rafmacalaba/data-use-mentions-extended huggingface dataset:
behind rapid GDP growth, structural change and industrialization operated at less than full power, implicitly resizing the extent of the African miracle, especially from a qualitative viewpoint.

All of these explanations have several shortcomings. First, they do not take into adequate account the fact that inequality as it is currently measured in SSA could be simply underestimated. The limitations of using consumption as the widely preferred welfare measure to investigate inequality in SSA, we argue, could be partly able to explain this conundrum. Our belief is that consumption as a proxy for well-being is well-suited to measuring poverty or in general the well-being of the bottom deciles (Meyer and Sullivan 2011), but its appropriateness as a proxy for well-being fades as one moves up the consumption distribution (Chancel et al. 2019). This is because the basket of goods and services commanded by top deciles is not well represented in standard consumption surveys, which, after all, are designed primarily for poverty measurement and hence focus on a fairly basic basket of goods.

3
This unit cost is then applied to the IBNET customer field to obtain an optimized asset value of water and sanitation services separately for each utility represented in the IBNET database:<sup>18</sup> (U ∗C I I (5)<sup>II II,ll,II</sup> Where KK is the estimated asset base for each service<sup>I</sup> DD) = UUIIUUII KK<sup>ll,II</sup> CCIIQQrrII I _l_ , of size TT<sup>II,ll</sup> _m_ for _s_ utilities in the IBNET database;<sup>II II,ll,II</sup> is the unit asset base for each service _l_ , of size _m_ ; and C I I are the customers in each service KK<sup>I</sup> _l_ , for _s_ utilities in the IBNET database. UUIIUUII KK<sup>II,ll,II</sup> CCIIQQrrII I TT<sup>II,ll</sup>

# **3.3 Estimating a country’s cost of capital and taxes**

# **3.4 Estimating depreciation and capital remuneration**

Once we have an estimate of the cost of capital and the _Unit K_ parameter, we can compute both depreciation and capital remuneration by calculating an annuity:

> 18 In case of missing sanitation data in the IBNET database, if only one of a utility’s number of sewerage connections or volume of wastewater processed was missing, the analogous data from the utility’s water coverage were used in its place (sewerage customers = water customers or wastewater treated = water sold). Utilities without any sanitation data were classified as wateronly providers.

> 19 Country-specific risk premiums can be found at Damodaran: http://www.damodaran.com.

9
period 1994 to 1999 after privatization exclusively. In both, freight and passenger transportation sectors, most companies reached high rates of productivity growth, 5.3% and 9.8% on average, respectively. And, as in the case of Brazil’s privatization, this increase is mainly concentrated on the output side. These results are globally confirmed by Estache et al (2004) with more recent data for Brazil.

Third, the literature also provides some useful insight on the timing of the efficiency effects of reform. In particular, Estache et al. (2001) also show that the major gains in efficiency realized by freight companies in Argentina and Brazil were achieved in the first year of operation. They also find that toward the end of the period efficiency drops for all the operators monitored. This tends to confirm the expectations derived from the use of price cap regimes with regular tariff revisions.

Fourth, the paper by Rivera-Trujillo compares the performance of the main North and Latin American railway systems and finds that between 1980 and 1999, the South lagged the North significantly on all efficiency counts. Particularly interesting is the observation that it also lagged the North in terms of the internalization of technological change. On average also, for that continent and during that period, technological change was higher than technical efficiency change.

Fifth, the recent paper by Estache, Trujillo and Perelman (2004) points to the importance of an integrated approach in dealing with safety and output concerns in the regulation of railway service. They do so by modeling an interesting innovative feature of the Brazilian “privatization” intended to ensure a close monitoring of the quantity-quality trade-off. With a price cap regime in the background, the privatization team spelled out, for each concession, two specific targets about output and safety, in terms of minimum net ton-kilometers carried each year and maximum number of accidents per trainkilometer during the first five years. The modeling approach relies on a non-parametric (DEA) index, introduced by Färe et al. (1995), which
and ‐0.56 respectively. The median district growth rate is also negative for Electrical and leather manufacturing industries.

