rafmacalaba/data-use-mentions-extended huggingface dataset:
<mark>can improve educational attainment and thus have an associated effect on other social and environmental factors. This is further confirmed by Lleras‐Muney (2002; see also Grenet 2013) who shows that legally requiring children to attend school for one more year increased educational attainment by about 5 percent. Furthermore, the study uses data from the 1960, 1970, and 1980 U.S. censuses in conjunction with changes in compulsory schooling between 1915 to 1939 to</mark> establish that each year of additional schooling can reduce mortality by 3-6 percent.

<mark>Other researchers have used the onset of compulsory schooling law changes to estimate the returns to schooling in the República Bolivariana de Venezuela (Patrinos and Sakellariou 2005), the Netherlands (Levin and Plug 1999), Australia (Leigh and Ryan 2008), Sweden (Card 2001), Ireland (Callan and Harmon 1999), Türkiye (Patrinos, Psacharopoulos and Tansel 2021), the United States (Harmon and Walker 1995), for example. In Europe, Brunello, Fort and Weber (2009), using data from 12 European countries show that compulsory school reforms significantly affect educational attainment, especially among individuals belonging to the lowest quantiles of the distribution of ability. There is also evidence that additional education reduces conditional wage inequality, and that education and ability are substitutes in the earnings function. Aparicio and Kuehn (2017) use data from 31 European countries to find that educational attainment is a key factor for understanding why some individuals migrate and others do not. The authors suggest that individuals moving from low to medium part of the education distribution are less likely to migrate across countries for employment. Other uses of compulsory schooling laws to obtain causal estimates have been used in mortality studies (e.g., Albouy and Lequien 2009; Gathmann et al. 2015), health (e.g., Kemptner et al. 2011), crime (e.g., Bell et al. 2016), religion (e.g.
vastly devoted to examining the importance of resource reallocation for aggregate productivity growth. Economic growth models emphasizing the role of creative destruction may explain the link between firm-level dynamics and aggregate productivity growth. A new firm enters the economy with new technology and competes with existing firms with old, conventional technology. As newer, more-productive plants become successful, less-productive ones are driven out, and resources are reallocated from incumbents to new entrants. As a result, the economy’s overall efficiency level rises, and aggregate productivity increases. Following the most common approach to decomposing aggregate productivity growth (e.g., Baily et al. (1992) and Foster et al. (2001)), we can quantify the role of plant-level reallocations in aggregate productivity growth.

# **3.4 Decomposition Methods and Results**

Using plant-level data, we examine the extent to which changes in the share of outputs across plants affect the aggregate productivity growth. There is a growing literature on the decomposition methods of aggregate productivity. While we primarily focus on the method by Foster, Haltiwanger, and Krizan (hereafter FHK), we also use the Dynamic Olley Pakes method developed by Melitz and Polanec (2015).

First, following Baily et al. (2001) and Foster, Haltiwanger, and Kriza (2001), we have decomposed the time series changes in aggregate productivity into components that reflect withinplant productivity growth and other effects that reflect the reallocation of shares across plants including the effect of entry and exit:<sup>13</sup>

> 13 See Foster et al. (2001) for the reviews of previous studies using different decomposition methodologies and measurement issues.

14
a key issue with the occupational data is that many people and even household heads do not report an occupation. Based on IPUMS data (Henderson and Kriticos, 2018), we believe this occurs because many of these people are farmers with agricultural land who work in other sectors as well. We note this non-reporting fraction is noticeably higher at almost 50% in rural areas.

Finally, there are the income measures. We construct measures of income for the household by adding together all income from self-employment, labor income, and capital or land income. In the surveys, respondents report income receipts of various forms, such as cash and in-kind wage payments, business incomes, remittances, incomes from the rent of property and farmland, private and government pensions, and sales revenue from agricultural produce. These receipts are also reported as taking place over a variety of time intervals, so to be consistent, we convert all income receipts to monthly intervals. Land income from crop sales or rents is generally only available at the household level, making it difficult to ascribe these income sources to any particular household member for an individual-level analysis. And the same comment applies to non-agricultural businesses owned by the household head or others in the family. For this reason, we will focus on total household income. We will also look at wage income of individuals in families which do not own agricultural land. We note that in a preliminary exercise with a smaller sample and different definitions of spatial units, we found that non-farm and farm households in cities had similar agglomeration effects to household incomes (Henderson and Kriticos, 2018). We do not explore that dimension here, especially in this sample, where the proportion of defined farm households is small.

