rafmacalaba/data-use-mentions-extended huggingface dataset:
# 4.8 Summary of core questionnaire and modules

To summarise, the proposed topics for any core questionnaire and those for which optional modules might be developed are presented in Table 2. The core questionnaire would be fairly short and many of the optional modules would have to be country-specific.

# S. DESIGN AND ORGANISATIONAL ISSUES

# 5.1 Selection of countries for the WHS

The -OR for this report suggest that the WHS would be a programme to sponsor studies of health in a broad cange of developing countries. The experience of the World Fertility Survey suggests that comparative studies of some topics are best undertaken by including data from as many and as varied countries as possible while other investigations find it more profitable to concentrate on a limited number of countries with certain features in common. It should also be noted that only a programme of surveys that included such populous countries as China or India could be considered in any way representative of the developing world. It therefore seems important to decide at the outset whether the countries involved in a WHS should be selected to maximise their differences or whether country health studies should be concentrated where they are judged most important. At least four axes of variation seem relevant: existing health conditions, sociopolitical structure, the organisation of the health services and geographical location.

As was emphasised in section 2, survey-based investigations of health are not appropriate in every country. The value of such a survev depends on the nature of existing health information systems, the capacity of national institutions to conduct a survey without disrupting other activities and the extent to which the results will be used to improve planning and management of the health services.

In the selection of countries the WHS should avoid duplicating the efforts of the UNHSCP national health surveys. One possibility is that 35
Firstly, we reproduce the results by applying weights to the sample of businesses to generate the weighted average treatment effects. The weights employed were based on firm size, sector, and region. This did not have a qualitative impact on the findings (see Table A2 in the Appendix), but as expected, the level of variance increases once weights are applied. Table A2 is restricted to all respondents who participated in both listing experiments. This shrinks the sample size to 2,272 but does not meaningfully change our results. Finally, we reproduce our analysis, including all respondents who refused to answer these questions and treated them as if they did not evade taxes. This still shows that substantial evasion is likely to be present.

# **5.2 Heterogeneous Effects**

The double list experiment was included as part of the WBES which collects extensive information on firm characteristics and the beliefs of top managers or owners, which means that there are many dimensions along which heterogeneity in the main findings could be explored. We rely on a machine learning algorithm to identify the dimensions where substantial and consistent heterogeneity exists. Specifically, we use a causal forest technique<sup>10</sup> to identify where the greatest heterogeneity occurs across 27 potential dimensions that are captured in the survey prior to the double list experiment questions. A list of these 27 dimensions together with a measure of their relative heterogeneity in tax evasion prevalence is presented in Table A3 in the Appendix. We use this inductive approach to identify heterogeneity as ex ante, it is not immediately clear which dimensions will exhibit the most significant variation in tax evasion. Importantly, we focus our analysis on the dimensions where substantial and consistent heterogeneity exists in both list experiments. There are instances where differences across some dimensions are meaningful in only one of the list experiments and where the direction of heterogeneity varies. As we are interested in the most robust results, we do not focus on these dimensions where heterogeneity is not substantial or consistent across

> 10The causal forest function
# **Fig. 3c: Urban shares in**

<!-- Start of picture text --> unorganized sector<br>Unorganized Outpu<br>0.80<br>0.70<br>0.60<br>0.50<br>Unorganized Employme<br>0.40<br>0.30<br>Unorganized Plants<br>0.20<br>0.10<br>0.00<br>1989 1994 2000 2005<br>Urban shares<br><!-- End of picture text --> Source: Ghani, Grover, Kerr, 2012 What attracts new entrepreneurs and workers to cities? Ghani et al (2011b) quantify the factors and traits of cities and industries that systematically predict stronger entry levels of new establishments in the formal and informal sectors. They use cross-sectional establishment-level surveys in 630 districts in India spread across 35 states/union territories. They quantify entrepreneurship as young firms that are less than three years old, and define entry measures through employment in these new establishments. They examine the role of demographic traits in cities (age profiles, population, population density), structural traits of cities (education of the local labor force, quality of local physical infrastructure, connectivity to major cities), business climate traits (stringency of labor laws, household banking conditions), and the impact of agglomeration economies in formal and informal sectors. They develop metrics that unite the incumbent industrial structures of districts with the extent to which industries interact through the traditional agglomeration channels (Marshall 1920). This conceptual approach has also been used to describe location choice decisions and city structures in several advanced economies. The first agglomeration channel is proximity to customers and suppliers, which reduces transportation costs and thereby increases productivity. The second is the Chinitz effect. Chinitz (1961) argued that the large, integrated steel firms of Pittsburgh depressed external supplier development. By contrast, New York City's much
|_(Table C.4 continued.)_||
|---|---|
|Sole (Index)|Index of woman’s sole decision-making in seven decisions in the<br>household:<br>1. Decision for her to take up work outside the home for income,<br>2. Decision for her to accept a promotion<sup>85</sup>,|
||3. Decision to purchase large household appliances,|
||4. Decision about large house repairs,<br>5. Decision to purchase clothing and jewelry for herself,<br>6. Decision for her to take a bus to run an errand,|
||7. Decision for her to visit a friend in the neighborhood.|
||For each decision, an ordinal variable was coded such that<br>1=Woman decides alone without needing permission,<br>0=Woman decides alone, but needs permission, OR<br>Woman is involved in joint decision-making, OR Others decide, but<br>need the woman’s permission, OR<br>Woman is not involved.<br>I create dummy variables for each ordinal category and decision, and<br>then compute a standardized index following Anderson (2008) and<br>O’Brien(1984).|

