rafmacalaba/datause-encoder-data huggingface dataset:
size of the circles corresponds to the number of transactions at the circle center. The triangles represent the estimated focal points, and the solid lines mark the unit price (left panel) and expenditure (right panel) schedules. At left, the downward orientation of the unit prices is clear. On the right, the changing slope of the expenditure line represents the drop in unit prices as quantity increases. The clustering of purchases at focal quantities is also clear.

In each district there are some items that do not exhibit bulk discounts. Estimated unit price schedules are flat for 51 of the 126 item-district groups in the data (40%). Some items exhibit bulk discounts in every district, such as maize, cooking oil, kerosene, and tea leaves. Other items, including sweet bananas, cooking bananas, onions, salt, sugar, and dried sardines, have downward-sloping price schedules in three or more districts. Cigarettes, beans, rice, cassava, and matches only exhibit discounts in 1 or 2 districts.<sup>13</sup> A natural concern with price schedules estimated from observational data is that unobserved quality variation could be misinterpreted as bulk discounts, if higher quality versions of each item are disproportionately purchased in smaller quantities, and unit prices factor in quality. There are three reasons to believe that this is not the case here. First, as described in Section 3, we are able to use the item descriptions in the diaries to create highly standardized item groups. This mitigates unobserved variation from different product types being grouped together. Second, there is no quality variation across quantities in the market survey price data, because enumerators asked vendors to provide prices for different quantities of the exact same item. Yet, we see clear evidence of bulk discounts in those data (Table 3, columns 1-3), and there is no clear pattern as to whether the market price data or the survey data exhibit larger discounts (4 out of 10 coefficients in column 3 of Table 3 are larger in magnitude than their counterparts in column 5). Finally, while nearly half of observed purchases take place exactly at a focal point, the other half are priced above or below the estimated price schedule. One might believe that some of this variation reflects unobserved quality, and, if that is true, that wealthier households may systematically pay prices above the price schedule while poorer households pay prices below it (because demand for quality is increasing in wealth). In Appendix S3 we show that deviations from the price

> 13Why discounts emerge for only some items, and why they persist despite apparently robust competition in retail markets, are open questions not addressed in this paper. See Attanasio and Pastorino (2015).

15
Assuming that time series data for factor market variables at the sector level is not available,<sup>2</sup> this section describes how sectoral representation of MS models can be improved. In MFMod, each country has a minimum of three sectors: agriculture ( _Pt_<sup>_AGR_</sup> _Yt_<sup>_AGR_</sup> ), industry ( _Pt_<sup>_IND_</sup> _Yt_<sup>_IND_</sup> ), and services ( _Pt_<sup>_SRV_</sup> _Yt_<sup>_SRV_</sup> ), where _P_<sup>_i_</sup> is the value added deflator and _Y_<sup>_i_</sup> is value added at constant prices for sector _i_ . Input-output (IO) tables are available for many (but not all) countries for certain years, enabling us to establish a _top-down_ correspondence between intermediate demand and changes in sectoral value added.

As described in Burns et al. (2021), the integration of an IO table improves the intersectoral responses to shocks. However, this option remains unsatisfactory given that most of the shocks occur from the demand side and are then mapped to sectors, precluding us from modeling sectoral shocks that then translate back to the demand side of the economy. For example, a shock to water tariffs should affect agriculture demand for water as well as manufacturing and in turn affect aggregate demand through the price channel. These relative price shifts are not accounted for in previous methodologies. One way to include relative price shifts, is to model the sector in the form of translog cost functions (Berndt and Wood, 1975), which will be the starting point of the specification of sectoral accounts under the constraint that we do not have data on wages and rental rates.

In Burns et al. (2021), aggregate real value-added (GDP at basic prices), _Y_<sup>_FCST_</sup> , is already determined. Given that the demand side of the economy remains the same as in Burns et al. (2021), total value-added is simply written as the market price GDP, _Y_<sup>_MKTP_</sup> , minus net real indirect taxes, ( _NITt_ ): The GDP deflator at basic prices, _Pt_<sup>_FCST_</sup> , remains a function of marginal costs, _MCt_ , and the output gap, _Y_<sup>�</sup> _t_ : This allows us to move from aggregate value-added to sectoral value-added. We define sectoral shares to total value added as: 2When factor market data exist at the sector level, one can proceed to build out sector specific production functions and analytical formulas for marginal costs.

