rafmacalaba/datause-encoder-data huggingface dataset:
undergoing rapid structural change may affect data quality. Second, the measurement of informal, illegal and subsistence activities is often very approximate in poor countries, but is likely to be a relatively larger share of GNI than in higher income countries. Third, countries with weaker statistical systems may also lack adequate data sources and estimation methods for accurately measuring formal activities; business registers—a fundamental tool for conducting a sample survey of businesses—may be incomplete and outdated, and survey response rates may be poor. It is possible that, in some countries, under-estimation of formal activities may be as large as under-estimation of informal activities (Jerven, 2013).

Non-market production is another potential source of systematic biases in income figures. Market equivalent prices generally do not exist for measuring the value of most non-market production, such as government services, own-account production, and the output of non-profit institutions, and the value of non-market output, by convention, is proxied by production costs (wages, intermediate consumption, consumption of fixed capital) without adjustments for productivity or the full cost of use of capital. Because government sector productivity levels likely vary systematically among countries at different income levels, this may result in a systematic bias in income measures: an overvaluation in low income countries with low government productivity levels relative to higher income countries with more robust government productivity levels. This effect is, however, likely smaller than the effect of under-coverage of other activities in poorer countries (World Bank, 1989).

A question of the viability of international comparability may arise from the use by countries of different vintages of international standards for the System of National Accounts (SNA).<sup>11</sup> However, this is believed to be a relatively minor issue, compared with the lack of adequate data sources and estimation methods for data compilation, especially in poor countries where statistical capacity is often weakest. Countries are constantly working to increase their statistical capacity and improve their national accounts estimates, and new source data and improved methodologies have led to revisions to previous estimates of production and income. China, for example, increased its estimate of nominal GDP (and GNI) by 17 percent in 2006

> 11 The current standard is the 2008 version of the System of National Accounts (United Nations, 2008), although many low and middle income countries use the 1993, 1968 or even the 1953 versions.

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**<u>Table 10: Impacts on Taxes and Trust in the State</u>**

||Taxes and market<br>fees costs (US$)|Firm provides<br>formal receipts|Trust in<br>government<br>institutions<sup>a</sup>|Trust in institutions<br>dealing with private<br>sector<sup>b</sup>|
|---|---|---|---|---|
|Treatment 1: BRC|-0.140|-0.010|-0.006|0.006|
||(0.211)|(0.014)|(0.029)|(0.030)|
||0.505|0.456|0.841|0.840|
||_0.569_|_0.456_|_0.841_|_0.982_|
|Treatment 2: BRC + TPIN|-0.342|-0.015|-0.013|-0.014|
||(0.237)|(0.018)|(0.038)|(0.039)|
||0.149|0.402|0.729|0.727|
||_0.268_|_0.452_|_0.841_|_0.982_|
|Treatment 3: BRC + IS + BBA|0.398|0.012|-0.023|-0.008|
||(0.304)|(0.013)|(0.026)|(0.027)|
||0.191|0.356|0.365|0.753|
||_0.286_|_0.452_|_0.841_|_0.982_|
|Control group mean|3.383|0.211|0.000|0.000|
|Sample size|10,900|10,900|10900|10900|
|p-value: Treatment 1 = Treatment 2|0.289|0.784|0.846|0.619|
|p-value: Treatment 1 = Treatment 3|0.048|0.080|0.496|0.597|
|p-value: Treatment 2 = Treatment 3|0.013|0.117|0.774|0.887|
|p-value test of equality|0.085|0.234|0.814|0.939|
|p-value test of equality of treatment effects over time|||||
|Treatment 1|0.803|0.559|0.300|0.448|
|Treatment 2|0.997|0.779|0.710|0.937|
|Treatment 3|0.189|0.668|0.656|0.987|

Notes: Data pooled for all follow-up surveys, unless otherwise noted. Specifications include strata dummies, a variable representing the initial outcome at baseline, a variable indicating missing data at baseline, and dummies for survey round (except for trust in institutions dealing with private sector which only includes dummies for survey round). Z score index constructed following Kling, Liebman, and Katz (2007). Adjustments to control false discovery rate (FDR) computed following Benjamin and Hochberg (1995). p-values and q-values are reported below standard errors (q-values in italics). Clustered standard errors by firms in parentheses. *, ** and *** denote significant at the 10%, 5%, and 1% levels, respectively.

a Z-score on trust in government institutions includes trust in courts, police, and national government.

b Z-score on trust in institutions dealing with private sector includes trust in city council and tax authorities. There is not baseline information for both variables.

