rafmacalaba/data-use-mentions-extended huggingface dataset:
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 1. Infrastructure contribution to annual per-capit economic growth In CentralAfrican countries**

<!-- Start of picture text --> Percentage points<br>a. Infrastructure’s contribution between 2001-05 b. Potential contribution<br>1.6 4.50<br>1.4 4.00<br>1.2 3.50<br>1<br>3.00<br>0.8<br>2.50<br>0.6<br>2.00<br>0.4<br>1.50<br>0.2<br>1.00<br>0<br>-0.2 0.50<br>-0.4 0.00<br>Roads Power ICT<br>Roads Power ICT<br>Percentage points Percentage points<br><!-- End of picture text --> Source: Calderón 2009.

Poor infrastructure is a handicap for business in Cameroon. Evidence from enterprise surveys suggests that infrastructure constraints are responsible for about 42 percent of the productivity gap faced by Cameroonian firms, the remainder being due to poor governance, red tape, and financing constraints (figure 2a). Firms’ perception of infrastructure as a bottleneck to growth in Cameroon is typical of countries in francophone Africa. Customs clearance is the infrastructure constraint that weighs most heavily on the country’s firms, with power deficiencies in second place (figure 2b).

**Figure 2. Infrastructure deficits constrain firms’ productivity**

<!-- Start of picture text --> a. Degree to which infrastructure is perceived by firms as an<br>obstacle to growth (%)<br><!-- End of picture text --> <!-- Start of picture text --> Benin<br>Senegal<br>Mali<br>Cameroon<
*p < 0.1. Standard errors in parentheses. Standardized weights are used that give each country equal weight. The standard errors are clustered at a sector × country level. Measures for GVCB share and GVCF share are standardized. All regressions include controls for technology (computer use, computer use squared), FDI, skills, education, age, gender, sector FE, and sector FE interacted with GDP per capita. The specification follows that in Table 2, but for consistency with Table 3, Canada, China, Hungary, Macedonia (FYROM), Peru, Serbia, Singapore, Sweden, and Türkiye (for which wage data are not available) are excluded. Source: Authors’ calculations based on PIAAC, STEP, CULS (tasks), World Bank (GDP), EORA data. and Borin and Mancini (2015, 2019) (GVC participation measures).

# c. Quantifying the direct and indirect contribution of GVC participation to wage inequality

In this subsection, we use the estimated models to quantify the contribution of GVC participation to wage inequality in the reduced country sample of 38 countries, both directly and indirectly. We find that the direct contribution of GVC participation to wage inequality is negative in most countries (Figure 5a). In other words, higher GVC participation is linked to reduced wage inequality within countries. Some notable exceptions include the US and small countries intensively integrated into GVC, such as Ireland (high backward GVC participation) or Norway (high forward GVC participation). The results suggest a U-shaped relationship between GDP per capita and the direct contribution of GVC participation to wage inequality (Figure 5a). That is, the reduction in wage inequality is the strongest in upper-middle-income and bottom-high-income countries, but the smallest in low-income countries (which are weakly integrated into GVCs) and high-income countries. The Mincerian wage regressions suggest that the direct contribution reflects the positive role of forward GVCs for workers' wages in non-offshorable occupations in the industrial and business services sectors (Table 3).

In
comparator countries on both measures. The low revenue to wage bill ratio reflects both comparatively low revenue per worker and high labor costs (World Bank, 2017).<sup>6</sup>

**Figure 2.2: Labor productivity and revenue to labor costs ratio in services (2009)**

<!-- Start of picture text --> Revenues per worker Revenues to labor costs<br>100000 10<br>98009 9.7<br>80,000 8<br>8.0<br>7.7<br>71906<br>60,000 6 6.6 6.0<br>52051<br>40,000 4 4.5<br>37837 37165 3.8<br>31531<br>20,000 2<br>14056<br>0 0<br>Poland China RussiaTurkey_micro ECA Turkey Malaysia China Poland Russia ECA MalaysiaTurkey_microTurkey<br>Revenues per workers (2009 USD) Ratio revenues to labor costs (2009)<br><!-- End of picture text --> Source: World Bank Enterprise Surveys; World Bank (2017). Turkey_micro consists of firms with five or fewer full‐ time employees. All other figures include firms with five employees or more.

