rafmacalaba/data-use-mentions-extended huggingface dataset:
cases where a document exists no information is requested on gender of individuals listed on documents. This limits the scope for gender analysis, e.g. by checking the extent to which documented and perceived rights match.

# **5. Conclusion and policy implications**

This paper aimed to advance the debate on land data collection by going beyond conceptual issues and exploring if cost-effective implementation of a household-level SDG compliant module is possible and can offer insights as a basis for actionable policy advice and tracking progress on implementation. Zambia’s experience suggests that piggy-backing a slightly expanded SDG module on existing surveys, such as the

> 22 This is combined with a failure to document land use or tenure type for residential land for which the Prindex questionnaire only asks if a property has any land attached to it or not (Q36). Published data suggest that 27% of households access only a residence with no land attached, a figure more than double what emerges from LFS data.

> 23 Especially if located on state land, titled agricultural properties may be more easily transferred and used for collateral than the primary residence. 24 As only (co)owners can decide on land transfers or use of land as collateral, this reduces the sample size for any regressions involving these variables, as illustrated by the limited number of observations in table 8.

> 25 The literature documenting the presence and adverse impacts of intra-household informational imperfections even among monogamously married spouses reinforces this (Udry 1996). Recent experimental evidence supports the notion that spouses do not always communicate information about resources (Ashraf 2009) even if such strategic exploitation of informational advantages results in efficiency losses (Castilla 2019).

17
# **2 Related literature**

## **2.1 Globalization and taxation**

Our paper first relates to the literature on globalization and taxation. Since Adam Smith (1776), economists have conjectured that increased openness pushes governments to reduce taxes on the most mobile factors of production (e.g., high-wage workers, capital) and recover the revenue shortfalls by increasing the taxation of less mobile factors (Bates, Da-Hsiang, and Lien, 1985; Rodrik, 1997). This effect is thought to be particularly strong for countries competing for capital (Wilson, 1999; Kanbur and Keen, 1993).

In an important contribution, Egger, Nigai, and Strecker (2019) show that globalization led to a decline in the progressivity of labor taxation in OECD countries since 1994 (namely, an increase in labor taxation for the middle class and a decline for the top 1 percent). Our approach, which focuses on the changing balance of labor vs. capital taxation in both OECD and non-OECD countries, is complementary. For OECD countries, our findings reinforce Egger, Nigai, and Strecker (2019): we show that in addition to reducing labor tax progressivity, globalization has been associated with a sharp decline in capital taxation. This decline reduces overall tax progressivity (above and beyond the decline due to falling labor tax progressivity) given the concentration of capital income at the top.

Our paper also adds to the macroeconomic literature on the link between trade and taxes. Due to the lack of systematic statistics on the evolution of tax structures in developing countries, this literature has focused on high-income countries or a single tax (e.g., the corporate income tax).2 Our contribution to this literature is to build and analyze a new global dataset of effective tax rate, extending prior work which focused on developed countries (Mendoza, Razin, and Tesar, 1994; Carey and Rabesona, 2004; McDaniel, 2007). This allows us to uncover new trends (most importantly
**</sup>|<br>0.500<sup>***</sup>|<br>0.859<sup>***</sup>|<br>0.668<sup>***</sup>|
||(69.82)|(50.49)|(27.65)|(71.16)|(36.56)|
|Observations|35059|24440|10619|35059|10619|
|Adjusted_R_<sup>2</sup>|0.340|0.362|0.188|0.346|0.249|
|Controls|Municipality|Municipality|Municipality|Municipality|Municipality|

_Source:_ Authors’ analysis based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo. _Note: t_ statistics in parentheses;<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001. _Incomplete Primary_ refers to persons who attended primary school but did not complete it. _Complete Primary_ refers to persons who attended and completed primary school but did not move on to Junior High. _Incomplete Junior High_ refers to persons who attended Junior High but did not complete it. _Complete Junior High_ refers to persons who attended and completed Junior High education but did not move on to Senior High. _Incomplete Senior High_ refers to persons who attended Senior High but did not complete it. _Complete Senior High_ refers to persons who attended and completed Senior High but did not move on to University. _University_ refers to persons who attended and obtained a University Degree.

# **General Equilibrium Effects**

A counterfactual earnings distribution of informal employees has been constructed assuming they are employed by formal firms. From the point of view of these employees, the exercise ensures that
frameworks) for evaluations related to local conflict building on a mixed qualitative-quantitative approach.

The PODES survey collects information on village/neighborhood institutions. We have constructed variables which indicate the presence of religious groups, _adat_ (traditional law/customs) institutions, and the number of places for worship. We also used variables that indicate whether there is a BPD in the village and the gender of the village head. The BPD is a mandated democratically elected council in rural areas.

