rafmacalaba/datause-encoder-data huggingface dataset:
# **5. DISCUSSION AND CONCLUSIONS**

Malawi is a country in biomass distress. It is also one of the poorest countries in Africa. Our goal in this paper is to understand how biomass access varies across the country, how this varied access affects the poor and how the poor cope with scarcity.

Remote sensing data shows that a large proportion of the biomass available in Malawi is in the northern region, an area that is much less populated than the rest of the country. The South with much less biomass faces considerable scarcity. Overlaying biomass and poverty maps on each other shows that at the district level there is no one-to-one relationship between poverty and forest degradation.

Ninety-seven percent of rural Malawians depend on fuelwood as their primary source of energy for cooking. Almost eighty percent of households collect fuelwood directly from areas close by. Ninety percent of the poor collect their own fuelwood. On an average day when women go out to collect fuelwood, they spend one and half hours on this task. Thus, biomass scarcity matters for the welfare of rural Malawians.

Our analyses suggest that biomass scarcity is associated with small but significantly lower household welfare, particularly for the rural poor. At current high levels of scarcity, 80 percent of the rural poor households are likely to benefit moderately from an increase in biomass in the community. The districts that are likely to gain the most from investments in biomass are Lilongwe, Zomba, Dedza, Kasungu, and Mangochi.

There have been attempts in the past to invest in fuelwood in Malawi and to induce rural households to plant more trees. Unfortunately, these efforts have largely been unsuccessful (French 1986, Dewees 1995, Walker 2004,). Our results may provide an economic explanation for the households’ decision not to plant more trees despite various policy interventions. The net returns to households from participating in biomass related projects may not be adequate. This does not mean that Malawians do not perceive biomass to be scarce. Rather, it indicates that given various constraints, households may choose to not use scarce land and labor to plant fuelwood oriented tree crops. This suggests that understanding household responses to scarcity has to be integral to any strategy to resolve Malawi’s deforestation problems.

23
the 12 months prior to the COVID-19 pandemic.

# **3.2 Tax Administrative Data**

The main data source used in this paper are monthly administrative records of TOT filing and payments between 2015 and 2024. Given the nature of TOT, monthly declarations mainly include the identity of taxpayers and their total declared turnover – taxes are then automatically calculated. We complement these filing data with information on TOT payments<sup>12</sup> and also characteristics of taxpayers, such as their declared location and business type.

In Table 1, we provide key descriptive statistics of our sample of interest. We highlight several stylized facts. First, we observe a somewhat stable number of businesses declaring ToT in the 2016 - 2018 period (between 6,000 and 7,000 per year), followed by a decline in 2020 during the COVID-19 pandemic and a rebound thereafter. In 2023, the number of businesses filing increases by almost 70% to over 17,000. As shown in Figure 3, that increase was particularly strong starting in August-September, when the number of monthly declarations climbed from 10,000 to 14,000 by November. This expansion coincides with the roll-out of a strong registration campaign by KRA involving the hiring of new agents (Revenue Service Assistants - RSAs) to improve the authority’s presence throughout the country. Despite the expansion of registered small businesses as TOT payers, it should be noted that these numbers are still very likely only a fraction of total potential taxpayers: less than 3,000 taxpayers filing ToT in 2023 are registered as Nairobi residents, a city of over 4 million people. Furthermore, Nairobi City Council has over 200,000 businesses in its business licensing regime, many of which should be also part of the TOT regime. This suggests that subnational/county governments may have a stronger knowledge of the existence of unregistered businesses and the ability to tax them than KRA.

> 12Most commonly, we observe payments being linked to one declaration. In some cases, we observe payments for which no filing data exists. The main reason for that is that no declaration is generated when individuals use the KRA app to pay their TOT. This phenomenon occurs in approximately 10% of the payments across the entire sample.

16
related to policies and institutions within the sector than to trade openness _per se_ (World Bank, 1999).

