies do not incorporate risk considerations. A high return from injecting capital in a firm is not synonymous with making a sound investment because of risk. Hence more work is required to illustrate that these enterprises would be willing to borrow or able to obtain financing.

> 12 Using survey data for Guatemalan micro-entrepreneurs, Wydick (1999) also shows a positive association between credit use on upward class mobility, but it is difficult to draw causal implications from the analysis. 13 Note that this work also has important theoretical implications. If returns to capital are very high at very low levels of investment as in the Sri Lanka and Mexico experiments above, then capital-constrained entrepreneurs should be able to enter and grow to a desired size by reinvesting profits earned in the enterprise. Hence capital constraints will have short term costs but fewer long term effects on outcomes, questioning the validity of Banerjee and Newman (1993) type models where credit constraints lead to permanent poverty traps.

35
But the food and fuel price shocks, though the largest and most protracted since 1900 (GEP 2009) did not seem to have an appreciable effect on world growth, unlike the previous oil shocks. Several factors may explain this: the compensatory effect of other commodity price increases for several importing countries, a decline in fuel intensity in the world due to technological factors<sup>9</sup> , a decline in the fuel needs of the developed world as they have moved away to service- led economies, and perhaps a substitution away from fuel as a response to rising prices over time. _In addition_ , monetary policy in the advanced countries was accommodating, contrasting with the contractionary policies of the previous period aimed at fighting inflation. While all the above factors contributed to resilience in world growth, it is possible that had the increase been sustained for a longer period there would have been negative effects on growth, particularly in some emerging markets, and particularly if tighter monetary policy had eventually been adopted to contain inflationary pressures. Recent estimates from FAO suggest that the postcrisis scenario for food prices does not augur well for developing country food importers as they are projected to rise again when the global downturn ends.<sup>10</sup> Commodity price developments remain a source of volatility in world markets.

A striking difference between booms in “real” factors as opposed to the booms in financial markets is that finance is “all pervasive” and the magnitude and swiftness with which a shock can spread is markedly higher. Every sector of the economy depends on the financial sector for transactions across space and time; the more developed the country, the more this tends to be the case. The very role of finance is to provide links between activities (intermediation) where none would have existed through the production or consumption process.

Second, the financial sector is notable for the speed with which changes in prices can occur.

> 9  OECD oil-intensity, defined as million barrels a day consumed as a proportion of GDP, decreased from 1.07 during 1977-80 to 0.57 during 2004-06

> 10 FAO’s latest Food Outlook suggests that the index of food prices climbed to 168 points in Nov 2009, the highest level since Sept. 2008. This is 21% below the peak in June 2008 but prior to 2007-2008; this index had never exceeded 120 and had usually been below 100.

5
percent of non-account-holders in that country cited cost as a reason for not having a formal account. But fixed fees and high costs of opening and maintaining accounts also often reflect lack of competition and underdeveloped physical or institutional infrastructure.

A second important barrier is documentation requirements. By limiting eligibility, these may exclude workers in the rural or informal sectors, who are less likely to have wage slips or formal proof of domicile. Because of legitimate concerns about fraud and money laundering, however, there is a reasonable limit to how much documentation requirements should be relaxed, and this line likely varies across countries.<sup>19</sup> Another important barrier to formal account ownership is proximity to a bank. For example, 47 percent of non-account-holders in Tanzania reported distance as a reason why they don’t have an account and Tanzania also ranks near the bottom among developing economies in bank branch penetration by area, averaging less than 0.5 bank branches per 1,000 square kilometers.

Trust in banks—or distrust—can constitute a barrier that is difficult to overcome, and suggested causes have been linked to cultural norms, local governance, economic crises, and uncertainty about the future (for example, Bjornskov, 2007; Guiso et al., 2004; Sapienza and Zingales, 2009). For example, respondents in the former Soviet Union—which has been plagued by episodes of government expropriation of bank assets—were almost three times as likely as adults in other regions to choose “[I] don’t trust banks” (31 percent).<sup>20</sup> These self-reported barriers and the information gathered by the survey on account use raise the following questions, which we can test econometrically: First, do we continue to find a

> 19 For additional information on documentation requirements and money laundering, see: <u>http://go.worldbank.org/0PHO7X3QA0.</u>

> 20 In the core Gallup World Poll questionnaire, respondents are asked to rate their trust in banks and again respondents in the former Soviet Union—banked and unbanked—report the least amount of trust.