# 5 Determinants of Employment Growth in Ghana

## 5.1 Key local determinants of employment

In our analysis below, we explore the extent of agglomeration economies on employment growth in Ghana, following Glaeser (1992) and Combes (2000), amongst many others. The previous section suggested some important trends in Ghanaian manufacturing‐ especially the growing dominance of Greater Accra and the decline in particularly wood and furniture, which were two industries not concentrated in Accra. We seek to explain these patterns through the lens of agglomeration economies. We test for localization and urbanization economies using several measures. These are specialization, competition (localization economies) and population density, average plant size and diversity (urbanization economies).

We also add in human capital in the district‐industry in the 2003 NIC, using the proportion of skilled workers in each industry‐district to test for human capital externalities. We use distance from Accra and Kumasi as a proxy for transport costs to larger markets, and, in the case of Accra, to export markets. We use the level of imports in the industry to control for external competition, which is assumed to affect all industries equally, after controlling for transport costs proxied by distance.

Combes (2000) argued that employment growth as well competition, diversity, average size and specialization within _zones d’emploi_ ‐industries should all be normalized using the level for the country as a whole in each industry, which he terms normalization, and which allows comparison across sectors. This was undertaken in the original Glaeser et al (1992) paper for some but not all variables, which Combes (2000) remedied. We now describe each of the urbanization and localization economies variables and their normalizations.

In examining the determinants of employment growth, we use a normalized measure of employment growth in an industry‐district between the 2003 and 2014 firm censuses as our dependent variable, following Combes (
the life skills trainings was 93.8%, and 85% completed at least five of six sessions offered. According to administrative data collected in a subset of villages, the entire sensitization lasted approximately 1.5 hours on average, approximately 250 people attended the sensitization per village, and village chiefs and imams were consistently present. Approximately 18% - 30% of beneficiaries in attendance saw the film with their husbands and 31% with their children. Approximately 87% of beneficiaries in the psychosocial arms reported remembering the film’s protagonist Amina in the follow-up survey.

_<mark>Measures</mark>_ The following description of measures is adapted from Bossuroy et al. (2022) Supplementary Information.

_Economic outcomes. Consumption_ was used as a proxy of poverty. It assessed total daily consumption per adult equivalent, which is the sum of daily household food and non-food consumption as well as expenditures on education, health care, and celebrations, divided by the number of adult equivalents per household. _Food security_ is assessed with the Food Insecurity Experience Scale (FIES) (Ballard et al., 2013), which asks eight yes/no questions about a household’s food security over the last year where 0 = “yes” and 1 = “no” and we present a raw sum. The questions are “have you or other members of your household 1) been worried about not having enough food, 2) been unable to eat nutritious and healthy foods, 3) had to eat a smaller variety of foods, 4) had to skip a meal, 5) eaten less than they thought they should, 6) run out of food, 7) been hungry but did not eat, and 8) gone an entire day without eating?” 34
*<br>**work****||||||||||
|Street|6.7|12.4||||||||
|Bar|40.9|34.7||||N/|A|||
|Brothel|33.1|28.0||||||||
|Other|19.3|24.9|<0.01|||||||
|**Mean**<br>**price/client**|6.6 (9.6)|6.2 (5.5)|0.27|||||||
|Missing data: T|otal income (Wa|ve 1 & 3 bala|nced pan|el, n=21, Wave|1 & 2 balanc|ed panel|, n=11) sex wo|rk income (W|ave 1|
for low-literacy settings, offer promise to reduce recall bias (Daum et al., 2019; Kilic et al., 2024). If implemented well, mobile phone-based time use diaries, coupled with higher-frequency physical activity tracking data collection (i.e., at sub-minute data collection frequency, without additional data collection cost), can potentially improve the predictive power of machine learning models.