# **III. HYPOTHESES AND SPECIFICATION**

# **_a. Hypotheses_**

In general, trade liberalization can affect the environment through several mechanisms, such as inter-jurisdictional competition to lower standards, transfer of pollution abatement technology, cross-border spillovers, or changes to the overall scale of economies. The various effects of trade on environmental quality can be divided into three components: how trade affects the overall _scale_ of the economy; how trade affects the _techniques_ of production, and how trade affects the _composition_ of industries (Copeland and Taylor, 2003). But the most direct effect of trade liberalization on the environment would be through the composition of industries and hence much of the focus of the literature has been on dissecting the composition effects of trade. Trade liberalization leads to specialization, and countries that specialize in less (more) pollution-intensive goods will have cleaner (dirtier) environments.

Given Vietnam’s comparative advantage in labor-intensive goods and relatively weaker environmental regulations compared to its main trading partners (such as Japan and EU), there is concern that as Vietnam continues to expand its international trade it may be specializing in pollution intensive industries. The primary objective of this study is to examine the composition effect of trade liberalization in Vietnam and form policy recommendations relating to its trade and environmental policies.<sup>6</sup> A retrospective analysis of Vietnam’s experience with partial trade liberalization (as a result of its participation in the AFTA and the USBTA) in the past few years will enable us to provide policy recommendations to reduce potential negative effect on the environment as Vietnam prepares to join the WTO later this year, and as the AFTA comes into full effect. While the trade obligation under the WTO are more comprehensive than the

> 6 Composition effect measures the increase in pollution that is likely to result as a result of a change in composition of output and exports, following a move towards free trade.

6
# NOTES

1The Sudanese data used in this paper were collected in 2007. We began work on this paper in 2009, before South Sudan seceded from Sudan in July 2011. Thus, our sample includes firms in what is now South Sudan. Throughout the text, “Sudan” means Sudan prior to the secession.

2A network consists of links between the nodes in a finite collection of nodes (for example, firms). See Jackson (2009).

> 3For this statement to be true, it is sufficient that ρ is smaller than one over the maximum degree of any agent (Jackson 2009).

> 4To illustrate the point, let _N_ = 2 , α = − 1 and ρ = 2 . If _y_ 2<sup>=</sup> 0 , then the _y_ 1<sup>≥</sup> 0 constraint is

binding and _y_ 1<sup>=</sup> 0 . If _y_ 2<sup>=</sup> 1 then _y_ 2 = − 1 + 2 × 1 = 1. Thus, we have two equilibria: ( _y_ 1, _y_ 2) = (,0 0) and (1,1).

5The online appendix can be obtained at <u>http://soderbom.net/Fafchamps_Soderbom_Online_Appendix_2013.pdf</u> and at <u>http://wber.oxfordjournals.org.</u> 6Heterogeneity across firms has increasingly been recognized in the recent literature; see, for example, Melitz (2003) and Melitz and Ottaviano (2008).

7There are two reasons to estimate (3) in terms of its absolute deviation and not in a covariance form as in (2). First, most of the outcome variables we investigate are binary; as a result, the only information they contain is whether _yi_ = _y j_ . In this case, (3) boils down to a linear probability model because _yi_ − _y j_ = 1 if _yi_ ≠ _y j_ and 0 otherwise. Second, in the more general case when _y_ is continuous, model (3) captures the main idea behind (2) but offers the advantage of being more 44
practices, and quality of materials.

# **3 Data and Descriptive Statistics**

Our main source of information is the _Sistema de Avaliação da Educação Básica_ (SAEB) provided by the Brazilian Ministry of Education between 2005 and 2017. SAEB is a learning quality assessment implemented biannually in Brazil that provides standard test scores in Mathematics and Portuguese, and collects detailed information about students, teachers, and school principals. The SAEB assessment is undertaken at the end of each educational cycle: 5<sup>_th_</sup> grade of primary education, 9<sup>_th_</sup> grade of lower secondary education, and 3<sup>_rd_</sup> grade of upper secondary education. Because virtually only primary and lower secondary education are managed by municipalities in Ceará, and are therefore part of the _RBF_ mechanism, our regression analysis is restricted to 5<sup>_th_</sup> and 9<sup>_th_</sup> grades. Importantly, to reduce the influence of gaming in our findings, note that SAEB is _not_ part of _RBF_ indicators in _Ceará_ . Instead, the RBF uses an independent state-level assessment called SPAECE. An additional advantage of SAEB is to provide unique city identifiers that allow us to explore geographic properties of schools in our identification strategy and control for city fixed effects. In 2005, only a representative sample of schools participated in the assessment; from 2007 on-wards it included all public schools with at least 30 students enrolled in the 5<sup>_th_</sup> or 9<sup>_th_</sup> grade.

