rafmacalaba/data-use-mentions-extended huggingface dataset:
demonstrating that the control variables included help to explain achievement variation but are not driving our results. Moreover, we estimated models with quadratic time trends, allowing for non-linear trends in achievement, and with different years dropped from the analyses. In both cases the results for mathematics did not change substantially. Finally, we re-estimated results with original survey weights representing the target populations of 15-year-olds in every country. These results show learning loss estimates for the global population represented by the PISA sample rather than for the average across countries. Again, the results were almost identical to those presented above using senate weights that give equal weight to every country (see Annex Table A3). For brevity, we present the main estimates only and descriptions of the regression model applied. Full results are available upon request from the authors.

# **5. Global Economic Impact and the Need for Learning Recovery**

Our model predicts that every week of full school shutdown will result in a 0.44-point achievement decrease. Using the UNESCO database, we estimate that the average learning loss across all educational systems is approximately 9 points. The literature offers several estimates on how PISA scores translate to economic growth. Although the range of estimates available in the literature vary depending on the set of countries, estimation methods, and control variables, we used the coefficient of 1.74 (Hanushek and Woessmann 2010). The average loss of 9 points (9 percent of a SD) for 199 educations systems (recognized by the UNESCO school closure database) translates to an average of 0.15 percentage points of GDP growth losses, ranging from zero (if schools remained open) up to 0.57 points. This enormous GDP loss in nominal values using World Bank GDP 2021 data equals $17 trillion of economic loss. These estimates are similar to other studies (see, for example, Azevedo et al. 2021; Doty et al. 2022; Psacharopoulos et al. 2021).

To avert some of these losses, then learning recovery is
the model specified in equation 1, where we model heterogeneity by GDP and the percentage of sales that are direct exports. Coefficients show estimated effects of a one standard deviation change in average temperature differences based on the fully saturated model with third-order polynomials. Columns labeled “low” evaluate the estimated model at the 10th percentile of the percentage of direct exports and columns labeled “high” evaluate the estimated model at the 90th percentile of the percentage of direct exports within each group of countries listed at the top of the table. Mean temperatures are 15C in HICs, 17C in UMCs, and 23C in LICs + LMICs. We report standard errors clustered at the Enterprise Survey strata level in parentheses.

39
it seems plausible that respondents here too are over-reporting as the recall period increases.

# Inputs

There are two main results regarding the effect of recall length on agricultural inputs (we analyze organic and inorganic fertilizer use in this study): first, respondents tend to report higher fertilizer quantities as the recall length increases. Second, respondents are slightly less likely to report incidence of fertilizer use, that is whether or not any fertilizer was applied to a given plot.

We use a linear OLS specification with a full set of controls to estimate the effect of recall length on quantity of inorganic fertilizer applied per plot, conditional on fertilizer use. The effect size is large in Malawi IHPS 2016/17 at 4.3 kg per plot per month (8.8 percent), and smaller in Malawi IHS4 at 0.9 kg (1.9 percent). In Tanzania NPS 2012/13, the coefficient has the same sign but is not statistically significant (Figure 12, Table A.15, Table A.16, Table A.17). Low fertilizer application rates lead to a small sample of households using inorganic fertilizer on their land at 675 of 7,322 plots, which may explain the recall effect being insignificant. The linear model is the preferred specification based on BIC’ analysis.

Based on a Probit model, we find a small negative recall effect on the binary variable whether any inorganic fertilizer was applied in the case of Malawi IHS4 and IHPS 2016, and whether any organic fertilizer was

