rafmacalaba/datause-encoder-data huggingface dataset:
While, proportionally, this number of imbalances is statistically expected through the randomization procedure, these are variables that may be correlated with our main outcomes of interest. In a robustness check, we include these variables as covariates to the first specification and find that our estimates do not change in sign or significance (see results section).

# _Program Exposure_

We first provide reassurance that our careful selection of radio channels was successful in terms of the similarity of their broadcasting by examining characteristics of their regular listeners using our baseline data. If programs are similar, we expect that they also appeal to broadly the same audience. Table 2 presents descriptive statistics by radio channel listenership. With the exception of age, we find no statistically significant differences between participants who reported to listen to SUD FM (control channel) and RTS (treatment channel) most of the days, respectively. In fact, the two audiences appear to be very similar with respect to gender, household size, income, main outcome variables, and knowledge about the new technology.<sup>31</sup> Program exposure is assessed in two ways as presented in Table 3: measuring listenership through verified lottery call-in records, and through self-response of radio listening practices. Overall, we find evidence that our encouragement approach worked. First, approximately 33 percent of our entire sample called the radio station during a call-in contest event. By matching telephone numbers, we track all call-ins made for the lotteries held during the radio sessions when solar lighting promotional clips were being run. The encouragement was also successful in incentivizing control and treatment households to tune to their respective radio channels. Both groups record statistically similar call-in rates and participation. Only 1 in 500 people called to the incorrect radio station (this was also balanced across experimental groups).<sup>32</sup> Second, we observe an increase of self-reported listening rates for both groups. We find that control households are 30 percentage points more likely to report listening to SUD FM as their main radio station compared to treatment households during the intervention period. Treatment households are 25 percentage points more likely to report listening to RTS. We also played a sequence of the radio clip as part of the end line survey and asked people to report whether they had heard it before.

> station (RTS) in a difference-in-difference regression, which accounts for these baseline imbalances of radio listenership.

> 31 The share of solar lamp owners is lower among participants with missing information about radio listenership which explains differences in the means between Table 1 and Table 2.

> 32 In this case, a control household assigned to listen to SUD FM was registered as calling in to RTS or vice versa for the treatment group.

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two year period, showing that in the absence of any intervention most informal firms stay informal. All three treatments had significant impacts on formalization, with the impacts larger as more supplementary services were offered: there was a 9.6 percentage points increase in registration in the first treatment group, 13 percentage points in the second, and 16.3 in the third, with these differences between groups all statistically significant. In contrast, information leaflets alone had no impact on formalization. We investigate whether governments can target these programs in a way to achieve even greater take-up levels by examining heterogeneity in impact according to key characteristics specified in a pre-analysis plan.<sup>1</sup> We find impacts are higher for male business owners, those with more education, those operating outside the biggest market in Cotonou (Dantokpa), and those that we classified ex ante as being more similar to businesses already formal using species classification (De Mel et al., 2010). Targeting on these characteristics could increase formalization rates to up to 27 percent, meaning that the majority of those offered the program would remain informal, even with targeting.

We then measure the consequences of formalizing for these firms. Formalizing leads to increased participation in business training, more formal accounting, lower tax harassment, and less taxes paid (due to a tax exemption in the year after formalizing). However, formal firms are not significantly more likely to obtain business bank accounts or loan financing, do not gain more customers, and have no significant gains in sales, profits, or standard of living. While the benefits of formalizing are thus modest, the cost of the intervention is not. We calculate an average cost of US$1,200-2,200 per firm formalized without targeting, and at least $600 per firm formalized even if targeting were used. This is large relative to the average monthly profits of these firms of only $79 and to the tax collection the government can expect to receive from such firms. As such, our analysis suggests that while introducing a simplified registration system offers at least time-saving benefits for firms that want to formalize, adding additional services or in-person visits to explain this new status is unlikely to pass a cost-benefit test.