The rich information in SAEB allows investigating a comprehensive set of outcomes. At the student level, it provides general characteristics such as gender, age, and race and specific household characteristics as single parenting, household size, number of bathrooms, and parental education. The teacher questionnaire asks about their level of qualification and experience in education, recent participation in training programs, basic contract characteristics, and pedagogical practices in the classroom (e.g., homework correction, materials used, and curriculum coverage). For school prin12
Lewis and Lockheed Studies in Brazil (Mario and Woolcock 2007;) Eastern Europe (Ringold, Orenstein and Wilkens 2003), India (Narayan 2000), Laos (King and van der Walle 2007), Peru (Nopo, Saavedra and Torero 2004), South Africa (Mwabu and Schultz 2003), and Vietnam (van der Walle and Gunewardena 2001), demonstrate the significant gap in average incomes between dominant groups and marginalized groups. For example, in Latin America, workers from indigenous and racial sub-groups earn less than white non-indigenous workers with the same educational attainment. Earnings of females from marginalized groups lag considerably behind those of males in their community particularly in countries with the largest indigenous or racial sub-groups (Bolivia, Brazil, Guatemala, Guyana, Peru) (Hall and Patrinos 2006).

**_Social-Psychological perspectives._** Two main lines of thinking are relevant to the education gender gap within marginalized groups, particularly as it affects achievement on tests. One explanation, relevant to individual performance, focuses on how a testing situation can “activate” gender or ethnic stereotypes, to the detriment of those who feel threatened by the stereotype. Activation of the stereotype creates higher levels of anxiety among the test takers, distracting them from the task and lowering their performance. Research on such “stereotype threats” has been carried out in dozens of laboratory experiments with respect to black/white, gender, social class and white/Asian differences in test performance (Steele & Aronson 1995). In all cases, performance declined when stereotypes were activated. For example, the math performance of high achieving white male students declined when they were told that Asian students usually outperformed whites (Aronson et al 1999). The test performance of high achieving black students was poorer when they were told the test was a test of ability than when they were told it was just a test about problem solving (Steele and Aronson 1995).

An example of stereotype threat in a developing country comes from an experiment carried out in India by Hoff and Pandey (2004). Low-caste junior high school boys performed equally well as high-caste junior high school boys at a task when the boys were strangers and had no information about each other, including about their caste. When caste was announced, however, the performance of low-caste boys dropped, while that of high caste boys improved. And the difference became greater when the boys believed success in performance depended upon subjective judgments being made by the researchers. Research shows, however, that discrimination based on stereotyped beliefs about abilities can be reduced by counterstereotypical information (Aguero 2005).

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TABLES & FIGURES

# **Table 7 - Diversification of activities in the municipality and impact of negative rainfall shocks**

||(1)<br>Total<br>distance of<br>barangays|(2)<br>% of<br>barangays<br>within less<br>than 5km|(3)<br>Total<br>number|(4)<br>Total<br>number|(5)<br>Total<br>number|(6)<br>Total<br>number|(7)<br>Total<br>number|
|---|---|---|---|---|---|---|---|
|dev_mean_all_a_pos_m1sd|0.017|0.016|0.016|0.016|0.016|0.016|0.016|
||(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|(0.012)|
|dev_mean_all_a_neg_m1sd==1|0.008|-0.069|-0.046***|-0.034*|-0.040**|-0.048***|-0.037**|
||(0.026)|(0.046)|(0.016)|(0.018)|(0.018)|(0.016)|(0.016)|
|Negative rainfall shock * Distance||||||||
|to market|-0.001***|0.045||||||
||(0.000)|(0.075)||||||
|Negative rainfall shock * N||||||||
|Commercial establishments|||0.000|||||
||||(0.000)|||||
|Negative rainfall shock * N||||||||
|Manufacturing establishments||||-0.000<br>(0.000)||||
|Negative rainfall shock * N||||||||
|Services|||||-0.000|||
||||||(0.000)|||
|Negative rainfall shock * N hotels||||||0.000<br>(0.000)||
|Negative rainfall shock * N Banks|||||||-0.000<br>(0.000)|
|Constant|5.532***|5.532***|5.532***|5.532***|5.532***|5.532***|5.532***|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|(0.004)|
|Observations|64,664|64,601|64,664|64,664|64,664|64,664|64,664|
|R-squared|0.003|0.003|0.003|0.003|0.003|0.003|0.003|
|MunicipalityFE|YES|YES|YES|YES|YES|YES|YES|

