rafmacalaba/datause-encoder-data huggingface dataset:
# **7 Conclusion**

Recent contributions to the economic growth literature have shown that the persistence of growth is as important as its magnitude, if not more. Many developing countries exhibit sudden bursts of rapid growth that last a short period of time, whereas the developed economies tend to display more gradual but persistent growth trajectories. Therefore, determining the factors that lead to either beginning or end of the growth spells promises useful information regarding the fundamental determinants of growth. These, in turn, highlight the importance of a careful investigation for time varying characteristics of growth.

This study demonstrates an application of the wavelet based techniques for analyzing the growth and volatility over different time horizons. Overall, these techniques perform well in separating the short-term and long-term changes in the GDP levels and growth rates. Localization in time and frequency by using these techniques enables us to identify the persistence of the impacts due to one-off events in the data. These techniques also enable us to decompose the association between the growth trajectories of two different economies into different time horizons. Combined with a lead and lag relationships, this co-movement analysis can provide the policy makers with important insights on how short and long term developments in partner countries may affect the domestic economy.

# **References**

- Burnside, C. and Tabova, A. (2009). Risk, volatility, and the global cross-section of growth rates. _NBER Working Papers 15225_ .

- Calderon, C., Chong, A. and Stein, E. (2007). Trade intensity and business cycle synchronization: Are developing countries any different? _Journal of International Economics_ , **71,** number 1, 2–21.

- Cohen, A., Daubechies, I. and Vial, P. (1993). Wavelets on the interval and fast wavelet transforms. _App. Comp. Harm. Anal._ , **1,** 54–81.

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Table A.4: Balance Tests: Sample means by firstborn sex, REDS

||1973-|1984|1985-|1994|1995-|1999|All years|1973-1984|1985-1994|1995-1999|
|---|---|---|---|---|---|---|---|---|---|---|
||FB|FG|FB|FG|FB|FG|FB-FG|FB-FG|FB-FG|FB-FG|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|SC|0.13|0.15|0.14|0.14|0.14|0.15|-0.008|-0.02|0.0001|-0.004|
|ST|0.07|0.07|0.07|0.07|0.08|0.07|0.003|0.0009|0.002|0.009|
|OBC|0.35|0.35|0.34|0.37|0.33|0.35|-0.02|-0.004|-0.03*|-0.02|
|Hindu|0.87|0.90|0.87|0.90|0.90|0.89|-0.02***|-0.03**|-0.03***|0.01|
|Muslim|0.08|0.06|0.08|0.06|0.05|0.06|0.02**|0.02*|0.02|-0.02*|
|Sikh|0.03|0.03|0.04|0.03|0.04|0.02|0.008*|0.004|0.008|0.02|
|Christian|0.01|0.01|0.01|0.01|0.01|0.02|-0.005|0.007|-0.0003|-0.01**|
|Mother’s age at birth|20.59|20.61|20.74|20.84|21.40|21.63|-0.13|-0.03|-0.10|-0.23|
|Mother’s years of education|2.74|3.06|3.84|3.73|5.37|5.52|-0.18*|-0.32*|0.11|-0.15|
|Father’s years of education|5.54|6.11|6.54|6.44|7.79|8.01|-0.27**|-0.57***|0.10|-0.22|
|N (1st births)|1,374|899|1,599|1,216|758|646|6,492|2,273|2,815|1,404|

NOTES: This table compares the socioeconomic characteristics of firstborn-boy (FB) and firstborn-girl (FG) families during the pre-ultrasound period and during the two post-ultrasound periods in the REDS sample. SC, ST, and OBC denote Scheduled Castes, Scheduled Tribes, and Other Backward Classes, respectively. The sample is restricted to first children who were alive at the time of survey.

Table A.5: Actual and ideal fertility and fraction of sons; pooling all families

||(1)<br>Actual|(2)<br>Ideal<br>Ac|(3)<br>tual - Ideal|
|---|---|---|---|
|**1. Pre-ultrasound: 1973-1984**||||
|Fertility|2.98|2.54|0.44|
|Fraction of Sons|0.58|0.57|0.01|
|N||23,414||
|**2. Post-ultrasound: 1985-1994**||||
|Fertility|2.17|2.47|-0.30|
|Fraction of Sons|0.55|0.56|-0.01|
|N||45,920||
|**3. Post-ultrasound: 1995-2005**||||
|Fertility|1.90|2.31|-0.41|
|Fraction of Sons|0.53|0.55|-0.02|
|N||44,813||

NOTES: This table reports the actual and ideal fertility and fraction of sons as reported by the mother, and the gaps between the two, over the three time-periods in our sample. Ideal fraction of sons = _idealboys_ + _ideal_ <u>(0</u> _._ 5 _kids∗idealeither_ <u>)</u> if _idealkids >_ 0, where _idealkids_ is the ideal number of total children; _idealboys_ is the ideal number of boys; and _idealeither_ is the number of children that parents are indifferent over the gender of, as reported by a woman. Data: NFHS.

