rafmacalaba/data-use-mentions-extended huggingface dataset:
), 159–209.

- Survey and Census Division, National Institute of Statistics, Niger (NIS) (2014). National Survey on Household Living Conditions and Agriculture 2011. Public Use Dataset. Ref: NER ~~2~~ 011 ~~E~~ CVMA ~~v~~ 01 M. Downloaded from `https://microdata.worldbank.org/index. php/catalog/2050` on 6 September 2019.

- Survey and Census Division, National Institute of Statistics, Niger (NIS) (2016). National Survey on Household Living Conditions and Agriculture (ecvma) 2014. Public Use Dataset. Ref: NER 2014 ~~E~~ CVMA ~~v~~ 02 ~~M~~ . Downloaded from `https://microdata.worldbank. org/index.php/catalog/2676` on 6 September 2019.

- Tack, J., A. Harri, and K. Cole (2012). More than mean effects: Modeling the effect of climate on the higher order moments of crop yields. _American Journal of Agricultural Economics 94_ (5), 1037–54.

- Tanzania National Bureau of Statistics (TNBS) (2011). National Panel Survey 2008-2009, wave 1. Public Use Dataset. Ref: TZA 2008 ~~N~~ PS-R1 ~~v~~ 03 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/76` on 6 September 2019.

- Tanzania National Bureau of Statistics (TNBS) (2012). National Panel Survey 2010-2011, wave 2. Public Use Dataset. Ref: TZA 2010 ~~N~~ PS-R2 ~~v~~ 03 ~~M~~ . Downloaded from `https://microdata. worldbank.org/index.php/catalog/1050` on 6 September 2019.

55
Policy Research Working Paper 7834

# **Abstract**

A significant body of research demonstrates that teachers and the quality of their teaching are crucial components of student learning. Many teachers in resource-poor environments have limited knowledge, skills, or motivation. Some impact evaluations have shown promising results from interventions to improve the quality of teaching. This paper reviews the existing body of evidence on what kinds of in-service teacher training interventions are most effective, and highlights the knowledge gaps. It reveals the dearth of detail on the nature of teacher training interventions and proposes a standard set of indicators—the In-Service Teacher Training Survey Instrument—for reporting on such programs as a prerequisite for understanding which interventions lead to improved student learning. Across a set of 26 programs with impact evaluations and student learning results, programs that provide complementary materials, focus on a specific subject, and include follow-up visits tend to show higher gains. Programs that use non-education professionals as trainers tend to have worse outcomes. Statistical power to identify these effects is limited, and use of these standard indicators in future impact evaluations will facilitate more precise inference.

This paper—prepared as a background paper to the World Bank’s _World Development Report 2018: Realizing the Promise of Education for Development_ —is a product of Office of the Chief Economist, Africa Region. 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 apopova@stanford.edu and devans2@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 accordingly. The findings, interpretations, and conclusions
# 5.3. Years of life lost

The impact of a reduction in the consumption of processed and ultra-processed foods on long-term income is derived from the reduction in years of life lost (YLL) because of mortality. Due to data limitation explained in section 4.2, the impact is estimated for the joint taxation on processed and ultra-processed foods.

The goal of this analysis is not to estimate the years of life lost related to dietary risks, as this is calculated using existing Global Burden of Disease estimates. Rather, we are interested in estimating the extent of the increase in income resulting from the reduction in YLL due to a lower dietary risk across households at the various levels of the consumption distribution. Potential gains in years of life lost and consequent increased income are determined based on the weight of processed and ultra-processed foods in households’ consumption in a given decile, and the price elasticity by decile. Income is calculated using household income per capita.

Table 7 shows the results in terms of income gains from reduction in years of life lost related to dietary risks. Every decile has income gains from increased expected income as a result of increased life expectancy, meaning that all values are positive. Notably, lower deciles have higher income gains, with the bottom consumption decile increase being almost 9 times that of the top consumption decile.

The pattern of income gains being higher for lower consumption deciles indicates that a tax on processed and ultra-processed foods also presents a progressive pattern when we analyze this specific impact. The progressiveness of taxation is due to higher elasticity for deciles at the bottom of the consumption distribution, as the other possibility for higher income gains would be a relative consumption of processed and ultra-processed foods among lower deciles in comparison to higher deciles, which is not the case, as shown in Table 2.

_Table 7_ – Income gains from reduction in years of life lost related to dietary risks

||**Processed and**<br>|**ultra-processed foo**<br>|**
harvesting season. These results are statistically significant at 5% for all but the short rainy season.

The second panel reports child labor statistics at the (unconditional) intensive margin. Results are mostly consistent with the patterns emerging from the extensive margin of work: boys work at the farm several more hours than girls regardless of respondent type. For the pooled data, this difference reaches 10 monthly hours, or 50% higher monthly hours than the mean for girls.

