rafmacalaba/datause-encoder-data huggingface dataset:
Table 5: Effect of participation in NEP on child development outcomes

||Obs.|NEP-B|NEP-I|P value Test B=I|
|---|---|---|---|---|
|Receptive Language|2895|0.418*|0.432**†|0.963|
|||(0.242)|(0.189)||
|Personal-Social Development: Composite Index|1509|0.315|0.540**†|0.361|
|||(0.304)|(0.261)||
|Behavioral problems: Externalization|1971|-0.118|-0.068|0.814|
|||(0.271)|(0.212)||
|Behavioral problems: Internalization|1887|-0.150|-0.088|0.770|
|||(0.268)|(0.203)||
|Executive Function|2879|-0.038|0.130|0.374|
|||(0.237)|(0.186)||
|Sustained Attention|2893|0.009|0.011|0.295|
|||(0.044)|(0.185)||

Note: Each line reports estimates from a separate regression. All regressions control for health center’s fixed effects. Significance levels: *p<=10%, **p<=5% testing individual hypotheses, †p<=10% testing multiple hypotheses.

Our estimates of the impacts of NEP on parental behaviors and home environments suggest that, in spite of their large magnitude, the medium-term impacts on language outcomes are plausible, because they are mirrored by sustained changes in parenting behaviors and home environments for participating households. Table 6 shows that NEP-I significantly improves the HOME index in the medium term by 0.66 SD (also robust to multiple hypothesis testing), and decreases negative discipline by 0.33 SD. NEP-I also significantly improves the Affection index measured by the PBC nurturing scale, by 0.36 SD. These are substantial impacts.

Regarding parental beliefs, we observe a positive and large impact of NEP-I on parental selfefficacy of 0.411 SD and on parental perceived impact of own behavior on child development by 0.446 SD (both robust to multiple hypothesis testing). We also observe an improvement of perceived social support of 0.37 SD. There are no significant changes in parental styles.

Finally, parents receiving the NEP-I treatment expect their children to reach particular developmental outcomes at a younger age than those in the control group about the age at which children. This is especially true in a “low-investment” home scenario (by 0.39 SD).

Again, our IV results in parental outcomes are robust to the inclusion of additional controls such household socio-demographic characteristics, caregiver endowments and outcomes at baseline, as shown in Table A9 in Appendix 2.

The estimates of the impacts of NEP-I on parental behaviors and home environments (in particular, on the HOME index) are as remarkable as the estimates on child language reported above, precisely for the same reasons already discussed: they are large in magnitude (especially when we take into account the low costs of the program), and they are long lasting. The fact that we observe 19
only find that there is clear evidence of crowding-in when financial development is low.

**Table 5:**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
||C<br>|umulative M<br>|ultipliers (ho<br>|rizon h in ye<br>|ars)<br>|
||h=0|h=1|h=2|h=3|h=4|
|||<br>|Below Media<br>|n<br>||
|Change in government investment|2.799*|3.189*|3.007**|2.768*|1.217|
||(1.498)|(1.730)|(1.370)|(1.516)|(2.084)|
|Observations|1,640|1,601|1,570|1,533|1,496|
|F-statistic|4.719|4.213|4.686|5.629|4.829|
|KP Underid stat|0.0248|0.0335|0.0244|0.0134|0.0215|
|No. of countries|82|82|82|82|82|
|||<br>|Above Media<br>|n<br>||
|Change in government investment|0.269|0.878|1.130|0.702|0.318|
||(0.635)|(1.167)|(1.487)|(1.413)|(1.687)|
|Observations|1,658|1,593|1,522|1,456|1,390|
|F-statistic|6.267|5.523|5.308|5.567|5.167|
|KP Underid stat|0.0183|0.0264|0.0292|0.0258|0.0316|
|No. of countries|80|80|78|77|75|

_Notes_ : *** p <0.01, ** p <0.05, * p <0.1. Dependent variable is change in private investment to lagged GDP. Robust standard errors in parentheses are Driscroll-Kraay standard errors to control for the presence of error cross-sectional dependence a ́ la Boehm (2020). All estimations include country and year effects and one year lagged dependent variable. “KP” is Kleibergen and Paap underidentification test.

