rafmacalaba/data-use-mentions-extended huggingface dataset:
variation across alternative samples and estimators for each country, but significant differences across countries.

We focus particularly on the variables that measure distance to the nearest market and its partition into secondary and primary road links. We find that distance response elasticities are substantially lower for primary road links, because their higher average vehicle speeds and lower maintenance costs reduce the effect of distance to market.

Econometric measurement of road quality effects enables us to estimate the deforestation impact of programs that upgrade secondary roads to primary status. To illustrate our methodology, we explore the impact of upgrading all secondary roads to primary status in the moist tropical forest regions of Bolivia, Cameroon and Myanmar. We find highly non-uniform spatial distributions, with significant impacts in some areas of all three countries, and effects that are more evenly distributed in Cameroon and Myanmar than in Bolivia.

With our model-based predictions in hand, we measure biodiversity risk in each 250 m cell using ecoregion information from WWF International and thousands of species range maps from IUCN and Birdlife International. We develop seven measures that incorporate biome status, species density, endemicity, and extinction risk. We find weak bivariate correlations for some of these measures, so we develop a composite index for each 250 m cell by selecting the highest of the seven, each normalized to the range [0 100].

We compute an expected loss measure for each cell by multiplying its composite biodiversity index by the predicted increase in forest clearing percent from secondary road upgrading. We map the results to identify priority areas with high expected losses, along with ecologically-critical road corridors within those areas. We find highly diverse patterns of 58
the amount of money a household must earn in order to afford a rental unit of a range of sizes (0, 1, 2, 3, and 4 bedrooms) at the jurisdiction’s Fair Market Rent (FMR).<sup>28</sup> This is based on the affordability standard of paying no more than 30 percent of income for housing costs. NLIHC’s Out of Reach report also provides comparisons of the annual equivalent of the housing wage to the local area’s median income as estimated from HUD (NLIHC 2019).

**Housing Affordability Index.** Housing affordability indices are widely used by the mortgage lending and real estate industries to assess the affordability of the residential sales market, based upon prototypical housing costs, primarily for potential homebuyers. The derived indicators allow for two or more points in time to be compared to determine if, on average, dwellings for sale have become relatively more or less affordable. While useful as a descriptive indicator of affordability, relative approaches do not generally provide a normative standard. They cannot be used to assess how many, and which kinds of households, can and cannot afford which properties for sale, or for that matter, the affordability stresses of owneroccupiers in their current dwellings.

**Amenity-Based Housing Affordability Index.** Fisher, Pollakowski, and Zabel (2009) developed the Amenity-Based Housing Affordability Index based on the rationale that a housing affordability index should take location factors into account because the goal of housing affordability policy should not only be to provide shelter, but also to supply units that are accessible to jobs, education, and other amenities. This index measures affordable housing stock in a given area for families within a given income range. From their application of this index in the Boston metropolitan area for families earning 50-80 percent of

> 28 In the U.S., the rent standard commonly used is HUD’s fair market rent (FMR). This standard is typically the 40th percentile rent of recently rented apartments
**Table 3: Foreign ownership and firm productivity**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||∆ln(TFP)|∆<sup>2</sup>ln(TFP)|∆<sup>3</sup>ln(TFP)|∆<sup>4</sup>ln(TFP)|∆<sup>4</sup>ln(TFP)|
|∆ln(FO)|0.000<br>(0.003)|||||
|∆<sup>2</sup>ln(FO)||0.001<br>(0.003)||||
|∆<sup>3</sup>ln(FO)|||0.002<br>(0.003)|||
|∆<sup>4</sup>ln(FO)||||0.011***|0.011***|
|||||(0.003)|(0.003)|
|Observations|34,128|32,231|28,344|18,138|18,138|
|Year Fixed Effects|Yes|yes|yes|yes|yes|
|Sector Fixed effect|Yes|yes|yes|yes|yes|
|Sector-Year Fixed<br>effect|No|no|no|no|yes|

_Source:_ authors’ calculations based in the Orbis dataset for Turkey. _Notes:_ The regressions are estimated by GLS. TFP is total factor productivity, computed using the Wooldridge-Levinsohn-Petrin methodology (WLP). FO is transformed as (FO _/_ 100) + 1. FO is the
> A pledge over an entire enterprise, the ―fonds de commerce‖, is also possible in many countries, including in Morocco, UAE, Tunisia, Palestine and Algeria. Enterprise pledges are the only way to take a pledge on a floating pool of assets, rather than identifying each individual piece of collateral (see Table 5 for an overview of scope of assets in MENA).

> 20 World Bank MENA SME Lending Survey 2010

> 21 _Hawkamah Report,_ Chapter 7 Egypt, Chapter 8 Jordan, Chapter 10 Lebanon, Chapter 12 Palestine, Chapter 13 Qatar, Chapter Chapter 15 UAE.

