rafmacalaba/datause-encoder-data huggingface dataset:
- Frongillo, E.A., M. de Onis, and K.M.P. Hanson (1997) ‘Socioeconomic and demographic factors are associated with worldwide patterns of stunting and wasting of children.’ _Journal of Nutrition_ 127(12), 2302–2309

- Fujii, T. (2004) ‘Commune-level estimation of poverty measure and its application in Cambodia.’ In _Spatial Disparities in Human Development: Perspectives from Asia,_ ed. Kanbur R., Venables A.J., and G. Wan (United Nations University Press). Forthcoming

- Galler, J.R., and L.R. Barrett (2001) ‘Children and famine: Long-term impact on development.’ _Ambulatory Child Health_ 7, 85–95

- Glewwe, P., H.G. Jacoby, and E.M. King (2001) ‘Early childhood nutrition and academic achievement: a longitudinal analysis.’ _Journal of Public Economics_ 81, 345–368

- Haddad, L., and R. Kanbur (1990) ‘How serious is the neglect of intra-household inequality.’ _The Economic Journal_ 100(402), 866–81

- Hardy, F., and Health Unlimited Ratanakiri Team (2001) ‘Health situation analysis Ratanakiri, Cambodia.’ Technical Report, Health Unlimited

- Haughton, D., and J. Haughton (1997) ‘Explaining child nutrition in Vietnam.’ _Economic Development and Cultural Change_ 45(3), 541–556

- Hentschel, J., J.O. Lanjouw, P. Lanjouw, and J. Poggi (2000) ‘Combining census and survey data to study spatial dimensions of poverty: A case study of Ecuador.’ _The World Bank Economic Review_ 14(1), 147–166

- Kakwani, N. (1977) ‘Applications of lorenz curves in economic analysis.’ _Econometrica_ 45(3), 719–728

- Kakwani, N., A. Wagstaff, and E. van Doorslaer (1997) ‘Socioeconomic inequalities in health: Measurement, computation, and statistical inference.’ _Journal of Econometrics_ 77, 87–103

- Kanbur, R. (1987) ‘Transfers, targeting and poverty.’ _Economic Policy_ 4(1), 112–147

- Kawachi, I., B.P. Kennedy, K. Lochner, and D. Prothrow-Stith (1997) ‘Social capital, income inequality and mortality.’ _American Journal of Public Health_ 87(9), 1491–1498

- Khorshed Alam Mozumder, A.B.M., T.T. Kane, A. Levin, and S. Ahmed (2000) ‘The effect of birth interval on malnutrition in bangladeshi infants and young children.’ _Journal of Biosocial Science_ 32, 289–300

- Kunst, A.E., F. Groenhof, J.P. Mackenbach, and EU Working Group on Socioeconomic Inequalities in Health (1998) ‘Occupational class and cause specific mortality in middle aged men in 11 european countries: comparison of population based studies.’ _British Medical Journal_ 316, 1636–1642

