rafmacalaba/data-use-mentions-extended huggingface dataset:
rejection occurrences is persistent over time<sup>8</sup> .

Finally, we ask the question whether the risk index computed using the EU market food safety requirements as a benchmark is relevant? The focus of the analysis is on public standards since the food alert database only reports shipments non compliant with the EU food safety regulation, and due to data limitations it was not possible to account for private standards in the empirical analysis. Private protocols play an increasingly important role in the governance of food supply chains. Public standards are becoming more performance and process based, they are developed to correct market failures, and therefore, tend to play a dominant role in preventing fraud and ensuring minimum standards for largely homogenous agricultural products. While, in many cases private standards build on the existing public standard infrastructure to provide an element of competition through quality di¤erentiation, as well as to facilitate e¤ective coordination in supply chains. Thus, a large share of the cost of compliance arise because of public regulations. Our risk measure while not accounting for private standard still captures the need for ...nancing to maintain access to developed countries’market. In addition, the EU regulation is in line with requirements set out by international standards as well as other domestic regulations–high income countries mostly. Besides, the EU is a major destination market for our African countries’export. It makes sense to account for requirements in this destination market. All together, this substantially supports our

> 8Although not optimal, this gives us some insight on how risk may evolve across sectors over time. The number of alerts at the product level is correlated at 75% across all years between 2001 and 2008.

9
# III.2.2. FDH and DEA Analysis: Health

This section considers the case of one input (public expenditure on health per capita in PPP terms) and four alternative output indicators: life expectancy at birth, DPT immunization, measles immunization, and the disability adjusted life expectancy (DALE) index which takes into account both mortality and illness. The efficiency frontiers for each indicator are computed using both the FDH and DEA methodologies. Figures 8a-d show the efficiency frontier for one indicator. The specific country rankings for two of the health indicators are listed in Table B.4 of Appendix B.