**Turkey’s services sector has seen little growth over the past 15 years, contrary to global trends.** Services value‐added has hovered at around 60 percent of GDP since 2000. The top panel of Figure 2.3 shows the positive association worldwide between the share of services value‐added in GDP and the real GDP per capita over time, where each dot represents a country in a given year. In other words, as countries get richer, they tend to increase the share of services in the economy. However, Turkey has largely shifted horizontally to the right—instead of upwards and to the right—indicating that the share of services in GDP has not
# **4.2. Robustness checks**

_Cost-of-living differences_ The consumption aggregates across the TZNPS survey rounds are expressed in 2022 prices using the World Bank Global Database of Inflation (Ha _et al._ 2021), as discussed earlier (Section 2.2). To assess the sensitivity of our results to the approach to standardizing prices – namely the degree of disaggregation of price adjustments by types of commodities, and by frequency of adjustment – alternative approaches were evaluated. Our preferred models relying on deflating prices using annual inflation at the level of commodity groups – food, utilities, and other non-food commodities – are compared to models relying on deflation by the traditionally used overall CPI deflators provided by the International Monetary Fund.<sup>13</sup> Deflation using the annual CPI disaggregated by groups is also compared to that using the quarterly CPI between the midpoints of survey rounds (Q1-2009, Q1-2011, Q2-2013, Q2-2015, Q2-2019, Q2-2021, and Q2-2022) and the monthly CPI (3/2009, 3/2011, 6/2013, 6/2015, 7/2019, 6/2021, 6/2022).<sup>14</sup> Figures 3–5 reveal that, across all models and pairs of base-target survey rounds (with a single exception for Model 4 in imputing into TA 1), annual commodity-disaggregated deflation performs not worse than and frequently better than non-disaggregated deflation. The advantage of commodity-disaggregated deflation is particularly notable in Models 8-9 when imputing into TA 1, Model 8 when imputing into TA 2, and Model 2 when imputing into TA 3.

Regarding the frequency of deflation, there appears to be no advantage to going below the annual level. Deflation using quarterly or monthly CPI performs no better and sometimes worse than deflation using annual (commodity-disaggregated) CPI. While commodity-disaggregated annual deflation performs notably better than IMF CPI in certain models and treatment arms, there

> 13 To access the database, visit: https://data.imf.org/?
# **References:**

Anós Casero, P. and Seshan, G. (2006), “Public-Private Sector Wage Differentials and Returns to Education in Djibouti”, World Bank Policy Research Working Paper 3923.

Blinder, A. (1973), “Wage Discrimination: Reduced Form and Structural Estimates”, _Journal of Human Resources_ , 8(4): 436–55.

Blinder, A. (1976), "On Dogmatism in Human Capital Theory", _Journal of Human Resources_ , 11(1): 8–22.

Blomquist, S. (1978), "Wage Rates and Personal Characteristics", Vancouver, B.C.: Department of Economics, University of British Columbia, Discussion Paper 78–20.

Gibson, J. (2009), “The Public Sector Pay Premium, Compensating Differentials and Unions: Propensity Score Matching Evidence from Australia, Canada, Great Britain and the United States”, _Economics Bulletin_ , 29(3): 2325–32.

Gregory, R. and Borland, J. (1999), “Recent Development in Public Sector Labor Markets” in Handbook of Labor Economics, edited by O. Ashenfelter and D. Layard. Amsterdam, North Holland.

Heckman, J. (1979), “Sample Selection Bias as a Specification Error”, _Econometrica_ , 47(1): 153— 162.

Heitmueller, A. (2006), “Public-Private Sector Pay Differentials in a Devolved Scotland”, _Journal of Applied Economics_ , 9(2): 295–323.

Holmlund, B. (1993), “Wage Setting in Private and Public Sectors in a Model with Endogenous Government Behavior”, _European Journal of Political Economy_ , 9(2): 149–62.

Jann, B. (2008), “The Blinder–Oaxaca decomposition for linear regression models”, _The Stata Journal_ , 8(4): 453–479.

