rafmacalaba/datause-encoder-data huggingface dataset:
remained high compared to the other countries. Moreover, the employment-to-population ratio for both sexes, but for women especially, is high compared to comparator countries. Throughout its transition to a market economy, its experience of financial and other crises, Vietnam has successfully maintained a high proportion of women within the labor force and in employment. This is consistent with previous findings (Pham et al., 2007).

**Table 1: Main labor market indicators - % - (2008)**

||Labor|for particip|ation (15+)|Employm|ent-to-Popul<br>(15+)|ation ratio|U|nemploym|ent rate|
|---|---|---|---|---|---|---|---|---|---|
||All|Men|Women|All|Men|Women|All|Men|Women|
|Cambodia*|80.4|85.9|75.5|64.8|65.1|64.5|-|-|-|
|Indonesia|67.2|83.5|51.1|61.5|77.1|46.1|8.4|7.6|9.7|
|Philippines|63.7|78.9|48.6|58.6|72.3|44.1|7.4|7.6|7.1|
|Thailand|73.4|81.5|65.8|72.4|80.4|64.9|1.2|1.3|1.0|
|Vietnam*|76.5|81.0|72.3|74.5|79.0|70.4|2.6|2.5|2.7|

_Sources: Laborsta (ILO) – Labor Force Survey data. *Cambodia (2004) 10+; Vietnam (2009, VET2)_

# _Low unemployment rates_

The evolution described above translates into low unemployment rates, and little difference in unemployment rates between men and women (Table 1). A low unemployment rate in a lower-middle-income economy cannot be interpreted in the same way as in developed economies. In the latter, comprehensive social security systems, and in particular unemployment benefits, mean that workers who are out of a job have the opportunity and time to look for another suitable job. The poorest people are therefore found among the unemployed. In middle-income countries that offer no or limited financial help to those looking for work, workers who are newly unemployed often cannot afford to stay out of a job very long and therefore take up new employment more quickly, even if the job is not suitable in terms of pay, required qualification, etc. Often, such jobs are found in the informal sector, which has much more flexible labor practices than the formal sector, but which also afford lower employment security. The unemployed are not therefore especially the poorest people; they are on the contrary those who can afford to remain unemployed. The structure and quality of employment therefore matter even more than unemployment in these countries.

There was a modest increase in the unemployment rate between 2007 and 2009. The introduction of unemployment insurance just before the financial crisis could have been associated with a large increase in the proportion of people registering as unemployed, 6
Table 4: Effect of the NREGS on risk index of crop <u>portfolio</u>

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|NREGS introduced in district|0.038<sup>_∗_</sup><br>(0.011)|0.072<sup>_∗∗_</sup><br>(0.017)|||||
|Cumulative expend., NREGS (log, lag)|||0.007<sup>_∗∗_</sup><br>(0.002)|0.015<sup>_∗∗∗_</sup><br>(0.003)|||
|Employment per JC generated, NREGS (lag)|||||0.002<sup>+</sup><br>(0.001)|0.002<br>(0.002)|
|Rainfall (deviation) at baseline||0.120<sup>_∗_</sup>||0.102||0.130<sup>+</sup>|
|||(0.051)||(0.073)||(0.075)|
|Risk index at baseline||-0.602<sup>_∗∗∗_</sup>||-0.659<sup>_∗∗∗_</sup>||-0.659<sup>_∗∗∗_</sup>|
|||(0.061)||(0.038)||(0.038)|
|Risk index _×_ Rainfall (deviation)||-0.110||-0.055||-0.069|
|||(0.142)||(0.097)||(0.099)|
|Bootstrap p-value of main treatment variable|||||||
|Rademacher weights:|0.107|0.047|0.072|0.015|0.326|0.388|
|Webb weights:|0.099|0.045|0.062|0.013|0.315|0.391|
|Observations|1087|1087|1087|1087|1087|1087|
|_R_<sup>2</sup>|0.068|0.443|0.066|0.431|0.058|0.389|

Notes: Estimation in FGLS. Dep. var.: Risk index of a household’s crop portfolio. Additional controls are variable inputs (log), area cultivated (log), share of area under irrigation, fertilizer application, HYV seeds application, labor sharing, annual income from off-farm activities (log), if household benefits from credit/training program, rainfall (deviation), rainfall (deviation, lag), self-reported shocks, and time trend. Standard errors (clustered at the district in cols. (1) and (2) and at the sub-district in cols. (3) to (6)) in parentheses. P-values are obtained by performing a wild cluster-t bootstrap with 4,999 replications and two alternative weights.<sup>+</sup> _p <_ 0 _._ 10,<sup>_∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗_</sup> _p <_ 0 _._ 01,<sup>_∗∗∗_</sup> _p <_ 0 _._ 001.