13
|||**Glo**|**bal**||**Adva**|**nced**||
|---|---|---|---|---|---|---|---|
||Adva|nced|Eme|rging|In|Out|**Emerging In**|
||In|Out|In|Out||||
|Signifcant & positive|18/19|18/19|14/28|22/28|15/19|14/19|13/28|
|Signifcant & negative|0/19|0/19|0/28|0/28|0/19|0/19|0/28|
|Median|0.49|0.58|0.33|0.52|0.52|0.51|0.36|
|Median t-stat.|4.2|6.3|2.1|3.4|5.1|5.2|1.9|

Table 3: Summary statistics of the loadings of the different factors. Significance refers to the 95% level, based on 10000 block bootstrap replications.

|In|fows-Outfo|ws|Infows|-Infows|Outfows-Outfows|
|---|---|---|---|---|---|
|Gl<br>Advanced|obal<br>Emerging|Advanced|Global-Advanced|Global-Emerging|Global-Advanced|
|0.73|0.27(0.47)|0.88|-0.16|-0.64|-0.34|

Table 4: Correlation of the loadings of the different factors. The number in brackets in the second column reports the correlation between the global factor loadings of the outflows of emerging countries, and the sum of the global factor loadings and the emergingcountry inflow factor loadings of the inflows of emerging countries.

39
potential controversial issues, so it is also natural that there will be disagreement regarding ethical considerations, both across individuals and across cultural settings, thus it is essential to have careful oversight and scrutiny of the research design and protocols. The project underwent several rounds of ethical review by several different committees and agencies, including the IRBs of the University of California, Berkeley and San Francisco, the Ifakara Health Institute, and the National Institute for Medical Research in Tanzania (NIMR). In addition, the study benefited from a (largely Tanzanian) ethical advisory group that reviewed the study protocol.

# **_Target Population_**

A key decision point is whether to target the general population of some region, or target a particular high risk group. The intervention may be on average more effective among high risk groups, but potentially have a smaller aggregate impact. The RESPECT study chose to target a general population. A pilot study by Galarraga (in process) in Mexico City is alternatively exploring the possibility for targeting a group of male sex workers. One concern with targeting a group such as sex workers is the political constraint to scaling up implementation due to stigma and/or the concern about the rewards attracting people to sex work; however the income value of sex work is likely to swamp the value of incentive payments, making that concern less salient.

In order to ease study logistics, RESPECT chose to implement the intervention within the context of Ifakara Health and Demographic Surveillance Site in rural southwestern Tanzania. We chose to recruit from ten villages; eight were rural and two were in Ifakara town. Rural villages are generally lower income, so a given reward level is likely to be more salient. Due to the limited number of villages, study arm randomization occurred at the individual level.

Demographically, we chose to enroll both men and women, as both could hypothetically respond to the program. A priori, there were concerns that women might not have sufficient agency to respond to the incentive regimen, while there were concerns that men‘s responses could be dulled if they used cash rewards to pay for new risky behaviors, thus it was of interest to study both genders. We also chose to enroll both 15
# Table 4: Regional Dierences, Second-Third Grade