40
App. Table 3a: Complete regression results for Panel A of Table 6

||Overall<br>growth<br>1989→2010|Overall<br>growth<br>2000→2010|1-Person firm<br>growth<br>1989→2010|1-Person firm<br>growth<br>2000→2010|Average 1-<br>person firm<br>employment<br>share 1989-2010|Tradables 1-<br>person firm<br>growth<br>1989→2010|Tradables 1-<br>person firm<br>growth<br>2000→2010|Tradables 1-<br>person firm<br>employment share<br>1989-2010|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Log population|-0.014|-0.043|-0.027|-0.008|-0.142|0.006|-0.065|-0.161|
||[0.030]|[0.072]|[0.040]|[0.045]|[0.078]*|[0.086]|[0.110]|[0.098]|
|Log population density|0.027|-0.000|-0.056|-0.008|-0.014|-0.074|-0.107|-0.008|
||[0.030]|[0.035]|[0.033]*|[0.021]|[0.041]|[0.085]|[0.070]|[0.057]|
|Share of population that is urban|0.112|0.129|0.116|0.078|-0.295|0.171|0.176|-0.150|
||[0.029]***|[0.044]***|[0.040]***|[0.034]**|[0.058]***|[0.081]**|[0.117]|[0.076]**|
|Share of population that is middle aged (15-49)|-0.085|-0.038|0.011|0.026|-0.039|0.014|0.098|-0.168|
||[0.049]*|[0.062]|[0.031]|[0.034]|[0.079]|[0.076]|[0.095]|[0.090]*|
|Share of population in scheduled caste/tribe|-0.039|0.043|-0.039|0.005|0.099|-0.100|0.008|0.065|
||[0.018]**|[0.040]|[0.034]|[0.024]|[0.042]**|[0.086]|[0.069]|[0.052]|
|Female labor force participation rate|0.034|0.143|0.025|0.009|0.015|0.026|-0.136|0.053|
||[0.024]|[0.050]***|[0.027]|[0.042]|[0.071]|[0.057]|[0.133]|[0.078]|
|Educated worker share (middle and up)|0.056|0.123|0.043|0.069|-0.249|0.084|0.088|-0.283|
||[0.042]|[0.097]|[0.036]|[0.065]|[0.094]***|[0.110]|[0.130]|[0.122]**|
|Literacy rate|-0.012|-0.035|-0.060|-0.030|0.314|-0.051|-0.040|0.387|
||[0.050]|[0.083]|[0.043]|[0.055]|[0.110]***|[0.130]|[0.143]|[0.142]***|
|Infrastructure: share of villages with electricity|0.024|-0.055|-0.010|0.031|-0.021|-0.036|0.112|0.071|
||[0.032]|[0.053]|[0.028]|[0.034]|[0.079]|[0.091]|[0.094]|[0.089]|
|Infrastructure: share of villages with paved roads|0.000|0.057|0.025|-0.061|-0.181|0.009|-0.209|0.133|
||[0.028]|[0.056]|[0.032]|[0.047]|[0.088]**|[0.092]|[0.112]*|[0.097]|
|Labor laws strength|-0.008|-0.012|0.034|0.004|-0.068|0.082|0.026|-0.122|
||[0.010]|[0.027]|[0.022]|[0.019]|[0.057]|[0.069]|[0.049]|[0.048]**|
|Travel time to nearest of India's ten largest cities|-0.011|0.001|0.007|-0.049|0.145|-0.048|-0.135|0.310|
||[0.026]|[0.047]|[0.020]|[0.043]|[0.063]**|[0.064]|[0.091]|[0.070]***|
|Share of households with a bank account|0.223|-0.253|0.044|-0.337|-0.580|-0.061|-0.683|-0.166|
||[0.165]|[0.388]|[0.160]|[0.224]|[0.537]|[0.464]|[0.813]|[0.561]|
|Observations|216|216|216|216|216|216|216|216|
|Adjusted R-squared|0.166|0.081|0.126|0.083|0.390|0.060|0.103|0.270|

Notes: See Table 6.
_For labor or for divorce?_ 2022; Gilbert and Ben Brik, 2022). To account for potential endogenous fertility choices postreform period, specifically women choosing to have more children to extend child custody period, we systematically restrict the sample to married women who stopped having children one year before the reform ( _the pipeline effect,_ (see Rasul, 2006)). The analysis spans over sufficiently long time periods (over 20 years for each country) to apprehend the effect of unilateral divorce legislation on spouses’ behavior (Jolls et al., 1997; Chiappori et al., 2002) with at least two periods before and two after the year of the reform.

**Table 2 – Descriptive statistics of full and working sample pre and post Chikak reform in Morocco**

||Mean<br>JK<br>(1)|<br>Pre-reform<br>J<br>(2)|K<br>Post-reform<br>J<br>(3)|K<br>Control<br>(post-reform)<br>J<br>(4)|K<br>Treatment<br>(post-reform)<br>(5)|
|---|---|---|---|---|---|
|**Marital status**||||||
|Married|0.89|0.87|0.90|1|1|
|Divorced/seperated|0.07|0.09|0.04|0|0|
|**Demographics**||||||
|Age|35.13|35.01|35.21|36.13|36.17|
|Age at first marriage|19.52|19.39|20.1|20.56|20|
|Number of children|2.61|3.31|2.14|1.39|2.53|
|Number of children under 5|0.85|0.87|0.72|0|0.93|
|Number of household members|<br>4.79|5.36|4.40|2.80|4.80|
|**Educational attainment**||||||
|No Education|0.56|0.65|0.49|0.51|0.48|
|Primary|0.20|0.15|0.24|0.21|0.26|
|Secondary|0.17|0.15|0.18|0.17|0.15|
|Higher|0.05|0.03|0.06|0.07|0.06|
|**D. Outcome variables**||||||
|Currently working|0.26|0.21|0.28|0.28|0.26|
|**N**<br>Source: Authors Calculations – D|19024<br>emographi|7645<br>c and Health Surv|11379<br>ey (DHS): Morocco|1239<br>(1992,2004) and HCP|5790<br>Household survey|

Source: Authors Calculations – Demographic and Health Survey (DHS): Morocco (1992,2004) and HCP Household survey (2007 and 2014).

