rafmacalaba/datause-encoder-data huggingface dataset:
di↵erent variables and the level of institutional trust. A positive coefficient (represented at the right of the red line) indicates a positive correlation.

Figure 6: Trust and socio-demographic characteristics: Coefficient plot <!-- Start of picture text --> Geographic and sociodemographic correlates of trust in government<br>Coefficient plot (ordered logit model)<br>Rural<br>Female<br>20 - 24 yo<br>25 - 30 yo<br>31 - 34 yo<br>35 - 40 yo<br>41 - 44 yo<br>45 - 50 yo<br>51 - 54 yo<br>55 - 60 yo<br>61 - 65 yo<br>over 65<br>1500 - 2999 Dhs<br>3000 - 7000 Dhs<br>7001 - 12000 Dhs<br>12001 - 25000 Dhs<br>Over 25000 Dhs<br>University diploma<br>Professional training<br>No diploma<br>-.5 0 .5 1 1.5<br><!-- End of picture text --> Data source: World Bank survey on trust (2021) This graph shows that institutional trust is tied to individual-level socio-demographic determinants, including gender, age and income. First, gender and trust are closely related. Men have considerably higher levels of institutional trust than women (respectively 62 and 48 percent report trusting executive institutions). This is congruent with Moroccan women’s lower levels of civic engagement and political participation and could be a reflection of more systemic patterns of social and political exclusion. Second, there is a positive linear relation between age and institutional trust. Levels of institutional trust increase with age, with about 49.5 percent 18-35-year-old reporting trust in executive institutions, compared to an average of 60 percent of individuals above 35 years old. This is consistent with the evidence that youth are more likely to participate in protests, vote less and emigrate more.

Finally, there is a U-shape relation between income and institutional trust: low- and high-income groups have higher trust levels than medium-income groups. Levels of institutional trust are disproportionately higher among the highest income group, with 81 percent of individuals earning above 25,000 dirhams per month reporting trust in executive institutions. Levels of institutional trust among individuals earning less than 3,000 dirhams per month reach up to 60 percent. Institutional trust plunges among individuals in the median income group (3,000-7,000 dirhams), with only 45 percent 23
6 income across different goods, household fixed effects and time varying village fixed effect panel regression techniques are applied to a three-round panel of about 1,800 rural households from two geographically and ethnically distinct regions in Tanzania— Kilimanjaro and Ruvuma, and a five-year household panel of 1500 rural households from two geographically and ethnically different provinces in western China—Gansu and Inner Mongolia. Estimates thus reflect revealed preferences in the market place derived from standard household budget surveys, as opposed to stated preferences, or revealed preferences derived from observed behavior in experimental settings.<sup>12</sup> Very few studies have examined the existence of mental accounting in developing countries or outside experimental settings.<sup>13</sup> To our knowledge, none have explored mental accounting by earned/unearned income as such in such settings.

Households are found to have a higher marginal propensity to spend unearned income on alcohol and tobacco, clothing, transportation and communication, while they are more likely to spend earned income on education. In most cases, unearned income is at the margin also more likely spent on non-staple food, while earned income is at the margin more likely used for buying staples. The results further confirm that men and women display clear differences in spending preferences, as has been widely documented in the literature. However, both men and women are also more likely to spend their unearned income on nonbasic adult and prestige goods such as clothing, transport and communication, and also alcohol and tobacco for men. Overall, the findings lend some credence to the age-old saying “Easy come, easy go”. The spending patterns from earned and unearned income sources differ, contrary to what standard economic theory holds, and in a manner that cannot be fully explained by intra-household dynamics or a series of other competing hypotheses, which are also examined.

The paper proceeds by reviewing the different income sources and consumption patterns across the rural settings studied here (section 2). The argument of mental accounting by effort is then presented more formally in section 3. The empirical strategy is described in section 4. Section 5 discusses the empirical findings, including those from the different robustness tests. Section 6 concludes.