|South|North|Dieren|ce|
|---|---|---|---|
|Panel A: Test Scores at Beginning of Second Grad|e (Baseli|ne)||
|Percent Correct: French<br>0.430|0.420|0.010|(0.027)|
|Percent Correct: Math<br>0.325|0.373|-0.048<sup>_∗∗_</sup>|(0.023)|
|Percent Correct: Oral<br>0.154|0.242|-0.088<sup>_∗∗∗_</sup>|(0.016)|
|Index Score (standardized)<br>-0.239|0.049|-0.288<sup>_∗∗_</sup>|(0.112)|
|Panel B: Test Scores at Beginning of Third Grade|(First Fo|llow-Up,|Control Schools)|
|Percent Correct: French<br>0.411|0.564|-0.153<sup>_∗∗∗_</sup>|(0.022)|
|Percent Correct: Math<br>0.434|0.569|-0.136<sup>_∗∗∗_</sup>|(0.021)|
|Percent Correct: Oral<br>0.233|0.383|-0.150<sup>_∗∗∗_</sup>|(0.022)|
|Index Score (standardized)<br>-0.629|0.100|-0.730<sup>_∗∗∗_</sup>|(0.100)|
|Panel C: Household Characteristics (First Follow-U|p, Cont|rol Schoo|ls)|
|Household size<br>8.625|10.216|-1.591<sup>_∗∗∗_</sup>|(0.412)|
|Number of children in household<br>5.050|5.551|-0.501<sup>_∗_</sup>|(0.276)|
|Head has any education<br>0.550|0.401|0.149<sup>_∗∗∗_</sup>|(0.050)|
|Percent of adult females with any education<br>0.261|0.224|0.038|(0.043)|
|Wealth index<br>-0.654|0.137|-0.792<sup>_∗∗∗_</sup>|(0.092)|
|Interview conducted in French<br>0.175|0.090|0.085<sup>_∗∗_</sup>|(0.041)|
|Panel D: Project Characteristics (Second Follow-U|p, Treat|ment Sch|ools)<br>|
|Months since project began<br>15.914|23.479|-7.564<sup>_∗∗∗_</sup>|(1.144)|
|Students helped draft application<br>0.800|0.547|0.253<sup>_∗∗∗_</sup>|(0.082)|
|Project included manuals<br>0.800|0.895|-0.095|(0.074)|
|Project included computer materials<br>0.029|0.121|-0.092<sup>_∗∗_</sup>|(0.042)|
|Project included teacher training<br>0.914|0.752|0.162<sup>_∗∗_</sup>|(0.062)|
|Project included management training<br>0.629|0.368|0.261<sup>_∗∗∗_</sup>|(0.093)|
|Project included building courses<br>0.971|0.821|0.151<sup>_∗∗∗_</sup>|(0.046)|
|Project included improving general education<br>0.563|0.456|0.106|(0.100)|
|Project included improving educational outputs<br>0.114|0.129|-0.015|(0.063)|
|Amount spent on principal (1,000,000s CFA)<br>0.082|0.034|0.048<sup>_∗∗∗_</sup>|(0.014)|
|Amount spent on teachers (1,000,000s CFA)<br>0.317|0.278|0.039|(0.058)|
|Amount spent on management (1,000,000s CFA)<br>0.128|0.041|0.087<sup>_∗∗∗_</sup>|(0.022)|
|Amount spent on students (1,000,000s CFA)<br>0.505|1.025|-0.520<sup>_∗∗∗_</sup>|(0.092)|

Each coecient reported is the dierence in test score between south and north (south-north). The mean test scores in the South for French, math, and oral at baseline are 0.400, 0.292, and 0.134 for females and 0.461, 0.358, and 0.173 for males, respectively, and at rst follow- up are 0.426, 0.430, and 0.215 for females and 0.457, 0.486, and 0.298 for males. Clustered standard errors in parentheses.<sup>_∗_</sup> _p <_ 0 _._ 10 ,<sup>_∗∗_</sup> _p <_ 0 _._ 05 ,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 21
- 34 -

More generally, the Bank has become more sensitive to the limitations of Its understanding with respect to adjustment policies. Its experience has shown the complexity inherent in designing measures to deal wlth rector and system-wide problems. The continuing fundamental importance of good project lending is being increasingly noticed. Just as a sound macroeconomic environment was found essential for successful project lending, so is the latter needed to carry out vital assistance that cannot be provided by the former. It has also become more aware of the uncertainties associated with the international environment and their effects on economic performance. Accumulated experience has also led to increased awareness of the political sensitivity of particular policy choices and the effects such sensitivities can have on the speed of policy Implementation.