17
**<u>Table 3. Customs Clearance Times in 2010 for IHC and Non-IHC Firms</u>**

||Distr<br>|ibution of de<br>|claration-l<br>|evel custom<br>|s clearance ti<br>|mes in 2010<br>|in hours<br>||
|---|---|---|---|---|---|---|---|---|
||Mean|25<sup>th</sup><br>percentile|Median|75<sup>th</sup><br>percentile|90<sup>th</sup><br>percentile|Standard<br>deviation|Coefficient<br>of<br>variation|Number of<br>import<br>declarations|
|IHC firms by the|||||||||
|end of 2013|6.66|0.40|0.84|1.72|3.64|45.46|6.83|23,516|
|Non-IHC firms by|||||||||
|the end of 2013|36.16|0.57|1.41|5.03|70.42|440.57|12.18|622,264|
|All firms|35.09|0.56|1.37|4.66|69.35|432.59|12.33|645,780|

Source: Authors’ calculations based on Serbian customs data.

Table 4 shows the distribution of monthly inspection rates in 2010 for firms that did and did not adopt the IHC program by the end of 2013. The mean inspection rate for IHC firms is lower than for non-IHC firms, and IHC inspection rates are much lower in the upper half
challenging to assess the impact of firm growth programs, as well as efficacy of business partnerships, using simple selfreported profits or sales.

One solution for addressing concerns about volatility in outcomes is to collect firm-level data through multiple surveys over longer periods of time (McKenzie 2012; Abebe 2013; de Mel, McKenzie, and Woodruff 2009). Although traditionally constrained by the large budgetary and logistical demands of collecting such detailed longitudinal data, technological advancements are making this approach more feasible. For example, Garlick et al. (2016) find that high-frequency mobile phone surveys offer comparable data quality, measure dynamics better, and cost less than conventional low-frequency in-person surveys. However, mobile phone surveys are limited in the 3
2</sup> has the shared prosperity index for 94 countries, up from 72 in the version published in September 2014.<sup>3</sup> In the latest GDSP, the shared prosperity index is calculated using household surveys circa 2007–2012. The average years for the first and the second surveys are 2006.6 and 2011.7, respectively.<sup>4</sup> With the paucity of household survey data as recently documented in Serajuddin, et al. (2015), it is remarkable that as many as 94 countries satisfied the substantial data requirements to calculate the shared prosperity index. Of the 134 countries included in PovcalNet,<sup>5</sup> however, only 71 countries (53 percent) are also included in the latest version of the GDSP. Using the World Bank’s regional classification, three regions are relatively underrepresented in the GDSP. For East Asia and the Pacific, the GDSP has 7 out of 18 countries (39 percent); for the Middle East and North Africa, 4 out of 11 countries (36 percent); and for Sub‐Saharan Africa, 15 out of 43 countries (35 percent). In the other four regions, the GDSP contains 45 out of 72 or about 70 percent of the countries in PovcalNet. IDA countries and countries designated as fragile and conflict affected states are also under‐represented. The GDSP includes about 35 percent of IDA countries and 65 percent of all other countries in PovcalNet. Of the 24 fragile and conflict affected states in PovcalNet, only five of them are in the GDSP.

Notwithstanding, the GDSP appears to be a fairly good representation of the developing world in terms of poverty incidence. The 71 countries that are present in both the GDSP and PovcalNet in total account for approximately 800 million of the poor population and 5.1 billion of the total population,<sup>6</sup> both of which

> 2 Available at: <u>http
.0**|**32.0**|**12.5**|
|The agricultural group|43.4|43.0|16.7|42.8|42.0|16.9|43.9|43.0|16.1|
|The low-skilled group|36.1|34.0|14.9|34.2|32.0|14.8|35.7|34.0|14.7|

Source: Own calculations based on ENEU survey 8
302). While tax authorities legally have between 30 and 60 days to put in effect a decision on a tax appeal in Chile, Ghana, and Vietnam, this time frame is stretched to 365 days in India.

# **3.3.1. Deductions**

Tax deductions are applied automatically to taxpayers satisfying the criteria established through legislation (Barreix et al _._ 2017). Tax deductions are intended to compensate disadvantaged individuals or to encourage specific behaviors or expenditures that would be beneficial for society (Poterba 2011; Doerrenberg et al. 2015). In Ghana, India, and Vietnam, tax deductions are applied if an individual has dependents. In Ghana, tax deductions are defined as a fixed amount of 3.45% income per capita for an individual who has a dependent spouse or at least two dependent children.<sup>15</sup> In Vietnam, a deduction of 7.89% income per capita is applied for each dependent. In India, tuition fees paid for the education of any two children qualify for income tax deductions. In Chile, no deductions can be applied based on dependents (Table 3.2).