39
16 each falling around 4 percent. It is even higher in Latin America (7 percent) and South Asia other than India (10 percent). By contrast, the number of extreme poor in India (though not in the rest of South Asia) is estimated to rise, by 4 percent.<sup>5</sup> Under the more moderate definition of poverty—those living on no more than US$2 per day—the number of poor in developing countries would fall by nearly 90 million compared to an aggregate baseline level of just under 2.5 billion in 2004, or by 3.4 percent (notwithstanding the number in India below $2 a day still increasing, but by just 1.7 percent).

# **_(b) GTAP Model Results_**

Hertel and Keeney (2010) draw on the widely used global economy-wide model of the Global Trade Analysis Project (GTAP). Their study adopts the same price distortions as the other studies surveyed here, and runs the same scenarios, but generates its own world price changes from the GTAP model for the multilateral trade reform scenarios. Those prices changes alter border prices for the various countries in the GTAP model, a subset of which have attached to them detailed household survey data. This permits the authors to say something about poverty impacts across a range of diverse economies using an internally consistent framework that captures the distributive effects of all factor income changes.

This multi-country study focuses on 15 developing countries: five Asian (Bangladesh, Indonesia, Philippines, Thailand, and Vietnam), four African (Malawi, Mozambique, Uganda, and Zambia), and six Latin American countries (Brazil, Chile, Colombia, Mexico, Peru, and Venezuela). Overall, it concludes that removing current farm and trade policies globally would tend to reduce poverty, and primarily via agricultural reforms (Table 5). The unweighted average for all 15 developing countries is a headcount decline in extreme poverty

> 5 The rise in India is partly because of the removal of the large subsidies and import tariffs that assist Indian farmers, and partly due to the greater imports of farm products raising the border price of those imports.
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Wong Poh Kam (2003). “From Using to Creating Technology: The Evolution of Singapore’s National Innovation Systems and the Changing Role of Public Policy,” World Bank Institute Working Paper, The World Bank, Washington DC.

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World Bank (2007). World Development Indicators, The World Bank Group, Washington DC.

Yusuf Shahid (2003). “Innovative East Asia: The Future of Growth”, Washington DC: International Bank for Reconstruction and Development.

Yusuf Shahid and Kaoru Nabashima (2007). “How Universities Promote Economic Growth”, Directions in Development, The World Bank.

27
# **3.1 Tax Administrative Data**

Administrative data from the TRA was utilized to analyze the impact of the intervention on taxpayers’ behavior. This data comprised of payment records for presumptive taxpayers (those with a turnover below 100 million) from January 2022 to March 2024. The data was reshaped from long to wide format, aggregating payments by the same individual within the same period to ensure a single sum per unique ID.<sup>1</sup> The reshaped data comprised 408,106 businesses.

Since randomization was implemented at the ward level, a taxpayer mapping exercise was necessary to estimate treatment effects, as the administrative data did not include ward information. However, the data contained other geographic identifiers such as street locations, regions, and tax districts. These identifiers allowed for matching each unique street location in the administrative data to its corresponding ward. The mapping process began by obtaining data from the National Bureau of Statistics (NBS), one for each region of mainland Tanzania. This data contained all location names within each region, from the highest to the lowest subnational division (containing columns for Region, Ward, Street, Village, and Neighborhood). Next, the street locations in the TRA data were cleaned by removing non-alphanumeric characters, punctuation, and special symbols, inserting spaces to separate strings, and converting all text to a common case. The matching process aimed to find the closest match between street locations from the administrative data and the Ward, Street, Village, and Neighborhood information from the NBS data, as unique street locations in the administrative data could signify not only a street but also a ward, village, or neighborhood. Using the Jaro-Winkler distance measure, matches with a similarity score below 0.3 were retained, and matching was restricted to the same region. This process identified the ward name corresponding to each street location in the TRA data, producing an output called the bridge, which linked street locations from the administration data with their corresponding wards as specified in the NBS data. In total, approximately 3,585 unique

> 1Some IDs represented multiple active businesses, which were not treated as duplicates since payments varied by period and amount.