The estimates also control for year dummies. Standard errors are clustered at the municipality level. *** p<0.01, ** p<0.05, * p<0.1 28
_discernment_ , defined as a participant’s average misinformation scores for misleading headlines minus their average score for headlines without misleading content. With this operationalization, discernment scores could range from -3 to +3 where a score of +3 indicates a participant rated all misinformation headlines as ‘Definitely misinformation’ and all non-misinformation headlines as ‘Definitely not misinformation’ for perfect discernment.

We also examine the disaggregated discernment score including the average ratings for the three misleading headlines and ratings for the three non-misleading headlines. As higher ratings correspond to judgements that a headline is using misinformation tactics, more accurate scores would be represented by higher scores on the misleading headlines (representing true positives) and lower scores on the non-misleading headlines (representing true negatives).

**_Sharing Misinformation_** _._ Adapted from Basol et al. (2021) and Roozenbeek and van der Linden (2020), sharing of misinformation was assessed with two headlines: one that did not use misinformation tactics and a headline that used misinformation tactics. Each participant was randomly assigned to one of two misleading headlines, a headline that used extreme emotion or a headline that used a false expert. Participants were asked to rate their likelihood of sharing each of the headlines on a four-point scale: (1) Very unlikely to share, (2) Unlikely to share, (3) Likely to share, (4) Very likely to share.

As with judgements of misinformation, we report three scores for sharing—discernment of sharing, likelihood of sharing misleading headlines, and likelihood of sharing non-misleading headlines. Discernment of sharing was calculated as the sharing score for non-misleading headline minus the sharing score for the misleading headline. Accordingly, participants with high positive discernment of sharing have _lower_ intentions of sharing misinformation, relative to sharing more non-misinformation.

15
<!-- Start of picture text --> Figure 2: Non-oil exports from AGOA beneficiaries to the US<br>7,000<br>6,000<br>5,000<br>4,000<br>No program<br>3,000<br>2,000 AGOA<br>GSP<br>1,000<br>0<br>1999 2000 2001 2002 2003 2004 2005<br>USD million<br><!-- End of picture text -->

# **4. Exports under AGOA Are Driven by Apparel**

Diversification into new export categories, reducing the dependence on exports of traditional primary commodities, is a major objective for many developing countries. Exports of apparel have provided an initial step on the route to more diversified exports for many developing countries. Through manufactures of apparel developing countries have been able to significantly increase and diversify exports with positive effects on incomes, employment, and poverty.<sup>7</sup> Apparel remains a key sector for many developing countries since they can exploit their comparative advantage in low labor cost operations whilst many apparel products remain subject to relatively high tariff barriers in the EU and the US. In addition, technology is relatively simple, start-up costs are comparatively small and scale economies are not important, all favoring production in low labor cost locations.