Future research in this area should document the predictive accuracy gains, if any, associated with using mobile phone-based time use diaries and sub-minute-level physical activity tracking data collection vis-à-vis relying on traditional, recall-based time use diaries and minute-level sensor data collection as in the case of our current paper. Fortunately, a follow-up study implemented in 2022/23 in Malawi (Kilic et al., 2024) - whose design has been informed by the analysis presented here - will 18
Class 2 for all slope categories. These speeds were then confirmed via consultations with partners.

## **_Equation 3_**

Where: Db: Distance to bank V: estimated vehicle travel speed of Dr: Distance to road V=<sup>Db −Dr</sup> Tb −Tr the household Tb: Time to bank Tr: Time to road However, DRCN / SUR / UR / VR road types can mislead as they indicate the relative importance, not the actual size or speediness, of a given highway segment. They may also reflect road future road development plans instead of the current road state. For example, DRCNs in some areas are paved whilst in others they are improved walking paths. The latter case is typical of mountainous areas and the hills and mountains of the Far West region in particular.

Therefore, some manual adjustments were made:

- All road segments above 2400m and all Far Western DRCNs in the Hills were assigned to class 4, walking paths, unless explicitly marked as paved within the RAI data set.

- Planned roads, VR and unpaved OSM highway segments tagged as smaller than “tertiary” were also assigned to class 4, paths.

- Similarly, SRNs in the Hill and Mountain areas of the Far West are usually rough dirt roads instead of paved highways. Therefore, they were assigned to class 3 instead of class 1.
# **I. Introduction**

Consumer price indexes (CPIs) play an important role for our understanding of poverty trends in Africa. This is because international poverty estimates rely on national CPIs to express nominal consumption estimates from household surveys in real terms and the base year of the international poverty line.<sup>1</sup> However, there are concerns that CPIs may not always adequately reflect changes in the cost of living. Recent studies suggest that consumption growth and poverty reduction in Sub-Saharan Africa in the recent past may have been stronger than widely believed (see Kenny 2011; Young 2012; Pinkovskiy and Sala-i-Martin 2014), though others have challenged this notion (see Harttgen, Klasen and Vollmer 2013). Underlying these differing views about levels and trends in poverty and living standards in the region are often concerns about missing or inaccurate data, including concerns about the accuracy of measured inflation (see Beegle et al 2016).

There are several reasons why CPIs may not correctly measure changes in the cost of living. The most well-known causes, which have received considerable attention in the United States and other developed countries, are commodity substitution bias, outlet substitution bias, quality change bias and bias from the introduction of new goods (Schultze and Mackie 2002; Hausman 2003). Additional caveats arise for CPIs in Sub-Saharan Africa, due to weaknesses in country statistical systems, and when CPIs are used specifically for the purpose of measuring poverty trends (Beegle et al 2016). These include concerns about the representativeness of CPI weights for the poor (see Günther and Grimm 2007) and urban bias of the underlying input data (see Gaddis 2016).

The Engel curve method introduced by Costa (2001) and Hamilton (2001) attempts to address some of the above biases, mostly commodity and outlet substitution bias. Engel’s law arises from the observation that the food budget share in household consumption declines with the increase in real household income. This suggests that movements in the food budget share over time can reveal
work in agriculture than older cohorts who live in the same areas (31.1% vs. 22.7% for men and 61.4% vs. 48.4% for women). This could be a reflection of the lower labor market skills of such individuals. This phenomenon could also indicate a structural shift in the economy as the proportion of population in Tajikistan employed in industries steadily declined from 13.0% in 1991 to 5.7% in 2005 and proportion employed in agriculture (including personal plots) increased from 44.7% in 1991 to 67.5% in 2005 (State Statistical Committee of Tajikistan, 2006).

Figure 3 shows us the ratio of female to male employment by region of residence to visualize industries with high level of female employment. The industries are ranked by overall share of total employment from highest to lowest. The total employment in the industries

> 26 Most of the employment data published by the State Statistical Committee of Tajikistan for earlier years, e.g. 1991, are available at the country or regional levels. The available raion-level employment time-series that are industry specific (with the industry being broadly defined into a handful of categories) start from 1991-1993 and the series are not disaggregated by gender.