12
# **B Data**

The data required for the model are:

- _E_ exports (% of GDP)

- _M_ imports (% of GDP)

- _RD_ tax revenue from VATs and sales taxes (%GDP)

- _RE_ tax revenue from export taxes (% GDP)

- _RM_ tax revenue from import taxes (% GDP)

- _RK_ tax revenue from capital taxes (% GDP)

- _RL_ tax revenue from labor taxes (% GDP)

- _TD_ tax rate on domestic goods and services

- _TK_ tax rate on capital (corporate tax rate)

- _TL_ tax rate on labor

- _αi_ labor-output ratio in production of _untaxed_

- _αf_ labor-output ratio in production of _domestic αe_ labor-output ratio in production of _exports_

Country-specific data are set out in Table 9. All country-specific data were obtained from IMF Statistical Annexes to country reports, available on the internet at www.imf.org. Table 9 also reports the year for which the data apply, and whether the country had a VAT in that year.

In the absence of data on labor-output ratios for all countries, the model uses the average ratios for the five countries of Table 10, supposing that the ratios are constant across all countries. These data are derived from social accounting matrices prepared by the International Food Policy Research Institute. Among other data, the SAMs provide for each commodity: the value-added by each factor; exports of each commodity; domestic demand for the commodity; and goods taxes paid. Let _Li_ denote the labor share of value added for each commodity _i_ . Let _Ei_ denote exports of each commodity. The first row in the table, the labor-output ratio <u>�</u> _<u>i</u>_<sup>(</sup><sup>_Li×Ei_)</sup> for exports is then . Commodities are classified as _untaxed_ if the taxes <u>�</u> _i_<sup>_Ei_</sup> paid on the commodity constitute less than 5% of the value of domestic output of the commodity, and _domestic_ if more than 5% is taxed. Let _Uj_ be the valueadded of untaxed goods. The labor-output ratio for _untaxed_ is then calculated <u>�</u> _<u>j</u>_<sup>(</sup><sup>_Lj×Uj_)</sup> as . The same procedure is used to calculate the labor-output ratio for <u>�</u> _j_<sup>_Uj_</sup> _domestic_ .

The administrative costs of tax collection presented in Table 5 are derived from various sources. For the United States: IRS Data Book, FY2002, available at: www.irs.gov/pub/irs-soi/02db30cs.xls; For other OECD countries – authors’ calculations based on the following tax agency annual reports: Australia – ATO Annual Report 2002, available at www.ato.gov.au; United Kingdom – Inland Revenue, Annual Report for the year ending 31st March 2002, available at www.inlandrevenue.gov.uk/pdfs/report2002.pdf; New Zealand – Inland Revenue 31
Table 2: Land market partcIdpaton In **1993** and **1998**

||Total Sample|NUplands|RedRiverDelta<br>aNorthCentral|Coast Central|Southest&<br>MekongDelta|
|---|---|---|---|---|---|
||||1993|||
|Rented in land|3.80%|7.20%|2.60%|1.30%|4.80%|
|Fixedrent|1.70%|2.40%|1.10%|0.30%|2.90%|
|Sharecropping|0.40%|0.20%|0.50%|0.00%|0.80%|
|Free|1.70%|4.60%|1.00%|1.00%|1.10<sup>0</sup>%|
|Boughtland|1.00%|0.70%|0.50%|0.00%|2.90%/o|
|Rentedout**land**|0.53%|0.20%|0.60%|0.30%|0.80%|
|Fixedrent|0.20%|0.20%|0.20%|0.00%|0.30%|
|**Share**cropping|0.03%|0.00%|0.00%|0.00%|0.20%|
|Free|0.30%|0.00%|0.40%|0.30%|0.30%|
|Sold land|0.30%|0.40%|0.00%|0.00%|1.10%|
||||1998|||
|Rentedinland|15.80%|11.00%|22.70%|8.00%|9.40%|
|Fixedrent|6.20%|2.20%|7.90%|6.40%|6.10%|
|Sharecropping|2.40%|2.00%|3.70%|0.30%|0.90%|
|Free|7.20%|6.80%|11.10%|1.30%|2.40%|
|Boughtland|7.20%|9.50%|6.10%|0.70%|10.50%|
|Rentedoutland|4.10%|2.00%|5.30%|5.00%|3.70%|
|Fixedrent|1.20%|0.20%|1.40%|1.00%|2.10%|
|Sharecropping|0.60%|0.20%|1.10%|0.00%|0.20%|
|Free|2.30%|1.60%|2.80%|4.00%|1.40%|
|Soldland|1.70%|2.00%|0.20%|0.00%|5.00%|
|Observations|2825|546|1326|298|655|