> 12 While the latter help greatly in establishing causality, they also require great care in addressing potential issues of randomization bias and framing (List, 2009).  The use of the more widely available, standard, panel household budget surveys to explore the hypotheses advanced here further facilitates re-examination of these hypotheses in other cultural settings. It also allows for considering a broader set of income sources and consumption sources than those examined in (laboratory and natural) experiments which are typically confined to particular consumption goods and/or  more precisely defined transfer (e.g. child allowance, coupon). 13 Duflo and Udry (2004) and Villa, Barrett and Just (2011) are noteworthy exceptions.
17 range of time periods and/or countries. Data availability is a major constraint in this exercise: our indicators need to be available in household surveys, since without such data we are unable to make the inequality adjustment in the UHC index. We begin therefore by outlining the data sources available for an exercise involving comparisons across countries _and_ over time. Clearly there is a risk taking this approach that one ends up with a set of indicators that is the lowest common denominator; a national monitoring exercise might choose a different (richer and perhaps more relevant) set of indicators.

# _Data sources_

For the service coverage domain, we require individual-level data on receipt of and need for health services, and on the living standards of the respondent’s household; such data are found most often in in a dedicated health survey. For the financial protection domain, we require household-level data on out-of-pocket spending on health services, and on the household’s consumption of budget items other than health services; such data are found most often in a household budget or expenditure survey although some multipurpose household surveys also contain such data. To compare across countries and over time we need definitions that are common across surveys – both over time within a country, and across countries.

Two multi-year global household survey initiatives provide a wealth of data on health service coverage, albeit with a strong focus in most countries on maternal and child health, namely the Demographic and Health Survey (DHS) conducted by 17
MULTILINGUAL CHILD ASSESSMENT Rural Kenya. _Early Childhood Research Quarterly_ .

https://doi.org/10.1016/j.ecresq.2019.01.002 Konsonen, K. (2005). Education in local languages: Policy and practice in Southeast Asia. In _First languages first: Community-based literacy programmes for minority language contexts in Asia_ (96–134). Bangkok, Thailand: UNESCO Asia and Pacific Regional Bureau for Education.

<mark>Lewis, M. P., Simons, G. F., & Fenning, C. D. (Eds.). (2016).</mark> _<mark>Ethnologue: Languages of the world,</mark>_ <mark>19th ed</mark> _<mark>.</mark>_ <mark>Dallas, TX: SIL International.</mark> Lieberman, E. S., Posner, D. N., & Tsai, L. L. (2014). Does information lead to more active citizenship? Evidence from an education intervention in rural Kenya. _World Development_ , _60_ , 69–83.

Manyonyi, J., Mbori, B., & Okwako, E. (2016). Attitude of teachers towards use of mother tongue as medium of instruction in lower primary schools in Bungoma South Sub-County, Kenya. _International Journal of Education and Research, 4_ (8), 315–334.

Musau, P. M. (2003). Linguistic human rights in Africa: Challenges and prospects for indigenous languages in Kenya. _Language, Culture and Curriculum, 16_ (2), 155–164.

Ozier, O. (2018). Exploiting externalities to estimate the long-term effects of early childhood deworming. _American Economic Journal: Applied Economics_ , _10_ (3), 235–262.

Pearson, B. Z., Fernández, S. C., & Oller, D. K. (1993). Lexical development in bilingual infants and toddlers: Comparison to monolingual norms. _Language Learning_ , _43_ (1), 93–120. Peña, E. D. (2007). Lost in translation: Methodological considerations in cross-cultural research. _Child Development_ , _78_ (4), 1255–1264.

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<!-- Start of picture text --> UK: Real Credit<br>(in percent)<br>1967 1977 1987 1997 2007 2017<br>year<br> Medium-term cycle Real credit growth (y/y)<br>80<br>60<br>40<br>20<br>0<br>-20<br><!-- End of picture text -->

