rafmacalaba/data-use-mentions-extended huggingface dataset:
There are no entirely comparable survey results for low-income countries, but the results from Gujurat, while not representative of the state, indicate that respondents perform worse on calculations of compound interest, and substantially worse on question testing understanding of inflation. Meanwhile, low levels of educational attainment, low levels of awareness of financial terms, and high levels of financial exclusion would lead us to predict poor performance on these three questions in other low-income countries. As FinScope survey results indicate, a large proportion of the population in countries such as Mozambique, Malawi, and Nigeria lack of awareness of basic financial products and concepts such as savings accounts, interest on savings, insurance, and loans.

The remainder of this section will discuss findings related to the demographic breakdown of these survey results, and other correlates of financial literacy. Demographically, we find that:

- Women have lower levels of financial literacy almost everywhere

- Financial literacy follows an inverted-U shape with respect to age

- Financial literacy is  associated with higher levels of income and educational attainment

- Geographic and racial/ethnic disparities in financial literacy are common

In high-income countries, surveys and studies show that:

- Financial literacy is correlated with retirement planning

- Financial literacy is associated with more sophisticated investment behavior

- Financial literacy affects debt and mortgage outcomes for individuals

- Financial literacy may even have other macroeconomic implications

In low-income countries, surveys and studies show that:

- Financial literacy is correlated with having a bank account

- Financial literacy affects insurance take-up

- Business literacy, as indicated by surveys of enterprises, may also be low

- Interest in financial education, however, appears to be widespread

# **_Women have lower levels of financial literacy almost everywhere_**

Lusardi and Mitchell (2011b) find that women in the U.S. are significantly less likely to answer questions correctly, and more likely to say that they don’t know the answer. (On the other hand, 9
“I have no trouble making and keeping friends.” The respondents were asked whether or not they felt that the statements described them.

Table 5 reports the statements and the proportions of men who responded yes or no to whether or not the statements described them. The remaining respondents gave equivocal answers or were unable to respond. Not only did the responses not vary significantly across castes, but the point estimates indicate that if anything, low-caste men have greater locus of control than their high-caste counterparts. For example, the second row shows that 84 percent of low-caste men disagreed that they had a hard time finding others to help them, compared to 77 percent of high-caste men. The results suggest that a caste difference in self-efficacy does not explain the caste difference in responses in the punishment survey.

26
an eigenvalue of at least one. We kept even items with low loadings in making the index, but since the loadings were small, they have a small role in determining the value of the factor.

For ECD, we examine:

- Literacy skills

- From the direct assessment: literacy interest; expressive language; expressive vocabulary; letter identification; letter sound identification, initial sound discrimination; listening comprehension; name writing; shape copying; receptive spatial vocabulary

- From the teacher report: letter, name and word writing, text directionality, letter names, and picture drawing

- Math skills

> 14 Poverty rates distinguish between poor, high-poverty communities (50% poverty rate or above) and non-poor, lower poverty communities (below 50% poverty rate).

> 15 Districts were randomly selected probability proportional to size (based on the number of schools), with replacement. Districts were drawn from within regions and based on the poverty status within a region (33% poor schools as cutoff).

> 16 If there are fewer than five schools within a district and strata, all schools were used (one to four).

> 17 Seven of the originally selected schools were unavailable (closed, in renovations, etc.) and random replacement schools, as much as possible from the same strata, were used.

> 18 Weather precluded completing one school.

> 19 Only one child did not consent; 97 child observations were not completed by enumerators, for an overall response rate of 96%.

> 20 Relative to the planned 2,552 child observations, this is a response rate of 56%. Only 10 parents, when reached, did not consent.

> 21 The appendix details the factor analysis; for full questions and responses see questionnaires, available at https://carolinekrafft.com/wp-content/uploads/2022/10/Diagnostic-KG-Instruments-ENGLISH-upload-2022.10.14CGK.pdf.