52
government debt and the increase in remittances, though again these factors might have been more beneficial to Stanbic than other banks.

# **Annual Profitability**

<!-- Start of picture text --> 0.1<br>0.08<br>Stanbic<br>0.06<br>UCB<br>R 0.04<br>O<br>A 0.02 UCB+Stanbic<br>(Virtual)<br>0<br>Sector<br>1996 1998 2000 2002 2004<br>-0.02<br>-0.04<br><!-- End of picture text -->

**Figure 1**

All models are estimated with a robust clustering method that accounts for both heteroskedasticity and correlation across multiple observations from the same bank. Because observations from the same bank are likely to be correlated, ordinary least squares (OLS) techniques can underestimate errors (thus overestimating significance levels). Although we do not present the results here in the interest of brevity, qualitative results are similar for models estimated either via simple OLS, OLS with White’s heteroskedasticity-consistent standard errors, or least median squares, though significance levels for those models are sometimes higher.<sup>18</sup> Models on the left-hand side of Table 2 include dummy variables to benchmark UCB’s profitability prior to the privatization, Stanbic’s preprivatization profitability, and the profitability of the merged institution after the transaction. Model 1 indicates that Stanbic was relatively profitable prior to privatization, while Model 2 indicates that UCB had relatively low preprivatization profitability. The insignificant coefficient for the

> 18 OLS and OLS with clustered standard errors yield identical coefficients. The only difference is in the standard errors, which affects significance levels, and thus the interpretation of results.

10
number of questions that are country-specific and other, globalized variables comparable across countries and time.

The survey for Egypt in 2016 provides for the first-time information on firm ownership and the decision-making participation by government officials and thus provides a direct measure of political connections. It also includes some other questions critical to firm growth in Egypt; for instance, if firms received energy subsidies, state procurement contracts, favorable exchange rates, etc. Importantly, the survey contains an innovation module with detailed questions on firms’ innovation activities. The 2016 Egypt survey includes manufacturing and construction as well as the following major private service sectors: retail & wholesale trade, hotel & restaurants, travel agencies, transport ICT, and real estate. It has a large sample size relative to other World Bank Enterprise surveys, covering 1,813 establishments. And it is a stratified survey, providing sampling weights that allow to estimate the economy-wide impact for these sectors for all establishments with at least 5 employees, limiting the survey to the formal economy. This size limitation is, however, not critical in our case since politically connected firms are typically formal and much larger, and so are their non-connected establishment peers.

We provide detailed information of the survey in Appendix A. Table 1 reports the descriptive statistics for these establishment-level variables, separately for politically connected (PC) and all other firms; additionally, “not politically connected” (NPC) firms are sub-divided into two categories of those with a PC firm in their (four-digit ISIC) sector—providing a measure of direct competition from PC firms—and those in sectors without PC firms. Consistent with previous studies, the descriptive statistics show that PC firms are older and much larger. We also find that they have closer ownership structures with the government, either through direct government ownership shares or having been established by the privatization of a state-owned enterprise (we control for both in the estimations below). The descriptive statistics also suggest that PC firms tend to innovate less frequently (for all measures except for having an internationally recognized quality certificate). Yet, PC firms have a higher labor productivity which appears to stem from their higher capital intensity. Importantly, PC firms are much more likely to have received subsidized inputs, a government contract, and investment financing from banks.

# **Measure of establishment’s political connection:**

8
plots and household workers increases by 0.5 and 1.9, respectively, between baseline and endline. This is much more than the increase of 0.2 and 0.7,<sup>10</sup> respectively, in the recall group. Both double difference estimators of listing bias due to recall – -0.3 for plots and -1.1 for household workers – are statistically significant.

**Table 4: Double difference estimator of bias in listings of plots and household workers**

||No. of cultivated plots listed<br>per household|No. of workers listed<br>per household|
|---|---|---|
|Endline|0.531***|1.885***|
||(0.063)|(0.118)|
|Endline * Recall|-0.320***|-1.116***|
||(0.088)|(0.166)|
|N|908|908|
|R2|0.156|0.393|

Note: Regressions control for household fixed effects *** denotes significance at the 1% level.

**Table 5** turns to explore whether bias in listings of plots and household workers accounts for recall bias in farm labor at the person-plot level. Column (1) repeats the benchmark estimate of recall bias in farm labor from **Table 3** . Column (2) further includes an indicator variable, which equals unity if the plot was listed at baseline ( _Baseline_Plo_ t) as well as its interaction with the main recall variable ( _Baseline_Plot*Recall_ ). This specification allows to test two hypotheses. First, season-wide hours per person per plot may differ between plots listed at baseline and plots that were added later, so that differences in the composition of plots between the recall and weekly visit groups may explain recall bias in the number of hours reported. Second, recall bias itself might differ between plots listed at baseline and plots added later. Column (3) uses an analogue specification but distinguishes between household members listed at baseline and those that were added later. Column (4) includes both sets of variables.

The results in columns (2) to (4) do not provide any evidence for the second hypothesis that recall bias differs between plots (household workers) listed at baseline and those that were added later, as the relevant interaction effects are insignificant in all three specifications. Conversely, we do see that reported seasonwide hours per person per plot are significantly higher for plots and household workers listed at baseline than for plots and household workers added later (columns (2) to (4)) and controlling for this characteristic turns the main effect of the recall variable insignificant.

Together, these results provide significant support for the first hypothesis that compositional differences explain recall bias in farm labor. Season-wide hours of farm labor per person per plot are higher for recall households because the latter fail to list several plots and persons. These omitted plots and persons have, on average, less farm labor than the plots and persons that were listed at baseline. In other words, weekly visit households report more ‘marginal’ plots and farm workers than recall households, which reduces their total average hours per person per plot.

> 10 This is the total effect of the Endline and the Endline*Recall coefficients (i.e. 0.531-0.320=0.2).

13
them individually or separately and whether she could sell them with anyone else’s permission. It also asks whether she has a savings account, either individually or jointly and whether she operates the account herself, signing checks or withdrawing money. The strength of this module is that it collects information about incidence, decision-making (except for savings), and channels of asset acquisition, but it does not collect information on asset values. Moreover, given its focus on women, the survey cannot be used to calculate the gender asset gap.

Only one LSMS questionnaire reviewed here, Nicaragua (1998), collected individual level data on the ownership of farm equipment; the Mexican (2002) and Uzbekistan (2005) questionnaires collected data only on decision-making.

4.3 Best Practices: Processes of Asset Accumulation In addition to data on the six broad categories of assets, a comprehensive analysis of individual net wealth by gender requires information on the processes of asset accumulation. Particularly important is data on credit and debt, and on how and when assets are acquired or lost.

Credit, as used here refers to money loaned out, whereas debt is what is borrowed. While most questionnaires ask about money that household members borrow, few ask about money lent out and who has lent it. It is important to collect data on which individual or individuals incurred the debt and are responsible for repaying it. A good model on the collection of this information at the individual level is provided in the LSMS for Paraguay (2000-01) and the IFPRI questionnaire for Ethiopia. Detailed modules on credit and debt with disaggregated data can also be found in some of the other surveys cited in Table A6.

Few of the LSMS inquire systematically about how assets are acquired. For instance, while several Latin American questionnaires solicit information about access to land at the household level, only Haiti (2001) does so at the individual level. The survey for Tanzania (1993) collects data on land acquisition at both the household and individual level. The LSMS surveys for Afghanistan (2007) and Vietnam (1993, 1997) inquire about the form of individual acquisition of housing. Data on the source of individual assets is important to discern the relative importance of inheritance and other gifting practices at marriage versus individual savings by gender.

Particularly useful for a gender analysis of asset ownership is data on the assets owned by the husband and wife prior to marriage. Good models are provided by the IFPRI questionnaire for Ethiopia and the KIDS questionnaire for South Africa, which collected this data by type of asset and source of acquisition. This data is important for understanding the timing of gender inequalities in asset ownership among spouses-before or after marriage-- and for discerning whether marriage helps women accumulate assets. Questions on asset acquisition need to be complemented by additional information on the prevailing marital regime to understand what might legally happen to the assets women bring to marriage: do they remain her assets, become joint assets of the 27
<u>Table 11: Real factor returns, percent change in 2030 relative to baseline</u>

