rafmacalaba/datause-encoder-data huggingface dataset:
political assumptions. The estimates from this paper should be considered as complement to the existing estimates in the literature by providing lower bound figures and filling the gap for certain sectors or regions.

# **II. Definitions and Previous Literature**

A range of estimates on infrastructure financing requirements applying different assumptions and methodologies for achieving various goals have been estimated over the years. Terms such as “needs”, “demand”, and “gap” have been used in the literature interchangeably generating some confusion when comparing results. In order to quantify “needs”, normative targets should be established usually assuming an optimal level of infrastructure. Different targets will lead to different estimated needs. “Demand,” as used in this paper, refers to the infrastructure investment requirements to satisfy the consumer and producer demand, based on predicted GDP growth and other growth trends. The terms “gap” is used as the difference between the estimated requirements and the actual level of investments.

There are various methodologies used in the literature to estimate infrastructure investment requirements; they can be broadly classified in two categories: “top-down” and “bottom-up” (See Annex I). As the data of macroeconomic variables are relatively more comprehensive compared with the data of individual infrastructure projects, majority of the studies in fact adopt the “top-down” approach to conduct the empirical analysis.

The most recent estimates of annual infrastructure investment requirements for Emerging Markets and Developing Economies (EMDE) are Fay et al (2011) which estimate the annual needs of US$0.9-1.1 trillion with an annual gap of US$100-300 billion by extrapolating estimates from Yepes (2008) using price inflation rate. A recent G24 policy paper, Bhattacharya et al. (2013), calculates investment annual needs of US$2 trillion and investment gap of US$1 trillion by extrapolating Fay et al (2011) using economic growth rate and price inflation rate. Both studies are extrapolations of previous studies and they do not account for actual changes in infrastructure stock since 2006.<sup>8</sup> McKinsey (2013)’s uses three approaches to estimate infrastructure investment requirements. First, they use the historical spending approach, which assumes that future needs will follow past spending as percentage of GDP. Using this assumption, they project spending to 2030. The main limitation of this approach is that the data are mainly limited to OECD countries and extrapolated for the rest of the world. Secondly, they use the stock of infrastructure approach that estimates its value by using a perpetual inventory model. Unfortunately, they only have data for 12 countries<sup>9</sup> and

> 8 Moreover, they fail to mention the list of countries for which data was not available.

> 9 The model was populated with data from the following countries: Brazil, Canada, China, Germany, India, Italy, Japan, Poland, South Africa, Spain, United Kingdom and United States

3
Lanjouw (2013), Dang et al. (2014) and Dang and Lanjouw (2016). In addition, these estimates use cross sectional data from the GEIH during the period 2008–2016 based on the official poverty line and a vulnerability line of US$ 10 dollar-a-day in 2005 PPP (i.e. US$ 13.2 dollar-a-day 2011 PPP), and considering the partial correlation coefficient computed with pseudo-panel data for each couple of years from 2008 to 2016. The partial correlation coefficients were 0.51, 0.58, 0.59 and 0.59 for 2008-2010, 20102012, 2012-2014 and 2014-2016 respectively.

The estimates in **Table 5** suggest that roughly 56 percent of the population remain in the same income categories (i.e., the sum of the cells in the main diagonal of the matrices), 20 percent experience downward mobility and 24 percent experienced upward mobility. Results show that the estimated poverty rate fell by 14.6<sup>18</sup> percent during 2008-2010, 9.4 percent in 2010-2012, 6.4 percent in 2012-2014, and 3 percent in 2014-2016; while the middle class grew in all these periods except for the last period (20142016). While 25.7 percent of the population was vulnerable in 2008, the vulnerable group increased by 14 percent, 3.7 and 2.7 percent between 2008-2010, 2010-2012 and 2014-2016, respectively; it fell 4 percent between 2012-2014.

The increase of the middle class is mainly explained by upward mobility of the population in the vulnerable group. In 2010, around 60 percent of the newcomers to the middle-class came from the vulnerable group. In 2016, 73 percent of the newcomers were vulnerable in 2014. Moreover, the increase of the vulnerable group during the last years is explained by a downward mobility of the middle-class. More precisely, the percentage of poor people joining the vulnerable group decreases each year. In 2010, around 59 percent of the newcomers to the vulnerable group were poor in 2008, but this percentage fell to 48 percent in 2016.