# **4.2 Basic Specification and Results**

All regressions have the following general specification:

- _ln_ ( _yijz f t_ ): Income of unit _i_ in location _j_ of type _z_ in country _f_ at time _t_ .

- _X_ : Vector of unit characteristics. _ijz f t_

17
with _ε_<sup>_b_</sup> _jt_ +1<sup>_∼N_(0</sup><sup>_, σ_</sup> _b_<sup>2)and</sup><sup>_εa_</sup> _ijt_ +1<sup>_∼N_(0</sup><sup>_, σ_</sup> _a_<sup>2).We further assume that the shocks</sup><sup>_bjt_are independent</sup> across sectors and the shocks _Aijt_ are independent across rms. Since _bjt_ and _Aijt_ are not observable, we infer them from data on operating prots as follows. From the denition of instantaneous prots we can write the combination of sector-level and rm-level protability shocks as Let _xijt_ denote the product of the rm and sector level shocks, with _xijt_ = _bjtAijt_ . We compute the right-hand size of (17) from data ( _πijt_ , _Kijt_ , _wjt_ ) and the estimates from the previous step ( _αL_ , _αK_ , _vM_ ). This gives us estimates for _xijt_ . To estimate the _A_ parameters, we rst compute deviations of _xijt_ from sector-year means (to get rid of _bjt_ ) and then regress this on one lag (no constant) To estimate the _b_ parameters we compute the sector-year means of _xijt_ and run the regression using the means, at the sector-year level In Table 1 in the paper, we report _ρa_ = 0 _._ 902 , _σa_<sup>2= 0</sup><sup>_._118,</sup> _b_ = 0 _._ 318 , _ρb_ = 0 _._ 777 , and _σb_<sup>2= 0</sup><sup>_._044.</sup>

# A.3 SMM estimator

The adjustment costs parameters are given by the vector Γ = ( _γ_ 1 _, γ_ 2 _, γ_ 3; _Ce, Cu_ ) ,
.2%|-19.3%|-11.0%|

_Notes:_ Authors’ computations based on the Annual Survey of Manufacturers Longitudinal Microdata file, 1990–2009. We report the values for the starting and the end years only since the series in between decrease rather smoothly (see our cepr discussion paper version for the full table). The means of the cdf are based on 257 industries and are not weighted (but the cdfs for each industry are weighted by either employment in the middle columns (2), or by sales in the right columns (3); see Supplementary Appendix S.1 for details). ‘Mean (1990–2009)’ refers to the mean of the _K_ -densities over the whole 1990–2009 period, on a yearly basis. ‘% change’ is the percentage change between 1990 and 2009.

The standard _K_ -densities are computed based on plant counts, i.e., distances between pairs of plants without any weighting scheme. This relates to the theoretical measures we have used in our model as given by (1). Yet, we can also compute weighted versions. In particular, we can weight pairs of plants by either plant-level employment or plant-level sales. For these weighted versions of the _K_ -density the unit of observation is the employee or a dollar of sales. A value of 0.086 at 50 kilometers using sales weights (see panel (3) of Table 2 for 1990) means, for example, that 8.6% of the industry sales are generated in plants that are at most 50 kilometers

> 4Table 9 in the Supplementary Appendix S.2 summarizes the _K_ -densities for the geographically most concentrated industries in 1990, 1999, and 2009, respectively.

7
# **1. Introduction**

Numerous empirical studies have investigated whether foreign aid effectively improves development outcomes in recipient countries. This literature has relied mainly on two levels of analysis. One focuses on the aggregate country-level impacts of aid, typically on economic growth or sector outcomes. Another takes a micro-level approach, with development projects as the unit of analysis, most often using donors’ own ratings of project outcomes as a measure of effectiveness.

This study bridges those two strands of research by focusing on the association between donor-financed projects and observable development impact, treating project ratings as intermediating variables. This enables us to ask whether project ratings convey information about those outcomes. We use ratings from projects undertaken in 183 developing countries by eight donors since the 1990s, concentrating on a few service delivery sectors with readily available data on beneficiary-level outcomes. We succeed in replicating previous findings of small positive effects of aid on sector outcomes. However, our results suggest that the project ratings convey little information about impact.