29
reallocate scarce labor from cultivation to nonfarm enterprises, and reduced frictions in land rental markets drive up the rental price of land.

We then analyze the impacts of the pilot Land Titling Program implemented in Ghana’s Central Region. We use a geographic regression discontinuity design (RDD) with three rounds of household survey data collected over a period of six years. We find that there are strong markers that the program was successful in registering land in the treatment group. However, land registration does not appear to translate into agricultural investments or increased borrowing. Instead, households decrease their amount of agricultural labor, accompanied by a small reduction of agricultural production and no changes to productivity, and appear to shift to non-farm economic activities. In particular, women’s profits from operating a business increase considerably. In line with this result, we observe that households decrease their land holdings amid a surge in land valuations.

This evaluation contributes to the literature linking property rights more generally and land titles in particular to investment and access to credit as well as household decisions related to production and the allocation of resources. DeSoto (2000) argued that secure land tenure can allow households to collateralize loans and thus obtain financing for investments. Given the regulatory environment in some countries, real estate may in fact be the only largely acceptable form of collateral. The additional capital obtained this way can be used to drive both farm and non-farm investment and has been found to trigger labor productivity and income (Field & Torero, 2005). This link has become a key argument for the role of land security in promoting development (Besley, 1995). However, there is mixed evidence on the impact of land titling programs on access to formal credit (Deininger & Chamorro, 2004; Galiani & Schargrodsky, 2010; Zegarra et al., 2008). In addition, those studies that have found empirical support for this relationship frequently qualify their findings in several ways. In particular, these evaluations of land titling programs highlight impact heterogeneity and the importance of
**Figure 1: Informal Jobs in South Asia**

<!-- Start of picture text --> 4.8<br>4.6<br>India<br>4.4<br>4.2 Pakistan Morocco<br>South Africa<br>Brazil<br>4 Lebanon<br>Yemen Mexico<br>3.8 Egypt Iran<br>Algeria<br>3.6<br>Tunisia<br>Turkey<br>3.4 Syria<br>3.2<br>3<br>6 6.5 7 7.5 8 8.5 9 9.5 10<br>ln(GDP per capita), 2008<br>ln(informal share of non-agricultural employment)<br><!-- End of picture text --> Source: OECD, 2009. World Development Indicators, 2010.

Note: 48 countries with available data shown. Chart uses latest data on informal share of employment available (1995-99 or 2000-07). GDP per capita is in 2005 constant PPP international $.

**Figure 2: Informal sectors and poverty rates**

<!-- Start of picture text --> 100<br>90<br>India<br>80<br>70<br>Morocco Pakistan<br>60<br>Brazil<br>Mexico<br>50 Yemen South Africa<br>Iran<br>Egypt<br>40<br>Tunisia<br>Turkey<br>30<br>0 10 20 30 40 50 60 70 80<br>Share of population below poverty ($1.25/day, PPP)<br>informal share of non-agricultural employment<br><!-- End of picture text --> Source: OECD, 2009. World Development Indicators, 2010.

Note: 45 countries with available data shown. Chart uses latest available data on informal
estimation approaches if one is interested in investigating this link. To somewhat mitigate this problem in our data, we lag growth rates by two years (e.g., the 5-year period 1990-1994 includes the standard deviation of growth rates between 1987-1988 and 1991-1992).

Our data further contains **changes in the share of urban population** of the total population, _durbanpop_ , from WDI. We include this variable in differences as we think that fast urbanization periods are correlated with growth spells. Our dataset further contains population density, _popdens_ , from WDI.