Lee, L.-F. (1978), “Unionism
Figure 5 **Learning: Direct versus Indirect Transactions** <!-- Start of picture text --> Direct Transactions Indirect Transactions<br>p<0.001<br>p<0.001<br>p<0.001 p<0.001<br>62% 20% 33% 65%<br>N=558 N=285 N=558 N=285<br>Mobile payroll Mobile account Mobile payroll Mobile account<br>.9<br>.6<br>.3<br>0<br><!-- End of picture text --> Notes: The figure shows the percentage of respondents making direct versus indirect sendmoney transactions by treatment, measured in survey data at endline. Panel (a) displays the share of respondents who made at least one send-money transaction from their own account for the mobile payroll account and mobile account groups. For reference, the mean of the control group is 12%, which is statistically distinguishable from both the mobile payroll and mobile account treatments. Panel (b) displays the share of respondents who made at least one send-money transaction intermediated by a mobile agent for the mobile payroll account and mobile account groups. For reference, the mean of the control group is 61%, which is statistically distinguishable from the mobile payroll treatment only. Error bars show 95% confidence intervals of the mean. Horizontal bars show p-values for _t_ -tests of equality between the treatment conditions.

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We discuss in this paper various challenges related to the SDGs in terms of identification and measurement methods, and we place a special emphasis on the quantitative analysis angle.<sup>3</sup> We examine global data but focus on poorer countries. In particular, we revisit basic questions that are often assumed as settled, but that on more careful thought may actually lead to different answers. For example, is the current list of SDG indicators appropriate for the stated development goals? Is there any overlap or potential conflict among these goals? Could progress on some indicators be interpreted in one way only, or more than one way? If the latter, how best to interpret progress? Should we evaluate a country’s performance as far as we can go back with the available data, or only within a recent time period? Since it is well-known that data are unavailable for a number of indicators, could this missing data challenge affect how we evaluate a country’s performance? Also given this missing data issue (and other potential interpretation issues), what viable alternatives are there to monitoring progress on all 232 indicators: could we instead track a core group of indicators or an overall index? While we do not claim to raise all these questions for the first time (see our discussion on the relevant literature in later sections), we aim to shed new light and offer more reflections on them.

We combine our own data analysis with insights gleaned from related studies to enrich our discussion. We analyze data from various data sources, including the World Bank World Development Indicators (WDI) database, the UN SDG database, as well as the nascent (but growing) literature on the SDGs. Building on the analysis and taking into account the political settings of the SDGs, we then offer further reflections on future data collection efforts for the SDGs and other related issues. In short, we aim to expose deeper issues hidden behind the various

> 3 For recent discussions on other substantial aspects of the SDGs
<!-- Start of picture text --> All Retail<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Population percentage Population percentage<br>Jun, 2020 (Gini = .971) Oct, 2020 (Gini = .983) Feb, 2021 (Gini = .973) Jun, 2020 (Gini = .926) Oct, 2020 (Gini = .946) Feb, 2021 (Gini = .938)<br>Cumulative sum of online sales Cumulative sum of online sales<br><!-- End of picture text --> Figure 14: Lorenz curve for online sales by wave <!-- Start of picture text --> All Retail<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>0 20 40 60 80 100 0 20 40 60 80 100<br>Population percentage Population percentage<br>Jun, 2020 (Gini = .879) Oct, 2020 (Gini = .891) Feb, 2021 (Gini = .879) Jun, 2020 (Gini = .827) Oct, 2020 (Gini = .883) Feb, 2021 (Gini = .848)<br>Cumulative sum of in-person sales Cumulative sum of in-person sales<br><!-- End of picture text --> Figure 15: Lorenz curve for offline sales by wave Another way of measuring inequality is showing the ratio between the median firm and the 90th percentile firm. Figure 16 shows this measure for the sales per worker by channel - offline vs online sales. As mentioned above, inequality is larger in the online sales market.
<br>DNs“© HK °o Ss& a=N 8 ==Es as = ero2.3 a2 baoS Presetas<br>5 Fs 2 = 388<br>ea32 8 8 SEs32<br>Sources: Eora database; and IMF staff calculations. o5<br>Note: See the list of country groups in Annex 3.2. =<br>1 Excluding sub-Saharan African countries. -<br><!-- End of picture text -->
MFIs benefit somewhat wealthier households rather than the poorest in some developing country contexts (Coleman, 2006; Kondo, et al., 2008; Takahashi, et al., 2010),<sup>3</sup> evidence from studies that use larger data sets from MFIs in many developing countries emphasize the diversity of approaches to outreach and profitability within the industry (Cull et al., 2007, 2009). In particular, NGO-based MFIs place greater

> 2 And experimental evidence on microsavings products has shown them to be effective in helping poor entrepreneurs to protect profits and grow their businesses (see, e.g., Dupas and Robinson, 2013 for evidence from Kenya).

