rafmacalaba/data-use-mentions-extended huggingface dataset:
expected to react negatively to the lower hypothetical amount of 50,000. One potential explanation for this lack of a dose-response relationship is that the lottery amounts tested were all sufficiently high (Tsh100,000 is equal to approximately 40 percent of mean annual income in the study population) that virtually all respondents would be motivated by the prospect of winning. Indeed, in-depth focus group responses suggested that many individuals in this context failed to calculate the relevant expected value (few have completed schooling beyond the primary level), and thus were likely making cognitive errors that led to greater salience of the lottery than would be rationally expected. Although we must be cautious in not over-interpreting these self-reported ―motivation‖ questions, and should await publication of further results from the more objective STI endpoints, this evidence does suggest that a lottery-based incentive system may be acceptable within this community and possibly be more cost-effective than a system of certain rewards.

# **Discussion**

Cash incentives have been shown to be effective at shaping behavior in a variety of health domains, from improving the uptake of health and educational services among the poor, to discouraging unhealthy or risky behaviors, such as substance abuse, smoking, and over-eating. These successes inevitably raise the question of whether they can also be applied in areas of sexual and reproductive health beyond contraception (Mauldon, 2003; Weeden et al., 1986), and in particular to HIV prevention (Haug and Sorenson, 2006). A review of countries‘ experiences with CCT programs for poverty alleviation and CM provides useful insights into how to design, implement and evaluate a CCTbased STI/HIV prevention intervention. The particular experience of the RESPECT study finds that such a program can be designed to be efficacious and acceptable.

Table 5 reports enrollee responses to questions regarding perceived success of the trial and which dimensions of the trial they liked and did not like. Although such responses must be interpreted cautiously in light of possible social desirability bias (not wanting to offend the interviewer) – indeed there is some
|
|<br>Other high-income countries|_5_|_13_|_-1.7_|_-28.4_|-3.5|-5.7|-3.2|
|<br>Eastern Europe and Central Asia|_16_|_18_|_0.2_|_7.4_|-1.0|-4.5|-0.8|
|<br>Southeast Asia|_1_|_1_|_-1.5_|_18.4_|-2.3|-8.4|-1.4|
|South Asia|_21_|_3_|_-1.0_|_24.5_|-1.0|-8.8|-0.6|
|Middle East and North Africa|_8_|_7_|_0.3_|_-3.3_|-1.6|-7.8|-1.5|
|Sub-Saharan Africa (excl S. Africa)|_5_|_13_|_1.1_|_-1.5_|-4.6|-7.5|-4.4|
|<br>Latin America and Carib.|_6_|_4_|_-0.5_|_4.9_|-2.5|-8.9|-2.1|
|**World**|**_100_**|**_100_**|**_0.0_**|**_0.0_**|**0.2 **|**-1.1**|**-1.0**|

> a Exports minus imports, both valued at f.o.b. prices as in the GTAP database 6.05

Source: Authors’ GTAP model simulation results and (for columns 1 to 4) the GTAP database
(Salehi-Isfahani (2012)).<sup>20</sup> As Figure 1 shows, Iranian households’ spending on education, which is the major source of education funding in Iran, increased by 67% over the 1995-2006 period. Spending on primary and secondary schools tuition is a significant share of total household expenditure on education in Iran (21%). Many of the best overall primary and secondary schools in Iran are privately funded (Source: Ministry of Education of Iran).<sup>21</sup> Also, parents believe that private primary and secondary schools offer a better education, an environment more conducive to learning, additional resources, and better policies and practices. Indeed, results from value added to cognitive achievement show that private school students averaged higher test scores than their public school counterparts. For example, children who attend private schools perform better in school final exams and the universities entrance exam and have better academic outcomes than those in public schools (Salehi-Isfahani et al. (2014); Rabiei and Salehi (2006)).

Evidence of how the 2006 sanctions affected children’s education can be found in the time series trends. While the enrollment rates did not change for primary and secondary education, attendance at the undergraduate programs

> 14Over these years, Iran’s economy has been under various economic sanctions. The first economic sanctions on Iran were imposed by the U.S following the Iranian Revolution of 1979. US sanctions were gradually expanded to the present level with a total embargo on all bilateral trade and investment. The studies show US sanctions’ economic and political effects have been insignificant (Alikhani (2000); Askari et al. (2001)). According to Hufbauer et al. (2012), the average welfare loss caused by US sanctions on Iran over the period 1984-2005 was around $80 million, less than 1% of Iranian GDP over that period.

> 15Education has expanded in Middle Eastern and North African (MENA) countries faster than in
differentials.