**Table 3.2: Taxpayer Eligibility for Deduction and Respective Amounts**

|**Country**|**Is the taxpayer eligible for any**<br>**deduction based on his dependents?**|**Deduction amounts (income per**<br>**capita)**|
|---|---|---|
|**Chile**|No|0%|
|**Ghana**|Yes|3.45%|
|**India**|Yes|Amount of tuition fees|
|**Vietnam**|Yes|7.89%|

_Source: Living Life data. Notes: Cost expressed as percentage of GNI per capita using the Atlas Method in current (2016) U
%) Estimated difference<br>B2.3. SOSD: Social Sustainability Index B2.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.97*** 0.9***<br>H<br>Kendall tau-b 0.89*** 0.89*** 0.72***<br>Spearman 0.95*** 0.92*** 0.68**<br>A<br>Kendall tau-b 0.89*** 0.78*** 0.56**<br>Spearman 0.93*** 0.93*** 0.9***<br>M0<br>Kendall tau-b 0.83*** 0.83*** 0.72***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .2<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference 0<br>.2<br>-.1<br>0<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _B3. Change in the weights assigned to each dimension.
export data. Nevertheless, the type of exchange rate choice does not impact significantly the size of the pass-through coefficients.

As it is visible in Table 3, a decline in international food prices does not transmit into domestic food markets and virtually in all MENA countries prices are highly downward-sticky.<sup>16</sup> Commodity price volatility is thus having an asymmetric effect on domestic prices: only price increases are transmitted, whereas consumers do not benefit from a decline in food prices. These results are consistent with the findings

> 16 The UAE and Yemen are the only exceptions.

-19-
the aid context, these models speak more directly to the importance of aid targeting than to any non-linear growth effects of aid.

The rest of this paper is organized as follows. In the next section we review some of the existing evidence on poverty traps. In Section 3 we discuss saving-based poverty traps, and consider the role of foreign aid in helping countries to escape from such traps. In Section 4 we consider the evidence for technology-based poverty traps. Section 5 concludes.

# **2. Existing Empirical Evidence on Poverty Traps**

In this section we briefly review some of the few existing empirical studies relating to poverty traps. One strand of the literature provides very reduced-form evidence based on crosscountry data. If poverty traps are an important feature of growth dynamics, then over time one would expect to observe a bimodal distribution of per capita income, with a group of poor countries clustered around the low-level poverty-trap equilibrium, and a group of rich countries clustered around the high equilibrium (see Azariadis and Stachurski (2004) for details). In a series of papers Quah (1993a, 1993b, 1996, 1997) has used non-parametric methods to estimate the evolution of the distribution of per capita income across countries. His results suggest the emergence of such a bimodal distribution of income. This finding is supported by a recent paper by Bloom, Canning, and Sevilla (2003) which provides evidence that, after controlling for countries’ geographic characteristics, the evolution of income across countries is consistent with a bimodal ergodic distribution. There is some controversy, however, regarding the bimodality of the long-run income distribution: Kremer, Stock, and Onatski (2001) argue that the dynamics of the world income distribution is better characterized by a prolonged transition, during which inequality may increase, towards a single-peaked long-run distribution. A similar result is obtained in Azariadis and Stachurski (2004b), who uses a parametric model of income dynamics derived from an explicit growth model based on Azariadis and Drazen (1990). They also find that the
to the fall of Suharto is difficult given that there are many confounders, such as globalization, technological progress, changing demographics, structural change etc.

19 As a prelude to that analysis, the online appendix C2 examines the relationship between changes in connectedness and changes in competition during the Suharto era, and shows that increases in the market share of connected firms were associated with attenuated competition as measured by the Z competition index which is based on 8 individual indicators of competition during the Suharto era.

20
.3 percent of Roma women participated in the labor market, compared to 49 percent of Roma males.<sup>3</sup> Limited access to jobs and socioeconomic rights keep Roma people trapped in a vicious cycle of poverty and social exclusion, where Roma women suffer the most, as they experience “double discrimination” (O’Higgins 2012).

**In the economic literature studying the labor market decisions of males and females, a strand of research has shown that biased conclusions can result from omitting the effects of family structure and the interdependence between one family member’s decisions and other family members’ conditions** . Moreover, household gender roles and restrictive social norms can create barriers for some members wishing to participate in the labor market. Becker (1981) considered that in order to navigate these barriers, family members should specialize in the production of the resource in which they have a comparative advantage. Our model focuses on spouses’ decision making, considering that the household head and his/her spouse are the main providers of resources. Additionally, defining the spouses as the unit of study helps to disentangle whether the observed gender gaps are due to differences in the labor market or due to differences in gender roles regarding leisure and home production.