It is also possible to break down the analysis by subgroups based on observable population characteristics such as gender and education level. When comparing welfare dynamics among male and female household heads, we observe that overall mobility is similar across genders and over time ( **_Figure 4 -7_** ). However, female household heads are slightly less likely to escape poverty in every period than their male counterparts. Thus, poverty shrinks by 15.1, 9.4, 6.3 and 3 percentage points for male household heads; while only by 13.6, 9.2, 6.7 and 3 percentage points for female household heads during 2008-10, 2010-12, 2012-14 and 2014-16, respectively. Despite the fact that poverty rates among females tend to be higher than for males (on average 4 percentage points higher), welfare dynamics are similar across both groups.

> 18 This number represents the percentage variation of the poor population between 2008 and 2010. As shown in Table 6, in 2008, 43 percent of the population were poor. By 2010, the poor population was 36.7 percent of the whole population. The percentage variation between the poor population on 2008 and 2010 is (36.7-43)/43 = - 14.6%.

16
Finally the fifth model specification suggests that:

- A firm in Lebanon is twice more likely than a firm in a Gulf country to have high employment growth (10% or more).

- A services firm is one and a half times more likely than a manufacturing firm to have high employment growth (10% o r more).

- A medium firm is 31% more likely than a small firm to have high employment growth (10% or more).

- A firm that had formal training is 35% more likely than a firm that didn’t to have high employment growth (10% or more).

- A firm that has a loan is 30% more likely than a firm that doesn’t have a loan to have high employment growth (10% or more).

Table 4: Logistic Regression Correlates High Growth to Explanatory Factors in Selected Arab Countries

|**Logistic Regression Results on**<br>**High Growth**|**9 Countries**<br>**SMEs**|**7 Countries**<br>**SMEs**|**6 Countries**<br>**SMEs**|**9 Countries**<br>**includes large**|**7 Countries,**<br>**includes large**|
|---|---|---|---|---|---|
|Offers formal training|+|+|+|+|+|
|Services sector|+|+|+||+|
|Medium size|+|+||||
|Has a loan|+|+||||
|Int’l Quality certification||+||||
|High regulatory compliance cost||+|+|||
|Innovated|||+||+|
|Exports||||+|+|
|Uses email for clients||||+||
|SME|||||+|

Source: World Bank Enterprise Surveys.

The findings indicate several important correlates of high growth in the sampled enterprises as follows; (Table 4).

- First, in all five specifications, firms that offer formal training to their employees are

- significantly more likely to experience high growth than firms that do not, controlling for a variety of other characteristics.

- Second, in four of five specifications, firms in the services sectors were more likely

- than those in manufacturing to have experienced high growth.

 Third, in two of the three SME models, medium-sized firms were more likely to have experienced high growth than small firms (some of these firms could have started as small firms and grown to be medium).

 Fourth, in two of three SME models, a firm having a loan was significantly more likely than a firm without a loan to be a high growth firm. This variable was not significant in the models including large firms, implying that at the margin, having a loan may be a more important factor for growth for SMEs.

- Fifth, in one specification, firms with an international quality certification were

- significantly more likely to experience high growth than firms without (Figure 24).

27
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12

**(7)**

Solving our model for the growth rate of **A,** we define **_PROD3_** _as:"_

**_PROD3= [GROWTH -_ 0.3** **_* CAPGROWTH -_ 0.7 * A(E *** **_0'(E))]_ /0.7**

# **_C Private Saving Rates_**

The data on private saving rates draw on a new savings database recently constructed at the World Bank, and described in detail in Loayza, Lopez, Schmidt-Hebbel and Serven **(1998).** This database improves significantly on previous data sets on savings in terms of accuracy, and both country- and year-coverage. These data draw on national accounts data, and are checked for consistency using individual country sources.

The private saving rate is calculated as the ratio of gross private saving to gross private disposable income. Gross private saving is measured as the difference between gross national saving (gross national disposable income minus consumption expenditures, both measured at current prices) and gross public saving (the public sector is defined as the consolidated central government).<sup>14</sup> Gross private disposable income is measured as the difference between gross national disposable income and gross public disposable income (sum of public saving and consumption).

Due to data availability, the private saving rate sample is slightly different from the sample used in the analysis of real per capita **GDP** growth, capital per capita growth and productivity per capita growth. Specifically, we have data available from **1971 - 1995,** so that we have five nonoverlapping five-year periods for the panel data set and **25** years for the cross-country estimations.