# **Figure VI-4: Growth of GDP and Credit Cycle Peaks**

## **A- Oil exporter**

<!-- Start of picture text --> BHR: Growth of GDP and Credit Cycle Peaks IRN: Growth of GDP and Credit Cycle Peaks<br>1967 1977 1987 1997 2007 2017 1967 1977 1987 1997 2007 2017<br>year year<br>Growth of GDP Peak Growth of GDP Peak<br>KWT: Growth of GDP and Credit Cycle Peaks OMN: Growth of GDP and Credit Cycle Peaks<br>1967 1977 1987 1997 2007 2017 1967 1977 1987 1997 2007 2017<br>year year<br>Growth of GDP Peak Growth of GDP Peak<br>ARE: Growth of GDP and Credit Cycle Peaks DZA: Growth of GDP and Credit Cycle Peaks<br>1967 1977 1987 1997 2007 2017 1967 1977 1987 1997 2007 2017<br>year year<br>Growth of GDP Peak Growth of GDP Peak<br>15 1 40 1<br>10 .8 .8<br>20<br>5 .6 .6<br>0<br>percent 0 .4 percent .4<br>local_peak_BHR local_peak_IRN<br>-5 .2 -20 .2<br>-10 0 -40 0<br>40 1 80 1<br>.8 60 .8<br>20<br>percent 0 .6.4 local_peak_KWT percent 4020 .6.4 local_peak_OMN<br>.2 0 .2<br>-20 0 -20 0<br>1 30 1<br>20<br>.8 20 .8<br>10<br>.6 .6<br>10<br>percent 0 .4 percent .4<br>-10 .2 local_peak_ARE 0 .2 local_peak_DZA<br>-20 0 -10 0<br><!-- End of picture text --> 37
this point in mind as a caveat.<sup>5</sup>

# **3 Estimation**

We now turn to an attempt at estimating _c_ following the logic of expression (7). There are two difficulties. First, as the model’s logic rests on a comparison of financial returns, we must find a way of valuing the return to subsistence activity. We do so in the simplest possible way, by using market prices to value output. However we do not have farm accounting data to assess profits as precisely as if we used corporate balance sheets. In principle, one would want to measure profits as the difference between output value (actual sales plus self-consumed output valued at producer prices) and intermediate consumption. However farmers typically report their income with considerable error, and cost data is non-existent. We go round these difficulties by taking consumption expenditures (reported much more accurately than income in household surveys) net of other (non-agricultural) revenue.<sup>6</sup> Second, farms must be classified as “subsistence” or “market” ones on the basis of a reasonable criterion. The most natural one is the proportion of farm output that is sold vs. self-consumed. However this settles only half the issue, because the cutoff must then be set somewhere.<sup>7</sup> We choose, as explained in section 3.2 below, to let the data generate the cutoff value endogenously.

## **3.1 Data**

The data is from Madagascar’s four household surveys (1993, 1997, 1999 and 2001), called in French “ _Enquˆete Permanente des M ́enages_ ” (EPM). Each is made of a household survey per se covering about 5,200 households on average and a “community questionnaire” collecting

> 5 We are grateful to an anonymous referee of the World Bank’s Policy Research Working Papers for attracting our attention to this issue.

> 6We actually also estimated our model using the output value only, assuming zero cost (since the data on costs is not available). The results we found are similar to the results we get with our measure of profits (consumption expenditures-non-agricultural revenue).

> 7See Hotchkiss (1991) for a similar discussion of part-time vs. full-time work.

9
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domestic indirect tax should feature symmetric treatment of imports and domestic goods, it was not obvious that this meant anything more profound than equal statutory tax rates. M o r e importantly, the failure to mention or discuss this issue in many country cases is probably an indication that it was not analysed, rather than evidence that domestic indirect taxes had no effect on protection. For example, an JMF-IBRD study on Thailand highlighted the existence of discriminatory taxation of imports under the Business tax which had not been addressed by earlier Bank work in the area.

C. Tariff Structure: While this review suggests that the Bank has often proposed replacing QRs with tariffs and almost always suggested that the level and the range of nominal tariffs be reduced, in few cases is a long term objective of reform spelt out beyond this limited prescription. Most studies are very disparate in their analysis and the recommendations indicate that there is little consensus on a desirable tariff structure.