14
Children under five were not included in our study. This decision was made because the migration crisis intensified between 2016 and 2019, meaning most Venezuelan children under five in 2022 were born in Colombia, thereby acquiring Colombian nationality.<sup>7</sup> was determined the of all household mem- _Eligibility._ Survey eligibility by nationality bers. Colombian children and adolescents were considered eligible if they were between the ages of 5 and 17, and both the child and their parents were Colombian nationals. Venezuelan children and adolescents were eligible under similar age criteria, provided that both the child and their parents were identified as Venezuelan migrants (not naturalized in Colombia) and had migrated to Colombia between 2016 and 2020, aligning with the period of the migratory crisis.

We specifically excluded children from binational households, which include those with one foreign parent and one native parent, or those who have obtained Colombian citizenship through recent naturalization policies. This approach was taken to focus the study on the of children without the ef- integration process immigrant potential confounding fects of the services and advantages available to those with dual citizenship.

Within each selected household, interviews were conducted with up to two children. In households with more than two children, one child from each age group was chosen a random selection to a broad of through process participate, ensuring representation experiences within the study’s scope.

_Sampling frame._ We use the 2018 population census data as a sampling frame for the Colombian sample. It allowed the identification of residential blocks and households with children in the desired age range. With this source of information, it was possible to identify the number of households and residential blocks with children and adoles-

> 7Although Colombia only grants nationality to children of Colombian nationals, it follows a _jus sanguini_ principle, the Colombian government has introduced reforms, such as the the program _Primero la Ni ̃nez_ to give Colombian nationality to children of Venezuelan parents born at times when diplomatic relations between Colombia and Venezuela were cut and hence, it was
results provide returns to height ranging from 1.4 percent to 4.5 percent per centimeter. In what follows, the Weil (2007) preferred value of 3.4 percent is used as the baseline. A reasonable range of estimates has 6.8 percent as the upper bound (corresponding to the mean estimated return to height across the 5 studies with estimates greater the Weil (2007) benchmark), and a lower bound of 1 percent as the lower bound (corresponding to the mean estimated return to height in the remaining 13 studies with estimated returns below 3.4 percent).<sup>22</sup>

# **A3.3  The Relationship Between Adult Height and Adult Survival Rates**

The second key ingredient in the calculation is the estimated relationship between height and adult survival, β������,��� . Weil (2007) estimates this using long‐run historical data on stature and survival rates for 10 advanced economies over the 20<sup>th</sup> century, where there is considerable variation within countries over time in adult height. In his sample, average height varies from around 164 cm to 180 cm, and he obtains an estimate of β������,��� �19.2 . To assess the robustness of this finding, the same relationship is estimated using data on female height collected in 172 DHS surveys covering 65 developing countries between 1991 and 2014.<sup>23</sup> In this sample, female height exhibits comparable variation to the historical dataset in Weil (2007), ranging from 148 cm to 163 cm. In the roughly half of the sample corresponding to non‐Sub‐Saharan African countries, a country‐fixed effects regression of height on adult survival results in a slope coefficient of 19 and a standard error of 3.6, which is extremely close to the Weil (2007) baseline estimate of 19.2. In Sub‐Saharan
We end by investigating the prevalence of opportunistic behavior by inspectors during the RCT period focusing on declarations receiving detailed comments but no valuation advice. Analogous to the analysis presented in Section 4.3, we examine whether inspector actions and customs outcomes vary, depending on the amount of tax revenue at stake, controlling for initial undervaluation.<sup>35</sup> The initial undervaluation is measured relative to the internal reference price and a new high-stakes indicator is equal to one if the hypothetical taxes exceed 10,000 USD (with hypothetical taxes being defined as the product between the declaration’s tax rate and the hypothetical value of the declaration calculated using the internal reference unit price). We find evidence of differential treatment by inspectors of high-stakes highly undervalued declarations in that they detect less fraud and adjust significantly less their value and taxes, but the impact on fraud is not statistically significant. Not surprisingly, these results are generally weak due to the small sample size and the effects are not robust to adjustments for multiple hypothesis testing. But overall, this evidence is consistent with detection of fraud combined with a lower than theoretically optimal adjustment, especially for declarations for which opportunities for graft are largest.

In summary, we find a significant response by inspectors to the provision of detailed comments. The fact that inspectors increase substantially their recording of fraud but that the tax adjustments are small and that they are smaller for high-stakes declarations suggests that inspectors are engaging in box-ticking behavior.

# **6. Conclusion**

The performance of customs inspectors is an important determinant of tax collection in developing countries but may be constrained by asymmetric information and inadequate monitoring of their behavior in conjunction with limited performance incentives. This paper has used uniquely rich administrative data and a randomized control trial to examine the impact of the provision of information to inspectors and of monitoring them on fraud detection, tax revenue, and clearance times.

