rafmacalaba/data-use-mentions-extended huggingface dataset:
Kishore, A., Rothler, Y., Ward, P., Jha, S., & Singh, R. (2016). Can information help reduce imbalanced application of fertilizers in India. _Experimental evidence from Bihar. International Food Policy Research Institute (IFPRI) Discussion Paper_ , _1517_ .

Gautam, M. (2015). Agricultural subsidies: Resurging interest in a perennial debate. _Indian Journal of Agricultural Economics_ , _70_ (902-2016-68359), 83-105.

_Gautam, Madhur; Laborde, David; Mamun, Abdullah; Martin, Will; Pineiro, Valeria; Vos, Rob. 2022. Repurposing Agricultural Policies and Support : Options to Transform Agriculture and Food Systems to Better Serve the Health of People, Economies, and the Planet. World Bank, Washington, DC_ Giné, X., Patel, S., Ribeiro, B., & Valley, I. (2019). Efficiency and equity of input subsidies: Experimental evidence from Tanzania. _American Journal of Agricultural Economics_ .

Gram, G., Roobroeck, D., Pypers, P., Six, J., Merckx, R., & Vanlauwe, B. (2020). Combining organic and mineral fertilizers as a climate-smart integrated soil fertility management practice in sub-Saharan Africa: A meta-analysis. _PloS one_ , _15_ (9), e0239552.

_Government of India. 2016. Economic Survey 2015-16, Volume I, Department of Economic Affairs, Economic Division, Ministry of Finance_ _Goyal, Aparajita; Nash, John. 2017. Reaping Richer Returns : Public Spending Priorities for African Agriculture Productivity Growth. Africa Development Forum;. Washington, DC: World Bank and Agence Francaise de Developpement. © World Bank._ Gulati, A., and P. Banerjee. 2015. “Rationalizing Fertilizer Subsidy in India: Key Issues and Policy Options.” Working Paper 307, Indian Council for Research on International Economic Relations. - <u>https://www.academia.edu/14970315/Rationalizing_Fertilizer_Subsidy_in_India Key_Issues_and_Policy_Options.</u
# 3.2.6 Demographic variables

The first of the demographic variables is _human capital_ (education). We rely on the log human capital index, _lhc_ , from the PWT 10.0, which linearly interpolates data on educational attainment from Barro and Lee (2013) and assumes a certain rate of return for primary, secondary, and tertiary education. Human capital is widely assumed to be a key driver of cross-country income differences (Mankiw, Romer, and Weil 1992; Hanushek and Woessmann 2015), but its approximation through attainment levels is problematic (Hanushek and Woessmann 2012). Given the lack of a better measure available across a wide range of countries for many decades, we rely on this standard measure.

To measure _inequality_ , we take the Gini coefficient of market incomes, _gini_mkt_ , from the Standardized World Income Inequality Database (Solt 2019). Following the literature (Brueckner and Lederman 2018; Scholl and Klasen 2019), we do not log-transform this variable. As discussed in those studies, there are several approaches to address apparent endogeneity concerns for this variable, and several arguments suggest various functional forms (changes versus levels and quadratic and interaction terms). After testing several functional forms, we opted to include this variable in first differences in our equation (see the next subsection).

To gauge potential demographic dividends, an extension of our model also includes the _employment rate_ , _emprate_ , as the fraction of the population that is employed, emp/pop from the PWT 10.0. Employment enters the production function directly and is thus considered a “proximate” and not “ultimate” driver of growth. Yet, some developing countries, including Bangladesh, have experienced drastic demographic changes. It is hence informative to understand the implications for growth and partially factor out this effect to understand whether it impacts other variables (for example, FDI or infrastructure).

Since demography can influence economic growth through various channels (Vandenbrouke 2021), we additionally experimented with other demographic variables, such as the share
1

# **1 Introduction**

Following a period of economic stagnation in the early 1990s, Tanzania has seen sustained gains in per capita output since 1995. The 2000/01 Household Budget Survey (HBS) offers the opportunity to assess how economic growth at the national level has impacted household consumption and poverty levels since the 1991/92 HBS. At the national level, the two surveys show only a small and statistically insignificant decline in the headcount rate. This fact, combined with the relatively rapid growth of recent years, has raised concerns that recent economic growth may not be substantially reducing poverty.