> Religious groups and _adat_ institutions are associated with higher levels of conflict.  One possible explanation may be that their presence reflects differences in norms about conflict resolution.  The qualitative work indicated that often varying expectations between different groups in the community, or between the community and the state, about how tensions should be resolved resulted in conflict.  The density of places of worship on the other hand is associated with lower levels of conflict, especially in rural areas.  This may support Varshneys’ hypothesis that institutions that can resolve tensions that exist between their constituencies play an important role in preventing conflict from arising and escalating.  The absence of a BPD in a rural village has no effect on conflict in the regression with provincial dummies, but does appear to be associated with lower levels of conflict in the regression that does not include these controls.  The presence of a female village leader is associated with lower level of conflict in rural areas.  This however, is still a rare phenomena: only 2 percent of the rural communities had a female village leader.

# **_Hypothesis: Better public or communal provision of security is associated with lower_**

**_conflict._** The PODES allows us to construct variables concerning the presence of formal and informal security in a village, including whether the community has organized its own security

29
In some countries (such as France) strict prohibitions are in place on the transfer of bank client data to insurers.<sup>11</sup> In other countries, opt-in or opt-out requirements are common.

Key required elements of any data protection regime allowing for data transfer are as follows:

- The consumer is made aware that data are being collected

- The consumer has access to his or her data

- The consumer can correct any errors and

- The consumer has the option of preventing that data from being shared with third parties (the definition of third party may or may not include related group companies).

A recent case review in Hong Kong SAR, China, concluded that where the right to transfer data is not specified in detail in the acknowledgment form, the relevant clauses in supporting material should be listed in the acknowledgement form (Office of the Privacy Commissioner 2011).

_Best practice example—Hong Kong SAR, China._

# **References**

Arena, M. 2008. ―Does Insurance Market Activity Promote Economic Growth? A Cross Country Study for Industrialized and Developing Countries.‖ _Journal of Risk and Insurance_ 75 (4): 921–46.

Davis, S. 2007. _Bancassurance: The Lessons of Global Experience in Banking and Insurance Collaboration._ CITY: VRL Knowledgebank.

Feyen, E., R. R. Lester, and R. de Rezende Rocha. 2011. ―What Drives the Development of the Insurance Sector? An Empirical Analysis Based on a Panel of Developed and Developing Countries.‖ World Bank Policy Research Working Paper 5572, World Bank, Washington, DC.

Hong Kong Office of the Privacy Commissioner. 2011. ―Transfer of Customers’ Personal Data by a Bank to an Insurance Company without Consent.‖ Report No. R11-1696, Office of the Privacy Commissioner, Hong Kong, China.

Lester, R. 2010. ―MENA Flagship Insurance Report.‖ World Bank, Washington, DC. Munir, A. B. 2010. Presentation
interpretation that we are in fact estimating the production function instead of, for instance, an infrastructure demand equation, or a combination of both relations.

Our estimates of the output contribution of infrastructure are significant not only statistically, but also economically. To illustrate this, consider an increase in the level of infrastructure provision from the cross-country median in the year 2000 (an index of -4.65, roughly similar to the value observed in Tunisia in 2000) to the 75<sup>th</sup> sample percentile in the same year (an index of -3.69). This would translate in a 7.7 percent (=0.08*(-3.69+4.65)) increase in output per worker.<sup>30</sup> Similar calculations show that: (a) an increase in infrastructure provision from the median level observed among lower-middle income countries (-4.67, roughly equivalent to Bolivia in the year 2000) to that of the median upper-middle income country (- 4.02, Uruguay) would yield an increase in output per worker of 5.2 percent, and (b) raising the level of infrastructure provision from the value observed in the median upper-middle income country to that of the median high-income country (-2.93, corresponding to Ireland) would raise output per worker by 8.7 percent.

# **5. Conclusions**

This paper adds to the empirical literature on the contribution of infrastructure to aggregate output. Using an infrastructure-augmented production function approach, the paper estimates the output elasticity of infrastructure on a large cross-country panel dataset comprising over 3,500 annual observations. The paper addresses several limitations of earlier literature. It uses a multi-dimensional concept of infrastructure, combining power, transport and telecommunications infrastructure into a synthetic index constructed through a principal component procedure. The econometric approach deals explicitly with the non-stationarity of infrastructure and other productive inputs, reverse causality from output to infrastructure, and potential cross-country heterogeneity in
may have played a role in the trend in labor income inequality in Latin America; it also documents how residual labor inequality is the most important factor in this trend. Section 5 concludes.