43
# **Middle-Income Traps:  A Conceptual and Empirical Survey**

Fernando Gabriel Im and David Rosenblatt<sup>1</sup>

**<u>Abstract</u>**

In recent years, the term “middle-income trap” has entered common parlance in the development policy community. The term itself often has not been precisely defined in the incipient literature. This paper discusses in more detail definitional issues on the so-called middle-income trap. The paper presents evidence in terms of both absolute and relative thresholds. To get a better understanding of whether the performance of the middle-income trap has been different from other income categories, the paper examines historical transition phases in the inter-country distribution of income based on previous work in the literature. Transition matrix analysis provides little support for the idea of a middle-income trap. Analysis of cross-country patterns of growth provides additional support for the conclusions in the paper, which closes with a general discussion of potential policy implications.

Key words: Economic growth, Convergence, Middle Income Countries JEL Codes: D30, E00, F02, F60, N10, O11, O40 Economic Policy Sector Board

> 1 World Bank, 1818 H Street, Washington DC 20433. fim@worldbank.org and drosenblatt@worldbank.org.

1
there is any impact of the increase in schooling on later-in-life outcomes. It is doubtful that less learning (lower-quality education) in the early years of primary school would lead to reduced fertility and improved future labor market outcomes. If anything, even for students that would have attended school anyway, this would likely attenuate estimates toward zero. The evidence of these long-term improvements and evidence of significant increases in literacy suggest that learning occurred at a level sufficient to generate consequential later-in-life improvements.

# **6.4.3 Conclusion of Ethiopian Civil War**

The long-simmering conflict in Ethiopia erupted in the late 1980s, with a vast majority of the fighting occurring to the north of the capital, Addis Ababa. Geocoded data from the Uppsala Conflict Data Program (Sundberg and Melander, 2013; Croicu and Sundberg, 2015; Allansson et al., 2017) make it possible to match deaths related to “organized violence” that occurred as early as 1989 to the zones used in the study.

To investigate whether characteristics of the areas most affected by the civil war are driving the results, zones are removed at two separate cutoffs and the models re-estimated. The first cutoff removes four zones that had over 4,000 deaths between 1989 and 1991; these zones contained over 75 percent of all deaths during this period. A less restrictive cutoff removes all zones with at least 500 deaths related to organized violence; these zones account for 96 percent of all deaths included in the data. Removing these zones from the data and re-estimating equations (8) and (9) generates similar sets of findings. The results reveal a similar pattern: 12 different first-stage specifications yield a consistent effect of the FPE intensity measure that ranges between 0.098 and 0.135. The 2SLS estimates show that each additional year of schooling led to between 0.254 and 0.550 fewer births for Ethiopian women, estimates that remain both qualitatively and quantitatively similar to the baseline findings of the article.<sup>19</sup>

# **6.4.4 Child Marriage Law**

In 2000, Ethiopia changed the minimum legal age of marriage from 15 to 18. In the following decade, regions throughout the country adopted the law (McGavock, 2015; Garcia Hombrados, 2018). However, the combined FPE and MTI analysis finds statistically significant delays in marriage only between the ages of 21 and 24.<sup>20</sup> This timing means that the result is unlikely to be related to the law outlawing marriage prior to age 18.

> 19These estimates, and those for the paper’s other outcomes, using the mortality cutoffs can be found in Panel L and Panel M of tables throughout Appendix Section D.2.

> 20These results can be found in Appendix Figure C.2.

17
rumors of impending bankruptcies and culminating in actual insolvencies. In addition, there were recurring rumours of fraudulent activities by entrepreneurs of Neuer Market companies, which harmed the market’s reputation irrevocably. Finally, the Neuer Market segment was closed and the companies that met the listing requirements were folded into the broader market.

The reasons for the dramatic decline of the Neuer Market include a narrow focus on high-tech companies, the lack of sophistication of both investors and the relevant regulatory authorities, and timing -- the Neuer Market took off just as the stock market bubble was beginning to inflate around the world. Another factor was a regulatory framework which, although it appeared robust, was quickly shown to be inadequate and unable to provide strong incentives for responsible corporate behavior.