<!-- Start of picture text --> Free Disposable Hull (FDH) Data Envelopment Analysis (DEA)<br>Life Expectancy vs Health Expenditure Life Expectancy vs Health Expenditure<br>KORMYSMUSARETT OOMN THADOMIDNKWTMEXBHSPHLCHLIRNMARBHRBLZKA ZAZECHNINDLKAEGYLBNGTMKNAFJIPERALBDZASY RL CAECUPAKGEOVUTVNMPRYATGBRATONVENKGZUKRTJKGRDHNDJ AMARMCPVUZBTKMSLVBOLNI CTURCOLMDABGRLVARUSSAUMNGVCTSLBGUYJORW SMDMABLRROMBRBPOLTUNLTUCRIPANURYESTMKD ARGSVKHUN HRV CZESVN KORMYSMUSARETT OOMN THADOMKWTIDNMEXBHSPHLCHLIRNMARBHRAZEKA ZBLZCHNINDLKAEGYLBNGTMKNAFJIPERALBDZASY RL CAECUPAKGEOVUTVNMPRYATGBRATONVENKGZUKRTJKGRDHNDARMJ AMCPVUZBTKMSLVBOLNI CTURCOLMDABGRLVARUSSAUMNGVCTSLBGUYJORW SMDMABLRROMBRBPOLTUNLTUCRIPANURYESTMKD ARGSVKHUN HRV CZESVN<br>BGDCOM BGDCOM<br>NPL NPL<br>SDNGHAYEMPNG SDNGHAYEMPNG<br>GA B SWZCMRKHMCIVAGOGI NLAOTGONGAMRTBENCAFHTICOGKE NZARBFAUGAMDGNERETHMLISENTCDBDIGMBRWATZAGNBZMBMWIERIMOZBWAZAFDJILSOZWE NAM GA B SWZCMRKHMCIVAGOGINLAOTGONGAMRTBENCAFHTICOGKE NZARBFAUGAMDGNERETHMLISENTCDBDIGMBRWATZAGNBZMBMWIERIMOZBWAZAFDJILSOZWE NAM<br>SLE SLE<br>300 400 500 600 700 800 300 400 500 600 700 800<br>Orthogonalized Public Expditure on Health Orthogonalized Public Expditure on Health<br>Daat Source: World Bank WDI Daat Source: World Bank WDI<br>(a.1) (a.2)<br>Immunization DPT vs Health Expenditure Immunization DPT vs Health Expenditure<br>KORMYSMUSARETT OOMN THADOMKWTIDNMEXBHSPHLCHL IRN MARBHRBLZAZEKA ZCHNLKAEGYGTMLBNSWZKNAFJIPERSY RDZAALBLCAECUGEOVUTVNMPRYATG BRA BGDCOMGHANPLTOKGZTJKBENUKRKE NGRDJ AMARMHCPVTKMUZBBDIRWASLVGMB N TZABOLZMBNI CTURMWIERIBWADCOLMDABGRZAFRUSLVASAUMNGVCTSLBGUYLSOZWEJORW SMDMABLRROMBRBPOLTUNLTUNAMCRIPANURYESTMKD ARGSVKHUN HRV CZESVN KORMYSMUSARETT OOMN THADOMKWTIDNMEXBHSPHLCHL IRN MARBHRBLZAZEKA ZCHNEGYLKAGTMLBNSWZKNAFJIPERSY RDZAALBL CAECUGEOVUTVNMPRYATGBGD BRA COMGHANPLTOKGZTJKBENUKRKE NGRDJ AMARMHCPVTKMUZBBDIRWASLVGMB N TZABOLZMBNI CMWITURBWAERIDCOLMDABGRZAFRUSLVASAUMNGVCTSLBGUYLSOZWEJORW SMDMABLRROMBRBPOLTUNLTUNAMCRIPANURYESTMKD ARGSVKHUN HRV CZESVN<br>YEM YEM<br>IND VEN IND VEN<br>PAKCIVLAOTGOUGAMDGSENPNGMOZ PAKCIVLAOTGOUGAMDGSENPNGMOZ<br>KHM KHM<br>CMRSDNGI NMLIETHGNB CMRSDNGI NMLIETHGNB<br>GA B MRTCAFHTISLE GA B MRTCAFHTISLE<br>BFA DJI BFA DJI<br>AGO AGO<br>COG COG<br>NGAZARNERTCD NGAZARNERTCD<br>
where _Y_ is the firm performance indicator, and _i, c,_ and _j_ represent firm, country, and local levels. _Y_ could be log labor productivity (or TFP), the labor productivity growth rate, the rate of sales growth, export intensity, or the investment rate.<sup>22</sup> _Firm_ is a set of basic firm-level controls including firm age, state and foreign ownership, and the ownership share of the largest owner. For the sales (labor productivity) growth rate equation we control for initial sales (labor productivity) three years ago to allow for regression toward the mean. _Geography_ includes log country population and a dummy equal to one for landlocked countries. _Infrastructure_ includes the telecom index (measured at the local, that is, the city-industry-size, level). _PoliInst_ represents the category of political and institutional factors, and includes log party years, any conflict in the past ten years, and ethnic fractionalization. _BusiEnvi_ indicates business environment consisting of firm-level foreign ownership, average country-industry foreign ownership, firing difficulty, the local average bribe, country-industry tariff levels, countryindustry competition index, and the average perceived level of crime at the local level. _Finance_ includes the local share of firms having access to an overdraft facility, and the local average for access to trade credit. We also include industry dummies, so that comparisons are between firms within the same sectors across countries. The error term captures unobserved variables and measurement errors.<sup>23</sup> Since the error terms within a country may be correlated due to omitted common factors or shocks, and many of our explanatory variables are aggregate variables, we

> 22 Log labor productivity is trimmed of tail one percent to avoid the outlier issue. Results are similar without trimming.

> 23 Since we have many control variables, and the coverage of both the Enterprise Survey and cross-country sources differ for some countries, dropping the observations with any variable missing would result in the loss of the majority of the sample, and
captures the differences in expenditure of the short and long list designs relative to the diary, ii which is the excluded category. ββ i is the error term which captures unobserved consumption expenditures. The results from equation 1 will help understand the percentage change in food and ee total consumption expenditures emanating from diary versus the recall designs.

In the second specification (equation 2), we estimate the model including household, head, and respondent characteristics that could potentially influence the performance of household consumption expenditure. These covariates include education, employment type, gender, access to improved water and sanitation.

i i i i (2) ii ii ii ii ii YY = ββ DD + γγ XX + δδ DD XX + ee where i represents a single household characteristic and is the coefficient of the interaction ii between a treatment dummy and a given household characteristic. We first estimate the model XX δδ controlling for and interacting treatment type with household head’s characteristics, and then repeat the same analysis for the main respondent.

