rafmacalaba/data-use-mentions-extended huggingface dataset:
Considerable variation remains in teacher absence rates across states with estimates ranging from 12.9 percent in Tamil Nadu to a high of 45.8 percent in Jharkhand. Teacher absence rates declined in 14 out of 19 states with significant reductions in 12 states. Five states (Tamilnadu, Punjab, Maharashtra, Orissa, and Chhatisgarh) now report teacher absence rates below 15 percent. On a population-weighted basis, the largest contributor to reductions in all-India teacher absence was Bihar, which is consistent with the widely-reported improvement in governance in the state during this period (Chakrabarti, 2013). In contrast, the significant increase in teacher absence in India’s most populous state Uttar Pradesh (population over 200 million), partly offset the reduction in other states.

Since Chaudhury et al. (2006) find a strong negative correlation between GDP/capita and teacher absence rates (both across countries and within Indian states), one way to interpret the magnitude of these changes is to compare them with the expected reduction in teacher absence that may be attributed simply to the economic growth that has taken place in this period. Using a growth accounting (as opposed to causal) framework, we can decompose the change in teacher absence into a component explained by changes in GDP/capita (as a proxy for ‘inputs’) and one explained by a change in governance (a proxy for TFP). Cross-sectional estimates from the 2003 data suggest that a 10 percent increase in GDP/capita is associated with a 0.6 percentage point reduction in teacher absence.<sup>14</sup> In the period between 2002 and 2010, real GDP/capita in India has grown 38 percent. Thus, growth in GDP/capita over this period should have by itself contributed to a reduction in teacher absence of 2.4 percent. Our estimate of the change in teacher absence rate is exactly in this range, and suggests that the reduction of teacher absence we document is consistent with a proportional increase in ‘inputs’ into
Policy Research Working Paper 6357

# **Abstract**

Least developed countries rely on preferential market access. Proof of sufficient transformation has to be provided to customs in importing countries by meeting Rules of Origin requirements to benefit from these preferences. These Rules of Origin have turned out to be complicated and burdensome for exporters in the least developed countries. Starting around 2001, under the United States Africa Growth Opportunity Act, 22 African countries exporting apparel to the United States can use fabric from any origin (single transformation) and still meet the criterion for preferential access (the so-called Special Rule), while the European Union continued to require yarn to be woven into fabric and then made into apparel in the same country (double transformation). This paper uses panel estimates over 1996–2004 to exploit this quasi-experimental change in the design of preferences. The paper estimates that this simplification contributed to an increase in export volume of about 168 percent for the top seven beneficiaries or approximately four times as much as the 44 percent growth effect from the initial preference access under the Africa Growth Opportunity Act without the single transformation. This change in design also mattered for diversity in apparel exports, as the number of export varieties grew more rapidly under the Africa Growth Opportunity Act special regime.

This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at jaime.demelo@unige.ch and aportugalperez@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited
# **Ignorance of bulk discounts**

Is it possible that many people in Tanzania simply do not know of the available bulk discounts? We are doubtful. When we conducted informal interviews with individuals in the study area, everyone was well aware of bulk discounts for a wide range of consumption items. Furthermore, many households personally experience non-linear prices. Column 5 of Table 3 shows the results of item-level regressions of unit price on quantity, with household fixed effects. Even _within household_ , bulk discounts are present. The members of households that purchase items multiple times—exactly those that are foregoing potential consumption by not buying in bulk—are surely aware of the available discounts.

# **Rationing consumption**

Could it be that people avoid buying in bulk as a way to limit their consumption? A sophisticated but present-biased agent would forego bulk purchasing in order to prevent her future self from over-consuming (Laibson, 1997; O’Donoghue and Rabin, 1999). Relatedly, shoppers may not trust other household members to control their consumption, and so may limit stocks as a form of rationing. In focus-group discussions we heard numerous variations on this theme.<sup>18</sup> If rationing is present, it is most likely to occur on items that are “temptation goods”—goods which, if held in stock, are likely to be consumed too quickly relative to one’s _ex ante_ plan. Because temptation goods are culturally specific, we conducted a survey in Tanzania to measure the temptingness of the study items. We invited Tanzanian staff members from recent survey projects to rank the study items on a five-point scale (1 = not at all tempting; 5 = tempting for essentially everyone who consumes the item). These respondents implement consumption surveys and speak regularly with households about their economic choices—in essence, they constitute a panel of experts. We asked respondents to

> 18The story of one respondent in Bukoba can be paraphrased as follows: “We know that we need 1 kilogram
# **3. Summary of Key Findings**