# 2. Data: From noise to signal for identifying regional trends

Most stylized facts on Latin America are produced using harmonized national household surveys from the SEDLAC Project.<sup>14</sup> The surveys are collected by the respective national statistics offices (NSOs) and harmonized through the SEDLAC Project. These microdata cover 17 countries over a span of 20 years, which account for 90 percent of the population in the region. Annex A provides more detail on the microdata used in this study for each country and each year. In addition, part of the analysis in section 3 uses aggregated information on República Bolivariana de Venezuela to present a more comprehensive picture on labor income inequality in Latin America. There is information on a yearly basis for all countries with the exception of Chile and Mexico, which collect data every two or three years, and Guatemala and Nicaragua, which collect household survey data about every five years.<sup>15</sup> All the surveys have a labor module to collect information on labor income after taxes, which is the main variable of interest in this study. Next, we provide a detailed description of the use we make of this variable.

An additional contribution of this study is the effort to minimize the intrinsic noncomparability of surveys over long periods of time. A salient characteristic of household surveys in developing countries is that they are living tools that undergo changes in structure, the phrasing of questions, sample design, and frequency, among other features, from time to time. There is thus a cost of comparability in the country series for those indicators that are more sensitive to the specific change. The large majority of the studies that are not country specific do not mention how they deal with the effect that the changes in the survey features have on the comparability of their estimates.
- Labor and training costs during the period between the delivery of an asset and its full entry into service.

- Costs due to the closure or relocation of a facility.

On the other hand, there are two elements of GFCF that are not included in FAI. _First_ , residential FAI is measured at the cost of the construction to the property developer. In GFCF, however, it is measured at the cost to the acquirer of the asset. The difference between these two concepts is the gross margin of the property developer (the difference between the construction cost and the selling price). The level residential investment has been corrected by adding this margin to residential FAI (Herd, 2013). _Second_ , since July 2016, research and development expenditure has been partially reclassified as a form of final capital expenditure rather than a cost element that is written off against profit in the year when the expenditure occurs. The overall result was to raise the level of GDP by 1.3% in 2016 and to increase the growth rate by 0.06 percentage point in the decade to 2015.

The NBS has not published a detailed breakdown of R&D spending by sector or type of expenditure. However, a comparison of the revisions to fixed capital formation in the 2015 and 2016 Statistical Yearbooks shows that the estimate of R&D expenditure included in GFCF was substantially lower than the estimate of R&D outlays published in the Science and Technology Yearbook. In the decade to 2014, just under 35% of outlays in the latter Yearbook were not classified as R&D. This presumably reflects a decision that a significant portion of development expenditure by companies should be regarded as a cost of production rather than an asset from which future benefits will flow.

A further measure of gross fixed investment can be derived from the data included in the Main Economic Indicators of Industrial Enterprises. This publication gives the results of a monthly census of financial data of all industrial enterprises with sales of over a threshold value that changes over time
leverages this analytical property to develop a method for estimating a household-level fullcoverage spatial deflator.

The method relies on a mixed empirical strategy. The estimation of the food component of the index is based on observed prices and quantities, typically derived from household surveys. By contrast, the nonfood component of the index is derived indirectly from multiple poverty lines, usually defined at the subnational level. For nonfood items, prices and quantities are rarely available, thus the proposed approach relies on expenditure, particularly the minimum required to achieve a fixed standard of living.

The proposed method offers two main advantages. First, it retains detailed information on the prices (unit values) and quantities involved in food expenditure. Second, it integrates this data with nonfood information derived from subnational poverty lines. This results in a deflator that, by construction, is fully consistent in terms of coverage with the consumption aggregate used for poverty and inequality estimates. This deflator may also be estimated at the household level.

A key limitation of the method is that the ability to capture accurately the spatial variability of the prices of nonfood items depends on the quality of the identification process in the estimation of multiple poverty lines (Ravallion 2016). The underlying assumption is that multiple poverty lines are utility consistent (Ravallion and Lokshin 2006). This assumption may be tested or analytically imposed (Arndt and Simler 2010).

Overall, the proposed method represents a significant advancement in measuring living standards by accounting for spatial differences in the prices of nonfood items, which comprise an increasingly large share of household consumption. This enhancement leads to more accurate measures of poverty and inequality.

19
, [30-34] (30), and [35+] (35). The omitted bin is [0-4] (0). We include the number of years of education ( _eduit_ ) and country-year-sample fixed effects ( _θt_ ).<sup>42</sup> Finally, we omit samples without at least 10 observations in each bin.

We use _γ_ as a measure of the returns to education. To estimate the returns to work experience we follow Jedwab et al. (2020a). For each bin one by one (5, 10, etc.), we

> 38Likewise, if we use city-level data as we did in Section 1., we only find very weak correlations between the PCC residuals estimated using tradable employment and the residuals based on the number of years of education. These results are not shown but are available upon request. Thus, urban tradable employment and urban human capital seem _unrelated_ for a given city size and the level of urban economic development.