9
- 25 -

NTBs in 1986 while these measures had a negligible impact on fuels. In Japan, close to 100 per cent of food and chemical imports were affected by NTBs while no Type I or Type II restrictions were applied to iron and steel products. Tables 6 and 7 also show that major differences exist in the typcs ot measures that are being employed in these two countries. In the United States the trade coverage ratio for hard core (Type I) NTBs is relatively close to the combined ratio for all Type I and II measures (11.9 as opposed to 16.8 per cent). However, in Japan the combined coverage ratio (36.9 per cent) is more than double that for the hard core restrictions. 19/

# IV. Summary and Conclusions

While major concerns have been expressed about the spread of nontariff barriers in developed countries, the lack of empirical information on the dimensions of the increase has affected the related policy debates. Using inventories of nontariff barriers in developed countries compiled for 1966 and 1986 this study develops quantitative information on the major expansion of NTBs that occurred over this 20 year period. In 1966 nontariff barriers affected 25 per cent of developed countries imports, while in 1986 this share had increased to 48 per cent. These figures show that $30 billion

> 19/ The reader should note that the su called Type II measures that appear to predominate in Japan's protectionist profile can be enforced in a way that their trade restrictive effect is more severe than that of Type I restrictions. Due to their nature, inventory tabulations of NTBs cannot provide any information on the trade effects of nontariff barriers, but merely document whether restrictions are or are not applied. It should be noted, however, that a heavy reliance on Type II restrictions to control trade can result in a major loss of transparency on the intent and effects of nontariff measures. It is also recognized that Japan's use of "informal" restrictions such as those on steel imports from Korea are
, Bronwyn H, Jerry Hausman and Z. Griliches. 1986. “Patents and R&D: Is There a Lag?” _International Economic Review_ 27(2):265-83.

Hausman, Jerry, Bronwyn H. Hall, and Zvi Griliches. 1984. “Econometric Models for Count Data with an Application to the Patents-R&D Relationship.” _Econometrica_ 52(4): 909-938.

Jaffe, Adam B.. 1986. “Technological Opportunity and Spillovers of R&D: Evidence from Firms’ Patents, Profits and Market Value.” _American Economic Review_ 76: 984– 1001.

Jaffe, Adam B., and Manuel Trajtenberg. 2002. _Patents, Citations & Innovations: A Window on the Knowledge Economy_ . Cambridge, Massachusetts: The MIT Press.

22
of IDPs (IDMC 2015). On the other hand, in many countries returns are subtracted from population estimates without knowing if sustainable solutions have been achieved.

- (f) The potential to manipulate data for political purposes, for example to show that a humanitarian situation has been resolved and direct attention elsewhere (e.g. in Armenia, the Russian Federation and Turkey) (Walicki 2009) or alternatively still requires attention and resources.

- (g) Aggregation of data from different locations/crises within a country makes it difficult to estimate how long groups have been in displacement.

# **Disaggregated data on location, accommodation and demographics**

Disaggregated data on location, accommodation and demographics are incomplete. UNHCR publishes disaggregated data for 60-80 percent of refugees and people in refugee-like situations, however the coverage is much lower for other populations of concern to UNHCR who are largely IDPs (see Table 5). Disaggregated data on IDPs who are not protected or assisted by UNHCR are collected by other agencies, including IOM, but data are not comprehensive and therefore not published in IDMC’s global reports.<sup>93</sup> The difficulties of collecting disaggregated data on locations of IDPs are compounded by the fluidity of IDP movements—IDPs might suffer multiple displacements or they might resort to changing locations as a coping strategy (e.g. moving between their homes and place of displacement or testing different locations before deciding where to stay) (Brookings 2011). In recent years, efforts have been made to improve data collection for IDPs living outside of camps by employing a range of techniques including: (a) profiling; (b) household surveys; (c) collecting information on IDPs who come to camps to visit family members or collect relief items; and (d) community outreach programs (Brookings 2013). However, there is no consensus or agreed best practices on the use of these methods in different contexts or stages of displacement (Brookings 2013).