This section highlights some of the key characteristics of informal entrepreneurs and their businesses based on the data covering seven countries and twenty-four cities. Informal businesses differ from formal firms along several key dimensions, which we also explore for countries where the survey data is available covering formal firms of comparable size. In five of the countries included in the analysis (India, Mozambique, Somalia, Zambia, and Zimbabwe), surveys of microenterprises, (i.e., formally registered business with fewer than five employees) were also conducted roughly at the same time as the Informal Sector Enterprise Surveys.<sup>7</sup> We use this data, where possible, to provide an overall comparison of informal businesses with micro-enterprises. Table 2 provides details and results of these simple comparisons across multiple dimensions. The analysis in this section is purely exploratory without aiming to uncover causal relations underlying the patterns that we highlight.

## **3.1 Characteristics of Informal Business Owners**

We find that owners of informal businesses across the seven countries are on average in their late 30s and early 40s, with an overall median age of about 36 years.<sup>8</sup> Only 3% of owners are over 60 years old. About half of the business owners are 35 years old or younger, ranging from 61% in Somalia to about 35% in India. There is no difference in age between female and male owners, with each averaging around 38 years of age, respectively. We find little age variation across the three groups of sectors covered by Informal Sector Enterprise Surveys; manufacturing businesses have the oldest owners (39.3 years on average), followed by retail (37.4) and other services (36.7).<sup>9</sup> Interestingly, the owners of informal businesses are similar in age to their counterparts in the micro-enterprises, averaging 37.8 years compared to 35.4
usage intensity, is a vector of firm control characteristics, including age and size clusters (micro, small, ccp cc medium, large)<sup>7</sup> as well as the monthly global price of oil, are sector-specific dummies to account for ii,tt XX sector-specific characteristics other than energy usage intensity, are country-level fixed effects to jj μμ control for country characteristics that are invariant in the short term, such as institutions or market kk θθ structures, are country-time fixed effects to control for time-varying macroeconomic conditions common for all firms in a given country that vary over time, are the time-fixed effects to control for kk,tt μμ time-varying global factors that affect all countries, sectors, and firms. The equation is estimated by OLS tt ωω using robust standard errors. <mark>Weights are employed in the analysis to equalize the emphasis on smaller versus larger countries.</mark> <mark>Note that relevant frm characteristcs are carefully considered for each regression: t</mark> o reduce omitted variable concerns, <mark>size dummies are incorporated when assessing labor productvity o</mark> r sales <mark>as the dependent variable</mark> s, while sales quartiles are used when employment is the dependent variable <mark>. The age of frms is controlled for because older frms may utlize energy efciency practces and generate outcomes diferent from younger frms.</mark><sup>8</sup> <mark>The overall efect of electricity price fuctuatons on a frm in the energyintensive (CPRS) sector is then given by: α + γ .</mark>

> 6 Bartelsman et al. (2004) highlight its importance in developing economies, where sales data often provide the most accessible alternative for productivity analysis.

> 7 Micro (1 to 5 employees), small (6 to 19), medium (20 to 99), large (100 or more employees); the omitted category in the regressions is “small
**|-1.576|=-5.797+|
||(8.628)|(7.953)|<br>(4.839)|(6.281)|<br>(6.845)|(8.090)|<br>(3.547)|
|Number of observations|1478|1478|1478|1478|1478|1478|1482|
|Number of unions|739|739|739|739|739|739|7Al|
|Within R2|0.058|0.089|0.073|0.080|0.062|0.073|0.082|
|Unconditionalmean(wave1,inFCFA)|33916.8|11893.9|1004.0|5578.1|7360.0|6381.1|6372.4|

Panel union fixed effect model estimates. Standard errors in parentheses are bootstrapped (300 replications); * p<0.10, ** p<0.05,*** p<0.01 Dependent variables: Level of cell non-food per capita expenditures per type of commodities

# Sample: monogamous wives in the two waves

F;: predicted failure probability estimated by a Cox survival model stratified by the polygamy status of the husband's father (see Table 1) All regressions include union fixed-effects

* Controls not shown: time, time squared, dummies for the deceased father of each spouse, household size, relative cell size, share of dependents, all step-1 controls (see Table 1) interacted with time

In all columns, we control for the level of non-food consumption (in log) Data source: PSF Survey, waves 1 and 2 (2006-2007 and 2010-2012)
|Deprived in 4 dimensions|5.9|18.6|
|---|---|---|
|Deprived in 5 dimensions|0.7|2.9|

Note: Percentage of individuals that are AROPE + and are deprived in multiple dimensions (using survey weights). Source: Own estimates based on 2021 Bulgaria SILC. We report the survey year and not the income year. AROPE definition based on EU2030 target.