> 85For the selected or nominated women who had been offered a promotion in the wave 2 survey, these questions refer to the decision-making about the actual offer instead of the generic question.

67
16%|45%|44%|
|Mexico|18%|16%|58%|56%|9%|9%|15%|15%|
|Nicaragua|23%|21%|16%|15%|32%|32%|28%|28%|
|Paraguay|54%|53%|25%|25%|18%|17%|4%|4%|
|Peru|5%|5%|41%|40%|23%|23%|31%|31%|
|Panama|21%|19%|18%|17%|18%|17%|44%|42%|
|Uruguay|97%|97%|3%|3%|0%|0%|0%|0%|
|Venezuela|37%|35%|7%|6%|28%|28%|28%|28%|
|_LCR Totals_|_30%_|_29%_|_29%_|_29%_|_25%_|_25%_|_16%_|_16%_|

Sources: 1) Population density is from CIESIN Gridded Population of the World 3.

2) Rainfed crop suitability data are from FAO/IIASA Global Agro-Ecological Zones. Notes: 1) LCR totals include all Latin American and Caribbean countries, including those that do not appear in the table.

2) There were a small number of coastal gridcells that were missing country codes, and were therefore omitted from the above table.

3) The denominator for the percentage was total area as given by the tabulated cells, and excluding those few cells with omitted country codes.

4) “Unsuitable” includes GAEZ classifications of ‘marginal’, ‘very marginal’, and ‘not suitable’.
# **References:**

- Berg, A. and A. Kruger. 2003. “Trade, Growth and Poverty: a selective survey.” _IMF Working Paper_ WP/03/30.

- Campa, J. M. and L. S. Goldberg. 2002 “Exchange Rate Pass-Through into Import Prices: A Macro or Micro Phenomenon?” _NBER Working Papers_ 8934.

- CEPAL (Comisión Económica par América Latina y el Caribe). 2001. _Panorama Social de América Latina 2000–2001._ Publicación de las Naciones Unidas, Santiago de Chile.

- Deaton, A. 1997. _The Analysis of Household Surveys. A Microeconometric Approach to Development Policy._ The World Bank and The Johns Hopkins University Press.

- Frankel J., D. Parsley and S.J. Wei. 2004. “Slow Passthrough Around the World: A New Import for Developing Countries?” _IMF working papers_ forthcoming.

- Goldberg P. and M. Knetter. 1997. “Good Prices and Exchange Rates: What Have We Learned?” _Journal of Economic Literature_ 35:1243-1272.

- Hanson G. 1997. “Increasing Returns, Trade and the Regional Structure of Wages.” _The Economic Journal_ 440:113-133.

- Hanson G. 2003. “What happened to Wages in Mexico Since NAFTA?” _NBER Working Papers_ 9563.

- Hertel T., W. 1997. _Global Trade Analysis: Modeling and Applications_ . The Cambridge University Press.

- Hertel T., W. and J.J. Reimer. 2004. “Predicting the Poverty Impacts of Trade Reform.” _World Bank Working Papers_ 3444.

- Ianchovichina E., A. Nicita and I. Soloaga. 2002. “Trade Reforms and Households Welfare: the Case of Mexico.” _The World Economy_ 25:945-72.