# _Variables in the “Medium Model”_

To measure countries’ **financial development** , we use the log of domestic credit to the private sector as a percentage of GDP, _lcredit_ , taken from WDI. The literature on financial development and its relationship to growth has grown extensively over the last decades (e.g., Levine, 2005; Arcand et al., 2015) and various studies and data sets on more detailed aspects of the issue have appeared (e.g., Svirydzenka, 2016). Credit over GDP, however, has the clear advantage of wide availability across countries and over time and has been used by several seminal studies in the literature on finance and growth. Brunnermeier et al. (2021) also confirm that credit expansion is rather a sign of financial deepening than the prelude for financial busts.

**Foreign direct investment** acts as a proxy for the activity of multinational corporations (Casella et al., 2023). We take the inward FDI stock as a percentage of GDP from UNCTAD. Since no stock data are reported before 1980, we fill the missing decade of data with an interpolation from FDI flow data (as a percentage of gross fixed capital formation). The sample correlation of both variables is high, 0.87. The variable is logged and labeled _lFDIstock_ip_ .

For **export diversification** , we rely on the export diversification index of the IMF (Papageorgiou
Table 9. Effects of Guerrilla Territorial Control on Main Outcomes, Controlling for Conflict

|_Panel A: Separating Disputed areas from Gov_<br>|_ernment controlle_<br>|_d areas_<br>|
|---|---|---|
|Night Light Arcsine|Wealth Score|Years of Education|
|(2013)|(2007)|(2007)|
|(1)|(2)|(3)|
|Guerrilla control<br>-0.127***|-0.170***|-0.438**|
|(0.0314)|(0.0587)|(0.188)|
|Disputed area<br>0.0851*|-0.0670|-0.230|
|(0.0473)|(0.0678)|(0.207)|
|Observations<br>3,652|3,630|3,637|
|Bandwidth (Km)<br>2.266|2.266|2.266|
|Dependent mean<br>3.536|-0.0160|6.573|
|_Panel B: Doughnut Hole Analysis (400 m)_|||
|Guerrilla control<br>-0.164***|-0.123**|-0.308**|
|(0.0520)|(0.0541)|(0.153)|
|Observations<br>1,564|1,555|1,555|
|Bandwidth (Km)<br>0.981|0.981|0.981|
|Dependent mean<br>3.245|-0.197|5.845|

_Note:_ The table presents results for the main outcomes but under different specifications that help discard the hypothesis that effects were driven by conflict. Panel A shows results
A comprehensive theoretical discussion of this methodology is beyond the scope of the present paper. In principle, the entropy correction methodology seems to provide a valid tool to produce utilityconsistent bundles and might thus enable the establishment of consistent location-specific PLs. Our purpose, however, is different. We question the validity of using location-specific poverty lines—with or without entropy correction—in the presence of substantial measurement errors that conform to a clear geographical pattern.<sup>16</sup> Any location-specific PL, being constructed based on a smaller sample than a national poverty line, has the potential to be more strongly influenced by measurement errors and to produce inconsistent, invalid, or counterintuitive results. There is a clear trade-off between the advantages of creating more locally-representative PLs and their relative robustness and consistency. There are also specific concerns regarding the entropy correction methodology, since to our knowledge the procedure doesn’t include any correction mechanism for the measurement errors described above; if the information used to construct the location-specific bundles is itself flawed, the entropy-adjusted bundles are bound to be affected by the same inaccuracy. As discussed in detail in Section 3.3, constructing location-specific PLs without entropy correction produce results that are very similar to the official PLs, which include entropy correction.<sup>17</sup> It follows that the impact of entropy correction is minimal, but that the problems in the raw data used to create these PLs can significantly compromise the resulting poverty figures.

# **3.1 Why focus on poverty lines?**

Some measurement error is always present in household survey data, and the IAF and IOF are no exception. Recalling in the analysis presented in Section 2, we identify 27 percent (2002/03) and 21 percent (2008/09) of observations (Sets A and B) as substantially inconsistent with other data and therefore likely to be inaccurate. This was a very conservative conclusion: early attempts to circumscribe the problem produced even broader sets of questionable observations. However, in