> **"** We get this result a follows. We first divide **_(5)_ by** Li and take logs. Note that due to the assumed functional form o(E) = Ei*o'(Ei), so that _ln(Hi/_ Li)= Ei*o'(Ei). Solving the equation for the log of productivity per capita and taking first differences results in **(7).**

> **"** Using a broader measure of the public sector, instead of the consolidated central government, would be analytically preferable. This, however, limits the sample size. Nonetheless, this definition of the public sector yields very similar results to those presented below.
no addition to real investment. Or the funds received may be passed on to another affiliate in the same multinational group. In such cases, inward FDI into a jurisdiction will exceed real investment there. On the other hand, inward FDI will understate foreign affiliates’ real investment to the extent that their tangible investment is financed in ways other than by cross-border transfers or retentions (but instead by, instance, local borrowing or local issuance of shares). FAI data does not suffer from either form of misstatement of tangible investment.

The second and closely related key difference<sup>25</sup> is that FAI data see through to the location of the ultimate parent company, whereas in FDI data the ‘parent’ country typically refers to the location of the immediate investor.<sup>26</sup> This reporting convention for FDI creates additional and serious issues for the measurement of investment: whenever funds pass through an intermediate country the same underlying funds will be recorded twice in the FDI statistics, with the intermediate country as the destination country for the first observation and as the ‘parent’ country for the second. FDI data will thus double count (or more) investments whenever conduit transactions arise between the ultimate parent and the final host country. This double counting, which is rooted in the equity method of accounting, also gives rise to double counting of profits for any company with intermediate affiliates at the micro level (Blouin and Robinson, 2019). At the global level, double counting of multinational investments would imply a misleadingly high level of aggregate FDI, inflated by pass-through funds that are recorded multiple times.

An example illustrating these core differences between FAI and FDI data—which will also prove helpful in understanding some later results—is provided in Figure 1, with implications summarized in Table 1. It takes the case of an ultimate parent in country UP which has a controlled affiliate in a country of intermediate ownership IP, that affiliate in turn being the immediate parent of another affiliate in host country H. The ultimate parent injects $2

> 25There is a third difference, which is in the threshold for ownership/control: FDI data comprise all foreign interests with 10 percent or more voting power, while the FAI statistics have a higher threshold of 50 percent.

> 26There are some other data available by ultimate investors, for example the OECD’s Inward “FDI by immediate and by ultimate investing country dataset” and from UNCTAD. For the former, however, coverage is limited and the distinct series by ultimate and investing countries are for stocks (not flows); for the latter, bilateral FDI by ultimate owner and host country is not publicly available after 2012 (Casella (2019)).

13
# **A Randomized, Controlled Study of a Rural Sanitation Behavior Change Program in Madhya Pradesh, India**

Sumeet R. Patil<sup>1</sup> Benjamin F. Arnold<sup>2</sup> Alicia Salvatore<sup>3</sup> Bertha Briceno<sup>4</sup> John M. Colford, Jr.<sup>5</sup> Paul J. Gertler<sup>6</sup>

**JEL codes** : I15, I18

**Key words** :  India, Madhya Pradesh, Total Sanitation Campaign, Randomized Controlled Trial, Impact Evaluation, Toilets, Open Defecation, Diarrhea, Parasite Infection, Weight, Height, Growth, Anemia

**Sector Board:** WAT

> 1 University of California, Berkeley and Network for Engineering and Economics Research and Management (NEERMAN, India), corresponding author.

> 2 Division of Epidemiology, School of Public Health, University of California, Berkeley.

> 3 Community Partnership Program, Stanford Cancer Institute, Stanford University.

> 4 World Bank, Water and Sanitation Program.

> 5 Division of Epidemiology, School of Public Health, University of California, Berkeley.

> 6 Haas School of Business and School of Public Health, University of California, Berkeley.
Table C.3: Baseline Parameters (continued)