- Nicita, A. 2004. “Who Benefited from Trade Liberalization in Mexico? Measuring the Effects on Household Welfare.” _World Bank Working Papers_ 3265.

- Singh, I. L.
)vvaavv(yyii2 −ββ vvaavv(xxii)ββ An alternative way to estimate ρρ= is to further assume that there is a cohort fixed effect in the σσεε1σσεε2 error terms and aggregate all the time-invariant variables to the cohort level and use the following equation ρρ i i i (C6) where the error term i includes a cohort fixed effect and the error i . ii yy = ββ ′xx + εε Note that the standard errors of estimates based on the synthetic panels can in fact be even ii εε ττ νν smaller than that of the true (or design-based) rate if there is a good model fit (or the sample size in the target survey is significantly larger than that in the base survey; see Dang and Lanjouw, 2023, for discussion).

Tables C.1-C.6 present the estimation results using Equation (C1) for all the countries and survey rounds. Tables C.7-C.12 present the descriptive statistics of the estimation sample. These tables show that while most of the time-invariant characteristics show similar distributions across survey rounds (and satisfy Assumption 1), some do not. For example, these include the shares of household heads achieving primary education or secondary education in the Arab Republic of Egypt during 2012-2015 (Table C.7). But the differences are practically very close to 0. Table C.13 presents the estimates for using Equations (C5) and (C6), where are estimated using Equation (C5) for all countries with cohorts being defined by age interacted with household heads’ education. ρρ ρρ We also provide alternative estimates for using Equation (C6). Using these estimates, Figures C.1 and C.2 offer qualitatively similar results to Figures 5 and 6. ρρ 78
Table A.9: Baseline balance, restricted to sample with midline student assessment data

|||(1)||(2)||(3)|T-|test|
|---|---|---|---|---|---|---|---|---|
||Co|ntrol|In-|Kind|Reco|gnition|Dif|erence|
|Variable|N|Mean|N|Mean|N|Mean|(1)-(2)|(1)-(3)|
|_Student-level (grade 2)_|||||||||
|Math|5528<br>[84]|0.049<br>(0.084)|5771<br>[80]|0.093<br>(0.075)|5409<br>[86]|-0.100<br>(0.088)|-0.044|0.149|
|Language|5528<br>[84]|0.050<br>(0.076)|5771<br>[80]|0.063<br>(0.074)|5409<br>[86]|-0.025<br>(0.074)|-0.013|0.075|
|Female|5517<br>[84]|0.482<br>(0.008)|5767<br>[80]|0.484<br>(0.010)|5400<br>[86]|0.479<br>(0.009)|-0.003|0.003|
|Age<br>_Student-level (grade 3)_|5349<br>[84]|9.485<br
The sub-national or local entrepreneurial ecosystem assessment can provide a clearer picture of the heterogeneity of relevant factors within country with customized diagnostic. A vigorous and dynamic entrepreneurial ecosystem is critical, but it requires investment and time to mature. The availability of better resources combined with good institutions tend to facilitate the process of creating new firms, the expansion of young firms, and the technological catch-up of firms overall. Yet, the key factors necessary to strengthen an entrepreneurial ecosystem (e.g., knowledge, human capital, entrepreneur talent, managerial capacity) are structural and demand investment, resources, and time to mature. Identifying the potential of entrepreneurial ecosystems at the sector-regional level can provide a more precise diagnostic of the entrepreneur problem, given that the decision of starting a business, scale up, and innovate, happens in a particular place surrounded by specific complementary factors.

To identify the potential of local entrepreneurial ecosystems, we propose to start by assessing the agglomeration of firms in terms of diversity and quality in relevant economic activities. While indicators on entrepreneurship performance and pillars of the ecosystem are relatively limited at the aggregated level, they are even scarcer at the sub-national level in developing countries. At the same time, many developing countries have some sort of establishment census available, which gives information on characteristics of firms associated with age, sector, and other features that can be used as a proxy for quality (e.g., education of the entrepreneur, size, formal status, experience as exporter or importers, innovation). The assessment can address two purposes: First, by observing the diversity and quality of firms geographically agglomerated in a given economic activity, this exercise provides a proxy for the endowment factors, which are usually difficult to observe in developing countries. Second, if a country is looking to promote policies that support entrepreneurship performance in a given activity or value chain, this exercise can be used a filter to identify specific geographic regions with more potential as candidates for deep dive analysis.