The remainder of the paper is organized as follows: Section 2 describes what is meant by formalization in Benin, and the potential benefits and costs associated with becoming formal; Section 3 details the intervention, the sampling, the study design and the data; Section 4 describes program implementation and take-up on program components; Section 5 presents the

> 1 This study was registered in the AEA RCT Registry on October 7, 2014, prior to any follow-up survey data being collected <u>https://www.socialscienceregistry.org/trials/515</u>

4
the World Bank’s City Development Strategies II program had either moved their city hall and administrative offices in this way, or were in the process of doing so. (Chreod 2005) In this type of land market activity, municipalities act as an aggressive, profit-maximizing monopolist. Local officials have formalized strategic guidelines as to how municipal governments should seek to gain control over all land leasing transactions, so as to control the volume and location of land supply and maximize municipal revenues.<sup>6</sup> Municipalities are required to compensate land users who are re-settled with cash and alternative housing. Analysis of the pattern of land leasing in Shanghai demonstrates that the areas leased for commercial development tended to maximize _net_ leasing revenues, after compensation costs, confirming the entrepreneurial mindset of the municipality. (Fu, Somerville, Gu and Huang, 1999).

Despite municipalities ’ability to create new supplies of urban land, land leasing is a transitional infrastructure-financing strategy. The supply of land available for leasing eventually will run out. Shenzhen, the pioneer in land leasing, aggressively expanded its urban boundaries for 15 years. By now, the potential for further expansion or new land leasing has almost been exhausted. Shenzhen’s asset management company, in fact, has turned to buying and selling land-use rights in other urban areas of the interior of China as a way of continuing to use its entrepreneurial skills to generate revenues from land transactions. By contrast, land leasing as an infrastructure-financing tool is gathering speed in later-developing regions of Western China and the rustbelt Northeast.

In summary, China used land leasing as part of a strategy to jump-start urban infrastructure investment after decades of neglect. The ‘marketization’ of land in effect, converted land that was generating no economic or financial return under the old landholding system into urban infrastructure that is the cornerstone of national economic growth. Chinese authorities recognize that land leasing monetizes a finite asset and cannot indefinitely play such a prominent role in urban infrastructure finance. As discussed by Guo (chapter x), the cities that pioneered land leasing are now shifting strategy to rely more on the income streams provided by infrastructure assets to recover capital costs.

6 For example, the Vice Mayor of Chengdu authored a volume (Sun Ping et al 2003) emphasizing that (i) land leasing and land supply should be the exclusive responsibility of government, (ii) therefore land transactions, including those by state-owned enterprises (SOEs) on the secondary market, should go through the municipality’s Land Resource Center or be approved by it, (iii) the municipality should capture for itself at least 60 percent of the net value gains resulting from the sale of leasing rights for land originally allocated to municipal SOEs, and (iv) land leasing proceeds should be used to finance municipal fixed-asset investment.

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<!-- Start of picture text --> Lcrop1 Lcrop i Lcrop n<br>σCET 2 = -0.5<br>L L L<br>forest pasture crops<br>σCET 1 = -0.11<br>AEZ1 AEZi AEZ18<br>σvalue added = 20<br>L _ in value added<br><!-- End of picture text --> Figure 3. Land allocation in Birur et al. (2008) <!-- Start of picture text --> Lwheat Lcoarse grains Loilseeds<br>σCET 3<br>L L L<br>pasture sugar grains & oilseeds & protein<br>σCET 2<br>σCET 3 ≥ σCET 2 ≥ σCET 1<br>Lhorticulture Lother crops Lfield crops &pasture<br>σCET 1<br>L _<br><!-- End of picture text --> Figure 4. Land allocation in Banse et al. (2008a) Gohin (2007) and Bouët et al. (2008) analyze the impact of the EU Biofuel directive. Gohin uses a custom EU-15 CGE model. Gohin provides an extensive disaggregation of the farm sector with 32 agricultural activities and a detailed policy representation of the EU Common Agricultural Policy (CAP). He finds that the expansion of biodiesel production induces a change 9
Variables related to the _wealth of destinations_ (GDP and GDP growth, both introduced as log, and GDP per capita) have been collected from the World Bank or from national statistical offices (e.g. Taiwan, China) and change every year (this allows us to control to some extent for agglomerations due to import booms at certain destinations generated by high GDP growth rates). The expectation is that large markets are likely to have a larger number of buyers and therefore increase the chance of finding a suitable match and a sustained relationship (Brenton, Saborowski and Uexkull 2009). Moreover, developed countries with affluent citizens can be expected to have stricter quality criteria (private and public standards) that will significantly increase the sunk cost required to enter them, thereby making exits less likely once these sunk costs have been paid. Lastly, growing markets will provide more opportunities to sustain trade for longer.