> 10 Arthi et al. (2018) and Gaddis et al. (2019) find two countervailing types of reporting bias. First, end-of-season recall modules lead to an over-estimation of reported labor inputs per plot (‘recall bias’). Second, end-of-season recall modules lead to an undercounting of plots listed and household members reported to have worked on them (‘listing bias’). Our results echo those findings: we find longer recall periods are associated with fewer plots listed and higher labor inputs
CAMEROON’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE African Republic increased by about 10 percent from 1.31 to 1.43 million tonnes. The bulk of trade flows are from Cameroon to Chad (452,000 tonnes, of which 76,000 tonnes for gasoline and 64,000 for containers) and from the Central African Republic to Cameroon (252,000 tonnes, of which 237,000 tonnes of wood). Flows from Cameroon to the Central African Republic amount to 126,000 tonnes (of which 21,000 tonnes for gasoline and 20,000 for containers) while flows from Chad to Cameroon (57,000 tonnes), mostly involve cotton exports (49,000 tonnes) (World Bank 2011c).

# Roads

## **Achievements**

Cameroon’s road density is greater than that of peers. The density of the country’s total road network is 72 kilometers per 1,000 km2, higher than the average for Africa’s resource-rich countries at 59 kilometers per 1000 km2. But Cameroon’s road density is still behind the level of the continent’s middleincome countries, which have an average of 318 kilometers per 1,000 km2 (table 3).

Twenty-seven percent of Cameroon’s rural inhabitants have access to all-season roads, as measured using geographic information system (GIS) tools, slightly above the average GIS rural accessibility in resource-rich countries. But evidence from household surveys suggests that 51 percent of the rural population lives within 2 kilometers of an all-season road (table 3).

**Table 3. Cameroon’s road indicators benchmarked**

|Indicator|Unit|Resource-<br>rich<br>countries|Cameroon|Middle-<br>income<br>countries|
|---|---|---|---|---|
|Road network density [a]|km/1000 km2 of land area|59|72|318|
|Classified road network
The share of total employment in agriculture, construction, finance, and.distribution increased in the 1987-94 period while it decreased in manufacturing, mining, transport/communication, and services; and slightly more in the latter than in the three former sectors.

# **2.3 Earnings**

Until 1982 real average wages across sectors as well as real wages in manufacturing were characterized by a positive trend, which was reversed to a negative trend in 1982, see Figure 5. The highest level of average real earnings was in 1982, being 39 percent above the 1979 level (see Figure 6). This was mainly due to the launch of minimum wages (see below).

**Figure 5 Total and manufacturing real wages (Zim $ per year)**

<!-- Start of picture text --> 7500<br>700000<br>Totreal San ioge a ( B<br>4500 / \<br>4000 \<br>3500/<br>3000 - , I I I .<br>Source: CSO and African Regional Database (World Bank).<br><!-- End of picture text --> 8
|20.9|20.4|20.2|
|(as % of total population)|||||||||||
|**Growth Rate**|**1990-95**|**1995-2000**|**2000-05**|**2005-10**|**2010-15**|**2015-20**|**2020-25**|**2025-30**|**2030-35**||
|**(in percent)**|||||||||||
|Total population|2.96|2.99|2.82|2.65|2.38|2.03|1.82|1.59|1.35||
|Active population|3.37|3.41|3.45|3.06|3.15|2.95|2.72|2.39|1.96||
|youth population|3.3|3.9|3.3|2.77|2.13|2.53|1.97|1.04|1.23||

**Source: World population projections 1994-95.**
# **1 Introduction**

In the core of the monopolistic competition model with differentiated goods pioneered by Dixit and Stiglitz (1977), consumers benefit from having more varieties of final goods. These benefits stem from lower unit costs of imports and these lower costs are the source of the welfare gains. However, most studies focus on the conventional sources of gains from trade, such as productivity improvement due to increasing returns to scale, trade-induced innovation, technology spillover, and improved market efficiency because of import competition (Chen and Ma, 2012). These studies often assume a constant set of products over time, and this leads to systematically understated welfare gain calculations.

This paper estimates the comprehensive gains from import variety in emerging markets and developing economies (EMDEs), particularly for each of 28 Asian (including one Pacific Island nation) and East African economies during 1995-2021, following the seminal works by Feenstra (1994) and Broda and Weinstein (2006). The gains from variety for the economies were estimated, using six-digit harmonized system (HS) product data. We estimated more than 100,000 elasticities in total and with the elasticities, we constructed an exact price index to measure the welfare gains from variety growth. This method is consistent with the theory of monopolistic competition and is robust in empirical applications (Feenstra, 1994), from which the quantitative analysis of gains from variety started.