In a number of cases the Bank suggested that the ultimate objective of tariff policy should be a uniform nominal tariff. In some cases increasing the tariff on inputs while lowering the maximum tariff (on final goods) is a method to approach a uniform nominal tariff objective. While the recommendation to narrow the tariff band is consistent with movement towards a uniform tariff, some Bank studies express concern over the reduced protection for downstream industries as a consequence of the necessary increase in tariffs on inputs. Others indicate that protection of upstream industries is needed and recommend raising input tariffk to achieve this objective. In many studies the suggested treatment is to increase the tariff on inputs to narrow the ERP on final goods while ensuring that there is some tariff escalation, i.e. final goods are taxed at a higher rate than inputs. One study (Turkey) expressed concern that tariff escalation causes final goods to have an ERP higher than the nominal tariff and recommended reducing final good tariffs on this account. Some amount
**<mark>Table 2 - Difference in mean of children’s and mothers’ outcomes before the refugees’ arrival in 1991/1992 (0-5 Vs. 6-16 years old cohorts)</mark>**

||**(1)**<br>**Children 6-16 at**<br>**the time of the**<br>**crisis in refugee**<br>**affected areas***|**(2)**<br>**Children 0-5 at**<br>**the time of the**<br>**crisis in refugee**<br>**affected areas***|**Overall**<br>**Diff**<br>**(1)-(2)**|**P-Value**|
|---|---|---|---|---|
||||**_Children characteristics_**||
|Height-for-age Z-score|-2.171|-1.995|-2.024<br>-0.176|0.163|
||(0.110)|(0.061)|(0.054)<br>(0.126)||
|Stunting Prob (HAZ<-2SD)|0.563|0.532|0.537<br>0.030|0.554|
||(0.047)|(0.021)|(0.019)<br>(0.051)||
||||<br> <br>**_Mother characteristics_**||
|Mother's weight(kilos-1decimal)|534.871|526.723|528.246<br>8.148|0.272|
||(6.775)|(3.020)|(2.764)<br>(7.406)||
|Deliveryat hospital|0.450|0.405|0.413<br>0.045|0.311|
||(0.041)|(0.019)|(0.017)<br>(0.045)||
|Age of respondent at 1st birth|18.290|18.510|18.469<br>-0.219|0.409|
||(0.239)|(0.115)|(0.104)<br>(0.265)||
|Respondent Currently working|0.748|0.714|0.721<br>0.034|0.382|
||(0.035)|(0.017)|(0.016)<br>(0.039)||
|More thanprimaryeducation|0.555|0.620|0.608<br>-0.065|0.140|
||(0.040)|(0.019)|(0.017)<br>(0.044)||
|More than secondaryeducation|0.032|0.015|0.018<br>0.018|0.241|
||(0.014)|(0.005)|(0.005)<br>(0.015)||
|N<br>**Source:**1991/1992 Tanzania DHS. .(*)Ka|155<br>gera, Kigoma, Geita, Mara, M|679<br>wanza and Shinyanga. St|834<br>834<br>andard errors in parentheses.||

29
Our experiment provides some of the first well-identified evidence of patient-driven demand for excess medical treatment. Most directly related to our work are Kravitz et al. (2005) and Currie, Lin and Meng (2014), who conduct audit studies where “simulated patients” or SPs (trained actors who are not actually sick) request a specific treatment. Kravitz et al. find that doctors are more likely to prescribe antidepressants when SPs ask for them. Currie, Lin and Meng find that direct SP requests for an antibiotic increased prescription rates at hospitals in China, although this effect disappeared when SPs indicated they would purchase their medications outside the hospital pharmacy. Our contribution is to develop a method to identify whether this provider compliance with patient requests for medication is willing (the doctor prefers to prescribe) or reluctant (the doctor would rather not prescribe, but goes along to avoid gatekeeping costs). Our experiment also has the advantage that it uses real patients, allowing for natural doctor-patient interactions where patients themselves choose whether to express demand for treatment in response to the subsidy.

