rafmacalaba/datause-encoder-data huggingface dataset:
“...focus on transplanting institutional practices from the West with little attention to their fit in the African context” (Hyden, 2007, p.16752). There is possibly as great a risk in transplanting ideas from the East. It would be naive to assume that all Africa needs to do is copy China’s specific policies to achieve China’s success. The period since 1980 has seen a sequence of (often radical) economic reforms in China, which moved the economy from being highly controlled to more market-oriented. Those reforms naturally reflected (relatively unusual) circumstances in China, and may make little or no sense as a blueprint for policy making anywhere else.

There are other reasons for caution. The lessons for Africa are not all about Chinese successes; some relate to aspects of China’s development path that Africa would prefer to avoid, such as the steep rise in inequality, which I return to. Nor is China the only success story that Africa might want to study. Neighboring Vietnam has done as well in terms of its pace of poverty reduction, and with some interesting policy differences to China.<sup>8</sup> And it can be argued that Africa should look first within its own region before turning to the Far East. There are many (old and new) success stories within Africa, and there is a body of research and practice related to African poverty, which Africans can already draw on.

It must also be acknowledged that there are constraints on Africa’s progress against poverty that China did not face. In this context, three differences stand out between China at the outset of its reform period and the typical African country today: African countries tend to have higher inequality, higher dependency rates and lower population density. On the first, at the time China had roughly the same “$1 a day” poverty rate as SSA today—namely a poverty rate of roughly 40% around the mid-1980s—income inequality was lower in China (a Gini index well under 30%) than found in all but a couple of countries in SSA today (Ethiopia and Mauritius).<sup>9</sup> This almost certainly means that African countries will need even higher growth in mean income than China to achieve the same pace of poverty reduction that China has enjoyed, given that the proportionate responses of poverty incidence to the rate of growth tends to be appreciably lower in high inequality countries.<sup>10</sup> Second, Africa’s high dependency rates—due mainly to higher fertility rates, but also high working-age adult mortality due to HIV/AIDS—are likely to constrain growth and poverty reduction. In 2006, 43% of the population of SSA was in the 0-14 age group, more than double

> 8 See, for example, Ravallion and van de Walle (2008).

> 9 See the inequality measures by country in World Bank (2005, Figure 2.9).

4
Notes: Informal output is based on the MIMIC model, in percent of official GDP. Informal employment is based on the share of self-employment in total employment.

A. Bars show the average share of informal output during 2011-19 (in blue; average self-employment rate in red). B. Lines show unweighted group averages.

**3.2 Informality in Indonesia**

# _Output informality_

> 21. **Indonesia generally exhibits lower output informality compared to the average of other EMDEs.** Output informality in Indonesia averaged around 38 percent of GDP in 2000-2007 before sliding down slightly to an average of 36 percent of GDP in the decade before 2020.<sup>6</sup> This is smaller than the average level in EMDEs by about 0.5 percentage point of GDP, while being slightly above the average level in EAP (figure 2). The declining trend in output informality in Indonesia was also accompanied by rising GDP per capita, which rose almost by six times from USD 770.9 in 2000 to USD 4,333 in 2021, a consistent trend observed across EMDEs.

**Figure 2. Output informality in Indonesia**

<!-- Start of picture text --> A. Levels of output informality B. Evolution of output informality<br>Percent of GDP Percent of GDP<br>2001-07 2011-19 42 Indonesia EMDEs EAP<br>40<br>40<br>35 38<br>36<br>30<br>34<br>25 32<br>30<br>20<br>Indonesia EMDEs EAP<br> Source: Elgin et al. (2021a), updated and benchmarked the MIMIC-model-based estimates by World Bank staff. BPS; World<br>Bank Staff Calculation. Bars (or lines) show simple group averages. Here EMDEs and EAP averges do not include Indonesia.<br>The shaded area is capturing the COVID-19 years.<br>2000 2003 2006 2009 2012 2015 2018 2021<br><!-- End of picture text -->

