rafmacalaba/data-use-mentions-extended huggingface dataset:
_EXPY_ we will use what we call _dynamic PRODY_ s, i.e. the _PRODY_ values of a certain service export may vary year to year. Hence, an increase i _n dynamic EXPY_ can be due to (i) an increase in the _PRODY_ of a service and/or (ii) an increase in the share of high _PRODY_ products in the export basket. This is important for two reasons. First, it gives us an opportunity to capture the effect of changes in PRODY on EXPY. We do not only want to measure the fact that countries are getting more and more engaged in higher value service export, but also that the service exports themselves are increasing in sophistication due to improvements in ICT and 3T‘s. In fact many richer and high-skilled countries are engaged in service exports boosting the _PRODY_ s of some service export categories.<sup>9</sup> Secondly, since the data is much less disaggregated for services, using _static PRODY_ fails to capture the movements to higher _PRODY_ service exports that are only sub-categories of the broader categories captured by the Balance of Payments data.

A second characteristic, and just mentioned, is that service export data is not collected in the same detailed manner as goods export data. This means we will end up with only ten broad categories of service exports. They are different enough to make the analysis interesting, but it of course limits the extent to which we can understand the mechanisms in play. For example, if a country – within the same service export category - move from a sub-category of services with presumed low _PRODY_ , to a sub-category of services with presumed high _PRODY_ that would not show up at all in the _static EXPY_ . As mentioned, the _dynamic EXPY_ would however capture this as it allows for the _PRODY_ s to change over time. Unfortunately though, even when comparing the _static EXPY_ and the _dynamic EXPY_ , due to the high aggregation of data it would be impossible to know if the increase in the _dynamic EXPY_ was created
Except for the Simples size-based tax exemptions and the R&D incentives, the remaining incentives have a sectoral focus and thereby generate distortions across sectors and activities. In addition, Simples also provides incentives to smaller firms, which can create threshold effects in preventing formal firm growth. The bulk of the implicit subsidies created by incentives go to firms in trade and services, and manufacturing industries. These differences across sectors erode a level playing field by favoring some firms and sectors over others.

# **_Additional government interventions: Product market regulations and competition law enforcement_**

Reducing cost of doing business and improving the adequacy of infrastructure are not enough to foster competition in markets and ensure domestic market integration. Effective antitrust enforcement and pro-competition regulation are also important. Even when transport and transaction costs are lower, or when costs of paying taxes, getting electricity and dealing with construction permits are reduced, there may be still firms exercising significant market power even in geographically large and integrated markets, for example, if they agree to collude on prices, or if there is a lack of regulation that prevents the abuse of dominance by firms that supply services in segments with features of a natural monopoly. Effective antitrust enforcement and procompetition regulation minimize these risks and are key pillars of a holistic competition policy.<sup>37</sup> In this turn, while this paper discusses specific policies that can enhance healthy market dynamics and competition, it does not include an assessment of Brazil’s competition policy implementation itself.<sup>38</sup> Available data on product market regulation reveal that government interventions in domestic markets have not been conducive to competition. Competition enhances productivity by improving allocative efficiency, enhancing productive efficiency, and boosting innovation. Aghion, Akcigit and Howitt (2014) also provide a compelling framework linking market competition to productivity growth through creative destruction, while Conway et al (2006) and Alesina et al. (2005) trace the positive impact of pro-competition product market regulation on productivity, and Barone and Cingano (
model, developed by González et al. (2017) and Fok et al. (2005), includes a standard logistic function that exhibits point symmetry with one slope parameter and one location parameter. Brzoza-Brzezina and Kotlowski (2018) is relevant to our empirical strategy because they employ the PSTR model to examine a nonlinear relationship between a country’s risk premium and net

> 6These two previous studies use actual debt data, whereas this paper uses a wide range of forecast data to exclude any effects from current economic conditions on interest rates as well as to consider the forward-looking behavior of financial markets.