# **5. Results**

# **5.1. Descriptive results**

# **5.1.1. Demographic characteristics by design**

Demographic characteristics in this study in general mirror those from other nationally representative surveys in Tanzania (Ministry of Finance, NBS, and World Bank 2020). With minor exceptions, **Table 1** shows that household characteristics relevant for consumption appear balanced across the three study arms, as do individual characteristics of both the household head 14
it is know that DSB has no aggregate effect on the population sex composition and that it does not cause a noticeable increase in aggregate fertility rates. Across all parities the sex of a child is random and

> 11 In Indonesia abortion is generally illegal; however, abortion is common. According to estimates derived from a sample of health care facilities, the annual abortion rate in Indonesia is 3.7 abortions for every 100 women of reproductive age. In Asia as a whole it is 2.9. Abortion clients are often married adults with unmet need for contraception. (Guttmacher Institute 2008, Utomo et al. 2001).

> 12 A defining feature of the program has been the use of targets rather than a change in attitudes (Hull and Hull 1997, Cammack and Heaton 2001). The targets have encouraged compulsory measures, including the practice of forced long-term contraception and sterilization. In the 1970s, the military was recruited to get as many women as possible fitted with IUDs. In subsequent decades, couples have been coerced by a combination of bribes, threats, and punishments. (Warwick 1986, Hafidz et al. 1992).

> 13 The sex ratio at birth and under 5 years old are slightly less balanced (1221 and 1076), as it would be expected (DHS 2007).

20
Figure 6. Kernel density graphs: time of sleep for men and women <!-- Start of picture text --> (6a) Recall versus smartphone: women (6b) Recall versus smartphone: men<br><!-- End of picture text --> <u>Notes:</u> (1) The estimates pool the Day 5, 8 and 10 samples for each treatment arm. K-S p-values for differences across treatment arms in (6a) and (6b): 0.000.

Therefore, the smartphone arm provides greater variation in intraday reporting and could potentially provide an easier way to avoid issues around understanding time in the day – given how early people are reporting sleep time. If time use statistics have typically been analyzed on a day-level aggregation, however, a natural question arises as to why we should potentially care about intraday time allocation. In addition to our understanding of how men and women organize their time, the distinctions we observed across treatment arms of reporting during morning versus evening hours also have important implications for difficult-to-answer questions around infrastructural interventions affecting time use. This includes, for example, the role of household electrification in supporting time allocation in different productive areas during evening and nighttime hours (see, for example, Agénor and Agénor, 2023; Ribeiro et al., 2021; van de Walle, et. al., 2017). Taking electricity and access to energy sources more broadly, high-quality intraday data on time allocation can help us better understand the role of energy infrastructure on individuals’ economic and other intrahousehold decision making. As an example, Appendix Table A6 presents OLS regressions showing that for women in the smartphone arm in particular, electricity access (as opposed to no access, or household lighting through battery-operated torches) are significantly associated with time allocation in nonfarm employment and leisure during the evening/nighttime, but with no effects for women in the recall sample.<sup>21</sup> The choice of survey approach to time
intervention, and in the types of firms receiving them. For example, the Zambia matching grant program gave out 63 grants, of which 23 were for less than $5000, 33 between $5,000 and $50,000, and 7 above $50,000; 39 percent of the firms were in manufacturing, 30 percent in services, 14 percent in agriculture, 6 percent in tourism, and the rest in other sectors.<sup>4</sup> In other programs the range of the grants offered can be smaller, but there is usually still large heterogeneity in the sectors. For example, the matching grants under the South Africa Black Business Supplier development project were capped at US$17,000, and restricted to firms with fewer than 20 employees. Even given these restrictions which make the firms more homogenous, a survey of 50 participating enterprises showed full-time employment with a mean of 9.9, standard deviation of 8.0, and 2004 revenues with a mean of 983,000 rand and standard deviation of 2,726,000 rand: that is, like the enterprise survey data, the standard deviation of sales is several times its mean. The Kenya project listed in Table 3 is notable as an exception, and an illustration that projects focused on microenterprises may serve many more firms than those focusing on SMEs.