# _Employment informality_

22. **Informal employment in Indonesia has also declined but remained higher than the average levels in other EMDEs and the EAP region.** Using self-employment as a proxy for informal employment, Indonesia has experienced a steady decline in informal employment since the 1990s (figure 3). However, there seems to be a reversal starting in 2016. A reversal that has been accelerated by the COVID-19 shock. Indeed, self-employment as a share of total employment increased from 49.9 percent in 2019 to 53.4 percent during the pandemic in 2020. Despite the historical decline, the level of employment informality in Indonesia is consistently higher than the level in the EAP region and other EMDEs. Using data on informal employment provided by ILO - a broader measure than self-employment - four out of five workers are informally employed in Indonesia, notably higher than the average informal employment rate in EAP or EMDEs (i.e., about 56 percent of total employment).

> 6 As shown in section 2, Output informality is proxied by MIMIC-based estimates or LFS estimates as elaborated in the previous section. The model-based MIMIC estimates are used mainly in cross-country comparisons, while the LFS estimates are used in the within-country comparison.

9
estimates from a large set of key informants identified within a geographical unit that is small enough to be able to provide an estimate with low measurement error. In other words, quick population counts are usually done at the lowest feasible geographic unit, as informants are more likely to provide accurate information on units that cover smaller populations. The standard practice for implementing such a count in Bangladesh - followed both by the censuses and the IOM’s count of Rohingya displaced in camps – focuses at the village/camp-block level. Camps and blocks cover, on average, 359 and 109 households each in Cox’s Bazar district (Table 1). The IPA quick count, however, was implemented at the union level using a small set of union-level legislators as key informants. While the union is the lowest administrative level that has an office, the size of the population included in a union does not lend itself to an accurate quick count. This is particularly true for Rohingya displaced, for whom no administrative data are available.

**<u>Table 1: Average population by administrative and geographic units</u>**

||**Number of units**|**Average number of**<br>**persons**|**Average number of**<br>**households**|
|---|---|---|---|
|**Hosts**||||
|Unions|110|20,484|3,758|
|Mauzas|339|6,647|1,219|
|Villages|1,150|1,959|359|
|**Rohingya displa**|**ced**|||
|Camps|34|26,739|6,271|
|Blocks|1,953|465|109|
|Source: 2011 Ban|gladeshcensusandIOM NP|M Round12.Note:Averages|forCox’sBazardistrict|

On average, a union in Cox’s Bazar covers 10.5 villages and 3,758 native/host households. The IPA count asked a small set of union-level key informants<sup>14</sup> to provide village-level data on refugee counts, disaggregated by “old” and “new” Rohingya displaced. This approach was not likely to generate accurate numbers on prevalence. Moreover, the inaccuracy was most likely to pose problems for the key population of interest in this listing: Rohingya displaced living in villages outside camps.

Given these concerns, we implemented a full household listing in a random sub-sample of IPA count villages to validate the IPA counts. These villages were stratified for refugee prevalence based on refugee counts in the IPA listing and population counts from the 2011 census. Three types of prevalence were defined based on these data, to form the strata for the validation household listing (Table 2). The validation listing was done in 33 villages randomly chosen from the strata without refugee camps in November 2018. On average, one village was chosen from each mauza, across six out of the seven upazilas in Cox’s Bazar district.

> 14  The key informants were government officials in union level offices, which are the lowest local government administrative units in Bangladesh. IPA went to the respective union offices and asked for population counts.

7
approach would result in a 25 percent (0.55 percent) overestimation of poverty at the $3.65 ($6.85) poverty line. Rural regions of Zagros and Northeast show an even higher poverty over-estimation f nf bias when using the MI approach, reflecting a relatively larger differential between Ii and Ii as well as relatively lower food budget shares.

The effect of different deflation methods on inequality measures is shown in Table 4. MI generally “mitigates” inequality in urban areas and “exacerbates” it in rural areas, with respect to SI, though magnitudes are very much dependent on the chosen measure. Considering these effects is relevant not only when inequality is the statistic of interest, but also when poverty dynamics is of interest (Ferreira 2012), particularly in settings where the mechanics underlying poverty changes are investigated (e.g., Datt and Ravallion 1992).