Third, in the majority of spells (six out of nine), the respective contributions of price changes and of real expenditure changes to nominal inequality trends were of opposite signs. Loosely speaking, in these episodes inflation inequality partially offsets real inequality. In some cases (e.g., Brazil 1988-1996) real and nominal inequality indices move in opposite directions. In others, the direction of change in nominal and real inequality is the same but the respective magnitudes are very different – e.g., in Mexico (1984-1989) we find, by any of the indices considered, a modest decline in nominal inequality, but a much bigger one in real inequality.

It is worth noting that these results arise from a decomposition of nominal inequality changes in which the contribution of price changes is calculated on the initial-year consumption basket, as can be seen from equation (4) above. Alternatively, the calculations could make use of the final-year basket, yielding the alternative decomposition As with (4), the first term in the right-hand side of (4’) captures the change in real inequality, while the second reflects the contribution of price changes. Crawford and Smith (2002) report that when inequality decompositions are performed over long time spells and based upon levels of inequality indicators, the results turn out to depend on the base year. However, when looking at changes instead of levels of the inequality indicators, they find that yearly variations are not sensitive to the selection of reference basket.

Hence, in our case we might expect that the choice between (4) and (4’) should not make a big difference. This is explored in the last two columns of Table 11, which report the results obtained using the decomposition in (4’). Comparison with the two preceding columns, based on (4), reveals only slight changes in the results. It also confirms the finding in Table 9 that when final-period baskets are used, the relative contribution of prices is diminished due
average of 51 percent reported in 2011. Growth in developing economies was even higher, increasing to 71 percent in 2021 from 42 percent in 2011—a more than 70 percent increase.<sup>1</sup> Financial account holders use their accounts for a range of purposes, such as storing money and making payments using electronic transfers, cards, the internet, or a mobile phone. “Accounts” can be with a regulated financial institution like a bank, credit union, or microfinance institution, any of which may offer internet or mobile phone-based access. Accounts may also be offered by mobile money service providers, which include telecommunications operators and “fintechs” (nonbank financial technology providers). In the case of mobile money, users often have the option of accessing their mobile money accounts through their mobile phones or using a brickand-mortar agent network. These agents are often located in kiosks or convenience-type stores. The Global Findex database defines _account ownership_ as ownership of an individual or jointly owned account either at a financial institution or through a mobile money provider.

The specific factors fueling the global rise in financial inclusion differ region-by-region and even country-by-country and may not be related to the economic bracket to which a country belongs. In Sub-Saharan Africa, for example, account ownership increased to 55 percent by 2021 from 23 percent in 2011, fueled by the adoption of mobile money accounts (33 percent of adults in the region have one). India, in contrast, achieved a 78 percent account ownership rate by 2017, largely driven by a government-led program that provided both digital IDs and bank accounts.

Access to a financial account can lead to a number of positive development outcomes for account holders. Research on the topic shows relationships between expanded account ownership and reductions in poverty (Beck, Demirgüç-Kunt, and Levine 2007); increased consumption (Karlan and Zinman 2012; Burgess and Pande 2005); increased productivity (Allen et al. 2016; Bruhn and Love 2009); greater investment in preventive health (Dupas and Robinson 2013
construct wealth by adding net foreign assets to the capital stock, constructed for all countries in the same way as we have described for China in Section 2 of the paper. We then express net foreign assets as a share of wealth, and average over all available annual observations between 1980 and 2000 to construct a single cross-section of 20year averages.

In Table 2 we first show the relationship between the net foreign asset position and per capita wealth relative to world average per capita wealth, where we define the world as the set of countries included in the regression. Not surprisingly, this relationship is strongly positive, indicating that on average capital tends to flow from rich countries to poor countries. This relationship is also quite consistent with the theoretical model of the previous section, where we have seen a positive relationship between per capita wealth and the net foreign asset position. What is also clear is that China is a very strong outlier in this relationship. We have already seen that China is a strong outlier in the relationship between capital stocks per capita and net foreign assets in Figure 5, and empirically per capita capital stocks and per capita wealth are very highly correlated across countries. The China dummy is equal to 0.19, indicating that on the basis of per capita wealth alone, we would expect China to have a net foreign asset position of -19 percent of wealth.

In the next three columns of Table 2 we introduce three measures intended to capture productivity differences across countries. The first of these is simply the loglevel of total factor productivity obtained from the very simple development accounting exercise that we described in the model calibrations of the previous section. The other two are two measures of institutional quality, which we expect will be important determinants of the attractiveness of a country for both domestic and foreign investment. The first is the ICRG measure of Expropriation Risk that has been widely used as a measure of property rights protection in the literature on institutions and growth. The annual data cover the period 1984-1997, and we average this
between savings and investment, and reform of inter-governmental fiscal relations that support greater public spending on health and education.