20
countries, both decentralized grids based on renewable energy and the inclusion of renewable energy in centralized energy grids have expanded (Baumert et al. 2005, Nouni et al. 2009, Deichmann et al. 2011). Solar photovoltaics (PV) and concentrated solar power (CSP) have emerged as particularly promising renewable technologies for addressing the energy/development nexus, while also mitigating greenhouse gas emissions. Both PV and CSP are carbon-free renewable technologies that are highly modular and thus relatively easy to build to scale and to maintain. PV in particular can be a very cost-competitive source of electric power in smaller-scale rural and peri-urban applications.<sup>1</sup> The attractiveness of solar electricity as a source of renewable energy has increased recently due to signicant cost reductions from advances in technologies and economies of scale in production. The fact that PV generated electricity has reached or become close to parity at the busbar in several countries has stimulated new investment in grid-based PV as well as more decentralized applications (Byrne et al. 2010). The total installed PV capacity in the world has increased from 1.5 GW in 2000 to 39.5 GW in 2010, which corresponds to an annual growth rate of 40% (REN21 2010). In addition, many developed and some developing countries have introduced policies (e.g. feed-in taris, higher electricity purchasing price, and rebates on installation) to further encourage the development of the solar PV market (Schmalensee 2011).

Though solar electricity has been seen by many to be an economically and environmentally attractive energy solution, it has its own challenges. In the next few decades, regulatory and institutional barriers can impede solar energy deployment, as can integration and transmission issues. In the longer term, the deployment potential of solar PV is aected by technological uncertainties and raw material scarcities. The economics literature on solar PV deployment has mainly focused on the short- and medium-term challenges related to regulation, integration and transmission constraints (for an excellent survey of these issues, see Baker et al. 2013). The long-run issues related to solar electricity deployment, such as technological uncertainties and material scarcities have largely been neglected in the economics literature, and remain an important gap to be lled.<sup>2</sup>

> 1In addition, non-electrical solar technologies also oer opportunities for modernization of energy services, for example, for water heating and crop drying (GNESD 2007).

> 2These issues are well recognized in environmental science and policy literatures (Jacobson and Delucchi 2011). This research adopts a longer-run perspective and largely revolves around the integrated assessment models (for a survey of solar PV in energy-economy integrated assessment models, see Baker et al. 2013), life-cycle assessment models (Fthenakis, Wang and Kim 2009) and expert elicitation surveys (Bosetti et al. 2012).

2
# **References**

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- Abaluck, J., Kwong, L. H., Styczynski, A., Haque, A., Kabir, M. A., Bates-Jefferys, E., Crawford, E., Benjamin-Chung, J., Benhachmi, S., Raihan, S., Rahman, S., Zaman, N., Winch, P. J., Hossain, M. M., Reza, H. M., Luby, S. P., & Mobarak, A. M. (2021). Normalizing community mask-wearing: A cluster randomized trial in bangladesh. (28734).

- Adao, R., Koles ́ar, M., & Morales, E. (2019). Shift-share designs: Theory and inference. _The Quarterly Journal of Economics_ , 134(4), 1949–2010.

- Bartik, T. J. (1991). Who benefits from state and local economic development policies?

- Berry, J., Fischer, G., & Guiteras, R. (2020). Eliciting and utilizing willingness to pay: Evidence from field trials in northern ghana. _Journal of Political Economy_ , 128(4), 1436–1473.