The competing risks analysis seeks to compare the impact of the variables outlined above on the hazard rates for exit from exporting to either an upgrade to a superior contract or a termination of the FPD without upgrade. Such a framework exploits the heterogeneity of contract terminations and is therefore a valuable extension of the single-destination model universally employed in the export survival literature. Ending a contract is not desirable, but there are, however, occasions when FPDs deaths are in fact transformations: a customer can start demanding a superior product and terminate orders for the current one.

The concept of upgrading is common in the firm portfolio and export diversification literature (Cirera, Marin and Markwald 2012). Competing risks analyses have also been made of firm exits from the market by liquidation or acquisition (Esteve-Perez, SanchisLlopis and Sanchis-Llopis 2010) and on the choice of export market between developing and developed destinations (Pisu 2008). While preserving the FPD for as long as possible is an obvious business strategy and policy goal, instances when the product exported to a particular destination is replaced by a superior one should be equally desirable, if not more.

A product upgrade can be requested by importers if their quality requirements have been met for simple products, or if they become satisfied with the reliability of the exporter for products which are less time-sensitive. The partners can choose to continue trading the inferior product as well (the two continue simultaneously), but in cases of actual upgrades, there may be little reason to continue wasting limited production resources on a basic or less profitable product, which may have been useful only as a signaling strategy. Example of such upgrades could be an export of raw wood replaced by exports of furniture, or plain shirts replaced by embroidered ones.

For the purpose of the current paper, upgrading is a re-coding of a subset of “deaths”. It identifies cases when another contract is started by the firm to the same destination after the end of the current one (but not later than 12 months in order to be 16
tructure), and international donors’ websites (World Bank, African Development Bank). Each source provides different sets of information, and a few of them include the exact location of the road projects. Most of the work consisted of bringing together sparse information about road transport projects in the DRC in one unified database and cross-checking the data using multiple sources of information. In addition to the information extracted from the previous sources, updates were added mostly by extensive online search. Lastly, we worked on avoiding duplicates. The database we obtained is the most complete one that exists, but unfortunately, some projects had information gaps, and it has been difficult to associate each project with its exact location.

Second, we constructed road segments based on the previous project list. Most of the previous sources of information do not provide the exact location of the project. Different methods were used to identify the location. For some projects, particularly from international donors such as the World Bank or the African Development Bank, documents detailing the initial commitment, rationales for the project, and disbursements over time can be found online. For others, the project’s title often includes the start and end city names or such information can be found online. Figure 4 shows the mapping of the projects we managed to localize.

Third, we put together a spatial panel of road projects by merging the map of projects with the PRIO grid cell - a grid structure that aids the compilation, management, and analysis of spatial data within a time-consistent framework.<sup>3</sup> We end up with a panel of grid cells with information on whether a road project is happening or not and whether it has been completed or is ongoing.

> 3These are 55km by 55km cells and a standard unit of analysis in the conflict literature. For a detailed description of the PRIO grid cell structure, see `https://grid.prio.org` .