**Table 4. Percent changes in inequality measures (MI-SI)**

||**Gini**|**MLD**|**Theil**|**Atkinson**<br>ε=0.5|**Atkinson**<br>ε= 2|
|---|---|---|---|---|---|
|National|-0.95|-2.25|-2.67|-2.25|-2.02|
|Rural|2.97|6.42|5.10|5.58|5.47|
|Urban|-2.10|-4.80|-4.85|-4.50|-4.35|
|Tehran metro Urban|-5.86|-12.87|-11.95|-11.58|-11.03|
|Caspian Rural|-0.05|-0.03|-0.38|-0.19|0.21|
|Caspian Urban|-0.81|-1.69|-1.63|-1.60|-1.40|
|Northwest Rural|0.61|0.62|-2.76|-0.64|1.32|
|Northwest Urban|0.49|0.98|1.09|0.98|0.66|
|Northeast Rural|5.17|11.66|10.99|10.77|10.57|
|Northeast Urban|-0.36|-0.75|-0.68|-0.68|-0.66|
|Central Rural|1.89|4.29|3.40|3.72|3.87|
|Central Urban|-0.80|-1.84|-1.65|-1.67|-1.74|
|Southeast Rural|9.68|19.62|19.04|18.62|13.31|
|Southeast Urban|-0.03|-0.11|-0.05|-0.07|-0.15|
|Persian Gulf Rural|6.26|12.94|14.32|13.00|10.07|
|Persian Gulf Urban|0.19|0.41|0.44|0.40|0.33|
|Zagros Rural|4.71|9.60|9.94|9.39|7.64|
|Zagros Urban|0.36|0.72|0.67|0.67|0.61|

_Source_ : Authors calculation based on HEIS 2019 33
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|Table 8: Ind|ices of Po|verty He|ad-Cou|nt, Gap, S|everity in|Indone|sia, 1996-1999||
|---|---|---|---|---|---|---|---|---|
|||1996|||1999||Percentage Ch|ange|
||Urban|Rural|Total|Urban|Rural|Total|Urban Rural|Total|
|Head-Count Index (P0):<br>Percent population in<br>poverty|3.82%|13.10%|9.75%|9.63%|20.56%|16.27%|152.3% 56.9%|66.8%|
|Poverty Gap ndex<br>(P1): Total expenditure<br>gap of those below<br>poverty line as percent<br>of total expenditures|0.53%|2.12%|_1.55%_|1.51%|3.61%|2.79%|183.0% 70.5%|80.2%|
|Mean expenditures of<br>the poor as a percentage<br>of the poverty line<br>(1- PI/PO)|86.01%<br>|83.84%|84.15%|84.30%|82.44%|82.87%|||
|Poverty Severity Index<br>(P2): Squared poverty<br>gap|0.12%|0.54%|0.39%|0.37%|0.99%|0.75%|201.6% 83.6%|91.9%|

This deterioration was also in line with a significant increased in the severity index of poverty (P(2)) both in urban and rural Indonesia. The worsening severity of poverty were also more apparent in urban than in rural areas (201.6 percent compared to 83.6 percent). This significant increase indicates that those at the lower tail of the expenditures distribution have become worse off.