Note: This table displays full and working sample means for several variables both pre and post Chikak divorce reform in Morocco. For the first three columns, we present full sample means pre and post reform. The remaining columns present working sample means for treatment and control groups. While the table shows means values for treatment and control groups post “treatment”, the empirical analysis employs a quasi-experimental framework that takes into consideration values for pre and post reform.

Tables 2, 3 and 4 display instructive descriptive statistics on the periods prior and post the introduction of the right to unilateral divorce. The main outcome variable is a dummy variable “currently working” which takes the value of 1 if the woman is employed at the time of the interview, and 0 otherwise. This variable is the most accurate representation of women’s labor market position. Between the pre and post reform periods the raw mean of women that declared being currently employed stagnated for The Arab Republic of Egypt 19
**Table 10 How Firms in Turkey Differ from the Firms in Comparator Countries in Their Growth Rates**

||Russia|Poland|Ukraine|Romania|All_ECA|EU8|EU10|
|---|---|---|---|---|---|---|---|
|Small|‐0.073|‐0.059|‐0.077|‐0.077|‐0.055|‐0.056|‐0.057|
||(0.025)***|(0.022)***|(0.023)***|(0.021)***|(0.011)***|(0.019)***|(0.016)***|
|Medium|‐0.058|‐0.038|‐0.066|‐0.064|‐0.056|‐0.051|‐0.050|
||(0.022)***|(0.022)*|(0.032)**|(0.042)|(0.012)***|(0.019)***|(0.017)***|
|Large|‐0.063|‐0.088|‐0.068|‐0.182|‐0.069|‐0.058|‐0.071|
||(0.024)***|(0.040)**|(0.037)*|(0.049)***|(0.017)***|(0.036)|(0.031)**|
|Small*Turkey|‐0.085|‐0.098|‐0.088|‐0.076|‐0.108|‐0.106|‐0.098|
||(0.039)**|(0.036)***|(0.036)**|(0.035)**|(0.031)***|(0.035)***|(0.034)***|
|Medium*Turkey|‐0.112|‐0.127|‐0.107|‐0.077|‐0.119|‐0.124|‐0.113|
||(0.041)***|(0.035)***|(0.042)**|(0.049)|(0.031)***|(0.034)***|(0.033)***|
|Large*Turkey|‐0.081|‐0.048|‐0.074|0.069|‐0.073|‐0.083|‐0.060|
||(0.048)*|(0.057)|(0.050)|(0.063)|(0.045)|(0.055)|(0.052)|
|Turkey|0.037|0.148|0.109|0.073|0.068|0.120|0.109|
||(0.046)|(0.032)***|(0.032)***|(0.034)**|(0.037)*|(0.033)***|(0.033)***|
|Constant|0.103|0.111|0.061|0.190|0.105|0.064|0.075|
||(0.037)***|(0.025)***|(0.045)|(0.032)***|(0.033)***|(0.052)|(0.030)**|
|Obs|1594|1088|1462|1128|7392|2292|2758|
|R<sup>2</sup>|0.188|0.191|0.187|0.188|0.117|0.130|0.121|

Robust standard errors clustered by region and 2‐digit industry are in parentheses. Additionally control for foreign and government ownership, age, export status, 2‐digit industry, and region fixed effects. Regressions for EU8, EU10 and All_ECA include controls for countries. *** p<0.01, ** p<0.05, * p<0.1.

18
stark relief, we assume that these deadweight losses make hold-up prohibitively costly. Our evidence, in fact, indicates that renegotiation of seasonal contracts is extremely rare.<sup>11</sup> Summarizing, the seasonal contract has two salient features: First, by serving as reference point in, and hence as a deterrent to, renegotiation, it protects relationship-specific investment (in our context, planting inputs); second, water allocations under the contract are unresponsive to the state of the world.

Let _τ_ denote the total transfer of groundwater at per unit price _p_ to irrigate a field of size _l_ . The optimal simple (i.e. single-price) contract solves The first term in the well-owner’s objective function (top line) is the expected revenue from crop production on his own plot net of cultivation costs, which is diminished when he sells water to a neighbor;<sup>12</sup> the second term is his total revenue from the sale. The participation constraint ( _PC_ ) stipulates that the crop revenue of the buyer net of _both_ cultivation and water costs cannot be negative. Finally, the incentive constraint ( _IC_ ) says that the transfer is maximizing the buyer’s net revenue, subject to the constraint that the promised amount cannot exceed the available supply of water in the lowest state of the world, _wL_ . Note that expectations are dropped in both the _PC_ and _IC_ because, under the contract, _l_ and _τ_ are fixed ex-ante. Thus, the seasonal contract offers an assured supply of irrigation to the buyer; the direct cost of production variability is borne fully by the seller on his plot.

words of Hart (2009): “Although our theory is static, it incorporates something akin to the notion of trust or good will; this is what is destroyed if hold-up occurs.” (p. 270). Alternatively, Herweg and Schmidt (2014) motivate the inefficiency of contract renegotiation using the notion of loss aversion.

> 11For each of the 873 well-buyer-crop combinations in which a seasonal contract was undertaken in _rabi_ 2011-12, our survey asks the borewell owner “Was the arrangement carried out as originally agreed?” with possible responses: “(1) Yes; (2) No, price increased; (3) No, price decreased; (4) No, contract terminated.” In all but one of these cases, the response was (1).

> 12Without loss of generality, we assume that the constraint that the water-seller’s cultivated area _ls_ cannot exceed his plot area _a_ is binding; i.e., the well-owner always fully cultivates his land before selling any groundwater. _Proof:_ Suppose not, then the optimal choice of _ls_ requires _Eg_ � _<u>wl−s τ</u>_ � = _c_ . However, equation

(1) _⇒ Eg_ � _lwU_ � = _c ⇒ τ_ = _w_ (1 _− ls/lU_ ) _,_ which is a contradiction because _τ_ cannot be state-contingent.

7
Policy Research Working Paper 9504

# **Abstract**

Transportation networks underpin socioeconomic development by enabling the movement of goods and people. However, little is known about how flooding disrupts transportation systems in urban areas in developing country cities, despite these natural disasters occurring frequently. This study documents the channels through which regular flooding in Kinshasa, the Democratic Republic of Congo, impacts transport services, commuters’ ability to reach their jobs, and the associated economic opportunity costs from travel delays. This assessment is based on transit feed specification data sets collected specifically for this analysis under normal and flooded conditions. These data sets were combined with travel survey data containing travelers’ socioeconomic attributes and trip parameters, as well as a high-resolution flood maps. The results show that (1) flood disruptions cause increases in public transit headways and transit re-routing, decreases in travel speeds, and thus travel time delays, which translate into substantial economic costs to local commuters; (2) accessibility to jobs decreases under flooded conditions, hindering the establishment of an integrated citywide labor market; (3) there are spatial clusters where some of the poorest commuters experience among the highest travel delays, highlighting socio-spatial equity aspects of floods; (4) certain road segments are critical for the transport network and should be prioritized for resilience measures; and (5) the estimated daily cost of flood disruption to commuters’ trips in Kinshasa is $1,166,000. The findings of this assessment provide disaster mitigation guidance to the Office des Voiries et Drainage under the Ministry of Infrastructure, as well as strategic investment recommendations to the Ministry of Housing and Planning.

This paper is a product of the Global Facility for Disaster Reduction and Recovery. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at yiyi_he@berkeley.edu, pavner@worldbank.org, and jrentschler@worldbank.org..

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **7.1.3 Aggregated data**

Aggregating the data is done using the same method as in all previous sections, where the nightlight cells across the province or district is summed up: where all nightlight cells _i_ in province or district _j_ are aggregated.

Since the tsunami only affected one province, it is easy to see the total damage done by it on Aceh. With our assumptions, the tsunami destroyed about 4 percent of the buildings in Aceh. This is clearer once broken down into district level damage. There were 6 districts affected by the tsunami, with Aceh Jaya, Banda Aceh and Pidie all experiencing damage of more than 20 percent. The other 3 affected districts - Aceh Barat, Aceh Besar and Bireuen - experienced damage between 5 and 10 percent due to the tsunami.