Using an instrumental variables strategy exploiting variation in the supply by an external third party of declaration-specific valuation advice, we have shown that
, such as Germany, Korea, Japan, or China, is therefore recommended to open up opportunities for trade in services.

Russia can build its services exports on the basis of its current strengths. First, its services are quite sophisticated (Box 1).<sup>11</sup> The second question is: What markets might service providers target? To reach an answer, a gravity analysis was performed using bilateral trade data to examine Russia’s potential for trade in services relative to its actual trade flows.<sup>12</sup> First, two separate regressions were run: one with country‐specific fixed effects (FE) to capture all factors that influence trade for all countries; and one without country effects but with the Services Trade Restrictiveness Index (STRI) measured so as to control for the trade‐ inhibiting effects of regulation. For all the countries discussed **Error! Reference source not found.** plots predicted bilateral trade levels for these two specifications.

> 10 The information about bilateral trade in services is not as detailed as for goods and presents potential interpretation problems. 11 Sophistication is measured by constructing an index of services export sophistication (EXPY). Any increase in a country’s EXPY indicates a shift toward exports of more sophisticated products. Hausmann et al. [2007] call the sophistication of a particular service PRODY; it is calculated as the weighted average of the GDPs of countries exporting that service. The problem with using the EXPY measure, however, is that it has two components that can increase or decrease EXPY: RCA and per capita income. The latter is very large for some countries; thus, even if the RCA for modern service exports is not high, the “sophistication” of a country’s services exports is found to be high.

> 12 The gravity model has used extensively in the international trade literature due to its intuitive empirical and theoretical appeal. Anderson and van Wincoop (2003), Feenstra (2004)
over time.<sup>21</sup> In Ghana, 75 percent of farmers report producing only or mainly for sale at the start of the season,

> 17 There is a surprisingly large difference in the average size of landholdings between the Malawi Agricultural Labor Survey and the Malawi IHS-4. Multiple factors may explain this – including the different levels of regional coverage (see Table A1), differences in question phrasing, slight differences in the timing of data collection, etc. In addition, unlike the IHS-4, which visited households only once, the Malawi Agricultural Labor Survey interviewed households repeatedly over the course of the season (i.e. all households were administered a pre-planting and end-of-season survey and a sub-sample of households received additional weekly interviews). As discussed in Gaddis et al, forthcoming, repeated interviews lead to increased capture of marginal plots and hence increase total measured land holdings at the household level.

> 18 Farming here includes animal rearing. In theory, the distinction between own-use production work and employment can also arise in other sectors, such as hunting, gathering, food preservation, etc. However, in developing countries, mixed production (for both own use and profit) is most common in agriculture, and employment estimates are most sensitive to farmers’ classification as employed or in own-use production work.

> 19 In all the analysis in this paper, we only report on individuals aged 15 years and above.

> 20 The start-of-season and end-of-season surveys were fielded, approximately, six to nine months apart (see Table A1).

> 21 In Malawi and Nigeria, the estimates refer to all individuals (15+) who had worked at least one hour on the family farm during the reference period (based on the surveys’ labor modules). In Ghana, the estimates refer to all individuals (15+) who stated that their _main occupation_ was working in agriculture (based on the question in the household member characteristics module). We obtain very similar results for Ghana if we consider individuals
# **3.2. Comparisons of establishment characteristics and formality across data sets**

We then focus on our universe of interest, comparing the formality of private non-agricultural establishments with 1-24 workers across the three data sets, using various definitions. We start by examining the distribution of firms along various characteristics across data sets to ascertain if they represent similar firms. Subsequently, we examine the patterns of formality across the common characteristics and data sets. We present our results descriptively and also test whether there are significant differences using regressions. Specifically, we pool the three data sets and run a logit model for probability of being formal, with all the common characteristics fully interacted with a categorical variable indicating the data source, to understand whether there are significant differences in formality across data sources.

# **3.3. Predictors of formality**

A key focus of our research is investigating what factors predict formality and whether formal and informal firms are comparable or different. We run logit models to predict formality. In “Model 1” we include only the few characteristics that are common across all three data sources. “Model 2” includes additional firm characteristics and can only be run on the EcC and ELMPS (with a somewhat different set of variables in each survey; see above). “Model 3,” which adds the owner’s characteristics, is only possible for the ELMPS.

