rafmacalaba/data-use-mentions-extended huggingface dataset:
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1982 as representing their initial stocks. Thus a unit of labor, for example, is that which earned one peso in 1982.16 The initial stock of money is taken to be the end of 1982 stock M2.<sup>1</sup> 7 The initial stocks of domestic bond holdings by private citizens was taken as total non-monetary savings held by the banking system (Pasivos no monetarios-instrumentos de ahorro).1<sup>8</sup> Holdings of foreign assets by Mexican citizens were derived in the following way. Zedillo (1986) derives a series of annual capital flight figures from 1970 to 1984. We have added these flows from 1970 to 1982 to arrive at an end of 1982 figure for holding of foreign assets by Mexicans. The resulting allocations are: Table 5.4. Initial Allocations

|Capital_A/_|<br>Labor|A/<br>Money g/|<br>Domestic Bonds A/|Foreign Bonds_i_|
|---|---|---|---|---|
|5.202|3.828|<br>3.311|2.328|2.189|

_A/_ In lOOOx billion pesos _hi_ In lOx billion US $ 16/ The initial allocations of capital and labor are derived from, Sistema de Cuentas Consolidades de la Nacion (1985). Table 1, page 1 and Table 20, p. 9. 17/ See International Financial Statistics (1985).

18/ See Indicadores Economicos (1986), Table I-H-24.
. In urban areas, three legal variables, except for the laws providing for equal inheritance rights, are positively and significantly associated with women’s chances of owning a house across all models. More genderequitable legislation providing for equal ownership rights for men and women is associated with a 15 to 18 percent increase in the probability to own the house; legislation valuing non-monetary contributions to the marriage is associated with a 11 to 16 percent increase; and legislation mandating equal remuneration for equal work is associated with a 10 percent increase in women’s chances of owing the house. In rural areas, equal ownership rights are associated with a 21 percent increase in women’s housing ownership, while the valuation of non-monetary contributions is associated with 12 to 20 percent increase in women’s housing ownership across the models.

In the case of land, the results are qualitatively similar, but the associations are weaker and significant for only a few legal variables (Table 2). In urban areas, women living in countries where laws provide for the valuation of non-monetary contributions are 8 to 11 percent more likely to own land. Controlling for current religion, historical cultural attitudes and a rich set of couple and household attributes, the other legal variables no longer remain significant. In rural areas, the laws providing for equal ownership rights are significantly associated with women’s ownership. For land, the fact that most WBL variables lose significance once the analysis controls for current religion and historical cultural variables (i.e. comparing columns (2) and (3) for both urban and

> 10 As noted in section 3, two countries (South Africa and Liberia) only collect data on housing and not on land ownership, which explains the smaller sample size for the regressions on land ownership. A few additional observations drop out once we control individual, spouse, couple and household characteristics due to missing values.

> 11 Unlike in section 2, where we reported the most recent WBL data (referring to 2020), the multivariate analysis
With regards to financing, the combination of high economic growth and high levels of investment as mandated by the education finance law of 2006 has led to unprecedented increases in resources for the education sector over the last decade. The increase in investment in education as a share of GDP from less than 3 percent in 2003 to close to 6 percent in 2013, paired with an average GDP growth rate of 6 percent over that same period (World Bank, 2014), has translated into a rise in public spending on education from about 500 million pesos to nearly 1.6 billion pesos (Albornoz, 2015). As with any education system, these resources have principally financed teacher salaries, followed by infrastructure and other capital investments such as information and communications technology (ICT) and other school equipment.

Concerning spending on teachers, there is evidence that this period witnessed both large increases in teacher numbers and teacher salaries. While the student base expanded by about 10 percent between 2003 and 2013 (DINIECE, 2004, and DINIECE, 2015), the recent teacher census revealed that the number of teachers expanded by more than 20 percent over roughly the same period (DINIECE, 2015). This has given Argentina a pupil-teacher ratio of 11, the lowest in Latin America after Cuba (OECD, 2013). With regards to teacher salaries, Bezem, Mezzadra, and Rivas (2012) find evidence that real salaries (in 2001 pesos) rose on average by 76 percent between 2003 (450 pesos) and 2012 (794 pesos). Still, this is less than the overall tripling of public resources for education, implying that the increased sectoral financing has meant a substantial rise in other schooling inputs. In particular, there was a large expansion in public school buildings constructed: 1,751, the most in half a century (Aboldornoz, 2015). The investment in ICT has also been impressive, representing as much as 50 percent of the infrastructure investment in the years 2011 and 2012 (Albornoz, 2015).

#
family planning, and maternal and child health. The more recent surveys did record data on school enrollment (for household members aged 6 to 25) and educational attainment (for household members aged 6 and above) as reportecl by a chosen respondent.