> 7Ebeke and Lu (2015) and Brzoza-Brzezina and Kotlowski (2018), who also use local currency bonds in emerging markets, control for exchange rate risk whereas Moore et al. (2013) controls for it indirectly by estimating the share of foreign investors that is affected by exchange rate risk. We include exchange rate risk in the baseline estimation but exclude it as a robustness check. Amstad et al. (2020), using the average credit ratings provided by Standard and Poor’s, Moody’s and Fitch find a slow and steady convergence of sovereign risk in local and foreign currency due to higher availability of foreign currency via FX reserves and smaller dependence on foreign currency borrowing (decline of original sin) over the past 20 years.

5
Table 2. Descriptive <u>statistics from educational administrative records</u>

||Pre (2018|-2019)<br>|Post (2020|-2021)<br>|
|---|---|---|---|---|
||Mean|(SD)|Mean|(SD)|
|_Outcomes_|||||
|Dropout|0.086|(0.281)|0.102|(0.302)|
|Effective promotion|0.802|(0.398)|0.823|(0.381)|
|Switched to public school|0.010|(0.098)|0.011|(0.103)|
|Switched to private school|0.032|(0.176)|0.023|(0.149)|
|_Student attributes_|||||
|Male|0.516|(0.500)|0.513|(0.500)|
|Age|10.722|(4.145)|10.953|(4.409)|
|Lives in urban area|0.414|(0.493)|0.407|(0.491)|
|Graduating|0.041|(0.198)|0.041|(0.199)|
|_Educational level_|||||
|Initial|0.146|(0.353)|0.148|(0.355)|
|Primary|0.570|(0.495)|0.576|(0.494)|
|Lower secondary|0.186|(0.389)|0.179|(0.383)|
|Upper secondary|0.098|(0.297)|0.097|(0.295
Tax-to-GDP prior to transition to middle income status, median <!-- Start of picture text --> Percent of GDP Percent of GDP<br>30 Median Mean 19<br>18<br>17<br>25<br>16<br>15<br>20 14 12.9<br>13<br>15 12<br>27.9<br>25.0 11<br>23.4<br>10 21.3 10 9.1<br>15.0 9<br>12.9<br>5 8<br>7<br>6<br>0<br>L to LM LM to UM UM to H<br>Years<br>T-10 T-9 T-8 T-7 T-6 T-5 T-4 T-3 T-2 T-1 T<br><!-- End of picture text --> _Sources:_ International Monetary Fund; Authors’ calculations. A. “L” denotes low income, “LM” denotes lower-middle income. “UM” denotes upper-middle income, and “H” denotes high income based on World Bank income classification. B. T denotes the year of transition from low to lower-middle income group. Dashed line denotes the inter-quartile range.

7 Some countries have transitioned more than once, likely due to volatility in estimated per capita income levels. To address this, we consider only the most recent year in which a country transitioned.

17
application of the regulatory framework governing trade. Surprisingly, the findings on large average firm size, more concentration at the top of the export distribution, low turnover rates, and high survival rates situate Argentina closer to the behavior of higher-income countries. Fernandez, Freund,

> 1The Exporter Dynamics Database is available at https://www.worldbank.org/en/research/brief/exporterdynamics-database. The indicators for all exporters are part of the database; other partitions of the information are used only for this paper.

> 2Non-automatic import licensing is a procedure introduced, for reasons other than SPS or TBT reasons, where approval is not granted in all cases. The approval may either be granted on a discretionary basis or may require specific criteria to be met before it is granted.

3
With regard to the calculation of household income, it is further worth mentioning that the value of farm output that is not sold and thus largely consumed (or stored) by the household is calculated at market prices and included as part of household income. Second, household incomes are deflated into 1986 prices, the first year of the RCRE survey, using the National Bureau of Statistics rural consumer price index for each province.

**_Rural Cadre Status_** _._ The measure of the political status used in our analyses is cadre status, which is constructed from a question of whether a member of the household was a township or village cadre ( _xiangcun ganbu_ ). Despite the simplicity of the survey question, the question provides a signal as to which households in each village are politically powerful.

**_Covariates_** . All models estimated include the households‘ weighted average years of education, share of laborers with special skills, productive assets per capita, arable land per capita, share of laborers and share of male laborers. Weighted average years of education and share of laborers with special skills help crudely to control for human capital. Share of laborers and share of male laborers are included in the regressions to control for household demographic characteristics that could affect household income. Finally, households‘ Communist Party membership is also included in our regressions to control for another measure of political status in rural China that could affect household income.