Our work also contributes to the literature on the pricing of health goods in developing countries. Most recent work in this area focuses on preventive health products like mosquito bednets and water purification, where beneficiary demand is low, use generates positive externalities, and large subsidies typically improve outcomes.<sup>7</sup> Use of curative products, by contrast, can generate negative externalities (e.g. by contributing to disease resistance (WHO, 2014)). Recent work on the pricing of curative health products has found mixed results. Cohen, Dupas and Schaner (2015) find that subsidizing antimalarials at private-sector “drug shops” dramatically improves access to lifesaving medication, but making the subsidy too high generates large amounts of overtreatment with only marginal additional gains in access. Importantly, these shops were staffed by retail workers, who were not expected to perform a gatekeeping function. In the public sector, Sautmann, Brown and Dean (2017) find that subsidizing health care for children (combined with close cost control) can improve access at the extensive margin significantly. Our – – paper contributes by offering evidence on misallocation and its drivers conditional on seeking care.

Our tests are specific to marginal changes in patient demand in response to the ACT subsidy and do not allow us to extrapolate how the different

> 7For bednets see Cohen and Dupas (2010), (Dupas, 2014), and (Tarozzi et al., 2014). For water purifications see (Ashraf, Berry and Shapiro, 2010) and (Kremer et al., 2011).

6
are purely exogenous (at the estimated rate _ζ_ = 0 _._ 026), and the model dynamics become similar to those in the dynamic factor demand model of Pindyck and Rotemberg (1983). The major impact is a significant drop in the use of both capital and energy (which are complements in the petrochemical industry). Energy, a flexible factor, falls by 10.3 percent in the first period. Energy consumption then increases, reflecting the rebound effect from exogenous energy efficiency improvements (Khazzoom 1980), but decreases in subsequent periods in conjunction with the drop in the use of capital. By 2020 energy consumption falls by 8.1 percent. Because of adjustment costs, capital falls gradually. Similar to the model of Pindyck and Rotemberg (1983), the adjustment of capital stock is fairly rapid; three-fourths of the total drop in capital occurs in seven years, so that substantial net disinvestment occurs during the first two or three years. In the absence of a price-induced investment response, the increase in the energy input price results in a substitution of energy and capital for labor and materials. The consumption of labor and materials inputs increases by 2.2 and 5.5 percent, respectively, by 2020.

<!-- Start of picture text --> 4.00% Labor<br>2.00%<br>0.00%<br>-2.00% Materials<br>-4.00%<br>-6.00%<br>-8.00% Capital<br>Energy<br>-10.00%<br>-12.00%<br>-14.00%<br>-16.00%<br>2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text --> Figure 4: Effect of $ 30 Carbon Tax on Energy Consumption in the U.K. Petrochemical Industry (Assuming Price Induced Investment Response) Figure 4 shows the effect of the same increase in the energy input price, now allowing for both exogenous and endogenous investment response to en27
(measured in constant 1994 dollars), close to fifty percent higher than its estimated level in 1994 (Chart 3).

Investment ratios are expected to rise in all developing country regions, albeit by different margins. The largest increase is expected to occur in East Asia, where investment ratios are already the highest-34 percent compared with a developing country average of 24 percent.<sup>3</sup> Gross domestic investment in East Asia could average as high as 38-39 percent of GDP in the next ten years, amounting to around $750-800 billion annually (at 1994 prices) and accounting for nearly 45 percent of total investment in developing countries (up from about 30 percent over the past ten years). Two factors account for the expected strong growth of investment in East Asia: large needs to expand and upgrade existing infrastructure capacity which is coming under severe strain as a result of rapid economic growth and urbanization; and investment requirements of a major shift in the industrial structure of these economies from predominantly labor-intensive manufacturing to capital-intensive industries.

With the breakdown of the old system, investment collapsed in many of the transition countries of Eastern and Central Europe and the former Soviet Union, recording an estimated average annual decline in real terms of more than 15 percent during 1991-94 for the group as whole. As these countries progress from stabilization to growth, investment will recover, and could rise in real terms at 5 percent a year or higher on average in the next ten years. Total investment in these countries could be on the order of $250 billion annually over the next ten years, measured in constant 1994 dollars.

> 3 This paper uses the standard World Bank country classification. Under this classification, developing East Asia excludes three newly industrialized, high-income East Asian economies-Hong Kong, Singapore, and Taiwan, China. For a fuller description of the country classification, see World Bank (1995b).

6