> 92 The absence of
Figure 8. Gender gap by presence of children and type of activity (men minus women). Hours per day Panel A. Market work <!-- Start of picture text --> 6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>With children Without children<br>Panel B. Unpaid domestic work<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>-6.0<br>With children Without children<br>Albania Argentina Colombia Ecuador Ghana Guatemala Iraq Mexico Mongolia Pakistan WBG Peru South Africa Timor Turkey Uruguay USA<br>Albania Argentina Colombia Ecuador Ghana Guatemala Iraq Mexico Mongolia Pakistan WBG Peru South Africa Timor Turkey Uruguay USA<br><!-- End of picture text --> Source: Authors’ elaboration based on survey data.

Notes: Children defined as 14 years of age or less except for Pakistan and South Africa where they are defined as 6 years of age or less.

45
by state Public Works Departments (PWDs). Though national highways constitute only about 2 per cent of the total road network, they carry 40 per cent of the total road traffic [Infrastructure in India, CMIE, 1998]. Funds for state roads (consisting of state highways and district and village roads) come from the respective state governrnent budgets. State highways are also managed by PVVDs and carry about 30 per cent of the total traffic [CMIE, 1998]. The management of district and village roads is sometimes decentralized to local governing bodies within the state. Table **II** summarizes the sources of funding and management of different roads in India.

Data for this analysis is available for national highways and for total state roads, that is, state highways, district and village roads. Data on national highways is taken from the Basic Roads Statistics of India. There are several missing values because states fail to regularly update the information with the Ministry of Surface Transport. In addition, the publication of roads statistics was very irregular in the decade of the 1970s. Most of the missing values belong to that period. Data on state roads is available for 12 states"<sup>5</sup> and only upto 1987.

Table III presents means and standard deviations of the variables. Because of the diversity of sources, the time period covered varies across variables, so the number of observations is different for different variables. The empirical analysis tests the robustness of the evidence in the face of changing samples when variables are excluded or included in the analysis.

# **IV. Empirical Evidence**

# **A. Effect of elections on tax revenues**

The analysis of the effect of elections on taxes begins with a simple specification

> '<sup>5</sup> The excluded states are Assain and Kerala. Data is available compositely for the states of Punjab and Haryana.

16
To address this, we also included in our instrument a survey experiment designed to quantify experimenter demand effects in the responses of village officials. This experiment provided a randomized priming treatment that made more salient the ongoing data collection effort; the message emphasized either (i) that data collection was part of a research collaboration with the Indonesian Ministry of Home Affairs or (ii) that data collection was simultaneously ongoing with citizens residing in the same village. The randomization was conducted at the village level. Treatment assignment in this survey experiment is uncorrelated with turnover in equation (1): the RD point estimate is _τ_ =-0.093 (robust SE: 0.131, _p_ =0.372). Nonetheless, we control for this treatment assignment in all our specifications. We report the takeaways from this survey experiment in a companion paper, and, in general, we find limited effects of our priming intervention on a wide range of bureaucrat-level outcomes and attitudes.

# **7 Conclusion**

This paper studies Indonesian villages as laboratories of local democracy. We use electoral turnovers— instances in which an incumbent leader failed to secure reelection in the most recent village election—as natural experiments that disrupt the status quo in local governance. These turnovers typically bring new leaders to power with a mandate to improve governance and development outcomes. Because village bureaucracies serve as the key link between citizens and frontline service delivery, they are a central instrument through which such political change may affect everyday bureaucratic performance.

We show that turnover in local elections reshapes the bureaucracy, most notably through staff reshuffling and a reduction in the presence of bureaucrats with nepotistic ties. Bureaucrats serving under new leaders report higher morale and exert greater effort: they interact more frequently with citizens, become better informed about constituents’ priorities, and are more likely to take actions aligned with those priorities. Improvements in the flow of policy-relevant information between officials and citizens translate into meaningful improvements in public service provision, visible in both administrative records and citizen reports of service access
electrification through both grid-extension and renewable sources. The success, in particular in SHS dissemination in rural areas, had led to a request from the government for additional financing of $130 million in 2009, with $100 million earmarked for scaling-up SHS installation and other renewable energy based mini-grids in rural areas.