15
Dillon, A., Gourlay, S., McGee, K., and G. Oseni. (2016). “Land Measurement Bias and its Empirical Implications: Evidence from a Validation Exercise”, _World Bank Policy Research Working Paper 7597_ . Washington, DC: The World Bank.

FAO (1982). Estimation of crop areas and yields in agricultural statistics, FAO, Rome.

- Fasbender, D., C. Lucau (2012). “Identifying and modelling sources of uncertainty in GNSS area measurements.” _Proceedings of the 17_<sup>_th_</sup> _GeoCAP Annual Conference_ .

- Fermont, A., T. Benson (2011). "Estimating yield of food crops grown by smallholder farmers: A review in the Ugandan context.” _Uganda Strategy Support Program Working Paper_ No. USSP 05, IFPRI.

- Goldstein, M. and C. Udry (1999) ‘Agricultural Innovation and Risk Management in Ghana’, _Unpublished, Final report to IFPRI_ , IFPRI, Washington DC.

- Hofmann-Wellenhof, B., H. Lichtenegger, E. Wasle. (2008). “GNSS – Global Navigation Satellite Systems.” Springer-Verlag: New York, NY.

- IFAD (2010). _Rural poverty report 2011 New realities, New challenges: New opportunities for tomorrow's generation_ . Rome, Italy: IFAD.

- Istat (2008). _Farm Structure Survey 2009/2010. Survey on agricultural production methods 2009/2010. National Methodological Report (NMR)._ Rome: Istat.

- Joseph, George Gheverghese (1997). Geometry in India. In H. Selin. “Encyclopaedia of the History of Science Technology and Medicine in Non-Western Cultures”, Springer.

- Keita, N., and Carfagna. E. (2009). “Use of modern geo-positioning devices in agricultural censuses and surveys: Use of GPS for crop area measurement.” _Bulletin of the International Statistical Institute_ , the 57th Session, (2009), Proceedings, Special Topics
to achieve identification of all four parameters. Notice that (4’) requires, on the left hand side, estimates of the cross-sectional variance of residual income for each age group z, while (6’) requires that we use the panel dimension of our data set to estimate the covariances in individuals’ residual incomes viz over time. Thus, our estimation strategy exploits both the panel dimension and the repeated cross sections available in the data set. As in Caroll and Samwick (1997), we use residual income data at the individual level to obtain unbiased estimators of the terms on the left hand side of (4’) and (6’). Specifically, viz<sup>2andvizvi,z+1</sup> serve as as individual level ”observations” of the variance and covariance terms on the left hand sides of (4’) and (6’) respectively. We estimate our system of two equations ((4’) and (6’)) using a simultaneous, non-linear, seemingly unrelated regressions model (NLSUR) (as described in Gallant, 1975 and Amemiya, 1983). This permits the estimation of the two non-linear equations, with the cross-equation restrictions implied by the common parameters, simultaneously and achieves additional estimation efficiency by combining information from both equations (Davidson & MacKinnon, 2004).<sup>15</sup>

# IV. Data

Using the estimation methodology described in the preceding section, we estimate income mobility parameters using individual income data from Mexico. Specifically, the individual income data are taken from the Encuesta Nacional de Empleo Urbano (ENEU, Mexican National Urban Employment Survey) which was conducted by the Instituto Nacional de Estadistica, Geografia e Informatica (INEGI, National Institute of Statistics, Geography and Information), the primary statistical agency in Mexico, and the Secretaria del Trabajo y Prevision Social (STPS, Secretariat of Labor and Social Security), Mexico’s Labor Ministry.