3
Table 22: Pension fund returns (1967-90) Mean (Standard deviation) of annual real total returns (domestic currrency) Percent

||u|s||UK|Ger|many|Jap|an|Cana|da|Ne|therlands||Sweden|Dem|nark|Switzerla|nd|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Using holding period retunms<br>on bonds (all countries) and<br>fixed rate mongages<br>(US&Canada)|||||||||||||||||||
||2.2|(11.9)|5.8|(12.5)|_5.1_|(4.4)|4.0|(9.4)|1.6|(9.8)|4.0|(6.0)|0.2|(7.6)|3.6|(12.7)|<br>1.5|(6.4)|
|Average earmings|0.2|_(2.1)_|2.6|_(2.5)_|4.0|(3.1)|4.2|(4.2)|1.7|(2.8)|2.4|(3.2)|1.5|(3.5)|2.8|(3.6)|1.9|_(2.!_|
|Pordolio return less<br>average earnings|2.0||3.2||1.1||-0.2||-0.1||1.6||-1.3||0.8||-0.4||
|Govemunentbonds<br>|0.6|(14.4)|0.8|(I1.))|2.7|(14.9)|0.2|(12.8)|0.0|(12.1)|1.0|(13.1)|-0.9|(8.5)|3.4|(16.1)|-2.2|(17.6)|
|Market paper<br>|2.0|(2.5)|1.7|(4.9)|3.1|(2.1)|-0.5|(4.6)|2.5|(3.3)|1.6|(4.0)|1.3|(3.5)|1.6|(1.8)|1.2|(2.2)|
|Equities|4.7|(14.4)|8.1|(18.9)|9.5|(20.3)|10.9|(19.4)|4.5|(16.5)|7.9|(28.2)|8.4|(23.3)|7.0|(27.5)|6.2|(22.3)|
|Memo: usingredemption<br>yields on fixed rate<br>instrunents|3.9|(7.6)|6.3|(10.7)|5.5|(3.0)|2.9|(5.7)|4.1|(5.0)|4.3|(5.5)|2.8|(2.9)|5.8|(3.0)|2.2|(2.a)|

31a
comparisons that do point to statistically significant, and likely also practically meaningful, differences across countries. Our 2005 Control of Corruption indicator for example covers 204 countries, so that it is possible to make 20,706 pairwise comparisons of corruption across countries using this measure. For 64 percent of these comparisons, 90% confidence intervals do not overlap, signaling quite highly statistically significant differences across countries. And if we lower our confidence level to 75 percent, which may be quite adequate for many applications, we find that 74 percent of all pairwise comparisons are statistically significant. The benefit of improved precision of aggregate indicators with increased data availability over time can also be clearly seen from this calculation. Consider our 1996 Control of Corruption indicator, which was based on a median of only four data sources per country, as opposed to a median of eight sources in 2005, implying substantially higher margins of error in 1996. Of the 11,476 possible pairwise comparisons in 1996, only 45 percent are significant at the 90% confidence level, and only 58 percent at the 75 percent confidence interval.

We can also use this calculation to illustrate the benefits of making comparisons based on aggregate indicators that are more informative than individual indicators. Again for Control of Corruption in 2005, consider one of our individual data sources, DRI, which has an estimated standard error of 0.52, corresponding to the median of the standard errors of all of our sources for corruption in 2005. Note that this is of course much higher than the standard error of the typical country in the aggregate indicator in 2005, which is 0.19. Had we based our estimates of governance on just this one data source, only 39 percent of cross-country comparisons would have been significant at the 75 percent level, and only 20 percent at the 90 percent level. Although rarely acknowledged explicitly, all other measures of governance are subject to margins of error as well, which in our past work we have shown to be at least as large as those we calculate for our individual and aggregate indicators. This underscores the need for caution in making cross-country comparisons with any type of governance indicator.