**Moreover, the longitudinal analysis conducted shows that the Roma population faces high**

**rates of chronic income poverty and chronic material and social deprivation** . Further analysis of chronic poverty can lead to an identification of a sub-set of poor individuals facing particularly adverse circumstances and/or distinctive problems in escaping from poverty (Whelan et al., 2003).  In 2021, 60.3 percent of the Roma were at persistent risk of poverty, and 62.2 percent suffered persistent material and social deprivation (Table 13).<sup>56</sup> These proportions largely exceed the national figures of 17.0 percent and 16.8 percent, respectively. In addition, almost half (45.8 percent) of the Roma population experiences both persistent poverty and material and social deprivation, which is five times larger than the national proportion.

**These estimates indicate that Roma households may transition out of income poverty and material and social deprivation less frequently than Bulgarian households, indicating structural challenges individuals face in escaping this state.** A high persistent rate of severe material and social deprivation suggests difficulty in overcoming these circumstances, underscoring its policy relevance comparable to or greater than the absolute number of individuals in such households in a given year. These disparities may indicate systemic and longlasting challenges faced by the Roma population, which may require the need for tailored antipoverty policies different from those tackling poverty at one point in time, <mark>as efforts should prioritize preventing poverty from becoming entrenched over time.</
|Somalia|High<br>Frequency<br>Survey<br>(2017)|Multi-stage<br>stratified random<br>sample|Somalia (within secure<br>areas)|IDPs and host<br>communities|27,287<br>(82.3%)|
|---|---|---|---|---|---|
|Sudan|IDP<br>Profiling<br>Survey<br>(2018)|Stratified<br>cluster<br>sampling|Abu Shouk and El<br>Salam<br>camps,<br>and<br>neighboring and non-<br>neighboring Al-Fashir|IDPs and host<br>communities|17,645<br>(95.2%)|
|South<br>Sudan|High<br>Frequency<br>Survey<br>Wave<br>4<br>(2017)|Stratified<br>two-<br>stage<br>cluster<br>design|Urban areas of seven of<br>the ten pre-war states<br>(Western<br>Equatoria,<br>Central<br>Equatoria,<br>Eastern<br>Equatoria,<br>Northern<br>Bahr-El-<br>Ghazl, Western Bahr-<br>El-Ghazal,<br>Warrap,<br>and Lakes state).|IDPs and host<br>communities|4,554<br>(92.8)|

Data were available for all selected MPI indicators in every country, except for access to a bank account in South Sudan, where the remaining indicators in the financial security dimension are re-weighted accordingly.

# **_4.2 Description of the data_**

The relative sizes of different comparator groups – displaced and non-displaced populations, adults and children, and displacement status groups by gender –
future climate extremes to households. The methodology is applied to a case study in the coastal zone of Bangladesh, an area inhabited by around 43 million people and considered to be among the most climate vulnerable regions globally. Using a recently developed synthetic household survey dataset that contains the location and socio-economic characteristics of all households, and linking each household to the critical infrastructures they depend on, we map the baseline level of multidimensional poverty (MDP) in coastal Bangladesh. The MDP index is a combined metric made up of nine indicators based on the global MDP (developed by OPHI). We quantify how cyclone-induced flooding at-present and in the future (2050) can temporarily increase MDP in this region. Moreover, we evaluate the benefits of adaptation, in particular the wider economic benefits of hard infrastructure interventions by upgrading coastal embankments.

# **Research questions**

Several research questions are set to achieve this aim:

- What is the spatial distribution of MDP in Bangladesh’s coastal zone, and how does it compare to other poverty metrics?

- What is the cyclone-induced risk in terms of asset damages, population exposed and infrastructure services provision, and how will this change in the future?

- Are poor households disproportionally exposed to cyclone-induced flooding?

3
Chart 12 Global Factors (Percent) <!-- Start of picture text --> Global rate factor<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>1970 1980 1990 2000 2010 2020<br>Global inflation factor<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>1970 1980 1990 2000 2010 2020<br>Global output factor<br>5<br>4<br>3<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>-4<br>-5<br>1970 1980 1990 2000 2010 2020<br><!-- End of picture text --> Sources: Authors’ calculations based on the data described in Appendix Table 2. Notes: Global factors for policy rates, inflation, and output growth are estimated using a one-factor dynamic factor model for crosscountry data for policy rates (or shadow rates), inflation rates, and growth rates of industrial production. Shaded areas in Panel A indicate global recessions and downturns (Kose and Terrones 2015; Kose, Terrones, and Sugawara 2020).