Table 4 summarizes a half-dozen projects on SMEs being undertaken by the International Finance Corporation (IFC) in Africa. These projects are typically directed at firms in a particular sector, such as firms in, or linked to, the mining industry. As a result, the firms in these projects appear to be typically smaller in number, but more homogeneous, than those in the World Bank projects. In addition, the IFC offers business training through its _Business Edge_ program<sup>5</sup> in a number of African countries. Table 4 shows that numbers of firms participating in these programs in the last year are small in most countries (
are important, some results are sensitive to specific countries. Specifically, no P individual country is highly influential for 0 or 0 except for Algeria FFLLNNAAFF GG (discussed further below). 1 is fairly robust to the exclusions of individual ββ ββ countries, though estimates ofFFLLNNAAFF1P are sensitive to Qatar: excluding this country ββ makes 1P negative, although still insignificant.GG 56 ββ GG Given that Appendix Figure 7 identifies Algeria and Qatar as potentially ββ influential, in Panel F of Table 4 we re-estimate excluding these two countries.<sup>57</sup> The coefficients 0 and 1 are mostly unchanged (including 1 > 0, as in in the model’s second prediction). But the standard deviation of FFLLNNAAFF FFLLNNAAFF FFLLNNAAFF0P and ββ ββ ββ 1P are much higher relative to our main results. Even though the point estimates GG ββ still suggest GG 0 0P and 1 1P , we now cannot reject 0P = ββ PFFLLNNAAFF GG FFLLNNAAFF GG GG 0 or 1 1 at conventional levels. ββ > ββ ββ > ββ ββ FFLLNNAAFF GG FFLLNNAAFF A final concern is on the quality of the commodity revenue data. Appendix ββ ββ = ββ Figure 9 shows that there is some disagreement with respect to the size of resource revenues for some countries across the GRD and WCE datasets. This disagreement raises the concern that our main results may be sensitive to measurement error. To test that possibility, panel G reports the estimates dropping all observations if the reported change in resource revenue from GRD differs from the WCE by more than 3% of MAGDP (in absolute value). The results are very similar to those in panel B, except that 1P is higher relative to benchmark – although still insignificant – GG P and we fail to reject 1 1 . ββ GG FFLLNNAAFF Our final robustness test is of the 2SLS specification itself. As the coefficient ββ = ββ on the first-stage regression is close to one, an alternative specification is a reduced-

> 56 Although Botswana, Kuwait and Trinidad and Tobago are influential for 1 ,
the domestic market bought mainly T-bills to reap huge profits was temporarily broken. The domestic credit extended by the Central Bank to the Treasury reached record amounts. 53% of the limit of STA's were extended in only the first 3 weeks, which approximately corresponded to 30 % of the net foreign assets of the Central Bank, This liquidity pumped in to the system at a time when the demand for TL was low, led to several runs against the TL in January, February and March 1994. The Central Bank, prior to- and in between the official devaluations, was heavily intervening in the overnight market to defend the parityv°, and overnight rates did jump to compounded annual rates exceeding 1000% at times. (See Figure 14) The percentage depreciation of the TL was about 16, 6, 18, and 35 during the first 4 months of 1994 respectively. The TL dropped to 39,900 against the US$ on April 7, but recovered to 33,400 TL/US$ at the end of the month.

Now we turn to the story behind the run against the TL and the drainage of Central Bank foreign assets in greater detail. It was noted before that an important portion of the inflows of foreign capital to Turkey took the form of short term off-shore borrowing. Although it is not possible to see the discrepancy between the foreign assets and liabilities of deposit banks in IFS data'" (See Figure 15), many commercial banks held short positions in foreign exchange, and lent domestically at high rates, including to the Treasury. It is noted in different sources that banks had large open positions especially in 1993. Ozatay (1996) notes that open foreign exchange positions

**10** The Central Bank Governor resigned at the end of March 1994 as a result of the conflicts between him and the PM over the conduct of policy.