The second and more important contribution of this study is to describe and analyze the correlates of projects’ contributions to improvements in sector outcomes. Focusing on projects undertaken by the World Bank, for which more granular information and extensive text documentation are available, we use state of the art methods to assess what aspects of a project’s production process are associated with stronger outcomes. We first create what are called “text embeddings” of project documents using the latest generation of transformer models,<sup>3</sup> turning texts into numerical representations of their similarity and differences. Then, we train machine learning models to predict projects’ sector outcomes, and probe what features of the projects the model paid most attention to. We find that projects with what appear to be high degrees of tailoring to country context and concentration of funds in fewer sectors are associated with stronger outcomes. In doing so, we use newly available data on project characteristics and draw on methodological advances at the intersection of causal inference and econometrics with machine
# **5.  Distributional Impact of NO2 Pollution**

# **5.1 Pollution-Vulnerable Residents of Dar es Salaam**

To assess the impact of vehicular NO2 pollution, we focus on two vulnerable populations: children (0-5 years) and elderly adults (65+ years). We incorporate poverty in a composite vulnerability index for each grid cell by multiplying population in the groups [0-5,65+] by the estimated proportions of cell residents living in MPI-defined poverty, below $1.25/day, and below $2.00/day. For each variable, we use raster databases from the previously-cited WorldPop program at 1 km resolution.

Figure 7 displays the vulnerability index for the three poverty definitions. In each case, highvulnerability areas form roughly concentric rings around the city center. Nevertheless, there are evident differences in the pattern across poverty definitions. For MPI-defined poverty, the vulnerability index is greatest in Azimio, Mbagala and Makangarwe divisions. For a poverty line of $1.25/day these divisions remain in the most critical group, but they are joined by Tandika division in the south and a cluster of divisions northwest of the city center that includes Kigogo, Mzimuni, Mburahati, Makurumla, Ndugumbi and Magomeni. For a poverty line of $2.00/day the southern cluster disappears but five of six divisions in the northwest cluster remain.

# **5.2  Pollution Exposure Paths**

Figure 7 displays areas where potential vulnerability is high, but actual vulnerability will vary with exposure. To assess this component, we produce pollution exposure paths for each month by tracing pollution measured by Sentinel-5P in each grid cell back along the wind vector to its traffic sources. Figure 4 indicates the general direction of these effects as the wind direction changes: Exposure paths shift toward the northwest in August and September; toward the west in October, November and December; and toward the southwest in February and March. The winds shift the paths toward the north in April and May,
data from Mexico and Chile suggest that both the size, of and transitions into the self-employed sector behave procyclically.6 There is increasing evidence both in the sociology and economics literature that suggests a life cycle view of the trajectory between formal and informal selfemployment: in the absence of well-functioning credit markets and effective educational systems, workers may take formal sector jobs to accumulate human and financial capital and then quit to open their own business.<sup>7</sup> In sum, there is ample evidence suggesting that selfemployment is a desirable destination for many workers who voluntarily leave formal employment.

_2. In the absence of government or union induced rigidities, there is still strong evidence of "segmentation. "_ Recent work on Mexico challenges the customary view of the sources of labor market segmentation. Minimum wages are not binding (See Bell 1998) and the evidence suggests that union power is directed largely to the maintenance of employment and find no significant effect on wages.' As Marquez and Ros (1990), noted, and has been confirmed by later studies for Peru (Shaffner,1998)and Guatemala (Funkhauserl998), wages of similar workers rise with firm size, much as they do in industrialized countries. Further, Marquez (1990), and Abuhadba and Romaguera (1993) find evidence consistent with efficiency wage effects in the high correlation of wage differentials among Chile, Venezuela, and Brazil and the

**6** See Maloney (1997b) and Pages(1998). Pais de Barros finds no cyclical movement in Brazil. Saavedra in Peru finds a broadly countercyclical movement but this may be largely driven by secular trends.

7Aroca and Maloney (1998) model the transitions into informality as a destination of entrepreneurs and find evidence using logit techniques adapted to panel logit context.