Table 19: Aggregated Tsunami Damage by District and Province

|District|Province|Intensity|
|---|---|---|
|Aceh Barat|Aceh|0.071|
|Aceh Besar|Aceh|0.078|
|Aceh Jaya|Aceh|0.221|
|Banda Aceh|Aceh|0.229|
|Bireuen|Aceh|0.055|
|Pidie|Aceh|0.210|
|Aggregated|Aceh|0.042|

29
# **TABLE 18 Foreign Direct Investment By Country of Origin** (Millions of Dollars)

|Year|Total|USA|UK|Germany|Japan|Switzerland|France|Spain|Sweden|Canada|Others|
|---|---|---|---|---|---|---|---|---|---|---|---|
|1980|1,622.6|1,078.6|48.6|170.8|123.1|111.4|19.5|80.0|10.9|17.5|-37.8|
|1981|1,701.1|1,072.1|40.9|146.3|212.1|74.9|10.3|101.8|15.3|5.2|22.2|
|1982|626.5|426.1|7.4|39.9|65.4|23.1|6.8|40.4|-2.0|8.1|11.3|
|1983|683.7|266.6|49.2|110.0|3.8|16.2|110.0|12.7|29.1|22.1|64.0|
|1984|1,429.8|912.0|44.3|152.5|35.6|59.8|8.7|11.7|61.1|32.5|111.6|
|1985|1,729.0|1,326.8|56.3|_55.5_|79.3|141.2|10.7|14.0|5.5|34.9|4.8|
|1986|<br>2,424.2|1,206.4|104.3|218.5|142.2|34.1|316.9|93.7|24.6|40.6|242.9|
|1987|<br>3,877.2|<br>2,669.6|430.9|46.9|132.8|95.2|31.2|125.8|36.7|19.3|288.8|
|1988|3,157.1|1,241.6|767.6|136.7|148.8|86.3|152.4|34.1|32.5|33.9|523.2|
|1989|2,499.7|1,813.8|44.7|84.7|15.7|194.4|16.5|44.0|6.9|37.5|241.5|
|1990|3,722.4|2,308.0|114.4|288.2|120.8|148.0|181.0|10.4|13.3|56.0|482.3|
|1991|<br>3,565.0|<br>2,386.5|74.2|84.7|73.5|68.5|500.5|43.8|13.9|74.2|245.2|
|1992|<br>3,599.6|<br>1,651.7|426.8|84.9|86.9|315.3|69.0|37.2|2.0|88.5|837.3|
|1993|<br>4,900.7|3,503.6|189.2|111.4|73.6|101.7|76.9|63.5|2.4|74.2|704.2|
|1994|10,493.1|4,825.1|593.4|305.0|630.9|53.9|90.5|145.1|9.3|740.4|3,099.5|
|1995|8,077.1|5,265.4|213.5|548.5|155.7|<br>200.2|119.5|41.6|61.1|168.7|1,302.9|
|1996|7,396.4|4,966.5|74.4|193.9|139.3|<br>76.1|118.9|59.8|96.6|482.0|1,188.9|
|1997|10,795.6|6,460.6|1,814.3|467.6|342.3|28.7|59.0|263.5|7.2|202.5|1,122.9|
|1998|4,470.6|3,153.4|109.5|130.2|84.6|10.1|47.6|113.5|9.6|123.2|688.9|

Data from 1980-93 and 1994-98 are not strictly comparable due to a change in the methodology. SOURCE: INEGI 49
# **Bear Stearns**

# **Lehman**

<!-- Start of picture text --> Prices (rescaled to face value 100 for preferreds)<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Senior debt price Sub debt price<br>Preferred‐rescaled TruPS‐rescaled<br>Citigroup<br>Prices (rescaled to face value 100 for preferreds)<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Senior debt price Sub debt price Preferred‐rescaled<br>TruPS‐rescaled E‐TruPS‐rescaled<br>Jan‐06 Apr‐06 Jul‐06 Oct‐06 Jan‐07 Apr‐07 Jul‐07 Oct‐07 Jan‐08 Apr‐08 Jul‐08 Oct‐08 Jan‐09 Apr‐09<br>Jan‐06 Apr‐06 Jul‐06 Oct‐06 Jan‐07 Apr‐07 Jul‐07 Oct‐07 Jan‐08 Apr‐08 Jul‐08 Oct‐08 Jan‐09 Apr‐09<br><!-- End of picture text --> <!-- Start of picture text --> Prices (rescaled to face value 100 for preferreds)<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Senior debt price Sub debt price<br>Preferred‐rescaled TruPS‐rescaled<br>Jan‐06 Apr‐06 Jul‐06 Oct‐06 Jan‐07 Apr‐07 Jul‐07 Oct‐07 Jan‐08 Apr‐08 Jul‐08 Oct‐08 Jan‐09 Apr‐09<br><!-- End of picture text --> _Source: Bloomberg. Note: The green vertical lines indicate the date of the bankruptcy/intervention event for each bank._ 27
POVERTY AND INFRASTRUCTURE ACCESS IN SLUMS

# 3   Inequality within slums: comparing poor and nonpoor households

In this section, we examine residents’ access to infrastructure and services among households according to their welfare level. We looked for systematic differences in access between households below and above the poverty line. This approach allows us to see if slums are uniformly bad or if some residents face conditions that are worse than those of their neighbors. We use respective national governments’ expenditure-based poverty lines, adjusted for household size, to categorize slum households as either poor or nonpoor. Because no data on income and poverty level for households are available in our Johannesburg survey, we restrict our discussion here to Nairobi and Dakar.

As described above and in section 1, poverty was assessed for each household (for which size and age were also determined) according to household-specific poverty lines calculated in the field. Respondents were asked if their total monthly expenditures were above or below the computed amount. Those who responded that they spent less than the poverty level were considered poor.

## The development diamond

### **Poverty rates are high, but not everyone is poor**

As noted earlier, slum settlements in both Nairobi and Dakar have high household poverty rates—72 percent and 82 percent respectively—based on respective national poverty threshold levels. But, strikingly, not all slum residents are poor. About 1 in 4 or 5 of the slum households in the two cities are above the poverty line.

Poor households are larger. In both cities poor slum households are likelier to report having more household members living together than the nonpoor, and they are also less likely to be single-person households (appendix table 9). Although the proportion of female-headed households is similar among poor and nonpoor slum households in both cities, in Dakar the heads of poor households are found to be about five years older (53 versus 48 years) than those of nonpoor households.

### **Education: the poor are less likely to have secondary and postsecondary education**

With respect to education level among individual adults living in the slum settlements, those from poor households are systematically less likely to have secondary and postsecondary level education in both cities (appendix table 10).<sup>5</sup> The same finding holds when we look at the highest level of education within households (appendix table 11).

> 5 We also tested whether there was any systematic difference between poor and nonpoor households in terms of access to basic public health services, using BCG immunization rates as a proxy indicator. Encouragingly, we find that almost all (about 97-98 percent) slum children of 0-14 years of age are immunized against BCG in both cities, and there is relative little difference in the immunization rates among children from poor and nonpoor households.

15
Figure 4: The choice of how much to export: before and after the crisis, marginal effects <!-- Start of picture text -->
-4.414 -6.305<br>Subj. fin. constr. Obj. fin. constr.<br>2.581 2.852<br>Subj. fin. constr. X Crisis? Obj. fin. constr. X Crisis?<br>0.176 0.160<br>Foreign shock Foreign shock<br>-0.051 -0.034<br>Foreign shock X Crisis? Foreign shock X Crisis?<br>-0.045 -0.051<br>Domestic shock Domestic shock<br>0.001 0.006<br>Domestic shock X Crisis? Domestic shock X Crisis?<br>-6.000 -4.000 -2.000 0.000 2.000 4.000 -10.000 -5.000 0.000 5.000 10.000<br>(a) OLS (b) OLS<br><!-- End of picture text -->

<!-- Start of picture text --> 159.984<br>Obj. fin. constr.<br>-190.938<br>Obj. fin. constr. X Crisis?<br>-0.105<br>Foreign shock<br>0.243<br>Foreign shock X Crisis?<br>-0.194<br>Domestic shock<br>0.154<br>Domestic shock X Crisis?<br>-400.000 -200.000 0.000 200.000 400.000<br>(c) IV<br><!-- End of picture text --> 14
# **Figure A5. Implications of public investment quality for public debt dynamics: additional results**

**Impulse: instrumented public investment-to-GDP (version 2); Response: public debt-to-GDP**

_Source:_ Authors’ estimates.

_Note:_ Impulse-response functions associated with the local projection model estimated for high-PIQ and low-PIQ samples, indicating cumulative responses to a public investment shock at t=1 over the horizon of ten years. Impulse variable: instrumented public investment-to-GDP change. Response variable: change in public debt as a percent of GDP at horizons up to 10 years following the shock. The shaded area indicates 90-percent confidence bands.

41