> 39From the notes of the Excel database containing the data, we learn that the “database harmonizes scores across major international student achievement testing programs measured in TIMMS-equivalent units, where 300 is minimal attainment and 625 is advanced attainment.” Url for the database: https: //www.worldbank.org/en/publication/human-capital$ _\_ sharp$Data, last accessed 03-29-2022.

> 40From the notes of the Excel database containing the data (same Url as above), we learn that the “Learning-Adjusted Years of School are calculated by multiplying the [...] years of school by the ratio of most recent harmonized test scores to 625, where 625 corresponds to advanced attainment [...].”

> 41We calculate _potential work experience_ – which we call “experience” in the rest of the analysis – as follows: (i) For individuals with at least 12 years of education, we assume children start school at age 6 and calculate experience as age - years of
## **6.2 Market Expansion**

Another channel underlying the economic impact of Internet connectivity is the expansion in market access for both firms and consumers (Hjort and Tian, 2021). In addition to traditional "brick-and-mortar" markets, Internet access offers an additional market channel via "online markets", thus allowing firms to sell products outside the boundaries of traditional markets, at relatively low cost. The expansion in market access via online platforms (in addition to offline/traditional platforms) will result in increased revenue for firms with positive implications on profitability. The potential for high revenue is particularly important for investors as it increases the expected return on investment.

To test this channel, we use firm-level data from the WBES to explore the relationship between high-speed Internet connectivity and firm sales by estimating the following equation: _Ymict = φ ·_ 1 ( _Submar inect ×Connectedi_ ) _+ βXmt +_ Γ _i + ηct + μmict_ (4) where _Ymict_ is the outcome of firm _m_ in city _i_ , country _c_ at time _t_ ; _Xmt_ is a vector of firm controls (ownership type, firm size, and whether it is foreign or domestic), while Γ _i_ represent city fixed effects. All else remains as previously defined. Standard errors are clustered at city level.

In Table 7, we find a positive effect of Internet connectivity and the two measures of firm sales: log of total sales and sales per worker. These results provide suggestive evidence of the economic benefits of Internet connectivity on revenue, potentially signaling a high return on investment.

# **7 Robustness Checks**

Before concluding the paper, we address two potential challenges to our baseline analysis, namely: standard error clustering and extending the sample to include countries with prior connection to the first-generation SMCs.

## **7.1 Standard Errors**

We begin by showing the robustness of our analysis to alternative clustering. Given that one dimension of our treatment indicator–SMC arrival–varies at the country level,
, the results reveal a consistently negative and strongly significant correlation of poverty with subsequent growth. Its magnitude is economically significant too: a 10 percentagepoint decrease in the headcount poverty rate is associated with a rise in annual per capita real growth of 0.5% to 1.8%, depending on the precise specification of the empirical model. However, further analysis reveals that the magnitude and significance of the effect depend on the prevailing level of poverty. Specifically, when the level of poverty is low (below the sample median), the growth effect of poverty is not statistically significant. In contrast, when the level of poverty is high, changes in the poverty headcount rate show a significantly negative association with subsequent growth – i.e., 25
can stay above some subsistence level of consumption (Hoogeveen, 2003; Fafchamps & Lund, 2003; Kazianga & Udry, 2006). Fourth, certain households may have access to informal insurance whereby communities pool risks and provide support to those in need, although such insurance mechanisms tend to be most relevant to idiosyncratic shocks rather than covariate shocks or seasonal variation (Mace, 1991; Townsend, 1994; Ravallion & Chaudhuri, 1997). Fifth, government policy – and, in particular, access to social protection – influences the base level of income to which households have access: such social protection may be adapted to respond explicitly to shocks and seasonality (Bodewig, 2019).

The rest of this paper is organized as follows. Section 2 describes the data and methodology. Section 3 presents some descriptive statistics, showing the limitations on strategies for coping with and managing seasonality in the Sahel. Section 4 presents the main results, comparing key markers of welfare between the lean and non-lean season. Section 5 describes heterogeneity in the main results, including comparing rural and urban areas. Section 6 concludes and describes the policy implications.

# Section 2. Data and methodology

The main data source used in this paper is the ‘ _Enquête harmonisée sur les conditions de vie des ménages_ ’ (EHCVM), which captures detailed household-level microdata from Burkina Faso, Mali, Niger, Senegal, and Chad. The EHCVM includes a wide range of socioeconomic indicators, including consumption, employment and agricultural activities, food security, assets, education, and health. The EHCVM data are representative at the national and regional levels for each of the five countries.

The timing of the EHCVM makes it possible to assess the impact of seasonality in four of the five Sahelian countries surveyed as the data were collected in two distinct waves during the same agricultural year; the first wave corresponding to the harvest period and the second one to the lean season. The waves collected information on different households, so the EHCVM data do not have a panel structure. However,