Feenstra (1994) showed how to estimate the elasticity of substitution of individual products, and using these elasticities he offered the formula for an exact price index that can account for entry and exit of varieties. By doing so, Feenstra (1994) demonstrated that new product varieties lead to an increase in consumer utility. However, a comprehensive measure of the gains from import variety puts tremendous demands on data availability and was not realized until Broda and Weinstein (2006).

Applying Feenstra’s estimation technique, Broda and Weinstein (2006) estimated the welfare gain that the United States enjoyed through trade liberalization over 30 years by
it suggests that the additional items collected in 2022 might have a negligible impact on the 2010 consumption aggregate, which raises questions about the plausibility of this implication. Nevertheless, the comprehensive robustness checks performed, including the use of the SWIFT method and backcasting with a pass-through rate, collectively reinforce the robustness of the study's results. These checks confirm that the study's findings are not only resilient to variations in methodological specifications but also to different approaches.

# **V. Final remarks**

This study marks a significant advancement in the analysis of poverty and inequality trends in Bangladesh, showcasing the importance of innovative and robust methodologies in socioeconomic research. Central to its contribution is the reconstruction of these trends using a novel methodology, which incorporates previously unaccounted items into the consumption distributions of 2010 and 2016. This methodological innovation is crucial, as it not only aligns with but also validates the results against other benchmark approaches, thereby reinforcing the reliability of its findings.

The research is particularly significant for public policy, providing a nuanced and accurate depiction of poverty trends, which is vital for effective policy making. While the method introduces a refined understanding of these trends, it importantly does not alter the profile of the poor as identified using the official aggregate. This ensures the compatibility of the new approach with existing studies and recommendations, enabling a consistent poverty profile while simultaneously enhancing the understanding of temporal trends.

The study highlights the critical need for comparable consumption aggregates in analyzing poverty and extreme poverty headcounts, as well as inequality measures over time. For instance, the average poverty lines for 2022 were found to be 21% and 19% higher than the original 2010 and 2016 lines at 2022 prices. In contrast, extreme poverty lines showed a smaller increase of only 6% in both periods. These adjustments suggest higher poverty rates when considering the imputed consumption data. Specifically, the poverty rate in 2016 would have been between 1.7 and 2.3 percentage points higher, and the extreme poverty rate between 3.
--> Notes: The measures plotted are based on measures in the country-year level dataset (file CY.dta) averaged across the 2006-2008 period for each country for each country except Kuwait for which data is for 2009 and Portugal for which averages are across the 2003-2005 period. GDP per capita is in current USD per inhabitant.

The panels in Figure 1 show that more developed economies have a larger number of exporters as well as larger average exporter sizes.<sup>38</sup> More developed economies are also characterized by more concentrated export sectors, in terms of higher shares accounted for by the top 5 percent of exporters. Exporters in more developed economies exhibit a more diversified portfolio in terms of products and destinations. However, more developed economies are less dynamic in terms of exporter churning. In such economies, larger pools of more productive firms may already be operating in the exporting sector and taking advantage of the profitable opportunities in markets abroad, making it less appealing to potential exporters to attempt an entry thus reducing entry rates. If those firms are well established, their probability of exit is also

> 38 A similar pattern is found for median exporter size.