The Alternative Investment Market (AIM) promoted by the London Stock Exchange (LSE) has taken a different approach than the Neuer Market, and was fortunate enough to have better timing. AIM was launched in 1995 to meet the needs of small growing companies, and applies a less stringent regulatory regime than that of the “official list” of the LSE. AIM has created a light touch regulatory environment that sets out to attract a broad set of companies from different sectors and focuses on the needs of institutional investors. It also relies on the stronger investment culture and regulatory capacity that prevail in the United Kingdom.

For a company to list on AIM, there is no minimum level of shares required to be held by the public, no trading record requirement, no prior shareholder approval for transactions requirement, and no minimum market capitalization. However, a company must appoint a nominated advisor (nomad) who is responsible, among other duties, for warranting to the LSE that the company is appropriate for AIM. The company’s application for admission is pre-vetted by the nomad, not the LSE or the UKLA.

Since AIM opened, it has admitted more than 1900 companies and raised more than £17 Billion. While it too experienced some difficulties at the bursting of the high tech bubble, the FTSE AIM index has performed well in the last few years, going from a level of 602 at the end of 2002 to 1046 at end of 2005. AIM is considered to be the only truly effective second-tier market in Europe, and presents an alternative to listing on the Nasdaq for companies worldwide. This trend has strengthened since the adoption of the Sarbanes-Oxley Act in the United States, which has made it more expensive for smaller companies to list on the Nasdaq. Companies that list on AIM also do so because of the levels of institutional investment that it attracts, as compared with the United States where retail investors are dominant.

In Brazil, the Novo Mercado was introduced by BOVESPA in December 2000, and presents a different model altogether. It is a listing segment for companies that voluntarily decide to comply with corporate governance and disclosure 21
Figure C13: Poverty rates and GDP <!-- Start of picture text --> (A) Official definition<br>Urban Rural<br>80 80 MWI<br>60 60 NER<br>MWI AGO<br>UGA<br>40 UGA 40 TZALSO<br>TZA BFA<br>NER TCDETHGNB<br>20 TCDETHBFA LSO BGD AGO 20 GIN SEN BGDGHACIVMRT COL<br>GNB GIN SEN GHACIVMRT VNMEGY COLGAB VNMEGY GAB<br>7 7.5 8 8.5 9 9.5 7 7.5 8 8.5 9 9.5<br>Log of GDP per capita Log of GDP per capita<br>R2 = 0.2712 R2 = 0.4865<br>(B) DOU<br>Urban Rural<br>80 80 MWI<br>60 60 NER<br>MWI AGO<br>UGA<br>40 UGA 40 TZALSO<br>TZA BFA<br>NER TCDETHGNB<br>20 TCDETHBFA LSO BGD AGO 20 GIN SEN BGDGHACIVMRT COL<br>GNB GIN SEN GHACIVMRT VNMEGY COLGAB VNMEGY GAB<br>7 7.5 8 8.5 9 9.5 7 7.5 8 8.5 9 9.5<br>Log of GDP per capita Log of GDP per capita<br>R2 = 0.4119 R2 = 0.4544<br>(C) DB<br>Urban Rural<br>80 80 MWI<br>60 MWI 60<br>AGO<br>NER UGALSO<br>NER<br>40 UGATZA 40 TZA<br>TCD<br>20 TCDETHBFA LSO AGO 20 ETHGNBBFA GIN SEN BGDGHACIV COL<br>GNB GIN SEN BGDGHACIVMRT VNMEGY COLGAB MRT VNMEGY GAB<br>7 7.5 8 8.5 9 9.5 7 7.5 8 8.5 9 9.5<br>Log of GDP per capita Log of GDP per capita<br>R2 = 0.4879 R2 = 0.3893<br>Poverty rate in urban areas Poverty rate in rural areas<br>Poverty rate in urban areas Poverty rate in rural areas<br>Poverty rate in urban areas Poverty rate in rural areas<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: GDP per capita is measured in PPP (constant 2017 international $). For the DOU and DB methods, WorldPop 250m is used. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method, and the categories “Core” and “Suburb” for the DB method. Poverty is measured using the $2.15 poverty line.