Until recently, the ENEU was the primary survey instrument for
- 14 -

in Mozambique are MZN3,000 to 7,000, depending on industry. Labor quality may have to be taken into account, when local labor costs are considered. Primary school enrolment rates in 2007 are used to control for this.

|**Table 2. Summary statistics of variables used for firm locational model estimation**|
|---|

|Variable|Obs|Mean|Std. Dev.|Min|<br>Max|
|---|---|---|---|---|---|
|No. of existing firms in 2008|43808|275.5|577.1|3.0|<br>3423.0|
|Road density (km per 100 km<sup>2</sup>)|43808|11.9|14.4|0.0|<br>72.7|
|Transport cost to a port (US$ per ton)|43808|25.3|16.5|0.1|<br>74.9|
|District population in 2007|43808|163391.5|115413.8|9506.4|682691.4|
|Population density (per km<sup>2</sup>)|43808|343.7|780.6|0.8|<br>3203.6|
|Local monthly wage (Metical)<sup>1</sup>|43808|2705.9|1656.8|150.0|<br>7883.0|
|Primaryschool enrolment(%, gross)|43808|117.1|24.3|56.4|<br>239.7|

1 Average monthly wage at the district level, based on the 2008 Agriculture Sample Survey. In case no data are available in a given district, the closest district data is used as a proxy
9 degree in which the shape of a polygon deviates from its equal-area circle. It is calculated as the share of the total area of the urban extent that is inside the equal-area circle about its center of gravity (Angel et al., 2010b). An _Exchange_index_ equal to 1 corresponds to a perfect circle. As the _Exchange_index_ moves towards 0, the shape of the polygon becomes irregular, elongated, and noncompact. The smoothness of the perimeter is measured with the _Perimeter_index_ , which indicates how smooth is the perimeter of the urban extent. It is calculated as the ratio of the perimeter of the equal-area circle and the perimeter of the shape (Angel et al., 2010b). A _Perimeter_index_ equal to 1 indicates a totally smooth perimeter, found in a perfect circle. A _Perimeter_index_ close to 0 indicates a highly irregular perimeter, which is very common in natural/organic cities located in rugged topography. Finally, the level of fullness is quantified with the _Fullness_index_ , which measures the presence of built-up areas within the urban extent as a fraction of the urban extent area. It is calculated as the fraction of the total area of the urban extent that is built-up. This measure can be understood as the complement of measures such as porosity or sprawling (Burchfield et al., 2006). A _Fullness_index_ equal to 1 corresponds to a totally compact/built-up city. A _Fullness_index_ close to 0 represents a sprawling city with lots of empty spaces within its borders. In this study, the _Fullness_index_ was calculated using the built-up presence by epoch layers from Global Human Settlement Layer project (GHSL) at 250 meters of spatial resolution for 1990, 2000 and 2014 (Pesaresi et al., 2015; Pesaresi et al., 2016).

Next, we study two dimensions of a city structure related to two dimensions of connectivity: first, the structure given by the layout of the road network, and second, the connectedness of its segments. We measure urban structure with the _Circuity_avg_ , which takes values close to 1
ceteris paribus. Surprisingly, the age of the firm is never statistically significant. In both manufacturing and service sectors, size is negatively correlated with productivity growth.<sup>39</sup> **Figure 3 FDI Flows into Europe, All Sectors, 1985–2009** <!-- Start of picture text --> Europe total 1,200<br>FDI inflow 1,000<br>($ billion) 800<br>600<br>400<br>200<br>0<br>100%<br>Inflow share EU15 South<br>among<br>subregions 80% EU15 North,<br>(percent) Continental,<br>60% European Free<br>Trade Area<br>40%<br>EU candidates/<br>eastern<br>20%<br>partnership<br>EU12<br>0%<br>1985 1990 1995 2000 2005 2009<br>Source: World Bank staff calculations based on United Nations Conference on Trade and Development (UNCTAD) data.<br><!-- End of picture text --> Table 2 presents the estimation results for Eq. (1) for the EU12. Columns (1) and (7) show the results of the complete model split into manufacturing and services industries, while the remaining columns present the results using the various sub-indices of business operations.

Productivity gains are correlated with increases in the availability of private credit, stock of inward FDI, workforce education, and business environment—especially trade and taxes. A one standard deviation increase in the overall business regulation index is conditionally correlated with a 6.35 percent increase in productivity growth for the average manufacturing firm and 7.93 percent for the average service firm. A one standard deviation improvement in the tax regulations index is correlated with 4.77 percent and 7.10 percent increases in labor productivity for manufacturing and service firms, respectively.
of their landlords (Govender et al., 2011). In addition, 2011 Census data analysed by Rabe (2017) show that backyard dwellings in Cape Town are more likely to contain a single person, and less likely to contain more than four persons, than the average dwelling in the city, consistent with the view that backyard households are more mobile than the general population. Moreover, a study of backyarding in Greater Soweto (Johannesburg) indicated that backyard tenants are significantly younger than their landlords (36 vs. 56 years for household heads) and more likely to be foreign immigrants (Beall et al., 2003).