# **2.3  Changes over Time in Governance at the Country Level**

We now turn to the changes over time in our estimates of governance in individual countries. In Kaufmann, Kraay, and Mastruzzi (2005) we provided a detailed analysis of how to perform statistical inference on changes over time in the aggregate 14
TABLE A13: Impact of leader’s exposure on individual ECD tests

||**Leader ITT**<br>(1)|**S.e**<br>(2)|**P-Value**<br>(3)|**Obs**<br>(4)|
|---|---|---|---|---|
|**_Younger children (12 - 36 months)_**|||||
|ECD index (frst principal component)|-0.02|( 0.05)|0.68|624|
|Denver Personal-social|-0.03|( 0.07)|0.70|639|
|Denver Language|0.03|( 0.06)|0.56|633|
|Denver Fine Motor|-0.05|( 0.08)|0.49|630|
|Denver Gross Motor|-0.06|( 0.07)|0.42|629|
|**_Older children (37 - 84 months)_**|||||
|ECD index (frst principal component)|-0.14|( 0.05)|0.01|1,427|
|Denver Personal-social|-0.02|( 0.05)|0.72|1,449|
|Denver Language|-0.14|( 0.05)|0.01|1,449|
|Denver Fine Motor|-0.01|( 0.05)|0.85|1,449|
|Denver Gross Motor|-0.09|( 0.06)|0.15|1,449|
|Memory|-0.07|( 0.05)|0.19|1,449|
|TVIP (Vocabulary)|-0.13|( 0.05)|0.02|1,449|
|Self control|-0.08|( 0.06)|0.17|1,427|

Every row corresponds to a separate estimation (equation 1). All outcome variables are standardized using the mean and standard deviation of control households. All the regressions include controls for the stratification variables, child age and gender, enumerator fixed effects, and a dummy variables for a set of households surveyed in 2016, as well as community fixed effects. Standard errors reported in parentheses.<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 61
simple record keeping, many of the technologies described in this section can be adopted at reasonable costs by local governments.<sup>6</sup> It is important to pilot and test software to be sure it meets the specific needs in a given context. Knebelmann (2022) provides detailed illustrations from the experiences from over 15 property tax digitalization projects in different countries to show how satellite imagery tools and georeferenced data systems can help in detecting, registering, characterizing and valuing properties.

# **4 Technology and Trade Taxes**

Trade and customs taxes account for a notable share of total tax revenues in Africa where they account for 14 percent of tax revenues, relative to 4 percent in other regions (Figure 3b). Also, the majority of VAT revenues are collected at the border in many countries (World Customs Organization, 2017). For this reason, national customs departments play a crucial role in revenue mobilization, in addition to being instrumental in facilitating trade and exchange with foreign investors. At the same time, customs departments are often characterized by high levels of corruption – mostly due to the fact that tax officials enjoy strong discretionary powers in their profession, and risk-based systems of control and accountability are often missing or easily manipulated.

Technological innovations can significantly improve the administration of trade taxes. Similar to VAT, automated customs systems produce high frequency transaction level data that can be used for tax monitoring. It is not surprising that, particularly in developing countries, the initial, experimental attempts to digitalize and automate tax administration took off from customs offices, usually in parallel with broader reorganization reforms of customs bureaucracies. According to the World Bank Global e-Government Systems database, by 2014 low-income countries have invested much more in automating customs (97%), than tax management (57%), filing of taxes (32%), or online service portals (11%). For instance,

> 6Relatedly, geolocalized address information and a more accurate taxpayer register can improve enforcement on other tax types as well, such as income taxes.

15
29,311 migrants from the Soviet Union in 1966 among the constituent republics to yield the bilateral numbers for Australia (table A3).

# **{Table A3 here}**

In this simple example, only the data for 2001 are available. Where data are available for more than one census, the shares across all decades are averaged before estimating the bilateral numbers.