The implementation of SHS programs was carried through two different delivery models. The first model is implemented by the Rural Electrification Board (REB), the state-owned utility responsible for grid-electrification in rural areas. It was tasked to disseminate SHS through the fee-for-service SHS program, whereby the system would be installed and owned by REB and households would pay a monthly fixed fee for using the systems. The second approach is through a private implementing agency, the Infrastructure Development Company Limited (IDCOL), which sold the systems to households using a micro-finance scheme implemented by various private agencies, such as Grameen Shakti.

While the REB was able to provide SHS for about 12,000 households, the IDCOL reached over 320,000 households over the same period. The success of the micro-credit scheme lies mainly in the fact that ownership approach is more acceptable to rural households than the fee-for-service approach. Moreover, these private delivery agencies have more practical knowledge in providing micro-finance and greater reach at the community level. In particular, Grameen Shakti has played an important role in the dissemination of SHS in rural Bangladesh and its credit program has reached many low-income households (Asaduzzaman et al, 2008).

The key lesson from the Bangladesh experience is that while scaling-up the adoption of renewable energy technology depends critically on private sector participation, public support is critical at the initial stage, by means of financing, technical assistance and institution and human capacity building, until a critical mass is reached for scaling up SHS dissemination. Lessons from the Bangladesh case study can possibly be transferred to other countries while taking account of local conditions.

# 4. **The 2005 Bangladeshi national survey**

A national household survey was conducted in 2005 for monitoring and impact evaluation of the
, and relatively low levels of implementation of the warning labels. In April 2013, the National Assembly passed the Tobacco Control Law Amendment Bill and Rules to close many of these loopholes. A major contribution of the amendment was the requirement that packages contain pictorial warnings. In addition, smokeless tobacco was included under the definition of tobacco; restaurants and indoor workplaces were included among completely smoke-free environments; the fines for noncompliance were raised; advertisements at points of sale were banned; and the sale of tobacco to minors was prohibited.

Although tobacco taxation has been recognized as one of the most effective strategies to reduce smoking, the tobacco tax structure has helped make cigarette prices in Bangladesh among the lowest in the region and in the world despite some of the highest tax rates (table 1). There is a variety of taxes on tobacco products in Bangladesh. These include supplementary duties on cigarettes, bidis, and chewing and pipe tobacco, duties on imported tobacco products and on tobacco leaf, and a 15 percent value added tax on all tobacco products.<sup>6</sup> Cigarettes are classed into four brand tiers, commonly known as slabs in Bangladesh, and the ad valorem excise tax rate is based on the administered retail price, which varies across these four price categories. However, the gaps between brands in the different tier categories create incentives for smokers to substitute less expensive brands in response to price increases and for manufacturers to position brands between price slabs to avoid paying higher taxes (ITC Project 2014). To minimize tax evasion, continuous tier structures were introduced in 2015,

> 5 Smoking is also restricted in restaurants, bars, and workplaces. However, these offer designated smoking areas.

> 6 The value-added tax is applied to the retail price.

3
_CHAPTER 6. UNIT LEVEL MODELS: GENERAL INDICATORS_ 51 Under homoscedasticity and with clusters equal to the areas, the predicted area effects _u_ ̃ _dcw,_ 2 are identical to those of the Pseudo EB in Guadarrama, Molina and Rao (2014), _u_ ̃ _dw_ , given in (6.19). Addition ̃ ally, if no survey weights are considered ( _wdi_ = 1 for all _i_ and _d_ ), _udcw,_ 2 reduces to the best predictor of the area effect _u_ ̃ _d_ of Molina and Rao (2010), given in (6.10).

