rafmacalaba/data-use-mentions-extended huggingface dataset:
investments. For portfolio investments, we use the Coordinated Portfolio Investment Survey (CPIS) from the International Monetary Fund (IMF) that measures the bilateral stock of portfolio assets, covering the period 2001-2014 for 75 source and 207 recipient economies. The data on cross-border syndicated loans are available at the transaction level and come from Thomson Reuters’ SDC Platinum database, covering the period 1996-2014. They comprise 111 source and 183 recipient economies. For FDI, we also use transaction-level data from two different sources. For M&As, we use Thomson Reuters’ SDC Platinum. For (announced) greenfield investments, we use the Financial Times’ fDi Markets. The M&A data span the 1990-2014 period and cover 139 source and 162 recipient economies. The greenfield investment data cover the 2003-2014 period, encompassing 157 source and 193 recipient economies.<sup>4</sup> The different datasets are not directly comparable in terms of their overall value of international investments. The CPIS annual data are on the _stock_ of bilateral investments, whereas all the other data are aggregated to bilateral annual _flows_ . In particular, we aggregate the transaction-level data to the bilateral level by summing the value of all transactions from a given source economy to a given recipient economy for every year in the sample. Because of their different nature, the value of portfolio investments is considerably larger than that of syndicated loans, M&As, and greenfield investments. Nonetheless, the overall patterns on their evolution across and within the different types of financial assets are still informative. The same applies to the comparisons of the number of connections of EAP with different regions and with itself.

To benchmark the extent of EAP’s financial integration with itself and other North and South economies, we estimate a gravity model including the following control variables: the GDP of the

> 4 North economies report consistently across the different databases, implying a good coverage of North-North and North-South connections. However, reporting of South-North and South-South transactions remains relatively sparse, arguably leading to an underestimation
from Planet – which features spatial pixel resolution of 3 to 5 meters – and use those predictions as inputs into the small area estimation model. We make use of two different CNNs: one to estimate the poverty rate (defined as extreme or moderate headcount poverty according to the CONEVAL definition) and one to recognize the land classification type of an image. We train the former using the 2014 MCS-ENIGH. Land classification subdivides images into coverage by building, road, water, grassland, forest, and background (all other classes). Because CNNs require square tiles of imagery for prediction, we train the CNN to estimate poverty or land type at the tile level, which are roughly 750 square meters each. The CNN predicts poverty or land type at this tile, and we use an area-weighted sum to aggregate the predictions to the AGEB – or sub-area – level. We then aggregate these AGEB-level predictions to municipalities, weighting by the 2010 census population.<sup>14</sup> The auxiliary information that augments the small area estimation models is therefore AGEB-level and municipal-level CNN predictions of poverty or land type. Summary statistics for the CNN generated auxiliary spatial data are shown in Table 2, and more technical details of the CNN are presented in Appendix A.

Intuitively, the poverty CNN uses all available visual characteristics in an image to predict extreme or moderate poverty as defined by the 2014 MCS-ENIGH. If these visual characteristics reliably predict poverty, then CNN model predictions can be extrapolated to areas not covered by the MCSENIGH and used to estimate poverty predictions for these areas. Adding this additional information should allow a small estimation model to have lower variance and higher predictive performance. While Babenko et al. (2017) demonstrate the performance of the CNN poverty predictions against a benchmark, incorporating this information into a small area estimation model improves the predictions in three ways. First, it enables the estimates to also benefit from auxiliary data on land classification. Second, it conditions the predictions on sample information through the empirical best predictor model,
planning services (Rosenzweig and Schultz 1989). This is especially so in Ethiopia where injectable contraceptives are the main method. Injectable contraceptives are ideal for women without education because they do not require any user action except the visit to a family planning clinic every 3 months.<sup>16</sup> In addition to the expected larger effect of family planning for women with no education, the age profile of fertility and the effect of other factors on fertility are likely to be different across education groups. Rather than assuming the appropriate specification given such interactions across education groups, the main sample is restricted to women with no formal education who have ever been married or lived together with a man. Among the original sample, 65 percent of women never attended school. Table 2 shows the descriptive statistics for this sample.<sup>17</sup> The main dependent variable is the number of children a woman had given birth to at the time of the survey (children ever born) which averages just over 4. The large number of births reflects the high fertility rate in Ethiopia, especially considering that the average age of the women in the sample is just over 28 years.<sup>18</sup> Age is captured using five-year age groups with the excluded category being aged 15-19. With the high population growth rate in Ethiopia younger cohorts are larger than older cohorts, but the percentage that have married or lived with a partner is smaller for young women compared to older women explaining the lower percentages of the two youngest age groups (15-19 and 20-24) in the sample. Just over half of the women are Orthodox Christian, a quarter are Muslim, and the remaining women are mainly other Christian. The remaining variables are the same community characteristics as in Table 1 used for the first stage.