8 See Maloney and Ribeiro (1998), and Hemandez -Laos (1998). Panagides and Patrinos (1994) find some wage effects, but these are likely to disappear when relevant firm characteristics, such as size,
1.8|-1.3|9.6|7.6|7.8|5.4|2.8|3.8|
|(in percent)||||||||||
|Real per capita private consumption<br>(in 1987 Naira)|1117|1003|852|864|893|878|877|851|917|

Source: Nigeria Unified Survey, March 1996

1. Table 3.1 shows that in 1986 nominal GDP grew by 1.8 percent, but the growth rate of real GDP was negative in 1987 at -1.3 percent. However, GDP growth picked up and reached a high of 9.6 percent in 1988 and has stabilized at 7.8 percent, 5.4 percent and 2.8 percent in 1990. 1991, and 1992 respectively. In 1993, the relative constancy in the growth rate from the prior year is attributed to the recovery in the services sector, which offset the steady but not increasing oil production levels (World Bank, 1994).

# **4. EVOLUTION OF POVERTY IN NIGERIA**

# **4.1 The Evolution of Mean Expenditure in Nigeria**

It is daunting to try to analyze income distribution in Nigeria. More so than in many developing countries, the availability of relevant data constitutes one of the most serious obstacles to analysis. We do not have an accepted population distribution for Nigeria by age and sex. Almost no national aggregate data exists for the rural areas that would allow us to make a confident judgement about intra-sectoral income distribution. Furthermore, Nigeria is an extremely heterogeneous society. We cannot expect that culturally associated understandings of what constitutes wealth and poverty and the ways people are grouped will necessarily be the same throughout the country. Thus if we want to understand the import of interpersonal, interstate and intersectoral income distinctions, this necessitates sensitivity to the variety of meanings attached to wealth and poverty in different parts of the
|0.0|1.9|

**_Governance Variables._** We use various indices to measure the broader governance environment in which a central bank operates. First, we use the Government Effectiveness index, as compiled by the World Bank. This index <mark>measures the "perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the</mark> government's commitment to such policies.”<sup>8</sup> The scale runs from -2.5 (lowest) to 2.5 (highest) and covers 204 countries. The index is developed by <mark>compiling and summarizing information from over 30 existing data sources that report the views and experiences of citizens, entrepreneurs, and experts in the public, private, and nongovernmental sectors. T</mark> he rationale for using this variable is that countries with strong governance and effective public sectors usually have well-implemented laws for their central banks. Relatedly, we also use data on central bank independence from the French Treasury. This variable measures the degree of central bank independence in each country. Central banks with no independence are assigned a value of zero, while those with substantial autonomy are assigned a value of 4.<sup>9</sup> The Directorate General of the Treasury of France surveys this and other institutional characteristics for 144 countries. The survey is based on perception data. The study's results are captured in the Institutional Profiles Database and were last updated in 2016. In addition, clear accountability frameworks and low corruption increase public

> 8. See https://www.msci.com/documents/10199/e092c439-34e1-4055-8491-86fb0799c38f <u>ators/h580f9aa5?country=BRA&indicator=388&viz=line_chart&years=1996,2018 and https://info.worldbank.org/governance/wgi/
excluding transport and distribution) on a gross and value added basis for Brazil, China, Korea, and the United States (Francois, Manchin, and Tomberger 2012). There are three sets of values in the table. The first, ”Gross Values” refers to exports by sector from standard balance of payments statistics. The second, ”Direct VA” refers to the direct value added contained in exports. These values reflect an adjustment to gross export values to include only the value of primary factors employed directly within a given sector in production for exports (or domestic absorption). The last set of values ”Total VA”reflect adjustments to gross export values to include both direct value added, and also value added in a particular sector exported indirectly through downstream linkages. This includes, for example, business services value added embodied in the production of steel for export.

> 7What follows draws in part on Francois and Hoekman (2010) and Francois, Manchin and Tomberger (2012).

6
- [15] Galiani, Sebastian, Stephen Knack, Lixin Colin Xu, and Ben Zou. 2017. “The Effect of Aid –

- on Growth”. _Journal of Economic Growth_ 22(1), 1 33.

- [16] Guardian. 2009. “G20 Declares Door Shut on Tax Havens.”2 April. http://www.guardian.co.uk/world/2009/apr/02/g20-summit-tax-havens. Accessed on 17 February, 2020.