> 3 For example, Coleman (2006) finds that participants in a credit program run by two NGO microfinance institutions in Northeast Thailand were significantly wealthier than nonparticipants prior to the program intervention. The difference in wealth was largely explained by the value of female-owned land across households.

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enforcement are less clearly in accord with the hypothesis. Before allowing for year-specific interactions, deforestation rates are 46% higher in the areas chosen for high enforcement in 2000-01. Compared to 1996-97, however, the odds of deforestation in these priority enforcement areas dropped by 17% in 1998-99, 20% in 2000-01, and 39% in 2002.

In general, the multiperiod regression reproduces the main findings of the individual period regressions. Deforestation odds are substantially lower on poor soils, far from roads, on high slopes, and in APPs.

# **6 Land value and deforestation in Mato Grosso**

Since enforcement against deforestation in Mato Grosso (and elsewhere) appears to have had limited success, the question arises whether this is due to the strength of economic pressure for deforestation, and whether policies could focus more successfully on deterring deforestation that yields only low economic returns. Hence, the objective is to assess the magnitude of economic pressure for deforestation in Mato Grosso by looking at regional differences in environmental costs (loss of biodiversity and carbon sequestration functionality) versus net economic gain (value of land less cost of conversion) from deforestation. This results in two hypotheses:

- (i) land value is closely related to road access, soil quality and rainfall (biome);

- (ii) deforestation rates are closely related to land values.

## **6.14 Statistical analysis of land values and deforestation in Mato Grosso**

To obtain first insight into the hypotheses, mean municipal-level land values for 2003, resulting from subjective estimation by INCRA technicians were assembled to be tabulated across road distance, soil quality and biome (INCRA 2004). However, municipios exhibit considerable internal variation in soil characteristics and road access. INCRA only considers land value 34
1

# I. **INTRODUCTION**

Summary statistics often conceal more than they reveal. Inequality measures are no exception. Much has been made, for example, of the fact that inequality has not tended to rise or fall systematically with economic growth, and the fact that increased volumes of international trade have not tended to generate higher levels of income inequality (cf. e.g. World Bank 2000, Dollar and Kraay 2004). And yet the relationships between growth and trade on the one hand and poverty and inequality on the other remain controversial. One possible explanation of this is that such statements overlook the possibility that economic growth and international trade reforms—like tax reform—result in both vertical and horizontal redistribution, and that the public may feel differently about changes in income inequality depending on how much of each type of redistribution is involved (cf. Ravallion 2004). Policymakers may feel less comfortable about economic growth if people who initially are equals experience different rates of income growth, and if a lot of people move up and down the income distribution as a result of growth. Given this, one might want to know more than the degree of vertical redistribution (e.g. how far the poor and non-poor experience similar proportionate changes in their income) and the size of any change in a summary measure of income inequality (e.g. how far the Gini coefficient changes during periods of economic growth). From a political economy perspective, too, knowing the roles of horizontal and vertical redistribution could matter: horizontal redistribution may be more likely to generate more political instability than vertical redistribution—losers may be better able to galvanize support for their cause if they can point to people who started out in similar circumstances but ended up with much higher incomes.

This paper decomposes the changes in income inequality associated with economic growth using a framework developed originally to decompose the redistribution associated with
2018b), MIT’s Economic Complexity Index (see, e.g., Hidalgo and Hausmann (2009)), and UNICEF’s SDG indicators for child welfare (UNICEF, 2018).

# **5.2. Empirical Illustrations**

We discuss in this subsection the relationship between the SPI scores and some major characteristics of a country such as its GDP, population size, and economic complexity. We subsequently offer a decomposition of the SPI by region, before providing some further empirical comparison between the SPI and the SCI.