Although recent experience with program lending may not have been fully satisfactory, it should not be inferred that such lending was not worthwhile. World Bank (1988) is an interim report on the accomplishments of adjustment lending as well as areas for continued improvement. Some programs have been extremely successful. Turkey, which has had five structural adjustment loans and three sector loans, Is probably one example of such success, and Colombia, Ghana, Korea and Thailand are perhaps others. Other programs, while not completely meeting expectations, have made Important contributions to the development potential of the adjusting economies. In a number of cases improved performance is already noted, although a causal relation between policy and performance is hard to establish. Among the contributions has been the Institution building which has accompanied
**Figure 1a: Average number of years of schooling for population of 15+ year-olds, 1960–2000**

<!-- Start of picture text --> 1960 1970 1980 1990 2000<br>year<br>7<br>6<br>5<br>Mean years of schooling<br>4<br>3<br><!-- End of picture text -->

**Figure 1b: Average number of years of schooling for population of 15+ year-olds, 1960–2000, by the world regions**

<!-- Start of picture text --> 1960 1970 1980 1990 2000<br>Year<br>LAC ECA MENA SAR<br>SSA EAP Advanced<br>10<br>8<br>6<br>Years of Schooling 4<br>2<br><!-- End of picture text --> The expansion has occurred at all levels, as is evident from figure 1c which presents the increases in gross enrollment from 1970–2000. In percentage terms, the higher the level of schooling the steeper the increase; thus, between 1970 and 2000, the average enrollment in secondary education went up by nearly 180 percent (from around 33 to 70 percent) while the enrollment rate in tertiary education nearly quadrupled (from less than 7 to 26 percent).

4
<!-- Start of picture text --> 100000<br>-<br>7<br>7<br>80000 zt<br>t<br>/<br>/<br>I<br>— 60000 I<br>®<br>a I<br>EI<br>)<br>==I<br>40000 I<br>!<br>I<br>)<br>7<br>20000 /<br>)<br>1980 1990 2000 2010 2020<br>Year<br>== Sporadic ——— LIR<br>----—- Total<br><!-- End of picture text -->
Despite the small share of firms with more than 50 employees, formal employment is concentrated in large firms. Firms with more than 50 employees represent fewer than 3 percent of formal firms, but employ more than 50 percent of formal workers. Medium firms (10–49 workers) account for close to one-fourth of total employment; small firms employ 21 percent of formal workers. The distribution of formal workers across firms by size, and the greater exposure of small firms to the minimum wage have implications for the potential employment effects of the minimum wage.

Table 1 also shows that the minimum wage is strongly binding for formal firms in Turkey. In the quarter before the 2016 minimum-wage increase, more than half of the formal workers in formal firms received a wage roughly equal to the formal minimum wage. There is a clear relationship between the size of firms and the wages of workers. Microenterprises and small firms pay the lowest wages in the formal sector, and they employ a large share of workers at the minimum wage. Shortly before the 2016 increase, 93 percent of formal workers in microenterprises earned the minimum wage. By comparison, about 70 percent of workers in medium firms earned the minimum wage, and 40 percent of workers in large firms earned the minimum wage. Thus, any increase in the minimum wage thus results in sizable labor cost expansions among formal firms, but particularly within small firms.

Figure 3 reports the total number of formal firms in the EIS administrative dataset by quarter in 2012–16. The number of formal firms in Turkey rose steadily from the first quarter of 2012 to the last quarter of 2015. The number of formal firms then dropped during the first quarter of 2016, right after the new minimum wage, and this number then continued to decline in the following quarters. Similarly, the growth rate in formal employment fell during the quarter immediately after the minimum wage rose; formal employment then continued to decline in absolute terms in the following quarters (Figure 4). This paper investigates whether the relationship between the minimum-wage increase and the drop in the number of formal firms and in formal employment is causal.

# **4. Identification strategy**

## 4.1 Measuring exposure to the minimum-wage increase

The main challenge in identifying the effects of the 2016 minimum-wage increase is related to the nationwide implementation of the reform. The approach followed by previous work to address this 11