- [17] Huizinga, Harry and Gaetan Nicodeme, 2004. “Are international deposits tax-driven”. _Journal of Public Economics_ 88, p. 1093-1118.

- [18] Johannesen, Niels. 2014. “Tax Evasion and Swiss Bank Deposits”. _Journal of Public Economics_ 111, p. 46-62.

- [19] Johannesen, Niels and Gabrial Zucman. 2014. “The End of Bank Secrecy? An Evaluation of the G20 Tax Haven Crackdown”. _American Economic Journal: Economic Policy_ 6(1), –

- 65 91.

- [20] Johannesen, Niels and Jukka Pirtill ̈a. 2016. “Capital Flight and Development: An Overview of Concepts, Methods, and Data Sources”. WIDER Working Paper, No. 2016/95.

- [21] Johannesen, Niels, Thomas Tørsløv, and Ludvig Wier. 2018. “Are Less Developed Countries More Exposed to Multinational Tax Avoidance? Method and Evidence from Micro-Data”. WIDER Working Paper, No. 2016/10. ( _World Bank Economic Review_ , forthcoming).

- [22] Johannesen, N. and Stolper, T., 2017. “The deterrence effect of whistleblowing–an event study of leaked customer information from banks in tax havens. ”Working Paper

- [23] Johannesen, N., Langetieg, P., Reck, D., Risch, M. and Slemrod, J., 2018. “Taxing hidden wealth: The consequences of US enforcement initiatives on evasive foreign accounts. ”
|**Table 3: Historical land expansio**<br>|**n and recent l**<br>|**and demand ex**<br>|**pressed in me**<br>|**dia reports**<br>||||
|---|---|---|---|---|---|---|---|
|**Region**|**Cultivated**|**land area (mil**|**lions of ha)**|**Annual c**|**hange (%)**|**Land de**|**mand 2009**|
||**1961**|**1997**|**2007**|**1961-1997**|**1997-2007**|**Mn ha**|**year eq**.|
|Sub-Saharan Africa|134.6|192.2|218.5|1.60|2.63|39.7|21.8|
|East Asia & Pacific|183.9|235.7|262.8|1.44|2.72|8.0|4.6|
|Eastern Europe & Central Asia|291.5|263.6|241.7|-0.77|-2.19|4.6|n.a.|
|Latin America|102.6|160.9|168.0|1.62|0.71|3.2|2.2|
|Middle-East & North Africa|77.9|91.3|89.0|0.37|-0.23|1.4|n.a.|
|South Asia|197.9|212
from the PIP and what is provided by the applicant. Applications are considered on a first-come first-served basis, with no prioritization or selection criteria. To be accepted, a requested intervention must help to achieve product compliance with EU traceability and food-safety (pesticide residues) regulations. Upon acceptance, a protocol stating the actions to be implemented by each party on a cost-sharing basis (50% for each, except for smallholders who are expected to contribute only 20%) is signed. Wages and investment costs are _de facto_ excluded. The actions listed in the protocol are chosen among a menu offered by the PIP under its five components; however, the combination in each protocol is specific to a firm and varies across beneficiaries. So far, most of the financing has gone to training costs, technical support and the development of a food-safety toolbox containing crop protocols, GAP guidelines, and Hortitrace, a traceability software developed by the PIP. The program’s first phase has covered 21 countries<sup>13</sup> and 320 export companies. Out of those, 219 firms benefited from the ‘good company practices’ component (advice and assistance for setting up sanitary quality and traceability systems, and certification pre-audits). 153 benefited from training under the capacity-building component.

# **2.4 The PIP’s Evaluation**

An evaluation of the PIP’s first phase was undertaken in June 2008. The appendix’s valuation matrix summarizes the report’s findings and details objectives, expected results, performance indicators, and outcomes. Performance indicators are both quantitative and qualitative. The evaluation relies on trade data reported by the firms themselves and from Eurostat. Additionally, a survey was conducted among PIP beneficiaries and EU importers. As highligthed in the appendix’s evaluation matrix, program impacts are evaluated at the aggregate level. Outcomes for beneficiary firms are generally reported without controlling for location, size or experience in exporting FFV to the EU market, with the exception of specific objective (S1), for which