24
# **Tables and Figures**

## **Table 1. Descriptive Statistics** **_Using Program-Level Data from the WBSCPS and Administrative Sources_**

|**Variable**|**Mean**|**Std.**<br>**Dev.**|**Min.**|**Median**|**Max.**|**Obs.**|
|---|---|---|---|---|---|---|
|**Panel A. Programquality determinants**|||||||
|_Infrastructure_|||||||
|Has enough equipment or tools for practice|0.72|0.45|0|1|1|2,103|
|Program offers at least one online class|0.35|0.48|0|0|1|2,103|
|_Training and curriculum_|||||||
|Teaches numerical competencies|0.80|0.40|0|1|1|2,103|
|Promotes work under hardship or pressure|0.84|0.36|0|1|1|2,103|
|Curriculum is fixed|0.70|0.46|0|1|1|2,103|
|Time assigned to practical training (%)|46.45|16.56|4|50|80|2,072|
|<br>Graduation requirement|||||||
|Test|0.40|0.49|0|0|1|2,103|
|Thesis or research project|0.37|0.48|0|0|1|2,103|
|Internships outside institution are mandatory|0.58|0.49|0|1
and Gurung, 2002).

As a result of this “liberalization of international migration”, the share of international migrants opting for these newer destinations increased dramatically between census years 1991 and 2001. Although there was a modest increase in the number of absentees between 1991 and 2001, migrants per 1000 population actually declined from 35 to 32 between 1991 and 2001 (see Figure 1). In terms of the composition however, whereas 90 percent of those absent from home were in India in 1991, the share had dropped to 75 percent in 2001. During the same period, the fraction of migrants that were in the Middle East increased from 1 percent to 12 percent and the fraction of migrants that were in East Asian countries went up from roughly 2 percent to 4 percent. (Figure 2).

Historically, the primary reason for migration out of Nepal has been inadequate round the year employment within the villages. This is also reflected in the stated reasons for migration in the most recent censuses. In 1991, 61 percent of the population absent from home were away seeking employment. This number had increased to 78 percent in 2001 (see Table 1). Given the cultural practice of patrilocal exogamy, it is not surprising that dependency or migration after marriage is the second largest self-reported reason for migration. While 95 percent of all international migrants seeking work were in India in 1991, the number had declined to 75 percent in 2001. In contrast, the Middle East which hosted 1 percent of all employment-seeking international migrants in 1991 was hosting 18 percent in 2001. There was a similar increase for East Asia as well.

There are two distinct aspects of this diversification of international migration destinations in Nepal that are highly important for us in this paper. The first is the large difference in the magnitude of wages that migrants could expect to earn in these newer destinations in comparison to the “traditional” destinations that were mostly urban areas within Nepal and Indian cities. Although wage data are not available, this is somewhat manifest in the
prosperity (defined as a sustainable increase in the income and well-being of the bottom 40 percent of the population) in every country. Household surveys are key to monitoring the progress towards the Twin Goals, and obtaining socioeconomic, behavioral, multi-sectoral information to deepen the understanding of income poverty and inequality (Deaton, 1997, 2016). As aptly put by Deaton (2016:1222), “[t]he documentation of how people live, how much they spend, and on what, has long been used as a political tool, to make visible the living conditions of the poor to those in power, to shock, and to agitate for reform... Today’s household surveys typically collect information on household incomes and/or (often detailed) expenditures, as well as demographic, geographical, and other characteristics of household members. Their official purpose is often to collect weights for consumer price indexes, but they are also used to calculate national and global estimates of poverty and inequality.” International poverty estimates computed by the World Bank rely on household surveys from more than 130 developing countries (World Bank, 2016). Frequent, inter-temporally comparable household surveys are critical for measuring and monitoring shared prosperity (Narayan, et al. 2013; World Bank, 2015a; Atamanov et al. 2016).