In the absence of such data (disaggregated data for the _same_ destination country in _other_ census years), the world is disaggregated into destination subregions. Origin countries in the same subregion are then assumed to have a similar propensity over time to send migrants to a particular destination country in a subregion for which data are lacking as they do to other countries in that subregion. For example, assume that the census data for Morocco in a particular year include the origin category All West Africa but no individual data on migrants from Ghana and that there are no bilateral data on Ghanaian migrants in other Moroccan censuses. In this instance, migrants from Ghana are assumed to have a similar propensity to migrate to Morocco as they have to other countries in North Africa. Data from other countries in North Africa (Algeria, Egypt, Libya, and Tunisia) are then used to calculate the propensity of Ghanaians—relative to migrants from other West African countries—to migrate to each country in North Africa. These propensity shares, which sum to one, can be applied to the All West Africa aggregate category from the Moroccan census to disaggregate it into the constituent West African countries. Equation 2 expresses this propensity measure: In equation (2), _G_ denotes the set of comparable destination countries (Algeria, Egypt, Libya, and Tunisia in the example above); _R_ is the set of origin countries (All West Africa); _n_ is the number of census years for which data exist; and _f_ is the number of countries in region _G_ . In short, this is the relative propensity of origin country _o_ to send migrants to subregion _G_ relative to other countries in its own region ( _R_ ). Where appropriate data for the subregion cannot be found, the set of all countries in the world is used.

_APPENDIX 5. CALCULATING GENDER SPLITS_ When gender splits are missing, the preferred option is to divide the world into subregions. Then it is assumed that the gender ratio of an origin country‘s emigrant stock in a specific decade is the same for each destination country in that subregion. The missing gender ratio in an origin country‘s emigrant stock can then be calculated using 30
of nonmigrants a few years after return, although it does not fully close in Bangladesh and Pakistan, mostly driven by a lower participation of returnees who came back at an older age. As reported in other contexts, we find that being over 50 has a stronger negative impact on labor force participation among return migrants compared to nonmigrants, suggesting that savings overseas may induce some return migrants to retire early from home labor markets (Dustmann and Kirchkamp 2002). That being said, returns at an older age are the exception rather than the rule in our sample of South Asian countries, as workers from South Asia typically migrate during their youth and return before the age of 40.

We also find that the occupational choices of return migrants systematically differ from those of nonmigrants. Return migrants who are wage employed are more likely to be employed in the construction sector, the main employer of labor migrants from South Asia in the main destinations. As evidenced in other contexts, we find that return migrants are more likely to be self-employed in non-agriculture compared to nonmigrants in Bangladesh and Pakistan, but not in Nepal.

Compared to prior studies in other contexts than South Asia, we find limited evidence for a returnee wage premium for returnees who become wage workers, except in Pakistan. The estimated returnee wage premium relative to nonmigrants is only 5 percent in Bangladesh and Nepal, and statistically insignificant. It is larger in Pakistan and statistically significant. One likely explanation for the smaller returnee wage premium in the context of South Asia is the low-skilled and brawn-based nature of the occupations migrants are employed in overseas, which may limit human capital accumulation at destination. Given the physicality and tediousness of occupations at destination, migrants may also not be willing to work in these same occupations and take advantage of their experience abroad once they return home at an older age. We report, however, that the minority of return migrants who find employment in the construction sector back home - the main sector of employment at destination - experience a large and statistically significant wage premium compared to nonmigrants employed in the same sector.

The paper is organized as follows. Section 2 provides some contextual background on temporary migration from South Asia. Section 3 introduces the data for Bangladesh, Nepal and Pakistan used in the paper and reports descriptive statistics. Section 4 provides details on our empirical estimation specification. Section 5 reports and discusses our descriptive results. Section 6 concludes.

4
18 as the number of items on the core mathematics test that the teacher claimed to have taught during that school year.

# Analysis Aoproach

Students were divided into four sex-by-school type groups: (a) girls in girls' schools; (b) girls in coeducational schools; (c) boys in boys' schools; and (d) boys in coeducational schools. The two dependent measures examined in the paper are (a) mathematics achievement and (b) stereotypic views of mathematics. We present means and standard deviations (s.d.) of all independent and dependent variables initially included in the two causal analyses examined in the study, and the statistical significance of mean differences between the four groups on each independent measure were tested by one--"y'analysis of variance (ANOVA).