# _Data on education outcomes_

The education variables analyzed here are based on the answers to three questions about those aged 6 and above: whether they had ever been to school; if they had ever been to school, what was the highest level of schooling attended; and what was the highest grade attained at that level. Those aged 6 to 25 were asked, in addition., whether they were still "in school" (if they report ever attending). In the rest of this paper, children who report being "in school" are referred to as being enrolled.

The countries have been grouped into eight regions for the analytical purposes of this paper. These are, ranked roughly from lowest to highest enrollment of girls aged 6 to 14 from the poorest households: Western and Central Africa, North Africa, South Asia, Eastern and Southern Africa, Central America and the Caribbean, East Asia and the Pacific, South America, and Middle East and Central Asia..

# _Measuring wealth using DHS data_

The DHS do not ask about household income or consumption expenditures, the variables usually used to rank households according to their standard of living. The surveys carried out since 1990 do however include two sets of questions related to the socio-economic status of the household.<sup>3</sup> First, households are asked to report about ownership of various assets, such as whether any member owns a radio, television, refr-igerator, bicycle, motorcycle, or car. Second, DHS III are those that have been carried out since 1994 This analysis is limited to datasets with the requisite education and asset information.

3This section relies heavily on information contained in Filmer and Pritchett (1998 and 1999a).

6
such as managing cadres of health workers and teachers to deliver basic health and education services on which poor households rely, and implementing infrastructure programs for local agriculture and economic growth. Finan et al (2015) conclude their review of the personnel economics of the state with the observation that when government bureaucracies have to perform multiple complex tasks, questions of how to structure the principal-agent relationships within these bureaucracies receive few answers from existing research. This paper is beginning to fill the gap.

We provide evidence consistent with economic theory of complex principal-agent relationships in which non-pecuniary motivation and selection of different types of agents influence organizational productivity. We gather unique survey data to measure selection traits–integrity, altruism, personality, public service motivation, risk aversion, cognitive ability–of bureaucrats and politicians who make-up the complex organization of local government to provide the first evidence of whether (which) selection traits are correlated with organizational productivity. We provide robust evidence that it is political integrity in particular that matters for service delivery, to the exclusion of other traits. This evidence is particularly relevant for understanding local state capacity for policy implementation in poor countries. This is because budget and human resource constraints in poor countries can limit the extent to which highly trained professionals can be attracted to the public sector, or be given sufficient incentives to make them accountable for their performance. On the other hand, politics is flourishing in poor countries– with widespread participation of citizens as voters and as contenders for political leadership at local levels (World Bank, 2016a)–whether for ill or good. The evidence in this paper shows that local politicians can exert significant influence over the functioning of bureaucracies at the frontlines. Policy efforts to build capacity of the local state would do well to take seriously the role of political selection, going beyond incentives, when crafting the principal-agent relationships (who is tasked with doing what) in local bureaucracies.

35
between the social and private benefits of the training seems weak for the same reasons.

# 5-5. _Psychological effects_

The increases in the control group’s adoption rates in 2008 may also be attributed to a psychological effect. It is only human to show that one is doing well, even if this involves some pretense. In the follow-up survey, some entrepreneurs may have exaggerated how well their firms were doing. Moreover, the control group may have been loath to admit that they failed to benefit from the training program. It is not difficult to imagine that some of them exaggerated not just the adoption of the recommended practices but also their business results. The follow-up survey data on the participants may have a similar problem. The participants may have been tempted to please us by exaggerating their benefits from the training program. If both participants and non-participants exaggerated the adoption rates and business results, the net impact on the training effect estimates will be small.

# **6. Conclusion**

As in the rest of the world, there are a number of industrial clusters in Sub-Saharan Africa, but unlike clusters in other regions, these clusters have not yet achieved successful industrial development. Their low performances have been attributed exclusively to factors outside firms, such as poor infrastructure and unfavorable governance. By contrast, problems within firms have seldom been scrutinized. Based on a randomized controlled experiment in Ghana, this study demonstrates that entrepreneurs in a survival cluster are unfamiliar with standard business practices. It also indicates that participation in a rudimentary management training program improves the business practices and 22
# EXBCUTIVE **SUMMARY**

1. The scale and magnitude of water vending activities in metropolitan areas of developing countries has not been widely realized, nor has the value of information on such water vending systems been adequately appreciated by water resource engineers or policy analysts. This paper presents a case study of water vending in Onitsha, Nigeria--a city of 700,000 people and one of the most important market towns in West Africa. The paper illustrates that a rapid reconnaissance survey of water vending activities and the willingness of households to pay for improved water services can yield valuable inforn tion for water supply planning and unexpected policy insights. Such studies are very inexpensive relative to the capital costs of urban water projects and should become a standard part of water supply project design and planning in developing countries.