Improvements over the past two decades notwithstanding, challenges in household survey data availability for monitoring poverty and shared prosperity remain substantial. During the 10 year period between 2002 and 2011, Serajuddin et al. (2015) document that 57 countries have zero or only one poverty estimate. This implies that in over a third of the world’s developing or middle income countries, there is no meaningful way of monitoring poverty or shared prosperity for that period. Out of 35 countries that have two poverty estimates over the ten-year period, the poverty estimates were more than five years apart for 20 countries, resulting in poverty monitoring efforts being dated. Thus, among the 155 countries for which the World Bank monitors poverty
6|106.8|104.3|93.1|97.8|97.5|95.2|90.9|99.4|99.2|101.2|
|1 child|100|107.2|106.6|109.5|98.1|101.1|106.9|103.7|91.9|96.5|94.8|93.5|88.3|97.5|96.4|97.7|
|2 children<br>3 and more|100|108.6|111.1|108.5|103.7|108.2|104.4|102.8|92.7|97.7|101.0|96.2|95.2|102.4|103.7|107.0|
|children|100|116.5|129.5|112.7|97.6|103.8|109.8|109.5|101.7|105.3|104.6|103.6|91.5|97.3|102.7|103.6|
|singleparent|100|101.8|109.1|101.8|97.6|100.7|130.1|106.7|89.7|96.4|94.6|91.5|90.5|107.0|99.1|114.3|

Source: Authors’ estimates based on HBS data.

28
Figure D.2. Probability of Women’s Marriage Before Age 18 Post the Gujarat Riots of 2002 <!-- Start of picture text --> .2<br>.1<br>0<br>-.1<br>Marriage Year<br>Data Source: NFHS-4<br>Diff-in-diff Coefficients<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014<br><!-- End of picture text --> _Note_ : This figure plots the difference-in-differences estimates from specification 2 using NFHS-IV dropping all flood affected districts. The outcome variable is an indicator of women’s marriage before age 18. The control states only include the bordering states of Gujarat, which are Maharashtra, Rajasthan and Madhya Pradesh. Standard errors were clustered at the state-year level 40
under most governance structures— are an important part of the explanation why sector performance outcomes are so seldom what they are expected to be in the long run.

Even if the nature of the accountability challenge has been identified, however, it is not straightforward how this understanding should help improving sector-governance. Policy strategies are complicated by the very nature of private agendas in politics, which also entails resistance against any measures that may reduce the opportunity to pursue those agendas. Even if accountability-increasing initiatives are promoted by civil society organizations, the development community, political groups, the press, and others, the process to improve political performance might be extremely slow in some countries with continued weak performance at the sector level as the result.

The crucial importance of macro-political factors could be seen as throwing into question the relevance of sector-level recommendations. If the bigger governance indicators are what matters, one may ask if we could just as well give up on sector-specific governance until the accountability issues have been “fixed.” This would be a misinterpretation, however, since the macro-political accountability indicators are no more than accumulated information about decision making—and the overall impression of how sector governance decisions are being solved. The political regime in a country is a framework condition—at least in a short- to medium-term perspective. Policy recommendations can be promoted most easily at the sector-level and this alone defends a sector-level approach for better policy-choices. The question is if also accountability can be better incentivized from this level and how to adjust recommendations to the given political environment framework conditions.

There are difficulties in promoting political accountability from the sector level, however. Some overall accountability issues are addressed in this section: the design of constitutional rules adopted to implement democracy in a given country; the reliance on political self-discipline to enforce rules; and lack of coherence between sector specific rules and global rules in countries.

# **4.1. The design and practical effect of constitutional rules**

The functioning of democracy and separation of powers in a country are essential to
percent of GDP (GDP of Estonia); the Slovak Republic, 2.37 percent; Latvia, 1.65 percent; and Bulgaria, 1.59 percent. They also found that interestingly, two of the largest economies in the group, France and Germany, both had negative gains from variety. They argue that the reason for this is that these larger economies were already heavily integrated in the European economy and therefore did not experience the increase in product varieties as did the “new”, smaller economies.

Our analysis of 4,537 elasticities of substitution across 28 countries reveals that the

> 1As a reference, only a few papers are mentioned here. In addition to its welfare gain estimation, Broda and Weinstein’s (2006) paper is often cited for the import demand elasticity estimation. Already estimated data of 73 countries using the dataset from 1994-2003 is available at the following Columbia University webpage:http://www.columbia.edu/~dew35/TradeElasticities/TradeElasticities.html

2