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_Table 1.04 Extensive Regression Analysis with Additional Bureaucratic Control Mechanisms (High-Income Democracy Sub-Sample)_

||<br>|**Corruption Perceptions Index**<br>||
|---|---|---|---|
||**(1)**<br>**(GLM)**|**(2)**<br>**(GLM)**|**(3)**<br>**(GLM)**|
||**Additive**|**Additive**|**Factor**|
|**Higher EDD/NEDD***<br>**DEM**|||-0.60*<br>(0.33)|
|**Higher EDD/NEDD Ratio**|-0.50***<br> (0.06)|-0.46***<br>(0.10)|-0.30***<br>(0.09)|
|**High-Income Democracies**|||-0.82**<br>(0.27)|
|**Oversight Index**|-0.30**<br>(0.10)|-0.23*<br>(0.12)||
|**Government Effectiveness**||-1.02***<br>(0.09)|-1.00***<br>(0.05)|
|**Coordinated Market Economies**|-0.33<br>(0.48)|-0.34<br>(0.39)|-0.17<br>(0.35)|
|**Standard**<br>**Controls**|√|√|√|
|**Additional Controls**|||√|
|**Sample**|High-IncomeDem|High-IncomeDem|All|
|**Number of Observations**|19|19|52|
|**(Pseudo Log)-Likelihood**|-6.00|-6.86|-19.00|

Note: robust standard errors in parentheses. Dependent variable converted into fractional form for GLM estimations. Age of democracy, education attainment, latitude, (log) per capital income, and economic openness (long-term). Varieties of Capitalism Control is a dummy variable which takes the value of one for ‘Coordinated Capitalist Economies’* Significant at the 10% level, ** significant at the 5% level, ***significant at the 1% level. See Appendix A for details of variable descriptions). _Source: The Author_

# **The Effect of the EDD/NEDD Ratio on Non High-Income Democracies? (Negative Test)**

A final test of the robustness of the model is whether the consistent relationship between the EDD/NEDD ratio and its component parts remains a significant determinant of rent extraction in the sub-set of countries that are not high-income democracies. The model and its antecedent literature assume that a higher EDD/NEDD ratio, and by extension all of its component measures, should be _consistently_ associated with less perceived rent extraction. It follows, then, that if the model is correct, this conditional effect should break down when the EDD/NEDD ratio is used, to predict variation in rent extraction in non-high-income democracies.

This is because the micro-theoretical assumptions of the theory postulate that the EDD/NEDD ratio affects the efficacy of elections, and that means high-income democracies, based on the antecedent literature. Therefore, it is anticipated that, while individual component measures of the EDD/NEDD ratio may be positively or negatively associated with variation in rent extraction among non-high-income democracies, increments in the EDD/NEDD ratio as a whole should not be negatively and robustly able to predict variation in rent extraction in the absence of a high-income democracy context. This is because the efficacy of elections is non-existent and cannot, therefore, be affected by the EDD/NEDD ratio.

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# When Is There Enough Data to Create a Global Statistic?

_Daniel Gerszon Mahler, Umar Serajuddin, and Hiroko Maeda_<sup>_1_</sup> _JEL codes: C15, C18, C53, C82, Y10._ _Keywords:_ Aggregation, data, simulation, SDGs, statistics, missing data, monitoring, India.

1 All authors are with the Development Data Group of the World Bank. Corresponding author: Daniel Gerszon Mahler (dmahler@worldbank.org). We are grateful for comments received from Aart Kraay, R. Andrés Castañeda Aguilar, Bob Rijkers, Christoph Lakner, and Dean Jolliffe. We are also grateful for discussions on the topic from participants of the Interagency Task Team on Aggregation coordinated by the United Nations. The code for the paper is available at <u>https://github.com/danielmahler/Aggregation.</u>
relative to the baseline path. For illustrative purposes, Figure B4 depicts the fan chart corresponding to the gross financing needs.

**Sound fiscal and debt policies envisaged in the baseline scenario do strengthen debt sustainability even in the presence of macroeconomic shocks** . The stochastic DSA suggests two points relevant for policy makers in Mauritania. Firstly, macroeconomic volatility and uncertainty should be taken seriously because the fiscal and debt performance are exposed to adverse shocks. In particular, if unfavorable circumstances unfold, such as lower growth and higher interest rates, the government debt can eventually reach levels significantly above those anticipated otherwise. For instance, the public debt is expected to be 30 percent of GDP by 2023 but in a poor macroeconomic environment it can be as high as 40 percent of GDP. Secondly, it is noteworthy that the sound policies seeking balanced budgets and inexpensive concessional funding sources—which Mauritania is assumed to follow so far—do deliver a large reduction of public debt in 2013-23, not only in the baseline scenario but also in all the simulations undertaken. Arguably, even the public debt of 40 percent of GDP by 2023 that would result from very unfavorable shocks represents, for practical purposes, a notable improvement against the current ratio of 74 percent. In other words, regardless of the adverse macroeconomic shocks that may slowdown the pace of debt reduction, the fiscal prudence always proves to be effective to strengthen debt sustainability in Mauritania. This result, already highlighted in the baseline scenario, turns out to be robust to alternative macroeconomic outlooks that do bring into the picture the shocks the country is exposed to.