The survey data, however, provides only two snapshots in time and fails to represent the full evolution of poverty over the course of the intervening nine years. The primary purpose of this paper is to assess the likely trajectory of poverty rates over the full span of the period between the surveys. This is done by applying macroeconomic growth data to the micro-level household survey data. Changes in consumption are simulated year-by-year using unit-record survey data, under varying assumptions for growth rates and inequality changes. The growth data is drawn from national accounts statistics. The analysis follows the procedure outlined in Datt and Walker (2002) and Datt et al. (2003).

The paper also implements an extension to the Datt and Walker method. The original procedure was designed to project poverty rates forward from a single survey. When the task is to estimate the trajectory of poverty rates during the period between two surveys, as with Tanzania for the 1990s, an alternative method can be used. The method involves scaling national accounts growth rates for multiple parts of the distribution. Unlike the Datt and Walker method, this approach guarantees that the simulated distribution will closely match the distribution in the final survey year.

The paper is structured as follows. Section 2 describes the data sources, key findings from the Tanzania Household Budget Survey report, and issues surrounding the data. Section 3 presents some poverty and growth diagnostics: a decomposition of growth and inequality
(amount in pesos), due date, date of payment, days overdue, means of payment (cash or electronic), type of account (residential, retail store, factory), gender of the account holder, age of the account holder, linear front meters of the lot/property, assessed value of the property.

The municipality authorities required us to target blocks with eight to fifty accounts, neither very sparse nor very dense, which was the target for their mailing campaign. Figure 1 shows the distribution of accounts per block. Table A2 shows some descriptive statistics for the year 2019. Our sample size consists of 68,808 accounts distributed in 3,982 blocks. The frequency of payments is highly polarized. About 45 percent of the accounts paid the twelve 2019 monthly bills, and about 35 percent did not pay any bill at all.<sup>10</sup> We call these two core groups _always payers_ and _never payers_ , respectively. The proportion of always payers is relatively low (45 percent) and, therefore, leaves room for potential behavioral responses from non-compliant and partially-compliant neighbors, and this was compounded by the context of the pandemic, during which lockdown measures reduced payments even from highly compliant individuals.

For the randomization, power calculations, and simulations, we use baseline data from the year 2019. We rely on three different pre-treatment outcomes: (i) an indicator equal to 1 if the account paid the twelve monthly bills of 2019, (ii) an indicator equal to 1 if the account paid at least one bill in 2019, and (iii) an indicator equal to 1 if the account paid six bills or more in 2019.

# **4.3 Experimental Design and MDEs**

Following the notation in Section 3, the block-level treatment indicator is denoted by _Tg ∈{_ 0 _,_ 1 _,_ 2 _,_ 3 _}_ with distribution P[ _Tg_ = _t_ ] = _qt_ for _t_ = 0 _,
The landlord stands to gain, in this case, by renegotiating the contract terms or contract renewal ex-post. The tenant, aware of this potential for opportunism on the part of the landlord, knows that he is unlikely to recoup all the fruits of his investment. It is thus privately optimal for the tenant to underinvest. The remainder of this section sketches out a simple model to formalize this intuition.

> 7 Recommended quantities of FYM vary greatly by crop—at the high end, 10-15 cartloads (8000-12000 kg) for an acre of sugarcane, and even more for vegetables (Government of Pakistan, 1997).

> 8 We do not have information from the survey that would allow us to compute the proportion of family labor used for this task. However, data from northeast China, which has an agricultural technology not disimilar from that of rural Pakistan, indicates that 8 percent of annual family farm labor goes into FYM application (Jacoby, Li, and Rozelle, 2002).

> 9 Indeed, according to the data, harvesting jobs are nearly twice as likely as weeding jobs to pay piece rates, which rely on directly observing output or effort.

5
Christian, M., Bookman, N., Wilkenfeld, B., Miller, R. (2021). COVID19 and the educational equity crisis: Evidence on learning loss from the CORE data collaborative. Policy Analysis for California Education.