The new bootstrap procedure proposed by Van der Weide (2014) draws a sample of clusters from the present in the survey. Using the data from this bootstrap sample of clusters, the model (6.1) is fitted, and predicted cluster effects _u_ ̃ _dcw,_ 2 are calculated for the clusters _c_ that appear in the bootstrap sample of clusters. For those that do not appear, predicted cluster effects are set to zero. These predicted cluster effects are then retained in the generation of the census data of the response variable. In all the simulation experiments conducted by Corral et al. (2021), this new procedure, referred to as clustered bootstrap-EB (CB-EB), appeared to be seriously biased, while EB and Census EB performed better than ELL and CB-EB even in simulation scenarios without normality.

In an attempt to combine the positive aspects of each procedure, Corral et al. (2021) extended the Census Pseudo EB estimators of Guadarrama, Molina and Rao (2018), to the heteroscedastic nested error model by using (6.21) and (6.22), for the case when the clusters are equal to the areas ( _Cd_ = 1 for all _d_ ). They also incorporated the survey weights in the estimates of the variance components, as proposed by Van der Weide (2014). Furthermore, they adapted the parametric bootstrap procedure for MSE estimation of the Census EB, as
# **Impact of the Russian Invasion on Ukrainian Farmers’ Productivity, Rural Welfare, and Food Security**

# **1. Background and motivation**

With a total agricultural area of close to 45 million ha of very fertile land,<sup>1</sup> Ukraine has long been a major breadbasket and exporter of agricultural commodities. Beyond causing immediate hardship and triggering a large exodus of displaced people, the Russian Federation’s military invasion of Ukraine and the economic blockade of its Black Sea export routes have also led to sharp increases in grain prices <mark>(von CramonTaubadel 2022)</mark> and raised concern about global food security. While fears of such consequences have eased somewhat with partial opening of maritime export routes in the context of the UN-brokered grain deal, there is little doubt that the war and associated political changes will profoundly affect Ukraine’s farm sector and rural areas.

To better understand impacts on the welfare of Ukraine’s farm population as well as the profitability, food supply, and prospects for future development of the country’s agriculture sector, this paper reports results from a survey of small and medium sized farms for the 2021 and 2022 agricultural seasons. We aim to answer three questions: First, has the invasion created an imminent threat of decapitalization that might permanently undermine the rural economy’s capacity to invest including in human capital? Second, how have area cultivated, profitability, crop choice, and level of market participation been affected by the war overall, what are the underlying mechanisms, and how do war effects vary across farm size group and regions? Third, what is the extent of informality in Ukraine’s agriculture sector and how do productivity and war impact differ between farms in the formal and informal sectors?

On the first issue, studies show that, if it forces households to liquidate assets to survive, exposure to conflict can have persistent effects, beyond short term impacts on food security (George _et al._ 2020;
in the returns, i.e. countries that may have abnormally high or low returns, either because these returns are real or because of measurement error; (iv) Add country-level controls for factors generating cohort effects: whether a country had a Communist regime,<sup>27</sup> past child and adult mortality rates (including for HIV),<sup>28</sup> and past per capita GDP or population growth rates<sup>29</sup> for each country-decade from 1950 to 2010 (source: United Nations (2019)).

In addition, if agriculture exhibits lower returns to experience and given that developing countries have a higher share of their workers in agriculture (37% vs. 5%

> 27These include a dummy equal to one if the country ever had a Communist regime, the number of years the country has had a Communist regime, and the year the country transitioned to capitalism (source: Wikipedia (2019)). Since we are controlling for the country-specific mean year in the data, this also controls for how many years ago the country transitioned to capitalism.

> 28These include average child mortality rates (for 0-5 year-olds) and adult mortality rates (15-45) for each country-decade from 1950 to 2010 (source: United Nations (2019)) and average HIV infection rates for each country-decade from 1990 to 2010 (sources: World Bank (2018a); data unavailable before 1990). 29These include average annual per capita GDP growth rates (source: see footnote 15).