_Inequality._ We complement the discussions on poverty with a brief overview on what happens to inequality during the period. In table 9 we present the estimated changes in the Gini and Generalized Entropy (GE(a)) indices of inequality in the distribution of real per capita expenditures (PCE). We use two deflators to define real expenditures. In the first panel, we use the deflator which has a food share based on the actual consumption of the bottom 30 percent of population. In the second panel, meanwhile, we use the household specific deflator based on Engel's law. This second deflator is included because the first deflator is likely to underestimate the 30
Myles and Naylor (1996) state that the model developed by Gordon is a step forward but lies outside the mainstream of the social custom literature because psychic costs depend on the extent of evasion. They see no reason why such a relation should hold. They argue that if the psychic cost is due to the shame at prosecution then the extent of evasion is irrelevant. If it is due to the fear of detection, then it should be dependent on the detection probability rather than the extent of evasion. Based on the social custom literature where it is accepted that once a social custom is broken, all utility from it is lost, Myles and Naylor suggest a model in which a social custom utility is derived when taxes are paid honestly, but is lost when evasion is undertaken. In their model, taxpayers face a choice between evading or not. If a taxpayer chooses evasion, the standard model of tax evasion becomes operative. Myles and Naylor combine social customs and social conformity with the standard model which represents tax evasion as a choice with risk. Since then, further studies have also modeled this puzzle of tax compliance (see, e.g., Schnellenbach, 2010).

# _2. Measurement of Tax Morale_

Several studies have used the World Values Survey (WVS) to generate a proxy for tax morale (see Torgler, 2007a). The World Values Survey is a worldwide investigation of socio-cultural and political change collecting comparative data on values and belief systems among people around the world. WVS builds on the European Values Surveys (EVS), first carried out in 1981-1984. These surveys have assessed the basic values and beliefs of people around the world and have been carried out in about 80 societies representing over 80 per cent of the world‘s population. This large data set therefore permits cross-country comparison of people‘s tax morale that is based on representative national samples The WVS/EVS is particularly advantageous to this review because it covers a large set of Central Eastern and Former Soviet Union countries. Not only does the WVS/EVS pertain to a wide variety of religions and cultural traditions within each country, but it can assist an analysis of value changes over time. The WVS has been broadly used by social scientists.

To assess the level of tax morale the following question has been used: 7
officials and with little or no local discretion allowed in determining revenues. Again, the issue is not well-defineu. While the most telling feature to distinguish regional and local governments from agencies of the central government is "autonomy", the various degrees of autonomy observed in different countries does not lead to a ciear demarcation between subnational governments and deconcentrated agencies of the central government. Defining the flows of funds as intergovernmental transfers is, therefore, equally difficult.

These definitional issues are particularly troublesome in those countries with a francophone administrative tradition. While local jurisdictions exist, they are little more than administrative linits. Tax revenues of the locality are collected by the central government, additional direct expenditures are made by the central government through these local governments and all or nearly all "local" employees are directly under the authority of the center. Still, there are instances where the local jurisdiction is expected to take on additional public service provision responsibility.<sup>6</sup> Importance of Grants to Granting Governments The role of grants can be measured in at least two different ways-with respect to the revenues of the recipient government and relative to total spending activity of the granting jurisdiction. While the former See, for example, Local Revenue Administration Project, Local Revenue Administration in Burkina Faso. Phase II. Final Report, Metropolitan Studies Program Monograph No. 17, The Maxwell School (Syracuse, NY: Syracuse University, August 1985 [revised January 1986]).
Table A.9: Eect of a direct SLM training intervention on other farmers' access to contact farmers, adoption, and knowledge, includes administrative post xed eects

||Ctrl.Mean<br>[SD]|ITT|N|R<sup>2</sup>|
|---|---|---|---|---|
|Other Farmers' Knowledge and Adoption, unweighted|||||
|Knowledge score|0.341<br>[0.200]|-0.007<br>[0.012]|10,955|0.057|
|Number of techniques known by name|1.654<br>[1.538]|-0.001<br>[0.116]|10,955|0.020|
|Number of techniques adopted|0.845<br>[0.891]|0.003<br>[0.066]|10,955|0.053|

Sources: Contact farmer survey, 2010; Household survey, 2012, 2013.

Notes: Regressions include the same explanatory variables as models in Table 10, except replacing district indicators with administrative post indicators.

***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. CF=contact farmer; ITT=intent-to-treat; SLM=sustainable land management.