Psacharopoulos, G., Collis, V., Patrinos, H.A., Vegas, E. (2021). The COVID-19 cost of school closures in earnings and income across the world. _Comparative Education Review_ 65(2): 271287.

UNESCO (2020). UN Secretary-General warns of education catastrophe, pointing to UNESCO - estimate of 24 million learners at risk of dropping out. <u>https://en.unesco.org/news/secretary general-warns-education-catastrophe-pointing-unesco-estimate-24-million-learners-0</u> World Bank (2017). _World Development Report 2018: Learning to Realize Education’s Promise_ . Washington DC: World Bank.

Zierer, K. (2021). Effects of Pandemic-Related School Closures on Pupils’ Performance and Learning in Selected Countries: A Rapid Review. _Education Sciences_ 11(6): 252.

16
., S. E. H. Jensen, and L. H. Pedersen (2008), _The Welfare State and Strategies Towards Fiscal Sustainability in Denmark_ , in Neck, R. and J.-E. Sturm (ed.), Sustainability of Public Debt, MIT Press, 2008.

- Attanasio, O.P., Lucio Picci and A.E. Scorcu (2000), Saving, Growth and Investment: A Macroeconomic Analysis using a Panel of Countries. _The Review of Economics and Statistics,_ May 2000, 82(2): 182–211.

- Attanasio, O. P. and M. Székely (2000), _Household Saving in Developing Countries:  Inequality, Demographics and All That. How Different Are Latin America And South East Asia?_ , IADB Working Paper, Nr. 427.

- Auerbach, A. J. and R. D. Lee (eds.) (2001), _Demographic Change and Fiscal Policy_ , Cambridge University Press.

- Bailliu and Reisen (1998), Do Funded Pensions Contribute to Higher Savings?  A Cross-Country Analysis, _OECD Development Centre Manuscript_ , Paris.

- Butelman, A. and F. Gallego (2001), Household Saving in Chile (1998 and 1997): Testing the Life Cycle Hypothesis. _Cuadernos de economía_ , Vol. 38, Núm. 113, Santiago de Chile.

- Barro, R. J. (1974), Are Government Bonds Net Wealth, _Journal of Political Economy_ , Vol. 82 (6): 1- 46.

30
**Table 4. Employment**

|||**Midli**|**ne Survey**|||**Endl**|**ine Surve**|**y**|
|---|---|---|---|---|---|---|---|---|
|||Control|Treatment -|Control||Control|Treatm|ent - Control|
|||Group||||Group|||
||Obs|Mean|Coeff.|_p-value_|Obs|Mean|Coeff.|_p-value_|
|**Beneficiary woman**|||||||||
|Not employed & not<br>searching|951|0.551|-0.352 ***|_0.000_|875|0.629|0.050|*<br>_0.073_|
|Not employed & searching|951|0.234|-0.193 ***|_0.000_|875|0.127|-0.012|_0.498_|
|Employed|952|0.215|0.545 ***|_0.000_|875|0.244|-0.038|_0.145_|
|Day worker|952|0.014|0.007|_0.277_|875|0.016|0.001|_0.861_|
|Salaried|952|0.021|-0.010|_0.185_|875|0.016|0.000|_0.966_|
|Self-employed|952|0.166|-0.111 ***|_0.000_|875|0.213|-0.038|_0.130_|
|Public works|952|0.017|0.689 **
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

proxy for private investments in reformed sectors (Bergara and Spiller, 1997). Also, where there are extensive electrification programs, consumption may be interpreted as proxy for access to service. Indeed, industrial consumption has been used as a proxy for political influence (see Zelner and Henisz, 2000). Ultimately, appropriateness and accuracy of findings based on proxy indicators depend on the extent to which the indicators represent reality.

# **_Stock vs. flow indicators_**

Electricity sectors are dynamic systems that respond to factors such as demand growth, availability of new technologies, compliance with environmental regulations and policy change; reforms reshape the sector by introducing new processes and new dynamism.