37
## **c. Generosity (benefits as % of MYPP)**

## **d. Inequality, poverty, and poverty gap impact**

<!-- Start of picture text --> 63.1<br>Marginal Contributions to<br>0.01<br>Inequality<br>Change in Poverty Gap 1.94<br>Marginal Contributions to<br>0.99<br>Poverty<br>4.0<br>0.3 0.0 0.1<br>1 2 3 4 5 6 7 8 9 10 Disposable<br><!-- End of picture text --> _Source: Own estimates using CEQ and Bulgaria SILC 2021_ _Note: MYPP refers to Market Income Plus Pensions. Deciles are income deciles. Marginal contributions to poverty are measured using at-risk of poverty lines (AROP), fixed at the baseline scenario before the reform was implemented._

# **Finally, the fiscal cost analysis shows that, with a full take-up, the fiscal cost can increase substantially;**

**this represents an upper bound.** Under Policy Scenario 2, the simulations show a substantial cost increase, with the MSA program cost rising to almost 60% more than in the first policy scenario (146.5 million BGN more) and a similar cost for the heating allowance. The simulations show a substantial change in fiscal spending, with an increase of 350.3 million BGN for social assistance, 38.6 million BGN for the heating allowance, and a cumulative rise of 387.0 million BGN in the total fiscal cost (table 5). These changes underscore the financial implications of the policy simulation, highlighting a substantial expansion in government expenditure in these support programs.

**Finally, the fiscal cost analysis reveals that the fiscal cost can increase significantly with full uptake, representing an upper limit** . Under Policy Scenario 2, simulations indicate a substantial rise in costs
**11**

creditors, the efficiency of contract enforcement, and the quality of accounting standards. Thus, legal origin is connected to legal and regulatory characteristics defining financial intermediary activities. We discuss the relationship between legal origin and the legal and regulatory environment in more detail below. Here, note that legal origin has a profound impact on financial intermediary development.

Table 2 presents regressions of the financial intermediary development indicators on the dummy variables for English, French and German legal origin, relative to Scandinavian origin (which is captured in the constant). We extend the LLSV **(1998)** data set from 44 countries (with financial intermediary data) to 71. Some of the regressions also control for the level of real per capita **GDP.** The major message is that countries with a German legal origin have better developed financial intermediaries. While countries with a French legal tradition tend to have less well-developed institutions than other countries on average, this result does not hold when controlling for the overall level of economic development **.** In contrast, the dummy variable for a German legal tradition enters with a positive and significant coefficient even after controlling for the level of real per capita **GDP.** Also, as indicated **by** the P-values of the F-test that the explanatory variables do not explain the dependent variable, the legal origin variables explain a significant fraction of the cross-country variation of the financial intermediary development indicators.

**C. Legal origin and growth in a pure cross-section of countries**

# **_1._** _Cross-sectional estimator_

The pure cross-sectional analysis uses data averaged over _1960-95,_ such that there is one observation per country. The basic regression takes the form: GROWTHi **=** oa **_+_** _OFINANCEi_ **_+_ y'** [CONDITIONING **SET]i +** si,
evidence shows that certain characteristics of the survey such as how the questions are structured in the survey, the ordering and the wording used, the format of the interview, or the quality of the translation, all significantly influence the outcome of survey responses. For example, whether the question is formulated in a positive way or in a negative way is more likely to translate into a positive or a negative answer. In addition, Schwarz (1999) describes how closed vs open response formats or the rating scale can also bias the response obtained.

Further sources of bias in subjective measures include context, cultural differences, and social desirability. The context in which questions are formulated is likely to influence answers, mainly due to the framing of the respondents. For example, Kaplan and Pathania (2010) show how perceptions‐based indicators used to measure the quality of the business environment are highly correlated with the business cycle. Using data from the World Bank Enterprise Surveys (ES) the authors show how perceptions about the business environment worsen during periods of higher economic growth and, therefore, how changes in perceptions are not a good measure of changes in the business environment. Cultural differences are also relevant to how individuals understand and respond to survey questions. This is because aspects of cognition that affect response behaviors can differ across cultures (Johnson et al., 1997; Harkness et al. 2002). Village‐level characteristics, such as ethnic heterogeneity and social participation, for example, have been shown to influence perception measures of corruption (Olken 2009). Furthermore, there is a large literature showing how social desirability affects the quality of subjective measures. One example is the reluctance of individuals to express subjective opinions perceived as non‐desirable such as racial attitudes (Bertrand and Mullainathan, 2001).

A final important element that affects the bias in subjective measures is the reference or recall period (Schwarz, 1999). The time reference for the question or the recall period in the formulated question impacts the quality of the answer