2. The vast majority of Onitsha obtains its water from an elaborate and well-organized water vending system which has been created and is operated by the private sector. Approximately 275 tanker trucks purchase water from about 20 private boreholes and then sell it to households and businesses equipped with water storage facilities. Many of the households which purchase water from tanker trucks resell the water by the bucket to individuals who cannot afford large storage tanks or who cannot be reached by tanker trucks. There are literally thou!1ands of small retail water vendors in Onitsha. These small retailers not only sell directly to individuals, but also to other water vendors who carry two 4-gallon tins on their shoulders with a pole and deliver water directly to a customer.

3. During the dry season households obtain approximately 2.96 million gallons per day (mgd) from the water vending system, for which they pay about US$28,000. In 1987 the public water utility was supplying about 1.5 mgd during the dry season, only 50 percent of the amount supplied by the water vendors. For this 1.5 mgd the water utility only managed to collect about US$1,100 in revenues. During the dry season the private
administrative estimates are statistically indistinguishable in both settings, although the magnitude of administrative sales is larger. In fact, the CV is lower for the AAA measure, with a value of 0.86 compared to 1.07 in Ghana; and 1.58 compared to 2.01 in Uganda. Hence, AAA sales match administrative sales very well across two sources of administrative data.

> 9 Note that both sample sizes are small because we were limited in matching the same reference month across administrative data and the AAA survey.

23
istheproductionshareintheEUasshowninsectionA.1oftheappendix.</sup>

> 36 In calculating the variable (XI /MI )/εI the remaining variables that we employ are at the same level of aggregation as the production data. Moreover, the import demand elasticity we calculate is for the EU as a whole, as required by the model. For this we use the structural estimates of the parameter ann for each 3-digit product from Kee, Nicita and Olarreaga (2004) along with the EU’s imports to GDP ratio in that industry.

22
dependent variable is the number of births at the time of interview. Standard errors in parentheses are clustered by state. Baseline mean refers to the mean fertility of mother at time of interview, with the specific socioeconomic status who had their first and last birth in the pre-ultrasound period 1972-1984. The third column in every panel shows a test of the significance of the difference between the coefficients for the sub-groups shown in the first two columns. Data: NFHS. *** 1%, ** 5%, * 10%.
question of “are these subsidies welfare enhancing”. In answering that question, it will be crucial to determine (i) whether the CF’s induced new entry into lagging regions or simply re-allocated entry away from the South and Southeast regions, and (ii) what was the productivity effect on re-located firms.<sup>19</sup> Answering these questions was not possible with the data used in this paper, but may require new efforts in collecting survey data from entrepreneurs about what motivated their entry decisions, and about their inputs and outputs conditional upon those decisions.

> in regions with improvements in transport networks, agglomeration economies have very modest effects. 19 In addition to informing us on the value of the CF’s, this latter point will speak to the question of whether or not there is generally a tradeoff between regional distribution objectives and national economic growth. This question has broad implications beyond the current study.

26
**Table 3: Regression Results for Step 2: Spill-over of Domestic Food Price Inflation**

**to other Domestic Prices Inflation**

**And Root Mean Square Error of the Forecasted Domestic Price Inflation***

||**Using Output gap**|**Using Output Growth**<br>**gap**|
|---|---|---|
|**Using FAO food index**|||
|_Coefficient_|_0.4841_|_0.5918_|
|_R2 adjusted_|_0.7210_|_0.5868_|
|<br>_RMSE_|_46.4037_|_31.4460_|
|**Core**<br>**Using FAO Cereals sub-**<br>**index**|||
|_Coefficient_|_0.8782_|_1.0408_|
|_R2 adjusted_|_0.6168_|_0.6617_|
|_RMSE_|_40.2721_|_25.7254_|
|**Using FAO food index**|||
|_Coefficient_|_0.65497_|_0.1458_|
|_R2 adjusted_|_0.7161_|_0.6968_|
|<br>_RMSE_|_37.4357_|_33.6075_|
|**Non-Food**<br>**Using FAO Cereals sub-**<br>**index**|||
|_Coefficient_|_0.8463_|_0.5883_|
|_R2 adjusted_|_0.7555_|_0.7374_|
|_RMSE_|_32.0512_|_24.7098_|

**_Note_** : *All figures are calculated from coefficients estimated at 5% significance level at least, and RMSE is calculated for the period 2010M7- 2011M6.

Finally, there is evidence that international prices have asymmetric effect on domestic prices (Table 4). Following the approach described in section 4, domestic food price inflation is found to increase by only 5.4% when inflation of
# **3.5 Taking advantage of individual-level variables**

One key data constraint to global well-being measurement is that major surveys collect many outcomes at the household level rather than at the individual level. This implies that within-household inequalities are not observed even though well-being is typically not equally distributed within households. Importantly, this is a problem that affects not only inter-personal comparisons (Objective 1), but also cross-region comparisons (Objective 2) because the latter rely on the former.