Within this context, reform indicators may be classified as stock or flow. For example, the efficient level of installed generation capacity, transmission and distribution networks can only be achieved and maintained through investment and maintenance expenditure (flow) and new capacity (stock). Some reform measures take time to take full effect, e. g. regulatory commitment and credibility can be thought of as stocks built up over time.

A related and but different type of indicator are those that reflect the rate of change in a measure. Some activities in the electricity sector such as generation capacity and number of customers tend to increase over time. The objective of reforms is often to accelerate the rate of change in these variables. Such changes are generally best measured by rate of change indicators. However, despite their potential use, such indicators have not yet been used in electricity reform studies.

# **3. Criteria for selection of indicators**

# **_Measurability_**

For an indicator to be useful it needs to be based on a clear definition and to be measurable. This is equally important whether it is expressed in physical, monetary or 6
Report: Jobs. Washington, DC.

World Bank (2020). Lebanon Overview. Retrieved on June 17, 2021: <u>https://www.worldbank.org/en/country/lebanon/overview</u> World Bank (2021). Jordan Economic Monitor, Spring 2021. Retrieved on Ocotber 21, 2021: - <u>https://documents1.worldbank.org/curated/en/831601624470221546/pdf/Jordan Economic-Monitor-Spring-2021-Uncertain-and-Long-Trail-Ahead.pdf</u> 29
COL 20012011 COL 20012011<br>IND 20012011 IND 20012011<br>PER 20012011 PER 20012011<br>RUS 20012011 RUS 20012011<br>THA 20012011 THA 20012011<br>TUR 20012011 TUR 20012011<br>ZAF 20012011 ZAF 20012011<br>0 1,000 2,000 3,000 0 1,000 2,000 3,000 4,000<br>US$ mil. US$ mil.<br>Total LVAX Unskilled Total LVAX Skilled Total LVAX Unskilled Total LVAX Skilled<br><!-- End of picture text --> _Source:_ Authors’ elaboration based on LACEX.

# **6. Conclusions**

This paper has used the newly developed World Bank’s Labor Content of Exports database to provide some novel evidence on the wages and jobs in South Africa’s exports. As have other large commodity-dependent 32
only possible where good stock records exist – which is somewhat more common in the public sector than in the private sector, according to HAI. Applying the survey as designed by HAI and WHO typically costs between US$10,000 and US$20,000.<sup>12</sup>

# _Issues in measuring informal payments_

While informal payments vary significantly across countries, it is still feasible to measure them whenever surveys can identify a clear gap between what patients pay for public health services and what they are legally required to pay. Measuring informal payments poses special challenges in that patients and households are often uncertain about how much they paid for health care services and are often even less knowledgeable about how much they are legally obligated to pay. Different institutional, legal and cultural contexts also create significant variation across countries that can confound cross-country comparisons (Lewis 2000; Gaal et al 2006).

Generally, patients and households are considered to be better sources for data on informal payments. In particular, exit surveys at facilities are best suited to getting reliable answers from patients as to how much they paid for health care services in a setting where the researcher can compare what the respondent paid to an official fee schedule for the services provided. By comparison, questions in household surveys

> 12 Author‟s estimate based on interviews.

______________________________________________________________________________________ -31-
As an example, using a broad activity limitation question related to schooling among children: ‘is your child limited in the amount or the type of schooling you can have due to a physical, mental or emotional condition?’ Such a question does identify persons with perceived limited schooling opportunities due to a health condition. This question does not identify children with health conditions who have been able to access schooling, which is problematic. It is problematic that the outcome (employment or education) is part of the question. That makes it impossible to disaggregate across disability status. Using such a question, say in an environment where education becomes more inclusive through the provision of accommodations in schools, one would get a decline in the prevalence of disability over time but the negative correlation between schooling attendance and disability may persist or worsen as disability may include people with the more severe health conditions.

# **General Disability**

Censuses and surveys sometimes have a general disability question such as _“Do you have a disability?”_ This general question has sometimes been used with answers about disability types or impairments (e.g. blindness, deafness, paralysis) or with a yes/no answer as a screen for a follow-up question asking about disability type or impairment. Such a general question was very common in the censuses of the 1970s, 1980s, 1990s and 2000s (e.g. United Nations 1990).