Indeed, it is exports of apparel that have been at the forefront of the expansion of Africa’s non-oil exports to the US under AGOA. Exports of apparel to the US from AGOA beneficiaries have increased by 140 percent since 2000. Exports of apparel have grown much faster than exports of other non-oil products leading to a substantial diversification of overall exports. The share of apparel in these countries exports to the US has increased from 13 percent to 20 percent. All of the increase in apparel exports has taken place under AGOA, which offers substantial preferences for African countries since tariffs on many apparel products exceed 15%. This expansion took place while major exporters of apparel in Asia

> 7 Kabeer and Mahmoud (2004) suggest that the production of garments for export in Bangladesh has generated 1.6 million new jobs, most of which were captured by women. Many of these workers tend to be migrants from poorer areas. Kabeer and Mahmoud also find that wages for garment workers are double those of other workers involved in nontradable activities. There is, however, some evidence to suggest that workers in this sector are vulnerable to changing employment contracts and the increasing “casualization” of work (Nadvi 2004).

6
where Yt is the dependent variable, X′t denotes the vector of explanatory variables (column vector), εt is the error component, ht is the time-varying variance of the error; Ω _t-1_ is the information set available at t−1, ω, αi for i = 1,2,. . ., β are parameters. **Equation (11)** gives the conditional mean while **equation (13)** describes the evolution of the conditional variance. We adapt **equations (11)** and **(13)** to our study so as to investigate the determinants of maize price volatility in Burkina Faso.

We proceed in a two-step approach. Firstly, we pool 28 maize markets in order to estimate the average effect of market remoteness (i.e. transport cost, time distance between market i from Ouagadougou, Bobo-Dioulasso and Koudougou: the major consumption centers) on price level ( **equation 14** ), and secondly, on price volatility ( **equation 15** ): ln Pit = θ0 + θ1ln Pit−1 + θ2Trendt + θ3ln RERt + θ4lnIPt + ∑μi i Sit + ∑ʊj j Mjh + ρlnTC+ δ1lnBorder+ δ2Surplus+ εit **(14)** hit = λ0 + λ1ε<sup>2</sup> t−i<sup>+ Ω</sup> 1<sup>ln P</sup> it−1<sup>+ Ω</sup> 2<sup>Trend</sup> t<sup>+ Ω</sup> 3<sup>ln RER</sup> t<sup>+ Ω</sup> 4<sup>lnIP</sup> t<sup>+ ∑π</sup> i i Sht + φlnTC+ ω1lnBorder + ω2Surplus + vit **(15)** The specifications retained indicate that explanatory variables have been introduced in both mean and variance equations. Ln Pit and Ln Pit−1 are the natural logarithms of real maize price in market i at months t and t−1 respectively. Trend, RER and IP represent the monthly trend, the real exchange rate<sup>13</sup> and the international maize price respectively. S refers to seasonal<sup>14</sup> dummy variables (lean and harvest seasons) while M denotes maize market dummy variables. εit is the error term. Border is a continuous variable that measures the distance between market i and the nearest cross-border maize point with Ghana, Côte d’Ivoire, or Togo, four border points being considered because of their importance in terms of maize trade volumes. Surplus is a dummy variable which indicates whether the market is in surplus production area. Surplus equals 1 for maize-surplus regions and 0 for maize-deficit regions. In this study, we capture _(_ **_TC_** _)_ **_Transport Cost_** through three measures: time distance,

> 13 The real exchange rate and the real international price are computed as the ratio of the FCFA to the US dollar and then deflated using the Burkinabe Consumer Price Index. We also use the natural logarithm to smooth the series.

> 14 In Burkina Faso, seasonal variability is characterized by differences in prices between the lean (June to September) and the harvest (October to December) seasons.

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Policy Research Working Paper 8064

# **Abstract**

This paper provides novel evidence on status goods, using a series of field experiments with an Indonesian bank that markets platinum credit cards to high-income customers. In a first experiment, the paper shows that demand for the platinum card greatly exceeds demand for a nondescript control product with identical benefits, suggesting demand for the pure status aspect of the card. Transaction data reveal that platinum cards are more likely to be used in social contexts, implying social image motivations.

Combining price variation with information on the use of the card sheds light on the magnitude of the demand for social status. A second experiment provides evidence of positional externalities from the consumption of these status goods. The final experiment shows that increasing self-esteem causally reduces demand for status goods. This suggests that part of the demand for status is psychological in nature, and that social image is a substitute for self-image.

This paper is a product of the Finance and Private Sector Development 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 mkanz@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 expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team