# **3.4. Comparing Distributions of Predicted Probability of Formality by Formality Status**

After running the various models mentioned above, we predict the probability of being formal using each model and data source. We compare the distribution of the predicted probability of formality by whether or not firms are actually formal. We take the size of the overlap in the distribution functions of predicted probability across formal and informal firms as a test and metric of whether informal firms are similar to formal firms and thus might potentially formalize. We acknowledge this overlap is, in part, driven by the number of covariates included in the model
<!-- Start of picture text --> 60<br>55<br>50<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Arrival to gate arrrival to lodging lodging to payment payment to gate<br><!-- End of picture text -->

**Graph 3 – Boxplot analysis of cargo dwell time sequential components (container imports in Douala, 2009)**<sup>**13**</sup> Souce: CAMEROONIAN CUSTOMS, 2009 – Values in days

For all these steps however the fact that median values are 40 to 65% lower than means demonstrates the substantial impact of a minority of very long or abnormal delays. A specific look into the determinants of these very long delays is then necessary, and we recommend using the median rather than the mean (traditionally used in Douala and other ports) as the benchmark for long delays.

# **Shipment level analysis and the need for segmentation**

## **_Main conclusions from shipment level analysis_**

Since the dwell time issue came to the forefront in 1997<sup>14</sup> local stakeholders in the Port of Douala have implemented multiple monitoring tools that provide extensive shipment level data. Preliminary analysis of these data lead to conclusions that are consistent with previous research findings (notably Arvis _et al.,_ 2010):

##  **Broad-tailed multimodal distribution**

The first observation is that there is a large dispersion of values in the distribution of dwell times), with a broad-tailed shape quite specific to developing regions. This is symptomatic of the long storage patterns and opportunistic behaviors mentioned here before. This also reflects that the

> 13 For a better visibility whiskers have been limited to 1.5 times the Inter-quartile range.

> 14 Mostly because of a very serious deterioration
when all other variables are introduced (column 6).

Table 4(b) presents coefficient estimates of an alternative specification in which the gravity variables are interacted with the internet use measures for the origin and destination countries separately — i.e., not as the product of the two measures. Panel B presents coefficient estimates for the interactions with internet use in the origin country, while Panel C presents estimates for interaction with business internet use in the destination country. Once again, we find that greater internet use in both the origin and destination countries offsets the negative impact of distance on tourism flows and reduces the role of sharing a common border or speaking the same language in facilitating bilateral travel. Once again, however, the colonial relationship interaction does not have the expected sign and remains significant in the case of the origin country even in the fuller specification in column (11).

# **4.3 Estimation results: Google Trends data**

As a robustness check, we now turn to the use of our proxy constructed with Google Trends data. We can now explore a longer time horizon, spanning the 2004 to 2017 period, with a measure that is more directly Page **19** of **34**
from European merchants on trade routes, measuring distances, possible supply lines, and other relevant matters. Preceding the 1874 British march on the Asante capital of Kumasi, the Colonial and War Offices in London had corresponded with Francis and Andrew Swanzy, leading merchants on the Gold Coast, on local geography, rivers and their tributaries, food supplies and the means of carrying food.<sup>2</sup> Another prominent West Coast trader, John Holt, had to be reined in by British officials in his protest against the German annexation of the Cameroons, which British merchants in Lagos considered part of the British sphere of influence. The German government formally complained to the British government that John Holt was inciting the natives in Cameroon to rebel, and that the British Vice Consul Buchan, also a

> 2 Unilever Archives, Liverpool, UAC/2/33/AG/1/2/5. C. B. Brackenbury from the War Office was effusive in his expression of gratitude to Swanzy in his letter of September 31, 1873: ―The information you are so kind as to give us is among the very best we obtain and we are really thankful for it.‖

20
Other control variables include output, _Y._ The coefficient sign of output _Y_ , _ß1_ , is not unambiguously predictable. An increase in _Y_ normally leads to a higher total wage bill. If the cost increase is equally distributed between female and male labor, there should be no influence on _S_<sup>_F_</sup> . If the wage bill of male labor increases more than proportionally, e.g. due to better bargaining power, this results in a higher _L_<sup>_M_</sup> and/or _w_<sup>_M_</sup> , and _S_<sup>_F_</sup> is expected to fall ( _ß 1_ < 0).

One expects a lower _S_<sup>_F_</sup> and thus a lower cost share of _L_<sup>_F_</sup> in total wages when relative wages for male labor, _w_<sup>_M_</sup> _/w_<sup>_F_</sup> , as part of _CV_ rise ( _ß 2_ < 0). An increase in the capital intensity, _k_ , will increase _S_<sup>_F_</sup> , if capital is a substitute for male labor ( _ß 3_ > 0), but reduce _S_<sup>_F_</sup> , if capital is a substitute for female labor ( _ß 3_ < 0). A higher _tfp_ could increase labor demand in favor of female ( _γ2_ > 0) or male workers ( _γ2_ < 0). The influence of _trade_ on _SHS_ is not easily predictable, as trade could increase the relative demand for female ( _γ1_ > 0) or male labor ( _γ1_ < 0).

In the absence of gender‐specific wage data at the firm‐level, the model is modified in two ways: First, the analysis on trade and the female wage share combines the Enterprise Survey dataset with sectoral wage data by gender, available from Household Surveys. This assumes equal relative