Clearly, this is not a big issue for some dimensions that are almost equally distributed at household level, such as housing. However, key dimensions of well-being are typically unequally distributed within households. This is the case of the monetary dimension, as documented by a growing literature on intra-household decision making (Lise and Seitz, 2011).<sup>56</sup> Arguably, this is even more so the case for the health dimension, if only because health tends to decrease with age.

Yet, many household surveys contain some information on individual-level outcomes. This is typically the case for children’s schooling information or for the BMI information underlying stunting and underweight variables. The current practice in global multidimensional poverty measurement treats these individual level outcomes as-if they are household level outcomes.<sup>57</sup> For instance, under the World Bank’s MPM, a household is considered deprived in a schooling variable if one of its children is not enrolled in school (see Appendix 6.1). A natural alternative would be to switch the unit of analysis from the household level to the individual level. “Equal sharing” assumptions could generate individual level variables from the variables that are only available at household level, such as income of housing.<sup>58</sup> The advantage of such switch would be to allow accounting for the unequal individual outcomes observed in the data.

One disadvantage with such switch in unit of analysis is the risk of misinterpretation. Individual-level well-being indices obtained in this way
two-step conditional maximum likelihood and a falsification test in columns (1) to (2) and (3) to (4) respectively. All estimates include all individual characteristics described before and country fixed effects. All estimates use the weights. Robust standard errors clustered at the country level in parenthesis. * _p <_ 0.1; ** _p <_ 0.05; *** _p <_ 0.01.

that did not experience the slave trade. To assess the validity of the identification strategy, I present in this section a falsification test. The latter consist of estimating the impact of trust on tax morale using a sample of countries that did not experience the slave trade. The sample comes from the same wave of the WVS 2010-14 excluding African countries. The sample has the advantage of having the identical questions for all countries and includes 43 them in four continents.<sup>15</sup> As both trust in public institutions and the neighborhood are endogenous, I employ the same identification strategy as previously, except the intensity of the slave trade that is not used here as an instrumental variable. The two last columns of Table 5 report the reduced form estimates of the relationship between trust and tax morale. As it can be seen, there is no evidence that both trust in public institutions and the neighborhood and affect attitude toward taxes. The regression coefficients are highly insignificant. The F-statistic of the excluded instruments from the first stage regression is largely above the usual threshold.

> 15Azerbaijan, Australia, Armenia, Belarus, Chile, China, Taiwan, China, Colombia, Cyprus, Ecuador, Estonia, West Bank and Gaza, Germany, Iraq, Japan, Kazakhstan, Jordan, Republic of Korea, Kuwait, Kyrgyzstan, Lebanon, Malaysia, Mexico, Netherlands, New Zealand, Pakistan, Peru, Philippines, Poland, Qatar, Romania, Russian Federation, Singapore, Slovenia, Spain, Sweden, Trinidad, Turkey, Ukraine, United States, Uruguay, Uzbekistan, and
and involvement of parents, to name a few. These institutional factors represent about one-quarter of the between-country variations in performance on PISA 2000, and account for a large portion of students’ success (Fuchs and Wößmann, 2004, and Fertig and Schmidt, 2002). Fuchs and Wößmann (2004) find that students in systems that have external examinations or standardized tests perform better across all three subjects examined in PISA. Student performance seems to be higher when schools have more autonomy over decisions such as the hiring of teachers, textbook choice, and budget allocations within schools. The combination of these two elements is even more powerful: the performance effects of school autonomy are more beneficial in systems where external exit exams are in place, underscoring the idea that external exams are the “currency” of the school system (Fuchs and Wößmann, 2004). However, some of these findings seem to differ depending on a country’s level of development. While developed countries are able to translate increased autonomy into improved student learning, increased autonomy seems to harm student achievement in developing countries (Hanushek, Link, and Wößmann, 2013).

Among other institutional reforms in developing countries, pupil-teacher ratios (or class size in general) have been most widely studied, with very mixed findings (Hanushek, 2003, and Kruger, 2003). Teacher absenteeism and whether teachers assign homework are strong predictors of student success (Pelletier and Normore, 2007). Providing school meals is inconclusive, as is multi-grade teaching, while extending the school day shows more positive results (Glewwe et al., 2013).

Evidence suggests that longer school days do improve learning in Uruguay and Chile (Cerdan-Infantes and Vermeersch, 2007, and Bellei, 2009). There is some evidence that school autonomy in the form of school-based management (SBM) has resulted in gains in enrollment, reduced repetition and dropout, and raised parental participation (Gertler, Patrinos, and Rubio, 5