# 5 Robustness and validity of the instrument

In this section we conduct a battery of robustness exercises by re-estimating our empirical model with alternative instruments in bilateral and multilateral loans. We also use alternative model specifications to check for the robustness of our main findings. Finally, we use an alternative estimator in _kinky_ least squares to test the robustness and validity of our results.<sup>14</sup>

> 14 In annex D we look at the impact of alternative censoring rules on our main instrumental variable.

17
|P005746|1998|1999||5 Closed|Tunisia|
|---|---|---|---|---|---|
|P005746|1998|2000|6/23/2000|8 Closed|Tunisia|
|P005746|1998|2001|3/26/2001|10 Closed|Tunisia|
|P005746|1998|2002|6/28/2002|13 Closed|Tunisia|
|P005746|1998|2003|6/18/2003|16 Closed|Tunisia|
|P005746|1998|2004|12/18/2003|17 Closed|Tunisia|
|P005746|1998|2020|3/18/2020|18 Closed|Tunisia|
|P005748|1994|1994||2 Closed|Tunisia|
|P005748|1994|1995||4 Closed|Tunisia|
|P005748|1994|1996||5 Closed|Tunisia|
|P005748|1994|1997||6 Closed|Tunisia|
|P005748|1994|1998||9 Closed|Tunisia|
|P005748|1994|1999||12 Closed|Tunisia|
|P005748|1994|2000|6/30/2000|14 Closed|Tunisia|
|P005748|1994|2001|5/30/2001|16 Closed|Tunisia|
|P005749|1995|1995||1 Closed|Tunisia|
|P005749|1995|1996||3 Closed|Tunisia|
|P005749|1995|1997||5 Closed|Tunisia|
|P005749|1995|1998||7 Closed|Tunisia|
|P005749|1995|1999||9 Closed|Tunisia|
|P005749|1995|2000|6/29/2000|11 Closed|Tunisia|
|P005749|1995|2001|2/27/2001|13 Closed|Tunisia|
|P005749|1995|2002|5/13/2002|17 Closed|Tunisia|
|P005750|2001|2001|6/29/2001|1 Closed|Tunisia|
|P005750|2001|2002|2/19/2002|2 Closed|Tunisia|
|P005750|2001|2003|6/13/2003|6 Closed|Tunisia|
|P005750|2001|2004|5/6/2004|8 Closed|Tunisia|
|P005750|2001|2005|4/27/2005|10 Closed|Tunisia|
|P005750|2001|2006|7/1/2005|11 Closed|Tunisia|
|P005750|2001|2006|3/8/2006|12 Closed|Tunisia|
|P005750|2001|2006|6/2/2006|13 Closed|Tunisia|
|P005750|2001|2007|11/14/2006|14 Closed|Tunisia|
|P005750|2001|2007|5/30/2007|15 Closed|Tunisia|
|P005750|2001|2008|7/19/2007|16 Closed|Tunisia|
|P005750|2001|2008|3/24/2008|17 Closed|Tunisia|
|P005750|2001|2009|7/18/2008|18 Closed|Tunisia|
|P005750|2001|2009|12/8/2008|19 Closed|Tunisia|
|P005768|1978|1985||0 Closed|Yemen,Republic of|
|P005768|1978|1986||0 Closed|Yemen,Republic of|
|P005770|1978|1985||0 Closed|Yemen,Republic of|
|P005770|1978|1986||0 Closed|Yemen,Republic of|
|P005771|1979|1985||0 Closed|Yemen,Republic of|
|P005771|1979|1986||0 Closed|Yemen,Republic of|
|P005771|1979|1987||0 Closed|Yemen,Republic of|
|P005771|1979|1988||0 Closed|Yemen,Republic of|
|P005771|1979|1989||6 Closed|Yemen,Republic of|
|P005772|1979|1985||0 Closed|Yemen,Republic of|
# _8. References:_

- Becker. (1993). Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education. Third Edition. University of Chicago Press.

- Bush, Kenneth D. and Diana Saltarelli (2000). The Two Faces of Education in Ethnic Conflict: Towards a Peachbuilding Education for Children. UNICEF. Innocenti Insight.