The general disability question is problematic in terms of the validity of what it measures. First, it is unclear what such a general question measures as disability may mean different things to different interviewees. Additionally, impairments may be unknown to respondents, especially in the context of populations with limited access to health care or information. Furthermore, disability and impairments may be stigmatized and thus interviewees may not feel comfortable 7
2. _Balanced Sample:_ This sample includes the 535 matched households as well as the 472 households in the TUP survey who report not owning any phone. It excludes households that own a phone on a different phone network than the one who provided CDR. The motivation for this sample is to provide an indication of targeting performance in a regime in which CDR can be used to target all phone-owning households. In addition to applying sample weights from the survey, households that do not own a phone are downweighted so that the balance of phone owners to non-phone-owners (with sample weights applied) is the same as in the baseline survey as a whole (with sample weights applied, 84% phone owners).

3. _Full Sample:_ All 2,814 households in the TUP baseline survey for which asset and consumption data are available, with sample weights applied.

Note that the quota used to evaluate targeting changes for each sample, based on the number of households that are ultra-poor in the sample. For the matched sample, the targeting quota is 27.29%; for the balanced sample and full sample the quotas are 5.47% and 6.02%, respectively.

# **3 Results**

## **3.1 Performance of Targeting Methods**

Our first set of results evaluate the extent to which different targeting methods can correctly identify ultra-poor households. This analysis compares the performance of CDR-based targeting methods to asset-based and consumption-based targeting, using the sample of 535 households for which survey data and CDR data are both available.

An overview of these results is provided in Figure 1. Figure 1a shows the distribution of assets and consumption, as well as the distribution of predicted probabilities of being nonultra-poor generated by the CDR-based and combined methods, separately for the ultra-poor and non-ultra-poor. The dashed vertical line indicates the threshold at which point 27% of households are classified as ultra-poor; we use this quota because 27% of households in this sample were designed as ultra-poor by TUP. Figure 1b
their experience with previous projects of this nature, but we have to mobilize them quickly. We have to meet our goal of a certain number of SHGs.”<sup>21</sup>

# **b) Social mapping to facilitate local knowledge versus achieving numeric targets**

Once key stakeholders in the village agreed on the merits of the project, the next step was to identify households to include in the project. This method of identifying the ‘poorest of the poor’ (or POP in JEEViKA’s parlance) involved using census data and hamlet-level information, but also brought in the community to identify the most vulnerable households in a way that established greater ownership and legitimacy over the process.

Facilitators in both Phase I and II demonstrated a comparable understanding of this process. What set them apart is that in Phase I villages, ACs and CCs shared a healthy skepticism towards the process. They knew that it was susceptible to being captured by a few groups, that some voices were louder than others when identifying target households, and most importantly that this list would have to be revised with time as they gained a greater understanding of the community, and the community understood more about the project. In a nutshell, decisions about the community were made _and_ changed in the field, with facilitators corrected by participants and vice versa, thereby establishing a model of engagement that was neither entirely bottom up nor top down but which evolved collaboratively on the frontlines.

In Phase II villages, facilitators collected data entirely as a means of arriving at a _number_ of target households. The more qualitative data collected in this stage were not utilized during the process of mobilizing women. Moreover, while the ACs and CCs did the mapping exercise and gathered the necessary data, mobilization was ultimately left to CRPs from another village to whom the numeric target was transferred. However, the qualitative data and the informal knowledge on village dynamics collected in the initial stages were not transferred to these CRPs. As a result, often the households identified
|**871**|**1196**|**440**|**0.43**|**248**|**593**|**1279**|**590**|**0.7**|**74**|**0.70**|

Notes: Earnings are in PPP ($,2011). Ratio of medians is the ratio of the median earnings for teachers on a fixed term or temporary contract to that for teachers on a permanent contract. Teachers include survey respondents who self-identified as a primary or secondary school teacher and a wage worker. All teachers have secondary or secondary or post-secondary education. The top and bottom 1% earnings are trimmed. Surveys in The Gambia, Ghana, Liberia, Malawi, and Nigeria did not report whether a worker was on a permanent or fixed term/temporary contract. Source: Surveys listed in Table 1.

39