78
Figure 12: Income tax compliance improved for the top 0.1% after 2016

# (a) Capital income share

<!-- Start of picture text --> Capital Income /<br>Total Income<br>2013-15 2016<br>Amnesty Amnesty<br>20% Top 0.1%<br>15%<br>10%<br>5%<br>p99.5-p99.9<br>0 p99-p99.5<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> (b) Income tax <!-- Start of picture text --> Income Tax<br>(2015=100)<br>2013-15 2016<br>160<br>Amnesty Amnesty<br>Top 0.1%<br>140<br>p99.5-p99.9<br>120<br>100<br>80<br>p99-p99.5<br>60<br>40<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> _Notes:_ this figure illustrates the capital income share and the income tax in panels (a) and (b), respectively. Within the top 1% of the income distribution, we have further divided this group into three subcategories based on decreasing income: the top 0.1%, the subsequent top 0.4% (P99.5 to P99.9), and the following 0.5% (P99 to P99.5). Between 2015 and 2021, the capital income share for the top 0.1% of the income distribution saw a remarkable increase, soaring from 5% to over 20%. In contrast, the next 0.4% witnessed a more modest rise in their capital income share, while the capital income share remained relatively stable for the subsequent 0.5%. Due to the increased disclosure of capital income, the income tax paid by the top 0.1% surged by 60% during the period from 2015 to 2021. _Source:_ authors’ calculations using data from AFIP statistical yearbooks.

35
- [38] Coate, S. and M. Ravallion (1993), “Reciprocity without Commitment: Characterizationf and Performance of Informal Insurance Arrangements”, _Journal of Development Economics_ , 40: 1-24.

- [39] Comola, M. and S. Prina (2017), “Treatment Effect Accounting for Network Changes”, working paper.

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- [41] Cox, D. and G. Jakubson (1995), “The Connection between Public Transfers and Private Interfamily Transfers”, _Journal of Public Economics_ , 57: 129-167.

- [42] Cox, D., Z. Eser and E. Jimenez (1998), “Motives for Private Transfers over the Life Cycle: An Analytical Framework and Evidence for Peru”, _Journal of Development Economics_ , 55(1): 57-80.

- [43] Cox, D., B. E. Hansen and E. Jimenez (2004), “How Responsive are Private Transfers to Income? Evidence from a Laissez-faire Economy”, _Journal of Public Economics_ , 88: 2193-2219.

- [44] Dercon, S., J. De Weerdt, T. Bold and A. Pankhurst (2006), “Group-Based Funeral Insurance in Ethiopia and Tanzania", _World Development_ , 34(4): 685-703.

- [45] De Weerdt, J., S. Dercon (2006), “Risk-sharing Networks and Insurance Against Illness", _Journal of Development Economics_ , 81(2): 337-356.

- [46] Fafchamps, M., F. Gubert (2007), “The formation of risk sharing networks", _Journal of Development Economics_ , 83: 326-350.

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30
Comparing widows and remarried widows for a given age at dissolution suggests that, in urban areas and given their characteristics, widows fare better as widows than they would do as remarried widows whatever their age at widowhood (Table 17A1). Here again it is also the case that remarried widows would fare better if not remarried, irrespective of age at dissolution. In rural areas, the difference in the predicted consumption of these women according to whether they remarried or not is never significantly different from zero. The same results are found if we examine duration since widowhood (Table 17B1). After a divorce, whatever the age at divorce or the duration since divorce, women have the same consumption level given their characteristics whether they remarried or not (Tables 17A2 and 17B2).

## _6.2.4  Discussion_

Differences reflect both the consequences of a given marital trajectory and the selection processes that push or pull women into those pathways. One interpretation of these findings consistent with what we know is as follows. Women who become widows and remarry often do so out of necessity, encompassing the two non-mutually exclusive burdens underlined in the conceptual framework: a lack of resources and strong social pressure. Such women would appear to consist on the one hand, of a group of poorer women with little fallback position in the event of their husband’s death, and on the other, of women who, due to social pressure, are compelled to remarry, often within their late husband’s lineage. Their vulnerability is not captured by commonly considered observables such as education, age, place of residence, and the type of job held by the deceased husband, as otherwise we would expect to find them better-off when remarried rather than not (contrary to results presented in panel 1 of Table 16). Clearly the unobserved characteristics along which they differ from those who do not remarry explains part of the consumption gap. Comparing them with once-married women (less likely to be selected than widows), it appears that for them there is a large economic loss associated with widowhood at all ages. Remarriage does not, however, compensate them fully for such loss. Widows, on the other hand, do not seem to incur an economic cost from their marital status, pointing to a potential positive selection for those who do not (choose to) remarry.