Because there is no information in our survey data on migration of women the definition of access to family planning implicitly assume that a woman has spent her entire life in the area where she was found
Figure 6. Growth of world output, quarterly <!-- Start of picture text --> A. World output growth<br><!-- End of picture text --> <!-- Start of picture text --> A. World output growth B. World per capita output growth<br>Percent Percent<br>12 9<br>6<br>8<br>3<br>4<br>0<br>0<br>-3<br>-4<br>-6<br>-8 -9<br>1960 1971 1983 1995 2007 2019 1960 1971 1983 1995 2007 2019<br>Sources: Feenstra, Inklaar, and Timmer (2015); Haver Analytics; Organisation for Economic Co-operation and<br>Development; World Bank.<br>Note: Shaded areas show global recessions, which are identified using per capita output data and the algorithm in<br>Harding and Pagan (2002). Last observation is 2019:3. For details, see Appendix 2.<br><!-- End of picture text --> 35
products in rural areas is treated as informal and thus not subject to VAT in the model.

- E) In-kind transfers are allocated to households based on either age eligibility (education) or declared usage of these services (health). In the case of education values are imputed using information on public school attendance children’s age eligibility. In the case of health care, we use the information on declared use of health care services in the data and impute their value using estimates of intervention costs from Flessa et al. (2018) and Jacobs et al. (2019).

# **Macro-validation of the simulated fiscal policy elements**

The data used for this study allow us to analyze only a proportion of fiscal interventions since much of government spending and revenues cannot be directly assigned to specific households in the data. The CEQ methodology in Cambodia covers the dimensions of fiscal policy which account for about 19.3 percent of total government revenues and 23.2 percent of total government spending.

To test the quality of the survey data and modelled fiscal elements, total imputed/simulated taxes and benefits are compared against administrative statistics. The exercise aims to see how precisely the total taxes (transfers) collected from (paid to) households in the survey matches the total tax collections (spending) reflected in budget documents. Table 3 shows the results of the validation exercise.

13
338(8758):1–5.

- Grimshaw, D. and Rubery, J. (2015). The motherhood pay gap: a review of the issues, theory and international evidence. _ILO Conditions of Work and Employment Series No. 57_ .

- Havnes, T. and Mogstad, M. (2011a). Money for nothing? universal child care and maternal employment. _Journal of Public Economics_ , 95(11-12):1455–1465.

- Havnes, T. and Mogstad, M. (2011b). No child left behind: Subsidized child care and children’s long-run outcomes. _American Economic Journal: Economic Policy_ , 3(2):97– 129.

- Heckman, J. (1974a). Shadow prices, market wages, and labor supply. _Econometrica_ , pages 679–694.

- Heckman, J., Moon, S. H., Pinto, R., Savelyev, P., and Yavitz, A. (2010a). Analyzing social experiments as implemented: A reexamination of the evidence from the highscope perry preschool program. _Quantitative Economics_ , 1(1):1–46.

- Heckman, J. J. (1974b). Effects of child-care programs on women’s work effort. In _Marriage, Family, Human Capital, and Fertility_ , pages 136–169. NBER.

- Heckman, J. J., Krueger, A. B., et al. (2005). Inequality in america: What role for human capital policies? _MIT Press Books_ , 1.