11 The IFS data on the total foreign assets and liabilities of the Deposit Banks in Turkey (including state owned, foreign and commercial
12 global distortions dataset. Specifically, all modelers assumed the following: a fixed aggregate stock of factors (including no international mobility), with the exception of labor in the Nicaraguan and South African studies where some aggregate employment responsiveness to trade policy is allowed because of high unemployment in the baseline; possibly some sectorspecific capital and labor, but most capital and labor types are assumed to be intersectorally mobile with a common flexible rate of return or wage; and land is assumed to be specific to the agricultural sector but mobile across the different crop and livestock activities within that sector. The key agreed macroeconomic closure rules that each case study aimed to adopt are a fixed current account in foreign currency, to avoid foreign debt considerations, and fixed real government spending and fiscal balance, so as to not affect household utility other than through traceable changes in factor and product prices and taxes. Fiscal balance is achieved by using a uniform (generally direct income) tax to replace net losses in revenue from abolishing sector trade taxes and subsidies.

# **3. SYNOPSIS OF EMPIRICAL FINDINGS: GLOBAL MODEL RESULTS**

This section summarizes the results from two global models (denoted LINKAGE and GTAP).<sup>4</sup> The following section then brings together the results from nine more-detailed national case studies, before the lessons learned from both sets of analyses are drawn together. It would be surprising if all the studies came to the same conclusions, but the strength of this blend of

> 4 Results were also generated by Bussolo, De Hoyas and Medvedev (2010) making use of the global LINKAGE model and combining this with the newly developed Global Income Distribution Dynamics (GIDD) microsimulation tool (Bussolo, De Hoyos and Medvedev 2008). However, since the key inputs into the microsimulation from the LINKAGE model are just the labor income changes, those results are not directly comparable with the other studies surveyed here.
to only an age based criterion, was to arrive at a cohort that is more reflective of actual participants<sup>20</sup> Annex B explains more fully

> 19 Both rejoiners and repeaters constituted around 0.5 percent of total enrollment in middle school at baseline. Not only were these rates similar between stipend and non-stipend districts in 2003-04, there has been no differential growth in these rates between 2003-04 and 2007-08. School switching is another source of compositional change within the cohort. At baseline, around 2 percent of all children enrolled in middle schools switched from private to public schools while a similar proportion switched from public to private (the difference is statistically insignificant). So at baseline, compositional shifts stemming from re-entry, repetition and school switching accounted for some 5 percent of total enrollment in middle school level. There are no comparable data on school switching in the 2007-08 survey, but the change in repetition and rejoining rate at the middle school level remained comparable between stipend and non-stipend districts between 2003-04 and 2007-08.

> 20 The previous impact evaluation of the FSSP (Chaudhury and Parajuli 2008) used cohorts of children 10-14 years old from cross-sectional household surveys to triangulate the findings derived from school data. However, an examination of the age-grade

14
change substantially by urban and rural areas, and hence in a robustness exercise we discuss the impacts by geographical areas separately. Finally, the basic simulations neglect considerations related to the effectiveness of targeting, and assume that our hypothetical program is able to perfectly target the poor. To address this issue, we also discuss a simulation where we assume that targeting has 15 percent higher administrative costs than categorical transfers (so that the targeted program only distributes 0.425 percent of GDP), and that exclusion errors lead to missing 30 percent of the (randomly selected) poor, who do not receive the transfer.

Observe, also, that we look at the poverty and inequality impact of programs that are in addition to the ones that are already implemented. Ideally, it would be preferable to first subtract from households’ income all transfers that they already receive, and subsequently add our transfer from the hypothetical program. In doing so, it would be possible to provide an idea on the extent to which the coverage of current programs could be improved to enhance cost effectiveness. Unfortunately, we only have limited and imperfect data about the amounts transferred to households in each country survey. We chose therefore not to explore that avenue.

The simulations are based on household surveys from 13 Latin American countries that have been collected and harmonized as part of the Socio-Economic Database for Latin America and the Caribbean (SEDLAC), a partnership between the Center for Distributive, Labor and Social Studies (CEDLAS) and The World Bank. The list of countries and of surveys used is shown in Annex 1. The analysis uses the income aggregates provided in SEDLAC, which we convert in 2005 USD PPP.

# 4.  Results

## **Children**

We begin by looking at the poverty and inequality impact of targeted and categorical cash transfers programs to families with children up to 5 years of age. Several features emerge. First, we can observe a relationship between income per capita, and the effectiveness of the program in reducing poverty under both the