The analytic sample combined male and female students, with dummy-coded program variables for girls-only and boys-only schools. We employed an analysis of covariance (ANCOVA) analytic design, using the two dummy-coded program variables as covariates. Ordinary least squares regression is the major analytic method used with ANCOVA, allowing the estimation of the two program effects net of other variables in the model. After considerable exploratory work investigating the many variables included in the IEA study, concentrating particularly on classroom and teacher variables which might "explain away" the potentially spurious relationships between school type (single-sex or coeducational) and the two dependent variables, we arrived at the final analytic model..1./ Effects are presented as both standardized and unstandardized regression coefficients, and the standard errors of the latter are included to indicate statisti.cai
Figure A8: Correlation between turnover (% of new hires) and the share of migrants in each occupation for migrants from different regions Panel a. EU15 migrants <!-- Start of picture text --> 0 .1 .2 .3 .4<br>Share of new hires <12 months<br>Panel b. NMS migrants Panel c. Non-EU28 migrants<br>0 .1 .2 .3 .4 0 .1 .2 .3 .4<br>Share of new hires <12 months Share of new hires <12 months<br>.16<br>.14<br>.12<br>.1<br>.08<br>.06<br>Share of EU15 workers<br>.04<br>.02<br>0<br>.16<br>.16<br>.14<br>.14<br>.12<br>.12<br>.1<br>.1<br>.08<br>.08<br>.06<br>.06<br>Share of NMS13 workers<br>.04 Share of non-EU28 workers<br>.04<br>.02 .02<br>0 0<br><!-- End of picture text --> Source: EU-LFS (2018).

Note: Correlation based on the 136 ISCO-08 3-digit occupations that represent more than 0.1% of the total employment in the EU. The size of the circles represents the share of each occupation in the total employment in the EU. In grey, 95% confidence intervals are reported for comparison.

55
Private expenditures on education in Peru are about 2% of GDP, well above the OECD average of 1.3%. As in other countries, these expenditures vary across education levels and income quintiles. In 1997, households spent about 3 times as much to send an 18 to 25 year-old to school as they did to send a 6 to 11 year-old. (Most of the older individuals would be attending university or somte other tertiary institution, while the younger children would be in primary school.) Differences across income quintiles are starker: On average, households in the richest income quintile spent about 8 times as much for every child aged 6 to 11 attending school as did households in the poorest income quintile, 5 times as much for those aged 12 to 17, and 4 times as much for those aged 18 to 25. Still, by international standards, these differences in the expenditure patterns across income quintiles are modest.<sup>7</sup> In Peru, education at the primary and secondary levels is dominated by the public sector: Ministry of Education figures suggest that 88.2% and 84.0% of students in primary and secondary school, respectively, attend a public school; these percentages have been virtually unchanged since 1990, when they were 88.3% and 85.6%. The private sector plays a much larger-and growing-role at the tertiary level: 43% of all tertiary education students (university and nonuniversity) were enrolled in a private institution in 1997, up from 37.6% in 1990 (World Bank 1999b, Volume 2, p. 53).8 Public provision of education has traditionally been quite centralized in Peru. In 1991, however, the government initiated a policy of deconcentration to twelve regional governments. As a result, a growing fraction of resources has been transferred directly to the regions: In 1990, the Ministry of Education managed 71 % of total public expenditures on education, and the regions 17%, but by 1997 these figures had been reversed, with the ministry of Education managing 25%, and the regions 56% (World Bank 1999b, Vol. 1, p. 16).9 The regional governments which manage education do not have capacity to raise own revenues, and are appointed by the central goveniment rather than elected."'

> rates in the DHS (61.3%), likely as a result of differences in the wording of the question or in the timing of the survey during the school year.

> 7As a comparison, in the Philippines households in the richest quintile spend 20 times as much per student in primary school as households in the poorest quintile, 1 1 times as much per student in secondary school, and 11 times as much per student at the tertiary level (Schady 2001, p. 34).

> **8** See the caveat given in the Data Appendix on the reliability of enrollment and repetition statistics provided by the Ministry of Education.

> Autonomous universities and other government bodies manage the remaining 19%.

> **10** Municipal governments at the province and district levels, by contrast, are elected.

7