||<br>Un-skilled|<br>Skilled|<br>Total<br>labor|<br>Capital|<br>Land|<br>Natl. res.|Non-labor|Total|
|---|---|---|---|---|---|---|---|---|
|World|0.88|0.7|0.79|0.5|1.97|0.36|0.57|0.66|
|BRI Area|1.36|1.38|1.37|0.84|1.76|0.12|0.87|1.02|
|Cambodia|4.7|3.99|4.54|5.6|4.05|4.18|5.07|4.88|
|China|0.59|0.91|0.70|0.22|0.32|-0.09|0.19|0.41|
|Indonesia|0.56|0.66|0.59|0.25|0.37|0.24|0.25|0.36|
|Lao PDR|1.09|3.34|1.47|3.24|1.19|5.03|2.22|1.97|
|Malaysia|10.78|10.52|10.67|4.32|0.57|-1.59|3.59|6.28|
|Philippines|0.62|0.74|0.68|0.46|0.49|-0.1|0.45|0.52|
|Thailand|7.45|9.15|8.16|6.75|3.63|-1.93|6.32|6.82|
|Vietnam|2.31|1.85|2.16|1.94|1.24|-0.1|1.67|1.91|
|Rest of East Asia|2.81|1.79|2.19|0.69|2.16|1.75|0.81|1.3|
|Bangladesh|8.48|8.7|7.83|10.55|21.23|4.62|12.71|9.79|
|India|0.82|0.14|0.53|0|0.9|-0.08|0.21|0.34|
|Nepal|1.25|0.36|1.02|0.52|1.22|0.42|0.71|0.83|
|Pakistan|27.89|5.19|15.44|7.76|24.12|3.56|9.56|10.81|
|Sri Lanka|0.77|0.01|0.50|0.82|0.67|0.46|0.79|0.68|
|Rest of S. Asia|3.13|1.67|2.48|0.66|3.04|2.44|1.36|1.75|
|EU ECA|0.24|0.23|0.23|-0.01|1.75|0.45|0.06|0.11|
|Russian|||||||||
|Federation|1.2|1.57|1.40|0.67|2.83|0.63|0.7|0.89|
|Poland|0.08|0.1|0.09|0.13|1.29|0.02|0.16|0.14|
|Rest of E.|||||||||
|Europe|1.02|0.75|0.88|0.91|3.28|0.52|1.13|1|
|Kazakhstan|1.16|0.42|0.85|0.2|4.31|0.28|0.31|0.53|
|Kyrgyz Republic|26.07|7.95|20.42|11.71|6.55|13.19|10.16|13.08|
|Rest of C. Asia|1.98|1.34|1.76|0.74|2.61|0.5|0.93|1.22|
|Iran, Islamic<br>Rep.|4.8|4.41|4.58|1.16|5.92|0.27|1.14|1.57|
|Egypt,Arab Rep.|0.52|0.28|0.38|0.23|0.77|0.3|0.26|0.31|
|Turkey|4.72|4.52|4.65|4.13|4.14|-1.35|4.12|4.26|
|Rest of MENA|1.42|1.45|1.43|0.73|1.85|1.14|0.8|0.96|
|Kenya|4.48|1.7|3.76|0.87|4.38|1.3|1.13|2.22|
|Tanzania|4.65|2.35|4.02|0.67|3.97|1.04|1.2|2.61|
|Non-BRI Area|0.36|0.48|0.43|0.19|1.77|0.31|0.22|0.33|
|Ethiopia|0.69|1.1|0.78|0.58|2.75|0.52|0.87|0.92|
|Rest of SSA|0.94|0.21|0.62|-0.23|0.8|0.28|-0.11|0.23|
|Lat. Am. & Car.|0.37|0.01|0.20|-0.04|1.3|0.43|0.03|0.09|
|United States|0.09|0.55|0.38|0.45|3.52|0.11|0.5|0.42|
|Rest of High-inc.|0.66|0.66|0.66|0.34|1.81|0.04|0.35|0.5|
|Rest of W. Eur.|0.26|0.28|0.28|0.18|0.95|0.24|0.19|0.22|

- 54 -
the latter is a composite of the entry, trade, financial markets, bankruptcy, and contract enforcement indices. We obtain the _product-market_ index by averaging the scores of the five components.<sup>7</sup> We also compute an _overall regulation_ index by averaging the scores of all seven components. We choose to give equal weights to all components despite the strong correlation among the first five because we don’t have any priors about the importance of labor market or fiscal regulation relative to the others.

Figure 2 depicts scatter plots of the overall, product-market, labor, and fiscal regulation indices against the (the log of) GDP per capita of all countries in the sample. The product-market regulation index is strongly negatively related to average income, and so is the overall regulation index, reflecting the fact that it loads heavily on productmarket regulations. Labor regulation also has a negative correlation with average income, but it is smaller and not statistically significant. The relationship between fiscal regulation and income is strong but of the opposite sign as those of the other types of regulation: richer countries tend to have heavier fiscal regulation.

Finally, we use a governance index in order to assess the quality of regulation itself and the general context that determines how regulation functions. We construct this index using three measures from the International Country Risk Guide: absence of corruption in the political system, prevalence of law and order, and level of democratic accountability. The last panel of Figure 2 shows the close connection between our governance index and per capita GDP.

# **III.  Regulation, Economic Growth, and Informality**

Having described how the regulatory environment varies across countries, our objective for this section is examining whether regulations have an impact on economic growth and the size of the informal sector. Establishing the connection between regulation and informality is a first step in understanding how regulation affects economic growth. In assessing the effect of the regulatory environment, it is important to consider that the quality of regulation is profoundly affected by the institutional context in which it is imposed. Thus, regulation’s ultimate impact is likely to be affected by the country’s level

> 7 The term “product-market regulations” is taken from Nicoletti et al. (2000).

4
previous finding in our database. The table reports firm characteristics in the portfolio holdings of specialized and global funds. Our universe of firms include 10,143 firms, out of which 1,347 have stocks held by global funds and 2,211 firms have equity in the hands of specialized funds at some point in our sample. The average firm size in our sample has US$358 million in total sales, US$1,015 million in total assets, and US$400 million in market capitalization. However, the average size of firms in mutual fund portfolios is significantly larger than these sample averages, independently of the measure considered. Global funds invest in firms with an average size of US$1,365 million in market capitalization for example, more than three times as large as the sample average. Specialized funds also hold significantly larger firms: US$867 million in total sales. Therefore, these results support the existence of a foreign bias – foreign investors’ propensity to invest in larger firms than domestic investors. Furthermore, the average firm in the portfolio of global funds is significantly larger than that on the portfolio of specialized funds, independently of the proxy chosen for firm size. The bivariate analysis thus suggests that there is heterogeneity in how information asymmetries affect institutional investors with different investment mandates.

# _[Insert Table 2 about here]_

The evidence on leverage also supports our view that global fund managers would prefer to invest in firms less prone to information disadvantages than specialized fund managers. Although less emphasized in the literature, leverage can also be an indicator of the importance of information asymmetries. The mechanism is analogous to the one for firm size. Highly leveraged firms usually have greater future returns uncertainty. As shown by Coval (2003), larger variance 22
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 22. Senegal’s pattern of capital investment in infrastructure, benchmarked against comparator countries** Investment in infrastructure sectors as percentage of GDP, by source

<!-- Start of picture text --> 3.0 Senegal LIC-nonfragile<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>ICT Power Transport WSS ICT Power Transport WSS<br>Public ODA Non-OECD Private<br>% of GDP<br><!-- End of picture text --> _Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2009). _Note:_ Private investment includes self-financing by households. ODA = official development assistance; OECD = Organisation for Economic Co-operation and Development; ICT = information and communication technology; GDP = gross domestic product; WSS = water supply and sanitation; LIC = low-income countries.

# How much more can be done within the existing resource envelope?

Around $312 million of additional resources could be recovered each year by improving efficiency (table 22). By far the largest area of inefficiency lies in the underrecovery of costs, which accounts for 66 percent of the total ($206 million). Reducing distribution losses to a reasonable benchmark in the power and water sectors could save up to $73 million each year. Potential efficiency gains of about $17 million and $14 million per year are possible from improving budget execution (that is, the share of budgeted funds actually spent) and optimizing staffing levels, respectively. Increasing collection rates do not seem to be an issue in Senegal, except in the water and sanitation sector. Looking across sectors, the power sector is by far the most problematic and presents the largest potential for efficiency dividends ($206 million).

**Table 22. Potential gains from greater operational efficiency**

||ICT<br>Po|wer<br>|Transport|WSS|Total|
|---|---|---|---|---|---|
|Underrecovery of costs|—|133|n.a.|73|206|
|Overstaffing|n.a.|14|—|n.a.|14|
|Distribution losses|—|71|—|2|73|
|Undercollection|—|0|n.a.|2|2|
|Low budget execution|0|1|14|2|17|
|Total|0|219|14|78|312|
|_Source_: Derived from Foster and<br>_Note:_WSS = water supply and sa<br>— = Not available.|Briceño-Garmendia (2009).<br>nitation; ICT = information and c|ommunication tec|hnology.|||
|n.a. = Not applicable.||||||

Undercharging for power services costs Senegal about $133 million each year. In the power sector, the estimated average total cost of power was $0.29 per kilowatt-hour (kWh) in 2007, while the average effective tariff was $0.21/kWh, which hardly covered the operating and maintenance costs of $0.23/kWh.

38