|Parameter|Description|Units|Value|
|---|---|---|---|
|_βf_|AIDADS Marginal Budget Share at High<br>Income for Services from Processed Food||0.028|
|_βl_|AIDADS Marginal Budget Share at High<br>Income for Services from Processed Livestock||0.011|
|_βe_|AIDADS Marginal Budget Share at High<br>Income for Energy Services||0.049|
|_βw_|AIDADS Marginal Budget Share at High<br>Income for Services from Processed Timber||0.032|
|_βr_|AIDADS Marginal Budget Share at High<br>Income for Ecosystem Services||0.104|
|_βo_|AIDADS Marginal Budget Share at High<br>Income for Other Goods and Services||0.776|
|_γ_<sup>_f_</sup>|AIDADS Subsistence Parameter for Processed<br>Food||0.45|
|_γ_<sup>_l_</sup>|AIDADS Subsistence Parameter for Processed<br>Livestock||0.003|
|_γ_<sup>_e_</sup>|AIDADS Subsistence Parameter for Energy<br>Services||0.026|
|_γ_<sup>_w_</sup>|AIDADS Subsistence Parameter for Processed<br>Timber Products||0.027|
|_γ_<sup>_r_</sup>|AIDADS Subsistence Parameter for Ecosystem<br>Services||0.028|
|_γ_<sup>_o_</sup>|AIDADS Subsistence Parameter For Other<br>Goods and Services||0.346|
|_γ_<br>_δ_|Risk Aversion Parameter<br>Social Discount Rate||2<br>0.95|

25
Science, and PISA assesses Math, Reading, and Science. The cross-country correlation between these scores is high—that is, in countries where students do well in one subject, they tend to do well in other subjects too—so we would not expect using one or the other to lead to very different _LAYS_ estimates. This is demonstrated in Figure 4.4: all points are close to the 45degree line, meaning that the subject chosen does not make an appreciable difference to the estimate of _LAYS_ . (In all cases, the correlation and Spearman correlations between these various measures of _LAYS_ is above 0.98.)

**Figure 4.4:** **_LAYS_ using different subjects**

<!-- Start of picture text --> LAYS based on TIMSS 2015 Science scores LAYS based on PISA 2015 Reading or Science<br>versus TIMSS 2015 Math scores scores versus PISA 2015 Math scores<br>0 5 10 15 0 5 10 15<br>LAYS using TIMSS math LAYS using PISA math<br>LAYS (science)<br>LAYS (reading)<br>Source: Authors’ analysis of TIMSS 2015, PISA 2015, and Barro‐Lee data.<br>Notes: Numeraire country is Singapore. Illustration for case where learning starts at Grade 0 for TIMSS and age<br>6 for PISA. For TIMSS: correlation coefficient is 0.99; Spearman rank correlation is 0.99. For PISA: between<br>mathematics and reading: correlation coefficient is 0.99; Spearman rank correlation is 0.98; between<br>mathematics and science: correlation coefficient is 0.99; Spearman rank correlation is 0.99<br>15 15<br>10 10<br>5 5<br>LAYS using TIMSS science<br>LAYS using PISA reading/science<br>0 0<br><!-- End of picture text -->

# **_4.4 The choice of numeraire_**

Singapore is the highest performer on both the TIMSS and PISA mathematics assessments, which makes choosing it a logical choice for the numeraire in the illustrations of 22
|Treatment 2|0.030*|0.041**|0.018|0.042**|0.006|0.279***|0.034|0.044**|0.017|0.058***|0.040|-0.008|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|“Teacher|_(0.017)_|_(0.019)_|_(0.017)_|_(0.021)_|_(0.031)_|_(0.055)_|_(0.033)_|_(0.022)_|_(0.021)_|_(0.022)_|_(0.028)_|_(0.009)_|
|Delivery”|||||||||||||
|N|31,437|16,209|15,228|18,716|10,402|985|1,334|15,713|15,724|10,480|10,498|10,459|
|N Control|11,404|5,881|5,523|6,573|4,038|360|433|5,813|5,591|3,667|3,888|3,849|
|N Treatment 1|10,461|5,424|5,037|6,362|3,340|238|521|5,185|5,276|3,560|3,489|3,412|
|N Treatment 2|9,572|4,904|4,668|5,781|3,024|387|380|4,715|4,857|3,253|3,121|3,198|
|_R2_|_0.854_|_0.850_|_0.843_|_0.853_|_0.820_|_0.798_|_0.900_|_0.850_|_0.862_|_0.464_|_0.420_|_0.226_|
|||||* p <|0.05, ** p <|0.01, *** p < 0|.001||||||

_Notes:_ All regressions control for pre-treatment outcomes, demographic controls (including dummies for age, gender, academic year, and ethnicity), and municipality fixed effects. Robust standard errors are clustered at the school level in parentheses. All figures are rounded to three decimal places. _Source:_ Administrative data, school year 2016/2017, own calculations.

22
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