- Heckman, J. J., Moon, S. H., Pinto, R., Savelyev, P. A., and Yavitz, A. (2010b). The rate of return to the highscope perry preschool program. _Journal of Public Economics_ , 94(1-2):114–128.

- Heckman, J. J., Moon, S. H., Pinto, R., Savelyev,
for the other components. For the macro-framework, a government development plan can be used, or other growth regressions such as Barro’s can be employed. For the poverty module, one could use POVCAL or PovStat or even a microsimulation model.

19. Easterly (1999) refers to the current version of the growth model employed at the World Bank as the “financing gap model” in view of the fact that its most important uses involve determining growth prospects and the associated financing shortfalls.

20. One could add a fourth asset: bonds issued by the government to borrow money from the general public. This addition would not alter the basic logic presented in the text.

21. In a critique of the use of this model in short-term growth analysis, Easterly (1999) notes that, in both the neoclassical and endogenous growth models, the ICOR can be constant in a steady state. In the neoclassical framework, this steady state ICOR is equal to the ratio of the investment rate to the sum of population growth and the rate of laboraugmenting technical progress. Furthermore, this constancy does not imply a causal and proportional relationship between investment and growth. Finally, he argues against viewing the ICOR as an indicator of the quality of investment.

22. This is possible because, once imports are known, all other components of this identity are determined except private investment. In particular, real GDP is determined from the ICOR and the total investment during the previous period. Exports are projected exogenously. Private consumption is specified as a fraction of disposable income, and government consumption and investment are each a fraction of GDP.

23. The current level of the overall price index is equal to the money supply multiplied by the velocity of money, divided by nominal income.

24. The simplest version presented in textbooks is commonly called the Keynesian cross.

66
039<br>(0.071)|0.13<sup>_∗_</sup><br>(0.056)|
|Observations|777|777|777|777|

Note: Standard errors in parentheses.<sup>_<u>∗</u>_</sup> _p <_ 0 _._ 05 _,_<sup>_<u>∗∗</u>_</sup> _p <_ 0 _._ 01 _,_<sup>_<u>∗∗∗</u>_</sup> _p <_ 0 _._ 001 _._ Year dummies included. Data: survey on Bamako, Mali, 2009-2012. The table reports the probit regression of log of land prices on distance to city center. The sample is restricted to the residential plots that were Non Statutory at the time of transaction.

# 6.3 Prices

Next, we estimate the dierential land price gradients for residential plots with statutory and non-statutory rights. We restrict our attention to the land plots that have statutory rights at both dates of transaction and survey (S to S), and to those that are transformed from non-statutory to statutory rights between transaction and survey dates (NS to S). This restriction allows us to separately identify the land purchases by those among urban workers who purchase formal land and by brokers who purchase land from customary owners before formalizing it as described in our model.

Table 3 displays OLS regressions of the log of the sales price on various sets of price determinants. The rst column excludes the determinants associated with distance to the CBD and tenure. Controls include plot area, distance to paved main road, dummies for water connection, electricity connection and South bank location, and year dummies to control for land price ination. All coecients have the expected sign and are signicant at the 1 percent level (except for access to
|**Pillar**|**Indicator**|**Description**|**Source**|
|---|---|---|---|
|Pillar 1: Institutions|Government<br>effectiveness|Captures perception of the quality of public services, the civil service<br>and its independence from political pressures, the quality of policy<br>formulation and implementation, and the credibility of the<br>government’s commitment to its policies.|WGI (2016)|
|Pillar 2:<br>Infrastructure<sup>a</sup>|Per capita energy<br>use|Energy use, kg of oil equivalent.|WDI|
|Pillar 3: ICT<br>adoption<sup>b</sup>|Individuals using<br>Internet (%)|Individuals who have used the Internet, from any location, in the last<br>three months|WDI|
|Pillar 4:<br>Macroeconomic<br>stability|Output gap<br>_or_<br>Change in terms of<br>trade|Output gap<br>Change in terms of trade (log).|WEO<br>WEO|
|Pillar 5: Health<br>Pillar 6: Skills|Human capital<sup>c</sup>|Index of human capital per person, based on years of schooling (Barro<br>and Lee 2013) and returns to education (Psacharopoulos 1994)|Penn World Table|
|Pillar 7: Product<br>market|Taxes<sup>d</sup>|Top marginal income and payroll tax rate, percent|Fraser (2016)|
||Regulatory quality<sup>e</sup>|Measures perceptions of the ability of the government to formulate<br>and implement sound policies and regulations that permit and<br>promote private sector development.|
Minor (2011), this study estimates determinants of trade costs in a panel specification.

The regression equation takes the following form: _TCijt_ = β1 _RTAijt_ + β2 _tariffijt_ + β3 _LSCIijt_ + β4 _LPIijt_ where for any given country pair _ij_ , bilateral trade costs _TC_ observed at time _t_ are regressed on a wide range of candidate drivers. These include membership in a regional trade agreement (RTA); sector-specific bilateral tariffs; shipping connectivity (UNCTAD’s Liner Shipping Connectivity Index, or LSCI) and logistics (the World Bank’s Logistics Performance Index, or LPI); a proxy for trade policy uncertainty; and standard gravity indicators (distance, a common language, and a common border). In line with Osnago, Piermartini, and Rocha (2018), trade policy uncertainty is defined as the gap between binding tariff commitments and applied tariffs. To ascertain the role of policies aimed at facilitating trade, indexes of logistic performance and maritime connectivity are included.
regulations index by state.

Our final primary measure is the strength of household banking environments. A large body of work considers the link between financial constraints and entrepreneurship, as surveyed in Kerr and Nanda (2011). Our measure of local financing conditions is the percentage of households that have banking services as measured in the 2001 census. This measure is likely to be particularly reflective of financing environments for unorganized sector activity.<sup>7</sup> Beyond these core district traits, we will also model consumption per capita in the district as an additional control in robustness checks. Richer districts in India have more factories (e.g., Sharma 2005), and we will check whether this influences entry beyond the controls for existing industrial activity discussed next. Our control is calculated for 1999 at the district level.

6 These are Ahmedabad, Bangalore, Bhubaneshwar, Chennai, Delhi, Guwahati, Hyderabad, Kolkata, Mumbai, and Patna.

7 We have also tested additional district-level indicators, including metrics of caste/religious population, female-to-male sex ratio, conflict, trade levels, and general development levels (leading/lagging designations at the state and district level). These additional controls do not substantively affect the results presented, and we maintain the more parsimonious specification to mirror other work from outside of India. Iyer et al. (2011) investigate the relationship of India‘s caste system to entrepreneurship.

13
and Bhatt (2015) for India, we also show that the correlation coefficient between parents and child’s schooling has been increasing or remaining constant across cohorts, mainly driven by educational inequality in the parents’ generation. This result is not surprising in our context given that the correlation coefficient provides an absolute measure of intergenerational persistence after account is taken of a possible improvement in the distribution of education attainment because of education system reforms, such as the abolition of school fees, which increase average schooling and reduce variation in schooling. The education systems of all the countries in our sample have changed drastically since the 1960s. From a policy perspective, our result highlights the demand for targeted redistributive policies that can improve intergenerational mobility in the region. Moreover, putting in place a favorable environment for women who are less well off in terms of education might play a decisive role in promoting social mobility not only in the short run, but also in the next generation.

The rest of the paper proceeds as follows. Section 2 reviews the literature and places the study in the context of existing literature. Section 3 presents the analytical framework. Section 4 describes the data. Section 5 presents the results. Section 6 offers concluding remarks and describes potential policy implications.

> 2The data used in this study are comparable with the data set used by Hertz et al. (2007) for 42 countries and Azam and Bhatt (2015) for India. Moreover, the sample of Hertz et al. (2007) uses an old survey of Ghana.

> 3See section 4 on the construction of matched data on parents’ and children’s educational educational attainment.

3
P a g e <u>|</u> **38**

# **Understanding housing characteristics in prevailing market conditions**

The next step in making a market assessment is to understand the price and location characteristics of the accommodations families actually choose to live in and builders choose to supply. This can be measured at many different degrees of precision, depending on the final use of the data. For the purposes of this illustrative exercise, we surveyed a few dozen households<sup>12</sup> . The survey was structured around a housing typology based on expert knowledge. Samples were not randomly selected. The result of the survey, while not statistically significant, provides a snapshot image of the housing stock beyond the formal/ informal dichotomy. Any institutions interested in housing affordability should conduct such surveys, allocating enough resources to provide statistically significant results. Eventually a fully fledged housing census could be completed in each city and hedonic price indexes estimated to understand the housing market, but it is possible to get valuable insights with methods that are far less demanding.

> 12 Further details on sampling approach and data collection available upon request.

38
One contribution of our approach is the deduplication of the job postings across portals to ensure that only unique postings are considered. Overall, the deduplication leads us to consider only 12,321 vacancies in the final data set rather than the initial 16,888 vacancies, which is around 30 percent less.<sup>19</sup> Our deduplication algorithm consists in iteratively deleting double entries based on different time horizons up to one month of publication. Thereby, it takes into account two different aspects of job postings. On the one hand, it accounts for the possibility of lagged duplications when a posting is duplicated on a different job portal within a onemonth frame. Such postings are considered duplicates. On the other hand, the algorithm acknowledges that when firms repost an identical job ad at a later time, this may indeed refer to a different work opportunity. Therefore, if the repost is done after one month (whatever the portal it is posted on), this is _not_ considered a duplicate. A detailed overview of this procedure is provided in Appendix A.III. By deduplicating the raw data, we extend previous work on online vacancies in Kosovo, most notably that of Helvetas (2016), who reports raw counts of vacancies from 2015.

The text data used for the analysis, namely the job title and the job description, contain information on skills, minimum years of experience, and minimum education requirements in a raw text format (also called “unstructured” data). That is, the information of interest is specified as raw text in the respective columns from where information is first extracted prior to analysis. A textual analysis is conducted on the job description and job title to identify the incidence of skills, as well as education and experience requirements.

The textual analysis follows that of <mark>Deming</mark> and Kahn (2018) and relies on a dictionary-based approach. The authors study variation in skill demand for professionals across firms and labor markets.<sup>20</
**Table 2. Odds ratios from logit “Model 1” of extended formality by data source**

||**Pooled**|**ELMPS**<br>**2012 in est.**|**ELMPS**<br>**2018 in est.**|**Economic**<br>**Census**|**Establishm**<br>**ent Census**|
|---|---|---|---|---|---|
|**Data (ELMPS 2018 in est. omit)**||||||
|ELMPS 2012 in est.|1.349**|||||
||(0.147)|||||
|Economic Census|0.400***|||||
||(0.035)|||||
|Establishment Census|0.532***|||||
|**Ent. size (one worker omit.)**|(0.045)|||||
|2|1.655***|1.462|1.887***|1.814***|1.628***|
||(0.094)|(0.292)|(0.358)|(0.130)|(0.011)|
|3-4|2.414***|2.096**|1.394|2.651***|2.750***|
||(0.141)|(0.506)|(0.368)|(0.275)|(0.019)|
|5-24|5.661***|7.228***|9
**Table 2: Summary statistics.**

|Variable|Obs.|Mean|Std. Dev.|Min.|Max.|Corr. with LogExports|
|---|---|---|---|---|---|---|
|Colonyij|93744|0.010|0.099|0|1|0.0897|
|Colonizerij|93744|0.117|0.322|0|1|-0.0295|
|Contiguousij|93744|0.013|0.112|0|1|0.1459|
|Distanceij<br>|93744|8541.281|4687.455|10.479|19951.160|-0.1644|
|Exportsij<br>k|103512|8366.421|190979.700|0|2.02E+07|n/a|
|GDPi|82639|2.99e+11|1.17e+12|1.37e+08|1.37e+13|0.086|
|Languageij|93744|0.170|0.375|0|1|0.012|
|LPIi|67657|2.744|0.631|1.212|4.190|0.0703|
|Parts<sup>k</sup>|103539|0.500|0.500|0|1|-0.3455|

_Note:_ Exportsijk _contains 75,217 observations equal to zero._ 25