# **7. Conclusions**

Marital upheaval is frequent in Senegal. Using data from the 2006 Poverty and Family Structure household survey, we estimate that anywhere between 18.5 and 21.5 percent of evermarried Senegalese women have experienced widowhood, while somewhere between 13.2 and 22
Figure 5: The relative value of declared foreign or domestic assets by asset type

# (a) Foreign assets

<!-- Start of picture text --> Wealth value<br>(2015=100)<br>2016<br>1300<br>Amnesty<br>1200<br>1100<br>1000<br>900<br>Real Estate<br>800<br>700<br>Stocks<br>600 Deposits &<br>500 Currency<br>Real Rights<br>400<br>300 Vehicles<br>Accounts<br>200 Receivabl e<br>100 Other Asse ts<br>0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> (b) Domestic assets <!-- Start of picture text --> Wealth value<br>(2015=100)<br>2016<br>1300<br>Amnesty<br>1200<br>1100<br>1000<br>900<br>800<br>700<br>600<br>500<br>400<br>Stocks<br>300<br>Real Rights<br>200<br>100<br>0<br>2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022<br><!-- End of picture text --> _Notes:_ this figure decomposes Figure 3 by asset type and plots the relative value of different types of foreign or domestic assets in panels (a) and (b), respectively, in constant pesos (2015 = 100). The value of reported foreign real estate increased by more than 1,000% compared to 2015. Similarly, the number of people reporting to own foreign bank deposits and currencies, real rights and credits, and cars and boats increased by 380%, 240%, and 160%, respectively.

_Source:_ authors’ calculations using data from AFIP statistical yearbooks.

28
of those age 12. Likewise, 57 percent of 16 year olds and almost one-third of 18 year olds were attending primary school. The magnitude of the over-age problem in the Haitian educational system is also evident from the average age of children attending each grade, shown in Table 5. The average age of children attending first grade is almost 10, while the official age of entry is six. The problem is substantially more severe in rural areas, where the average age of attendance in grade one is 10.3, versus 8.9 in urban areas. At the end of primary school, sixth-grade students are on average almost 16, five years older than the prescribed age for the grade.

The existence of a large number of students who are over the normal age for their grades is a concern because their presence in the classroom may negatively affect other students. Evidence from other countries suggests that age heterogeneity in the classroom may decrease learning (Schiefelbein, 1975 and Schiefelbein and Wolff, 1992). Teachers with multiage classrooms face the challenge of adapting teaching methods to satisfy the interests and attention needs of children of different age groups at the same time.

The presence of over-age children can be driven by a mix of three causes: 1) late entrance to the school system, 2) grade repetition, and 3) dropping out of school and restarting the same grade when older. The fact that attendance rates increase with age over a substantial range suggests that the over-age phenomenon in Haiti is driven chiefly by late entrance to school. As Figure 7 shows, in 2005 attendance rates grew markedly moving from age 6 to 10, and the peak attendance rate was at age 14. We would not expect to see this pattern if the presence of over-age children were largely a result of grade repetition or dropping out and restarting school.

The slight decrease in attendance between 2000 and 2005 occurred during a time of political turmoil and economic decline. During this time international support from the United States, Canada, and other important donors was suspended, with international aid dropping to US$136 million in 2001-02 from a high of US$611 million in 1994-95.<sup>12</sup> Vulnerable populations tend to draw their children out of schools during an economic downturn, as a coping mechanism during periods of crisis (World Bank, 2007a). Civil unrest during this time also tended to disproportionately affect urban slums and other poor areas. This effect can be seen in the decrease in the attendance rates in this period for the three poorest quintiles, while for children in the two top quintiles attendance remained steady.

> 12 See Government of Haiti (2004).

9