6
deal with potential omitted variable bias and reverse causality issues, we employ the instrumental variables (IV) approach where the exposure to a nationwide property privatization reform, which happened only in the urban areas, and the local

> telemarketing and other forms (for more detail, see https://www.fbi.gov/scams-and-safety/common-fraud- <u>schemes). Those schemes differ from each other in many ways, from targeting subjects to operating tools.</u> For example, gambling scams take advantage of people’s willingness to win gambles. Fraudsters send email to potential victims and tell them that they need to pay a small amount of fee in advance to guarantee winning an imaginary big prize/lottery/rewards. Investment frauds encourage people to participate in fake and rare investment opportunities with absurd returns. Bogus products and services provide potential victims useless health care products, psychic services, fake career opportunities, and so on (Button et al., 2009). 6 At this stage, we have much less understanding of fraud victimization in rural areas, as will be implied in the literature survey. This topic is thus left for future research.

3
but would be a valuable avenue for future research. Here, we view the cooperative as performing the optimization above independently for each species.

_Testing Assumption of Members’ Response to Cooperative Prices_ To test the assumption that cooperative members increase their catch in response to an increase in the cooperative price, we use the daily transaction data with one observation for each recorded sale by a fishing team. In the model, a cooperative member _i_ ’s catch in period _t_ is given by the following: Taking logs gives the following: _log_ ( _qict_ * ) =  _log_ (2)  _log_ ( _Pct_ )  2 _log_ (1  (1  ) _Ht_ )  2 _log_ ( _H t_ )  2 _log_ ( _X t_ )  _log_ (  _i_   ) . (16) The first two terms on the right-hand side pose no complications for estimation, but the remaining terms do. First, _X t_ ,  , and  _i_ are unobservable to us. Second, _Ht_ is implicitly a function of other quantities from the model (see equation 8 above). In the case of either a nonexclusive cooperative or the exclusive cooperative with a large range species, _Ht_ is a function of _Pct_ , _Pmt_ , _X t_ , and parameters  and  . For an exclusive cooperative fishing a small-range species, the same is true, except _Ht_ is not a function of _Pmt_ .

These considerations and equation 16 motivate the following log-linear approximation: _log_ ( _qict_ * ) =  _i_   0 _log_ ( _Pct_ )   1 _log_ ( _Pmt_ )   _ict_ , (17) where<sup></sup> _i_<sup>is a fishing-team fixed effect capturing</sup>  _i_ ,  , and  , and  _ict_ is a residual. The key identification concern is that the residual is clearly correlated with _Pct_ (and, perhaps, with _Pmt_ as well). This is because the residual contains _X t_ , and _X t_ is chosen
poverty data and more than one time-dimension: Uganda. We have access to poverty data for 2012 and 2016. This is a period a few years after the most intense fighting took place in Uganda and, in fact, we need to use the more detailed ACLED data so capture any armed violence in this period. However, what we will be able to study in Uganda are regional recovery trends. If our framework above is correct we should see a reversion to the mean in the regions that were most violent before and which are now catching up in terms of poverty numbers with the rest of the country. And this is exactly what we see in the data.

|_Table 5: _|_Poverty and Confl_<br>|_ict History in Ugan_<br>|_da 2012and 2016_<br>||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
||<br>Poverty rate|<br>Poverty rate|<br>Poverty rate|<br>Poverty rate|
|Conflict debt|10.26<sup>***</sup><br>(1.606)|||1.928<br>(1.515)|
|County conflict debt||13.02<sup>***</sup><br>(0.975)||7.556<sup>***</sup><br>(1.103)|
|District conflict debt|||11.11<sup>***</sup><br>(0.730)|5.345<sup>***</sup><br>(0.592)|
|Sub-county fixed effects|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes