rafmacalaba/datause-encoder-data huggingface dataset:
- 12 -

# **III. BUNDLING OR UNBUNDLING PUBLIC TASKS?**

There are wide variations in how to involve the private sector in infrastructure. In the traditional contract theory, as shown above, the government decision is essentially dichotomous: whether a public work is produced in-house or contracted out to the outsider. And ownership is supposed to go with provision. For instance, in the model by Hart _et al._ (1997), public tasks may be multiple; but they cannot be separated and must be carried out by a single agent, i.e., either employees or a private firm. But infrastructure projects can consist of a number of elements: planning, financing, construction, operation and maintenance, management, service provision, and ownership. Therefore, various types of public private partnership (PPP) are possible (Table 3). For instance, ownership can be retained by governments, while contracting out management (i.e., management contract) or operation and maintenance (i.e., lease or concession). Governments can trade away all the elements to the private sector (i.e., divestiture). Governments can also provide public services using private financing (i.e., nonrecourse private financing).

- <u>Table 3. Main features of public private partnerships</u>

|Allocation of responsibilities|Management<br>contract|Lease|Concession|Divestiture|
|---|---|---|---|---|
|Asset ownership|Public|Public|Public|Private|
|Operation and maintenance|Private|Private|Private|Private|
|Capital investment|Public|Public/Private|Private|Private|
|Commercial risk|Public|Private|Private|Private|
|Duration|3-5years|8-15years|25-30yeas|Indefinite|

Source: Besant-Jones (2006).

Ownership can be separated from investment and operation. Besley and Ghatak (2001) assume that both government and agent care about the quality of public work, showing that the optimal ownership depends on the relative importance of the public and private good components. The critical assumption of Hart _et al._ (1997) is that the only government values quality or social investment in quality, whereas the agent does not value it directly. Conversely, when quality is public goods—i.e., both parties take quality into consideration— the party that attaches a higher value to quality should own the asset. This holds even if it is
variable consists in transforming the raw number of SMCs in (strictly increasing) cumulative difference, starting from 0 at the beginning of the estimation period to _K_ at the end when the SMC number increased by _K_ units over the estimation period. While this variable does not comprehensively reflect a country’ international broadband capacity, it has the merit of better identifying the treatment since SMCs deployment in recipient countries is a critical source of increased international connectivity, and since this variable is of staggered and ordered nature.

Then, we address the issues of heterogenous and dynamic treatment effects by implementing the extended _DiD_ estimator with multiple groups ( _DIDM_ ), periods and non-binary treatments of de Chaisemartin and d’Haultfoeuille (De Chaisemartin and d’Haultfoeuille (2022); De Chaisemartin and d’Haultfoeuille (2023); de Chaisemartin et al. (2022)).<sup>17</sup> We then estimate the following model, considering the effect of the cumulative difference in SMCs ( _CDit_ ) on the logarithm of fixed or mobile broadband prices ( _Yi,t_ ), controlling for the lagged number of SMCs ( _SMC_ Lit_ ), and additional control variables ( _Xit_<sup>_′_):</sup> In addition to the baseline control variables (including the regulatory quality), we added the lagged number of SMCs to account for nonlinearities in the effect of SMC variation on BB price depending on their number, as there may be for instance return to scale in their deployment (cost-saving hypothesis).<sup>18</sup> Last, since the mobile broadband price variable has a shorter time dimension, we had no option but to balance the sample between treated and untreated observation units by binning together the highest values of the variable, associated with few observations (using the _recat_ treatment_ option, as recommended by CDH), and to reduce the number of lags and placebos when using this price variable. We used a binning threshold of 15, based on the variable’s treatment distribution represented on Figure 14 in the Appendix.

_DIDM_ estimates of ( _δ_<sup>ˆ</sup> 1 ) are reported in Figure 9 below and detailed in Tables 18 to 20 in the Appendix. Graph (A) on Figure 9 reports estimates using the fixed BB variable and points to a 1% significant negative and dynamic effect of SMC arrivals, starting one year after the treatment onset, persisting and intensifying afterward. However, these estimates point to simple first-differenced, and therefore non-staggered, SMC variable would improperly reflect the staggered arrival of cables.

> 17Using the _did_  multiplegt_ Stata package. Other methodologies like Butts (2021) deal with spatial spillovers in difference-in-differences.

> 18We additionally control, in the following subsection, for market concentration to test the competition and regulation channels.

22
# **References**

- Alfaro, Laura, Şebnem Kalemli-Özcan, Vadym Volosovych (2008) Why doesn’t capital flow from rich to poor countries? An empirical investigation. _The Review of Economics and Statistics_ 90(2):347-368.

- Aschauer, David A (1989) Is public expenditure productive? _Journal of Monetary Economics_ 23(2):177-200.

- Barber, Brad, Adair Morse, Ayako Yasuda (2020) Impact Investing. _Journal of Financial Economics_ Forthcoming.

- Bernstein, Shai, Josh Lerner, Antoinette Schoar (2013) The investment strategies of sovereign wealth funds. _Journal of Economic Perspectives_ 27(2):219-238.

- Brest, Paul, Ronald J. Gilson, Mark A. Wolfson (2018) Essay: How Investors Can (and Can’t) Create Social Value. _J. Corp. L._ 44:205.

- Brown, Gregory W., Oleg Gredil, Steven N. Kaplan (2013) Do private equity funds game returns? SSRN Working Paper # 2271690

- Campbell, John Y., Yasushi Hamao (1992) Predictable stock returns in the United States and Japan: A study of long‐term capital market integration. _The Journal of Finance_ 47(1):43-69.

- Carhart, Mark M., Jennifer N. Carpenter, Anthony W. Lynch, David K. Musto (2002) Mutual fund survivorship. _The Review of Financial Studies_ 15(5):1439-1463.

- Caselli, Francesco, James Feyrer (2007) The marginal product of capital. _The Quarterly Journal of Economics_ 122(2):535-568.

- CDC Group (2019)Annual Accounts 2019: Towards a Decade of Action.

- Chari, Anusha, Jennifer S. Rhee (2020) The Return to Capital in Capital-Scarce Countries. National Bureau of Economic Research Working Paper No. 27675.

China Development Bank (2017) 2017 Annual Report

- Chinn, Menzie D., Hiro Ito (2006) What matters for financial development? Capital controls, institutions, and interactions. _Journal of Development Economics_ 81(1):163-192.

- Chinn, Menzie D., Hiro Ito (2008) A new measure of financial openness. _Journal of Comparative Policy Analysis_ 10(3):309-322.

- Cochrane, John H (2005) The risk and return of venture capital. _Journal of Financial Economics_ 75(1):3-52.

- Cole, Shawn, Leslie Jeng, Natalia Rigol, Benjamin N. Roth (2022) What do Impact Investors Do Differently? _Harvard Business School Working Paper_ 24-028.

26
The estimates for equation (5) are reported in Table 7 and Figure 5. The results suggest a significant retrenchment in gross capital flows; both domestic and foreign agents decline their cross-country outflows around both mild and severe crisis episodes for all income groups. During _one crisis_ episodes, _CIF_ and _COD_ decline at the onset of the crisis as well as in its aftermath, and even more so for high-income countries, if compared to the two years before the crisis. Similar statistically significant results are found around _more than once crisis_ episodes. Wald tests reported in Table 7 show that _CIF_ and _COD_ are significantly smaller during the crisis year relative to the pre-crisis average for countries from all income groups in our sample. The results however suggest that the fall in capital flows by domestic agents in the aftermath of severe crises is more short-lived and reversed during the following two years. Wald tests reject that _COD_ is statistically different in the aftermath of the turmoil episode if compared to its precrisis values.

The retrenchment in gross capital flows is not only statistically but also economically significant as shown in Figure 5. In high-income countries, _CIF_ reversers from 5.2 percent of trend GDP in the year preceding _one crisis_ episodes in the average country to less than -4.4 percent of trend GDP in the first year after the onset of the crisis, suggesting a collapse in flows of over 9 percentage points. Domestic agents behave similarly during these episodes. This retrenchment in gross capital flows around mild crisis episodes is also large in middle-income countries, where a decline of almost 4 percentage points takes place on average during the fiveyear window around mild crisis episodes, and slightly milder in low-income countries, with declines of about 1 percentage point of trend GDP over the same period. During _more than one crisis_ episodes, similar patterns are observed. Capital inflows by domestic agents decline from 15.7 percent of trend GDP in high-income countries to about 4 percent in the aftermath of the 22
# **1 Introduction**

An economy‐wide structural shift from employment in agriculture to non‐agricultural activities is a prominent feature of the development process.<sup>1</sup> At the level of the household, or family, the shift of labor from agriculture to industry and commensurate movement out of rural areas often proceeds incrementally, with individual family members migrating to urban or manufacturing areas while leaving other members behind. An important aspect of this gradual process is that family members in rural and urban areas remain linked, and this arrangement often benefits the household in numerous ways.<sup>2</sup> The decision to migrate, however, is influenced by institutional arrangements, both locally and in migrant destinations, that shape the benefits of migration and employment off‐farm. If poor institutions limit the function of land, labor or credit markets, they may raise or lower the expected benefits to individuals and households from moving out of agriculture. When an individual’s presence in the village and active work on land improves a household or family’s claim to land, then such endogeneity of land tenure security may influence migration decisions and shape the process of structural change.

An abundance of theoretical research suggests that insecure property rights may have important impacts on productivity, factor allocation and economic development.<sup>3</sup> A growing body of empirical research based on micro‐data has tested these theoretical predictions in a range of settings, but with primary focus on the impact of rights on investments or agricultural production.<sup>4</sup> More recent empirical

> 1The movement of labor from concentration in rural agricultural pursuits to urban‐based industry figures prominently in many of the classic works in development economics (e.g., Kuznets, 1955; Lewis, 1954 and 1958; Ranis and Fei, 1961).

2Transfers from migrants may be an important source of investment funds if local credit markets do not function well (Woodruff and Zenteno, 2007), or alternatively, migrants may provide insurance for households which remain behind (Giles, 2006; Rosenzweig and Stark, 1989).

3The mechanisms through which property rights affect economic activity are context specific and depend on complementary institutions (Besley and Ghatak, 2009a; Katz and Owen, 2009). Besley ahd Ghatak (2009b) identify four mechanisms through which clear and secure property rights improve efficiency in resource allocation and productivity. Feder and Feeny (1991) and North (1990), for example, emphasize the importance of secure property rights in supporting economic development. Further, Acemoglu et al (2001) suggest that variation in protection of property rights across countries led to significant differences in subsequent economic performance.

4 Evidence from the literature is mixed, though several studies support the view that secure land rights are investment‐enhancing and may affect cultivation techniques adopted. Land tenure security in Ghana is associated _Land Tenure Insecurity and Migration, Page 2_
# **The Determinants of Rising Informality in Brazil: Evidence from Gross Worker Flows**<sup>*****</sup>

Mariano Bosch _London School of Economics_ Edwin Goni _World Bank_ William Maloney _World Bank_ First version: October 1997 This version: October 10, 2007

**JEL: Gross worker flows, Labor market dynamics, Informality, Developing Countries.**

> * We are very grateful to Francisco Carneiro, Marcello Estevao, Gustavo Gonzaga Lauro Ramos, Jose Guilherme Reis and Gabriel Ulyssea, to participants in the NBER workshop on informality, October 2006 in Bogota, and to those at the University of Michigan conference on Labor Markets in Developing and Transition Economies, May 2007, in particular  Gary Fields, Ann Harrison, Ravi Kanbur, and Jan Svejnar, for helpful advice and reality testing of ideas.  All conclusions are, of course, our own.

> Correspondence: m.bosch@lse.ac.uk, egonipacchioni@worldbank.org, wmaloney@worldbank.org.
# **4. DESCRIPTIVE ANALYSIS**

Before turning to results, we examine birth-weight over time, to get a sense of the variation in the data. Figure 5 presents mean birth-weight and the 95 percent confidence region for beneficiaries and non-beneficiaries by year. Over time, as Plan Nacer scales up, the sample sizes of the beneficiary group grow, and the sample size of the non-beneficiary group shrinks. There is no difference between the beneficiary and non-beneficiary means in 2004. Starting in 2005, however, the mean in the beneficiary group is higher than the mean in the nonbeneficiary group, and the gap between beneficiary and nonbeneficiary grows over time. In the last three years (2006–08) the variation in results are significantly different from zero. Although this evidence cannot be interpreted as causal, it is consistent with improvement for the beneficiaries.

**Figure 5: Average Birth Weight (** **_grams_ ) Over Time**

<!-- Start of picture text --> 2004 2005 2006 2007 2008<br>Years<br>Non_Benef Benef<br>3325<br>3300<br>3275<br>3250<br>3225<br><!-- End of picture text --> _Note:_ The figure plots mean birth-weight by year and beneficiary status. The individual is defined as beneficiary if she was enrolled in Plan Nacer when she gave birth and if the clinic she visited during pregnancy was incorporated into Plan Nacer.

# **5. IDENTIFICATION AND ESTIMATION**

We investigate three questions: 17
# **Table 5: Engagement in Economic Activities**

## **OLS estimates, standard errors in parentheses, p-values in braces**

||**Enrolment**<br>**Only**<br>**(1)**<br>|**Engaged in IGA**<br>**Only**<br>**(2)**<br>**Aged 12-17 at B**|**Engaged in**<br>**Both**<br>**(3)**<br>**L**|**Enrolment**<br>**Only**<br>**(4)**<br>|**Engaged in IGA**<br>**Only**<br>**(5)**<br>**Aged 18-25 at B**|**Engaged in**<br>**Both**<br>**(6)**<br>**L**|
|---|---|---|---|---|---|---|
|**Disruption Effect**|-.166***|.191***|-.058|-.004|-.038|-.037**|
||(.044)|(.062)|(.043)|(.024)|(.056)|(.018)|
|**Treatment Effect in High Disruption**|.085*|-.133**|.095**|-.007|.028|.051**|
||(.049)|(.064)|(.038)|(.024)|(.060)|(.020)|
|**Treatment Effect in Low Disruption**|-.052*|.023|.008|-.021|.031|.001|
||(.030)|(.019)|(.027)|(.015)|(.031)|(.010)|
|**Difference [β3, p-value]**|{.020}|{.020}|{.062}|{.599}|{.966}|{.023}|
|**Control Mean at BL**|.519|.080|.296|.091|.576|.053|
|**Observations**|2,382|2,382|2,382|2,400|2,400|2,400|

**Notes:** ***, ** and * denote significance at the 1%, 5%, and 10% levels. Income Generating Activities (IGA) include both self-employment and wage labor. The main results are estimated with an ANCOVA specification. Control variables include: age, PPI score, household size, illiteracy, village size (nr of dwellings), village average PPI score, distances form key facilities (clinic, secondary school and market), a dummy equal to one if the village is a political stronghold (i.e. has a resident paramount and/or section chief), the number of NGOs active within the village before the Ebola outbreak (excluding BRAC), share of Christian households, distance from Freetown and distance from Kailahun. All specifications include dummies for the randomization strata (district) and errors are clustered at the unit of randomization (village).
polarization is negatively related to the incidence of conflict,but is not significantly associated with the intensity of the conflict, once it has started.

# **5.4 Relationship between poverty and conflict over time**

Since we have annual data on conflict related deaths for 1999, 2002, 2003 and 2004, we can document the relationship between poverty and conflict intensity over time, and we find that this does not remain constant. Table 6 regresses year-wise conflict deaths on dummies for the bottom three quartiles of poverty (i.e. the omitted category is the top quartile or least poor people), as well as the top three quartiles of literacy (the omitted category is the least literate people). We find that poverty or literacy rates are not significantly associated with Maoistcaused deaths before 2002 (columns (1) and (2)), while they are significantly associated with state-caused deaths in 1999 itself (columns (9) and (10)). In 2002, when the conflict escalated, deaths caused by the Maoists and by the state are highest in the poorest quartile of districts (columns (3) and (11)); similarly, deaths caused by both sides are highest lower in the most literate districts, compared to the least literate. This might indicate a timeline in which the state was repressing Maoist activity more strongly in the poorest areas, which in turn could have led to greater support for the Maoist movement in those areas, resulting in a very high number of deaths in 2002, when the conflict intensified strongly.

In 2003, the intensity of conflict is not higher in poorer areas (though high literacy areas still show significantly lower conflict). It is interesting that by 2004, when the activists had captured a good deal of territory, we see that the districts in the second quartile of poverty show slightly higher levels of conflict (columns (7) and (15)). A similar result holds with literacy, where we see the second and third quartiles of literacy having higher levels of conflict. This does suggest that the Maoist and state effort in the conflict are moving away from the poorest areas to the not-so-poor ones. However, these differences are not statistically significant, and hence these results are indicative and not conclusive. This time path is consistent with the graphs in Figures 4A and 4B: as we see, the poorest districts have the highest casualties caused by both Maoists and the state in 2002, while in 2004 it is districts in the third quartile 18
The ASEAN country with the highest GDP per capita growth rate during 1970-2010 was Singapore. According to Table 1, the income share of the 3<sup>rd</sup> (4<sup>th</sup> ) quintile was boosted in Singapore due to rapid GDP per capita growth during that period by around 7 (9) percentage points. In contrast, the ASEAN country with the lowest GDP per capita growth rate during 1970-2010 was Brunei. Growth of Brunei's GDP per capita during 1970-2010 increased the income share of the 3<sup>rd</sup> (4<sup>th</sup> ) quintile by around 3 (4) percentage points.

# **2.2. Effect of the Rise of the Middle Class on Economic Growth**

Brueckner and Lederman (2015) present instrumental variables estimates of the impact that various measures of income inequality have on GDP per capita. The authors use the residual variation in income inequality, ε, that is not due to GDP per capita as an instrument to estimate effects that exogenous variation in income inequality have on GDP per capita.<sup>4</sup> The relevant equation is: (2) ln(GDP p.c.)ct = ac + bt + θ1Inequalityct + θ2Inequalityct*ln(initial GDP p.c.)c + uct Brueckner and Lederman (2015) find that the effect of inequality on GDP per capita differs depending on countries' initial level of GDP per capita. The estimates that these authors provide are useful for computing effects that a rise of the middle class has on GDP per capita of ASEAN economies at different stages of these countries' economic development.

Table 2 reports predicted effects of a 1 percentage point increase in the income share of the 3<sup>rd</sup> quintile on GDP per capita of ASEAN countries. Column (1) reports predicted effects based on ASEAN countries' initial economic development as measured by GDP per capita in 1970; column (2) reports effects for recent levels of economic development as measured by GDP per capita in 2010. Specifically, for column (1) the predicted effects are generated by combining estimates reported in Brueckner and Lederman (2015) (see their column (4) of Table 1), with the values of countries' GDP per capita in 1970. For column (2) the predicted effects are generated by combining

> 4 To do this, one needs a consistent estimate of α in equation (1). This estimate is obtained from Brueckner et al. (2015). For papers that have applied such an instrumental variables strategy in the context of foreign aid and fiscal policy, see, for example, Brueckner (2013) and Fatas and Mihov (2001).

5
vate and public expenditures, say, in health, and that individuals have perfect information about the returns to their health investments. However, there is increasing evidence that decision-making by the poor is greatly affected by limited information (e.g. Bertrand et al. 2006; Banerjee and Duflo 2011; Dupas 2011b). This means that health programs have the potential to simultaneously deliver health services and induce changes in beliefs about the returns to health investments in the populations they serve, which could even lead to a reversal of potential crowding-out effects.

Beyond the literature on crowding-out effects of public programs, it is also important to mention how our study fits into the literature on malaria control programs and on information and health in developing countries. Providing information about the returns from using a technology can be an effective way to promote both take-up and use. Dupas (2011b) reviews several studies that show how the provision of information can effectively influence people’s health-seeking behavior, when they are not already fully informed about the health situation they face, when the source of information is credible and when they are able to process the new information. In other words policies may affect people’s behavior if they are able to change their beliefs. In a study of HIV in Malawi, De Paula et al. (2011) do not find strong evidence that HIV testing consistently affects people’s beliefs about their own HIV status (see also Delavande and Kohler, 2009), but they also show that downward revisions in beliefs about HIV status increase risky behavior, while the opposite occurs with upward revisions. In another study about HIV-related behavior, Dupas (2011a) shows that providing information on the relative risk of HIV infection disaggregated by gender and age has a significant effect on teenage pregnancy. The role of information in public health programs and health behavior in developing countries is also key in Madajewicz et al. (2007); Goldstein et al. (2008); Kremer et al. (2009).

Therefore, it is important to recognize how the availability of information about the benefits of using one technology plays a central role in public health policies. Borrowing from the literature in marketing and psychology, Dupas (2009) analyzes how the framing of information on the benefits of ITN use affects ownership and use of ITNs. She compares two cases: one which stresses the financial gains from a reduction in missed work and another highlighting the health gains from avoiding malaria. Using data from a randomized control trial (RCT) from Kenya, Dupas finds that neither take-up nor usage are 4