# _SPI and Country Characteristics_

We start first with examining the relationship between the SPI and a country’s log GDP per capita (Figure 1). We expect richer countries to have better statistical systems because they have more resources to allocate for statistical activities, and also because they tend to have more complex and diversified economies that require more data. Indeed, there is a statistically significant and positive correlation between a country’s SPI and its income level. Figure 1 suggests that a 10 percent increase in a country’s GDP per capita is associated with approximately a 0.7 percentage point increase in its SPI score (see the box inside the figure).<sup>24</sup> As discussed earlier, a country’s statistical system produces statistics that reflect the socioeconomic conditions of the nation. As such, the more complex (advanced) a country’s economic level is, the more likely that its NSS is more developed. Indeed, besides an inherently stronger demand for the NSS to keep track of its various economic activities, a richer country likely has

> 24 Alternatively, moving up an income category (as defined by the World Bank (2018a)) can see a country improve its SPI score by 4.9 percentage points (Figure 1.2, Appendix 1).

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tenor and cost of borrowing were probed, as were reasons for not using formal intermediaries (including bribery: 27 per cent report having to pay a bribe to get a loan). Among the other financial questions was: type of insurance used. Not many household level control variables were mentioned in the study report, reliance being placed instead on ecological regression using village characteristics.

An important recent initiative is a suite of household surveys recently conducted by the Finmark Trust with partners in five countries of Southern Africa (Botswana, Lesotho, Namibia, South Africa and Swaziland) with broadly comparable questionnaires in the five countries.<sup>17</sup> In contrast to many other studies, the focus of the Southern Africa surveys has so far been less on the knotty question of access to credit, and much more on cost and other barriers to use of services access to most of which does not require satisfying creditworthiness tests. Close attention is paid not only to the range of alternative financial services (life and general insurance and payment technologies) but also the reasons provided by households as to why they did or did not use the services and as to problems with using them. As such, these surveys carry some of the flavor of market research to a greater extent than the others mentioned above. An interesting feature is the use of household psychology: values and attitudes (whether family or community oriented or materialistic; general optimism; connectedness) as predictors of financial service usage. In addition, financial discipline and risk management strategies were explored. For example, households were asked questions as to what resources they had drawn on to meet various emergencies and other spending needs, or to absorb investible windfalls. They were also asked about their knowledge of and attitudes to different financial institutions (for the former making explicit use of service provider names, e.g. Barclays, FNB). As with the other market-research relevant aspects, this presumably enhances the commercial value and points to a way of spreading the cost of such surveys
<mark>3.7 Opportunities for NSOs to improve microdata accessibility</mark> Collecting microdata is costly, which may be one reason why relatively few microdata sets are collected in the MENA region. While the frequency with which microdata are collected may not change overnight, our search for microdata revealed opportunities for NSOs to improve their data accessibility at almost no additional cost. No MENA NSO makes price surveys (or at least item level price indices) publicly available even though such information would be relevant to a host of users. Almost all MENA NSOs possess recent population census data, but few make them publicly available. The exceptions are Morocco and the Islamic Republic of Iran where a sample of anonymized individual and household level data is available for download. <mark>Additional suggested practices that can improve accessibility of microdata on NSO websites are outlined below.</mark> Suggested practice 1: Provide an English version of the website <mark>While the primary audience for NSO statistics are nationals, many potential data users live abroad. Since English is understood by majority of people in almost every region of the world, it is best practice for NSOs to make available an adequate English version/translation of their website. At present, not all MENA NSOs have an adequate English version of their website. For instance, an English version of the website of the Islamic Republic of Iran NSO exists, but several data sets available on the Persian version of the website are not available on the English version. This includes the consumption (welfare), labor force survey as well as the population census data reported to have satisfactory openness in table 4. Consequently, non-Persian speakers would have difficulty identifying the wealth of data that is available, particularly as the Islamic Republic of Iran is exemplary in providing data access. All recent, available microdata sets are downloadable from the website, some like the household budget surveys at an annual frequency.</mark> Suggested practice 2: Provide a microdata catalog, data tab and a search