Kuijs and Wang (2005) calibrate a more capital-efficient growth scenario for China in which the economy grows at about the same rate as in recent years, but there is substantially less public savings (with the overall savings rate about one-fifth lower). So, in thinking about different savings scenarios, we consider one in which the status quo continues, and interpret this as a “stalled reform” scenario in which there continues to be a lot of inefficiency in the public sector and poor intermediation between savings and investment. In our “high reform” scenario we consider a decline in the aggregate savings rate of one-fifth.

# _What Will Happen to Productivity in China?_

Estimates of TFP growth in China are abundant, vary considerably.<sup>7</sup> In a recent very careful study Young (2003) concludes that various -- although not equally valid -- interpretations of Chinese data would result in TFP growth rates ranging from 0 to 6 percent per year! However, Young's preferred estimate is a very reasonable 1.4 percent per year, for the non-agricultural economy during the period 1978-1998. This estimate is in the same vicinity as Kraay (1996), who estimates a TFP growth rate of 1.8 percent per year for the period 1978-1994. These two estimates tend to be at the low end of those in the literature. For example, Borensztein and Ostry (1996) and Hu and Khan (1997) provide estimates of TFP growth of 3.8 and 3.9 percent per year over the period 19781994. In this subsection we make two points. The first is that relatively modest TFP growth rates for China are most likely higher than average productivity growth in the rest of the world, implying that China's level of productivity relative to the rest of the world has been rising over time. The second is that there is substantial scope for further
12 household health spending. By contrast, membership of GIS or LIS significantly increased the risk, by 20 percent or so.

# V. **SUMMARY AND CONCLUSIONS**

The results in this paper are consistent with the hypothesis that health insurance need not always reduce financial risk. Our measures of risk are the probability of an individual spending more than 5% of average income and the probability of an individual spending more than 10% of per capita household income on health. Our estimates of the effect of insurance on financial risk allow for the possible endogeneity of health insurance, either by allowing for a time-invariant fixed effect capturing unobserved risk that may be correlated with insurance status, or by using instrumental variables, where as instruments we use variables capturing the availability of or eligibility for health insurance. Results from two separate surveys (the China Health and Nutrition Survey, or CHNS, and the Health VIII baseline survey, or H8BS) suggest that during the 1990s China’s government and labor insurance schemes (GIS and LIS) increased financial risk associated with household health care spending. Evidence on the success of the rural cooperative medical scheme (CMS) in reducing financial risk is mixed: the CHNS suggests CMS increased risk, but not significantly, while the H8BS suggests that CMS significantly reduced risk. Two surveys (the CHNS and the Gansu Survey of Children and Families) both suggest that China’s new health insurance schemes (‘other’ and private schemes, including those that cover schoolchildren, as in the Gansu survey) may be associated with increased risk of large out-ofpocket payments. Where we find evidence of health insurance increasing the risk of ‘high’ outof-pocket expenses, the marginal effect is of the order 15-20 percent. In the case of the CHNS, our treatment of missing values is likely to result in this being an _underestimate_ .
the original HIPC Initiative. Neither the MDRI nor the original HIPC Initiative is associated with any change in infant mortality.

The results in Section 4 are robust to including a dummy variable for birth intervals less than 24 months (which increases the probability of infant death (WHO, 2005)), a variable for mother’s age, and age squared (columns (6), (7), and (8), respectively). Additionally, the within-country results (including mother-specific characteristics) in column (2) in Table 5 are identical to the those in column (1) in Table 9, which applies country fixed effects that vary by birth cohorts of the mothers.

There may be concerns with the validity of the data on infants obtained from the DHS. First, mothers may not accurately remember the birth and death (if applicable) dates of their children. If so, there may be problems with recall bias in the sample employed in this paper. This is not deemed to be a serious problem since the DHS apply rigorous tests to ensure the accuracy of the mortality data and mothers tend to remember important events such as the death of a child. Nevertheless, because there is a peak in the distribution of infant deaths at 12 months of age, I redefine the infant mortality variable as babies dying before of or in their twelfth month of age. The estimation result is presented in column (3) in Table 9. I observe no changes in the baseline findings because of this. Estimates are also robust to dropping the babies born before 1985 (column (4) in Table 9). Although the average effect and the significance level (the p value is 0.058) go down a little, the effects on the different sub-samples are stable to this change (results not shown).

I test if reported infant mortality is connected with debt relief at Decision Point of the HIPC Initiative by using DHS data on infant mortality from earlier surveys.<sup>8</sup
a 0.3 percentage points increase in price differences with respect to Delhi. This result implies that menu costs are in partly responsible of price differences in India. The result goes in line with the study of Crucini (2010) in Japan, which shows that price stickiness is one of the factors explaining the departures from the law of one price between Japanese cities.

The best model explains 70% of the total variance, which is more than twice of the explanatory power achieved in the retail analysis. The higher fit of the model suggests that wholesale markets are more sensitive to changes in fundamentals. The lack of explanatory power of the retail model may be explained by the fact that the lack of integration in wholesale markets directly affects integration in retail markets (as the two markets are strongly linked, as discussed in Section 3).

The law of one price does not hold in wholesale markets for all commodities. However, when testing product by product, for mustard oil and wheat the law of one price hypothesis is upheld. Just like in the case of departures from the law of one price in retail, results for wholesale suggest that there are unobservable factors that impede integration.

# Border effect

How costly is it to cross state borders in India pre‐GST? What do these borders imply in terms of deviations from the law of one price? Typically, the question of crossing borders has been studied in the case of international borders rather than intra‐national ones. However, India is a federal country, and during the period of analysis (i.e.: prior to the implementation of GST in July 2017), states often behaved like separate countries, exporting and importing from each other in a similar manner to international trade. At state borders, this resulted in state border authority checks on both parts of the border levying taxes, conducting road transport checks and commodity specific taxes.

Recent studies have pointed to how disruptive state‐border controls were in India. A recent World Bank survey conducted among
identified in broad categories; clearly, two households can each own a “fridge” but in one case it is 30 years old and works poorly while in the other case it is a fancy new model. Second, an important exclusion is that one cannot use fine geographic effects, such as at the level of the village, since one is constrained to estimating on a sample survey that will typically only cover a sample of villages (typically determined by the first stage of a two-stage sampling design). One does not know the geographic effect for the population, as required for implementing the PMT.<sup>23</sup> However, in one version we include community-level variables that go some way toward addressing this concern.

There is a degree of judgement required in selecting covariates. Here we consider various options, starting with a “Basic PMT” that seems to capture well the set of variables found in practice. We also consider “Extended PMT” methods that include variables that have extra explanatory power; while this provides a useful indication of the gains from more data, it is acknowledged that this version may not be easily implemented in the field. The <u>Statistical Addendum</u> provides descriptive statistics.

# **3** . **Measures of targeting and poverty**

An early strand of the literature formulated the targeting problem as that of choosing a schedule of transfer payments across types of households to minimize a measure of poverty subject to a budget constraint.<sup>24</sup> The subsequent literature has instead emphasized “targeting efficiency,” defined in terms of reducing targeting errors as defined below. Here we shall study both types of measures. We start with targeting measures.

**_Measures of targeting performance:_** The relevant counts corresponding to the joint distribution of _yijt_ and _y_ ˆ _ijt_ are shown in Table 1, which helps clarify our notation and some of the properties of our measures.

We focus on three main measures of targeting
5 negative – just as they could be for producing non-GM crops, which also are not captured in our model. On the one hand, many GM crop varieties have some attributes that are more environmentally friendly than their conventional non-GM counterparts. They also are less dangerous to farmers and the soil where they require reduced applications of pesticides. On the other hand, there is concern that some long-term and possibly irreversible negative environmental effects might occur in the future, although we are not aware of significant scientific evidence of such adverse effects.<sup>3</sup>

# **4. Model simulations and results of GM cotton adoption**

To simulate the economic effect of adoption of GM cotton, we assume total factor productivity (TFP) in cotton production would rise by 5 percent in most adopting countries, net of any higher cost of GM seed.<sup>4</sup> This output-augmenting, Hicks-neutral TFP shock is a conservative estimate of the gain to farmers, according to experience to date (FAO 2004, Table 7; Marra, Pardey and Alston 2002; Qaim and Zilberman 2003; Huang et al. 2004) and bearing in mind that typically, in a small number of years after GM cotton adoption is allowed, more than four-fifths of production moves to GM varieties. In India and Sub-Saharan Africa other than South Africa, however, we assume a TFP shock of 15 percent. Even that higher value is conservative for those countries, according to Qaim and Zilberman (2003), because those countries’ yields per hectare with conventional varieties are less than half the yields in the rest of the world (see last

> 3 Federoff and Brown (2004) give reasons why that null finding is not surprising from the viewpoint of a molecular biologist.

> 4 In the GTAP database, cotton is part of a sector called ‘plant-based fibers’ but it represents well over 90 percent of the value of that sector. The only country for which this is likely to be of any significance is