- Deolalikar, Anil (1997). "The Determinants of Primary School Enrollment and Household Schooling Expenditures in Kenya: Do they Vary by Income?". Working Paper 97-7. CSDE. University of Washington, Seattle

- Easterlin, Richard A. (1981). "Why isn't the Whole World Developed?,".Journal of Economic History, Vol. XLI(1), March. Pp. 1-9.

- Filmer, Deon and Lant H. Pritchett (2001) "Estimating Wealth Effects without Expenditure Data-or Tears: With an Application to Educational Enrollments in States of India" Demography. 38(1):115-132.

- King, Elizabeth M. and Anne M. Hill (1993). Women's Education in Developing Countries: Barriers. Benefits, and Policies. Johns Hopkins University Press.

Malcolm, Noel. (1998) Kosovo: A Short History. Macmillan.

- Lloyd, Cynthia (1994) "Investing in the Next Generation: The Iinplications of High Fertility at the Level of the Family". Pp. 181-202 in R. Cassen (ed.) Population and Development: Old Debates. New Conclusions.

- Mason, Andrew D. and Shahidur R. Khandker (1996) "The Determinants of Enrollment and Late Starting Decisions in Tanzania" HCD Working Paper. Poverty and Social Policy Department. The World Bank.

- Psacharapoulos, George (1994) "Returns to Investment in Education: A Global Update" World Development. 22(9): 1325-1343

- Schultz, T. Paul (1988) "Education Investments and Returns" in H. Chenery and T.N. Srinivasan (Eds.) Handbook of Development Economics. Volume I. Elsevier Publishers.

- RIINVEST. (2000) Establishment and Functioning of Local Government Institutions in Kosova (Research Report). Prishtina.

UNHCR (2000) The State of the World's Refugees. Oxford University Press: New York.

UNHCR (1999) The State of the World's Refugees. Oxford University Press: New York.

21
16

# 4. The hypothetical default scenario for analysis

Until recently, the U.S. sanctions regime maintained general license 9A as an avenue to service debts contracted by the Russian government before March 1, 2022. On April 6, 2022, it was superseded by general license 9B, and, on the following day by general license 9C.<sup>43</sup> The change foreclosed the possibility of debt service payments flowing directly from accounts in the name of Russian entities through U.S. financial institutions, which include the financial institutions’ foreign subsidiaries, but still allows creditors to receive them. It expires on May 25, 2022. In the U.K., general license INT/2022/1495176 allows financial institutions to perform “any activity reasonably necessary” to effect debt service payments of non-ruble securities issued by Russia until June 30, 2022.<sup>44</sup> The Russian Ministry of Finance has stated that it considers its obligations paid in full with the transfer of the amount in rubles to the NSD.<sup>45</sup> It seems possible for Russia to re-route payment through other institutions, maybe even including U.K. ones, and for bondholders to receive payment in the bonds’ currency of denomination. But, unlike in prior weeks, when Russia was able to use otherwise frozen reserves to at least avert default; the new regulations forced it to choose between uses for declining hard currency reserves.

> 43 _See_ <u>Russian Harmful Foreign Activities Sanctions; Russian Harmful Foreign Activities Sanctions General License 9C. The only apparent change between general licenses 9B and 9C was the addition of notes to</u> paragraph (1). In practice, the material changes to Russia’s ability to pay using U.S. financial institutions initially stemmed from 9B.

> 44 _See_ <u>OFSI General Licenses; General Licence INT/2022/1495176.</u>

> 45 _See_ Morgan Lewis Lawflash, _<u>Update: Russia Adopts Decree on Repayment in Russian Rubles of Debt to Foreign Creditors</u>_ (March 7, 2022); Harry Robertson, _<u>Russia says it sent $650 million bond payment in rubles after US Treasury blocked dollar transfers</u>_ <u>, Yahoo Finance (Apr. 6, 2022); Prospectus for the EUR 500,000,000 1.125</u> percent bonds due 2027 and EUR 1,000,000,000 2.65 percent bonds due 2036, at 195-96 (2021) (Explaining the status of NSD as the central depository).
Supplementary Appendix S2

# **BANGLADESH SURVEY OF FORMAL AND INFORMAL JOBS**

# **Conducted by RAND, USA and BIGD, BRAC University**

# **IDENTIFICATION OF THE SAMPLE AREA**

|**Sample Area Particulars**|**Name**<br>**Code No.**|
|---|---|
|Zila||
|Upazila/Thana||
|Union/Ward||
|Mouza/Moholla||
|PSU Number||
|Questionnaire Number||
|InterviewerName||
|Supervisor Name||
|Name of Person EnteringData||
|Date of Interview||

# **ENUMERATOR: PROVIDE INTRODUCTION / READ INFORMED CONSENT**

Will you be interested to participate in the second part of the survey, today?