**Given the Roma population’s low labor participation rate and lack of labor market opportunities, we are modeling the household decisions with a Unitary Spouses Joint Labor Search model. Search models are useful to understand labor market dynamics given the existence of frictions,**

> 2 The 2017 UNDP-WB-EC Roma Regional Survey is the most comprehensive survey to date on living conditions and human development outcomes among marginalized Roma households in the Western Balkans, as well as nonRoma households in the vicinity of Roma.

> 3 Population weighted regional statistics constructed with the 2017 Roma Regional Survey **.**

2
) would be necessary in order to assess the optimal scope of financial intermediation.

> 21Data source: Average Loan Size for All C&I Loans, All Commercial Banks (EAANQ), Federal Reserve Bank of St. Luis (FRED database)

> 22Data source: table 1.B, Wolff [2010].

> 23When profits from intermediation are included in income, the model is no longer linear and becomes harder to solve analytically. I therefore reserve this important quantitative exercise for future work.

27
INE) for 2007-17. The EEA draws its sample from a directory of formal firms with annual sales above 150 Tax Units,<sup>3</sup> based on administrative tax records. The survey stratifies the sampling frame by economic activity and firm size proxied by annual sales to ensure that it is nationally representative of Peruvian firms at the four-digit ISIC code level. For each year, given their sectoral importance, firms with annual sales above a certain threshold are selected using forced inclusion sampling (inclusion forzosa) to form

> 3150 Tax Units amounted to 517,500 in 2007 and 607,500 in 2017. The tax is called Unidad Impositiva Tributaria.

12
# **4.3 Counterfactuals**

We calibrate the model to assess the welfare and spatial impacts of new infrastructure investments. The counterfactual exercise is done in three steps. First, we calibrate the model to obtain the underlying parameters of the model for the baseline situation, without the new investments. Second, we update the trade costs and sectoral productivity parameters based on the new assumptions. Third, we use the model to obtain the new employment shares given the new transport costs and productivities, the wage per location, and therefore the real wage given the new equilibrium goods and housing prices.

We assume that electrification increases productivity in modern sectors and that transport investments increase market access by reducing transport times. To build the electrification counterfactual, we assume that access to electricity increases productivity in modern sectors, being manufacturing and services (see Moneke (2020)). To build the transport counterfactuals, we use the available road networks for each country, making assumptions about speed along the networks based on the type and condition of roads that are registered. Investments are assumed to increase the speed at which vehicles can travel along segments that are improved or build new links between locations. We assume trade costs to be iceberg costs, such that the costs between the origin location _o_ and destination _d_ are given by _dod_ = _max_ (1 _,time_<sup>_τ_</sup> ). Border costs are also added to travel costs, as detailed below.

We calibrate the model using spatial data for land, population, and sectoral shares from the sources used earlier. Because of the complexity of a three-sector model to converge in order to recover the initial sectoral productivities, we reduce the spatial disaggregation to fewer locations. Such aggregation also smoothed measurement issues of sectoral employment based on the DHS data. We divide the Lake Chad region into 24 regions, including 8 first-order administrative divisions in Cameroon, 6 in Nigeria, 8 in Chad. Figure A23 shows the share of agricultural employment and population for each of these subnational
that at very low inflation rates (less than 3 percent a year), inflation and growth are positively correlated while they are negatively correlated above that level. They also found out that the negative relationship is convex meaning that the decline in growth is larger at low inflation rates than higher ones.

The above empirical findings on the relationships between inflation and growth have been confirmed on both developing and developed countries’ economies. In a study covering Bangladesh, India, Pakistan and Sri Lanka, Malik and Chowdhury (2001) demonstrate that moderate inflation is helpful to growth and faster economic growth feeds back into inflation. They therefore recommended moderate inflation for growth of these economies. Using Vector Auto Regressive (VAR) techniques, Nell (2000) examines the relationship between inflation and growth in South Africa covering the period 1960-1999 and found out that inflation within the single-digit zone may be beneficial to growth, while inflation in the double-digit zone appeared to be detrimental.

# _Estimated inflation thresholds_

In the quest to explore the relationships between inflation and growth, several studies went further to establish a threshold level of inflation that constitutes a spot sign above which inflation becomes harmful to the economy. This category of papers is based on pooled data ordinary (OLS), generalized least square (GLS) or non-linear least squares estimators, within-between panel specific estimators, and panel smooth transition (PSTR) estimators.

Based on pooled data of 87 countries covering the period 1970-1990 and GLS estimators, Sarel (1996) finds a significant structural break at 8 percent of annual average inflation rate based on a panel data. The determination of the threshold is based on the minimization of the sum of squared residuals, and it allows the author to conclude that below 8 percent inflation rate, inflation has a slightly positive effect on growth but powerful negative effect on growth above that threshold. Along these lines, Khan and Senhadji (2001) examine the issue based on a data Page | 8