52
<mark>Collected in partnership with the Gallup World Poll in 2011 and again in 2014, the Global Findex interviewed approximately 150,000 randomly selected adults (age 15 and over) across countries in each survey round. This sample is representative of more than 97 percent of the world’s adult population, and nationally representative of each of the more than 140 countries included in the database.</mark><sup>17</sup> <mark>The Global Findex includes over 100 indicators of financial inclusion, summarized for all adults and disaggregated by key demographic characteristics—gender, age, and income. Because the Global Findex samples individuals, all the questions are about the respondent and specify whether the respondent should answer about him or herself alone or jointly with someone else. For example, the questionnaire asks, “Do you, either by yourself or together with someone else, currently have an account at any of the following places?” No information is collected on other assets.</mark>

# **4 Best Practices and Known Gaps**

Given the various approaches discussed above, in this section, we focus on what we can learn from the different approaches, what their strengths and weaknesses are relative to each other, and the research questions for which each is suited.

# **4.1 Defining and Measuring Ownership, Control, and Use of Assets**

In any discussion of women’s landownership, someone inevitably raises the issue that it is women’s access to land, not their ownership of land, that matters. Yet, no empirical analyses have analyzed the relevance of ownership and access to various outcomes. While access is important for women to produce livelihoods, ownership may imply more secure tenure rights. Figuring out how to identify these different issues within surveys is challenging. There are several key dimensions, including the right to use land and to keep the proceeds from the land and the security of tenure.

When considering ownership, the first challenge is to define it. Often, we simply ask the respondent whether he or she is an owner; occasionally we also ask if there is an ownership document. The potential of what ownership will mean varies across contexts. In some places, the state officially owns all the land and individuals only have use rights. Yet, when these rights are transferable, they are often similar in practice to ownership rights. In other

> 17 <mark>In economies where less than 80 percent of the population has telephone coverage, surveys are conducted face to face. This is also the case in countries where in-person interviews are the customary method of conducting surveys. In all other countries, the questionnaires are administered over the telephone.</mark> For more information, please visit http://www.worldbank.org/en/programs/globalfindex.

17
To classify the 14 sectors of economic activity into two groups (green and non-green), the paper considers whether their emissions per worker are below or above the median within each country (Table 7 reports the results for countries in LAC for which all data sources are available to calculate emissions per worker). In other words, each sector is considered green or non-green relative to the median sector emissions in each country. Since this classification is quite similar across countries (i.e., the same sector ends up as either green or non-green in most cases), the same green/non-green sectoral definition is used in every economy, including those for which it was not possible to estimate GHGE by sector. The classification to either non-green or green sector is clear from Table 7 except for Financial intermediation and Real estate (because they change status over time in Argentina, Costa Rica and Peru), which are classified as nongreen and green in this paper, respectively. The paper also assumes that this sectoral classification stays the same over time.

**Table 7 – Green (green color) and non-green (brown color) sectors**

||||||||**Year( **|**2000 +**|**)**||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Sector**|**Country**|**05**|**06**|**07**|**08**|**09**|**10**<br>**11**|**12**|**13**|**14**|**15**|**16**|**17**|**18**|
||ARG||||||||||||||
||BRA||||||||||||||
|1. Agriculture,<br>hi|CHL||||||||||||||
|untng,<br>forestry and|COL||||||||||||||
|,<br>fishin|CRI||||||||||||||
|g|MEX||||||||||||||
||PER||||||||||||||
||ARG||||||||||||||
||BRA||||||||||||||
|2 Mining and|CHL||||||||||||||
|.<br>uarrin|COL||||||||||||||
|qyg|CRI||||||||||||||
||MEX||||||||||||||
||PER||||||||||||||
||ARG||||||||||||||
|3.|BRA||||||||||||||
|Manufacturing,|CHL||||||||||||||
|Construction &|COL||||||||||||||
|Industrial|CRI||||||||||||||
|processes|MEX||||||||||||||
||PER||||||||||||||
||ARG||||||||||||||
|4. Electricity,<br>|BRA||||||||||||||
|gas, and water|||||||||||||||
|supply|CHL||||||||||||||
||COL||||||||||||||

13