The second stage provides an assessment
- **PanTex** , which is a built-up presence index derived from the grey-level co-occurrence matrix (Pesaresi et al, 2008)

- **Structural Feature Sets** (SFS), which are statistical measures to extract the structural features of direction lines (Huang et al, 2007)

These spatial features are calculated by comparing pixels with their neighbors and then reporting this value back to the individual pixel (in this case 10m). The number of neighboring pixels considered in the comparison is the scale, which varies by the feature being calculated. For most features, we use scales of 3, 5, 7, which correspond to squares of 3 pixels by 3 pixels, 5 pixels by 5 pixels, and 7 pixels by 7 pixels.

The remote sensing data used for Tanzania, while largely derived from publicly available imagery, was supplemented by proprietary data on building footprints produced by Ecopia and Maxar.<sup>10</sup> The publicly available layers considered for Tanzania included:

- **Night-time lights** , taken from the same VIIRS imagery used for Sri Lanka

- **Population estimates** taken from WorldPop, which is in turn derived from the 2011 census and distributed using measures of built-up area.

- **Precipitation estimates** taken from Weilcott and Matsuura (2018)

- **Elevation and Slope** taken from Jarvis (2008) and downloaded from AidData’s geoquery database

- **Built-up area** from the same GHSL data used for Sri Lanka, for the same periods.

- **Climactic region,** obtained from Kottek et al (2006). This indicator divides land into five main climate groups (tropical, dry, temperate, continental, and polar), with each group being divided based on seasonal precipitation and temperature patterns, based on a raster version of the map available at 0.5° resolution.

- **Crop yield estimates** for Maize, Sorghum, and Rice, from the HarvestChoice Dataverse
-high value vs. paddy and wheat- of crops grown. We use these to test for productivity effects that may have been triggered by program-induced risk-reductions, noting that, as the program targets the poorest groups, direct program effects should be most pronounced for small farmers.

Table 4 reports estimated impacts on wages received, overall (panel A) and allowing for heterogeneous program effects by gender (panel B). We find MNREGS-induced wage increases of some 10%, well above what is reported elsewhere. Most of this increase can be attributed to wages in the agricultural sector which are estimated to have increased by 11%, compared to a positive but insignificant coefficient for wages in the non-farm sector. Gender disaggregation suggests that program-induced wage increases affected males and females more or less equally though estimated rates were marginally higher for females who also seem to have experienced a marginally significant increase in non-agricultural wages.

> 16 While the data are extraordinarily rich, they suffer from two shortcomings. First, lack of information on the source (i.e. public or private) of nonfarm work makes it impossible to distinguish work performed under MNREGS from other types of non-farm casual employment. Second, as data on on-farm labor supply in 1999/2000 was not disaggregated by season, a direct test of the hypothesis that MNREGS-induced increases in dry season labor demand were key to smoothing consumption (Imbert and Papp 2015b; Klonner and Oldiges 2014) is not possible.

11
- [3] Barro, R.J. and J.W. Lee (2000): International Data on Educational Attainment: Updates and Implications, CID Working Papers 42, Center for International Development (Harvard University).

- [4] Bauer, T.K. and A. Kunze (2004): The demand for high-skilled workers and immigration policy, Brussels Economic Review, Forthcoming.

- [5] Bhagwati, J.N. and K. Hamada (1974): The brain drain, international integration of markets for professionals and unemployment: a theoretical analysis, Journal of Development Economics, 1, 1: 19-42.

- [6] Beine, M., F. Docquier and H. Rapoport (2001): Brain drain and economic growth: theory and evidence, Journal of Development Economics, 64, 1: 275289.

- [7] Beine, M., F. Docquier and H. Rapoport (2003): Brain drain and growth in LDCs: winners and losers, IZA Discussion paper, IZA Bonn.

- [8] Carrington, W.J. and E. Detragiache (1998): How big is the brain drain?, IMF Working paper WP/98/102.

- [9] Carrington, W.J. and E. Detragiache (1999): How extensive is the brain drain, Finance and Development, June: 46-49.

- [10] Cinar, D. and F. Docquier (2004): , Brussels Economic Review (special issue on skilled migration), forthcoming.

- [11] Commander, S., M. Kangasniemi and L.A. Winters (2004): The brain drain: a review of theory and facts, Brussels Economic Review, forthcoming.