- Bleakley, H. (2007). Disease and development: evidence from hookworm eradication in the american south. _The quarterly journal of economics_ , 122(1), 73–117.

- Bleakley, H. (2010). Malaria eradication in the americas: A retrospective analysis of childhood exposure. _American Economic Journal: Applied Economics_ , 2(2), 1–45.

- Borusyak, K., Hull, P., & Jaravel, X. (2021a). Quasi-experimental shift-share research designs.

29
for access to services. It is not the aggregate index that we need for this purpose but its components.

Once we recognize that not even the Alkire-Santos MIP can be considered a sufficient statistic for poverty assessments, and that it will need to be complemented by other measures for those things left out, we are essentially back to the standard approach of using multiple measures. Whether that still leaves a role for the MIP depends on whether we think that it captures an important subset of the relevant dimensions of poverty. Here the ambition to be “multidimensional” is in such marked tension with the need to obtain all dimensions for each surveyed household that one must question what role such an index can usefully play. Arguably it would be better to derive the best measure possible for each of a logically defensible set of grouped dimensions—such as “income poverty,” “health poverty” and “education poverty”.

Clearly that task will still require some degree of aggregation. How should that be done? Here too there is an important, but poorly understood, distinction between the approach taken by recent MIPs and standard approaches to measuring poverty.

# **3. How should we aggregate?**

One can distinguish two approaches to forming an aggregate poverty index. The first is to use prices (actual or imputed) to form a composite index for aggregate consumption, to be compared to a poverty line defined in the same space.<sup>9</sup> Ideally this is not just consumption of market goods and services, but should include imputed values for non-market commodities. For market goods, either their market prices or appropriate shadow prices can be used. For nonmarket goods the missing “prices” will need to be assigned on _a priori_ grounds or estimated. In practice, most poverty measures require imputations for missing prices, so this approach is a natural extension of prevailing practices. In principle we can broaden this approach to allow for non-commodity dimensions of welfare. The space defined by all primary dimensions of welfare (including commodities) can be called the “attainment space” (though the term “achievements” is also used in the literature), and the aggregation can be called “attainment aggregation.” The weights on attainments can be called “prices,” understood to include imputed prices.

> 9 Household consumption is typically normalized for differences in household size and the prices faced; equivalently the poverty line is so differentiated.

8
**Figure 4:** Zambia’s opaque external debts, 2010 – 2021

<!-- Start of picture text --> 15<br>Hidden debt (in 2021):<br>12 • USD 3.18bn<br>• 14% of GDP<br>9<br>6<br>3<br>0<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Initially reported external public debt<br>External public debt added through revisions<br>(USD billion)<br>Public external debt stock<br><!-- End of picture text --> _Notes:_ The figure shows the initially and most recently reported public and publicly guaranteed debt stocks for Zambia between 2010 and 2021 in billion USD. The gray bars show the initially reported debt stocks. The red bars show additional debt stocks added through revisions in subsequent reporting. The data is from our new database (see Section 3 and Appendix A).

revision implies that a sovereign debtor now reports a liability to the World Bank that it had not previously reported.

- All debt instruments enter the World Bank’s debt statistics with their nominal or face value. They are not adjusted for fluctuations in their market value. This implies that ex post data revisions do not reflect valuation changes; rather, they indicate changes in the underlying debt reporting.<sup>14</sup>

- Since the first publication of the World Debt Tables in 1973, the World Bank’s reporting guidelines have essentially remained unchanged. Section B.1 in the appendix provides a systematic comparison of the Debtor Reporting System (DRS) reporting manual over time and confirms that only minimal refinements have occurred over the past four decades.<sup>15</sup> This finding ensures that ex post data revisions do not reflect the evolution of reporting guidelines; rather, they can be traced back to changes in compliance with a stable set of rules over time.

**Sources and digitization:** Our new database combines more than 50 different editions (“vintages”) of the World Bank’s International Debt Statistics (2013–2023) and its prede-

> 14See Appendix Section B.2 below for a discussion of ex post foreign exchange rate revisions. We show that they are minuscule in size and cannot explain the revision patterns that we document.

> 15Appendix Section B.1 documents these minor refinements. Our results remain unchanged when eliminating vintages that introduced changes to the reporting guidelines.

9
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