# **A detour: stochastic DSA and scenario analysis**

**A stochastic DSA has technical advantages vis-à-vis the use of scenario analysis to address macroeconomic uncertainties and fiscal risks** . Scenario analysis is another popular methodology to investigate uncertainties and fiscal risks, and it is actually the approach adopted in the LIC DSF to formulate stress tests. Scenario analysis proceeds by introducing a few standardized shocks one-by-one into the DSA model and gauging their individual impact on the fiscal and debt performance. The modelling of shocks is extremely simple: their size is often chosen arbitrarily and not looking at countryspecific circumstances; little attention is paid to co-movements (covariances) between disturbances hitting key macroeconomic variables; and there is no consideration whatsoever regarding their likelihood of occurrence. A stochastic DSA, instead, takes all these modelling issues seriously and hence improves upon scenario analysis on purely technical grounds. Both approaches, therefore, should be seen as complements.

**A stochastic DSA also has advantages vis-à-vis scenario analysis for the formulation of fiscal and debt policies** . The stochastic DSA offers advantages on practical, policy-making grounds that derive from

11
_φ_ ( _L_ )<sup>_p_</sup> _φ_ 12( _L_<sup>12</sup> )<sup>_P_</sup> (1 _− L_ )<sup>_d_</sup> (1 _− L_<sup>12</sup> )<sup>_D_</sup> ( _yt − β_<sup>_′_</sup> _xt_ ) = _θ_ ( _L_ )<sup>_q_</sup> _θ_ 12( _L_<sup>12</sup> )<sup>_Q_</sup> _εt_ (9) where everything is as in (6) and _xt_ contains the Google Trends Index that we describe in the next section. The addition of this term allows us to model the information contained in the Google Trends data as a time-varying mean.

# **4.3 Index Construction**

In order to incorporate the information from the various Google Trends search keywords, it is desirable to synthesize the information in all of the Google Trends data into something more manageable. Formerly, authors used the Google Insights search categories data. This data is used in many of the studies referenced in section 2. However, previously such an index from Google Insights was usually not available outside of large, developed countries, so studies such as Carri`ere-Swallow and Labb ́e [2013] estimate their own. The advantage of having an index is mainly parsimony of information. Indeed, such an index may be of interest in its own right. Furthermore, in September 2012 Google merged some features of Google Insights with Trends and discontinued the aggregate search categories entirely.<sup>7</sup> To solve the keyword aggregation problem Carri`ere-Swallow and Labb ́e [2013] creates an index from multiple search terms by use of an expanding linear regression model described below. Other approaches rely on factor analysis techniques for dimension reduction such as unweighted least squares [Vosen and Schmidt, 2011] or principal components analysis [Stock and Watson, 2002]. These methods assume that there are some underlying, unobserved common factors for all of the series. We describe our use of statistical learning techniques for variable selection below.

We took several approaches to constructing our search indices. First, we applied the linear index approach of Carri`ere-Swallow and Labb ́e [2013]. This is a common approach in the literature and is an attractive choice mainly for its simplicity. Let **_X_** be our matrix of year-over-year percent changes for the Google Trends terms. We construct an index _It_ for these terms, for each series _yt_ that we wish to forecast in the following way. In each period, we estimate the weights _β_<sup>ˆ</sup> by using the observations up to time _t −_ 1 and fitting a linear model

> 7 `http://insidesearch.blogspot.com/2012/09/insights-into-what-world-is-searching. html` One may only speculate that it was discontinued because this task is very difficult to automate.

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