- [12] Domingues Dos Santos, M. and F. Postel-Vinay (2003): Migration as a source of growth: the perspective of a developing country, Journal of Population Economics, 16, 1: 161-75.

- [
As mentioned before, for sampled clusters, EB prediction makes use of the estimated cluster effects from the survey data to improve the point estimates of the poverty indicators and their errors. A crucial assumption under EB prediction is that model errors _ech_ and _ηc_ are normally distributed. This yields ˆ normality for _ηc_ given _ec,s_ , which leads to the proposed predicted cluster effect _ηc_ .

This EB addition implemented in the PovMap software uses an extended version of Henderson’s method III (Henderson, 1953) to obtain estimators _σ_ ˆ _η_<sup>2and</sup><sup>_σ_ˆ</sup> _e_<sup>2ofthevariancecomponents</sup><sup>_σ_</sup> _η_<sup>2and</sup><sup>_σ_</sup> _e_<sup>2thatin-</sup> corporate the sampling weights.<sup>12</sup> In step 2 of section 2 for the ELL, _ση_<sup>2</sup><sup>_∗_</sup> is drawn from a _Gamma_ distribution, however to avoid this assumption and because var [ˆ _η_ c] is unkonwn,<sup>13</sup> it is necessary to rely on bootstrap re-sampling to obtain the noise of the point estimates in similar fashion to ELL.<sup>14</sup> Even if the estimator is based on the EB method of MR (2010), the bootstrap procedure used to generate the census data is completely different. This new bootstrap algorithm tries to avoid the distributional assumptions by generating in the first step samples of clusters from the survey data set. The full procedure for obtaining the estimator of _τc_ , called here clustered bootstrap EB (CB-EB), is as follows:

1. Take a bootstrap sample of clusters (PSUs) with replacement from the survey data.<sup>15</sup>

2. Fit model (1) to the bootstrapped survey data from step 1. This
High to Extreme<br>40% 12 13 26 25 23 25 40% 5 9 8 8<br>30%20% 9 10 5 10 30%20%10% 1 1 4 5 6 5 4 6 4<br>10%<br>0%<br>0%<br>Proportion of developing countries<br>by level of state fragility<br>Proportion of developing countries<br><!-- End of picture text --> _Source:_ Authors’ calculations based on data from the World Development Indicators database and Marshall and Cole 2010. _Note:_ Figures above or beside each bar indicate the number of countries.

We also looked at several dimensions of trade--export sophistication and shipping connectivity, commodity versus noncommodity exporters as well as landlocked versus other countries. These associations are presented in detail in World Bank (2011a). In any case, export sophistication, shipping connectivity, and state fragility are likely to be correlated with a country’s level of 2009) close to 1.03 percent and a CPIA index of 3.00 in 2009. However, non-fragile states have grown, in per capita terms, at an average rate of 2.27 percent since 1990. The CPIA index for these countries is 3.68 in 2009.

15
the remaining analysis, however, we will maintain the focus on German influence given the greater incidence of German FDI with respect to other major partner countries.

## [Figure 2 about here.]

The positive effect of German ownership on employment is also confirmed when using our second approach (given by equation (2)). This approach reflects the actual timing of acquisitions and, therefore, allows us to compare the effects of early and late acquisitions. The results are reported in Table 2, while Figure A.7 shows the event study associated to the double robust estimation and confirms the absence of pre-trends in employment. This approach provides more conservative estimates, yet the effects remain large and indicate that the effect of German acquisition on firm-level employment is between 7.5% and 11%. The estimates by treatment year show that the effects are larger for later acquisitions, which are associated with an increase of more than 14% in employment from 2010 onwards. Overall, both set of results point towards substantial positive effects of German acquisition on firm size.

## [Table 2 about here.]

# **5.2 Differential effects of EU accession on German acquisitions across sectors**

Figure 3 shows the results of the event study, which are obtained by estimating equation (3). The results confirm our descriptive evidence and show an increase in foreign acquisitions by German shareholders in the post-accession period. Overall difference in differences results are shown in Table A.5 and are consistent across different measures of German influence. They indicate that a 10% increase in Germany’s export share is associated with a 8% increase in German ownership (0.04 percentage points over the average ownership of 0.5%). We do not find evidence of border regions receiving more FDI from Germany (Figure A.8 of the Appendix). As a falsification experiment, we employ the same specification on similar data for Romanian firms, also from the Orbis database. Since Romania acceded the EU in