1. Yes I would like to participate today

2. Yes I would like to participate another day: Date and Time 3. No, I would not like to participate Reason: ....................................................................................................................................... ....................................................................................................................................... ....................................................................................................................................... .......................................................................................................................................

.......................................................................................................................
# **6 Some Illustrative Experiments**

This section presents and discusses the numerical results associated with three types of shocks: a temporary terms-of-trade shock, a permanent cut in domestic credit to the government, and a poverty reduction program consisting of partial external debt forgiveness coupled with a reallocation of savings on debt service payments to three alternative forms of government expenditure: lump-sum transfers to households, spending on infrastructure, and outlays on education.<sup>3 6</sup> Both the short- and longer-run effects of these shocks are analyzed, with a particular focus on poverty as measured by the indicators described above.<sup>37</sup>

## **6.1 Terms-of-Trade Shock**

We first simulate the impact of a temporary terms-of-trade shock that takes the form of a transitory (one period only) 10 percent increase in the world market price of the agricultural traded good (see Tables 2 and 3). This type of shocks has indeed played a pervasive role in explaining changes in real incomes in sub-Saharan Africa and has been analyzed in a number of empirical studies (see, most recently, Dorosh and Sahn (2000)). Because we have assumed that product wages of unskilled agricultural traded workers are fixed (that is, the ratio of the nominal wage to the value added price of the good is constant), nominal wages will tend to match the increase in the producer price.<sup>3 8</sup> Had we excluded financial sector effects from the model, the demand for labor would remain constant and so would production of the traded good. But because we assume that firms in this sector borrow to finance wage payments, the effective price of labor includes the loan rate, as noted earlier. As we shall explain later, interest rates experience an initial decline after the positive terms-of-trade shock, implying that the effective price of labor goes down. In itself, this tends to increase the demand for unskilled labor. However, a decline in the real value of public investment,

> 3 6For a description of the IMMPA simulation program, which consists of both Eviews and GAMS versions combined with Ecel input and output sheets, see Chen et al. (2001). 37 1t should be kept in mind that the choice of a particular set of poverty measures always involves a value judgement and can have considerable bearing on simulation results and policy choices. The open architecture of IMMPA, however, allows users to program alternative measures if deemed necessary.

> 38 They do not match the increase in price of the agricultural good one to one because producer prices also reflect changes in intermediate input prices.

62
a key sector for economic development (Chenery and Watanabe (1958) and Hirschman (1961)).

However, the recursive analysis also reveals that the trace test for cointegration is not rejected in all investigated subsamples. In line with the earlier results, a stable longrun relationship seems only to form from around 1990 onwards.

Figure 3: Recursive Trace test for cointegration between agricultural and manufacturing sector GDP (1973 - 1998) <!-- Start of picture text --> The Trace tbst.<br>I so zo?<br>I 00~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~(<br>o75<br><!-- End of picture text --> _Source:_ Authors' calculations.

Of interest is also the development of the trace test for cointegration between the agricultural sector and public services. The null hypothesis of no cointegration is rejected from 1987 onwards which is much earlier than for the other cases.

For the manufacturing sector, the picture is even more interesting. While the graphical plots of the trace tests again seem to point to more stability between the sector components towards the end of the sample, the trace test for cointegration between the manufacturing and public sectors is rejected over the full sample, indicating a stable longrun relationship between these two variables.

The fact that there also appears to be a stable cointegration relationship between oil and public services is perhaps not such a surprising finding, given the importance of the oil revenues for the economy of Ecuador.

17
_A.3 A few examples of post disaster expenditures_

# **Seychelles**

Following the 2013 floods the immediate fiscal impact of the disaster was estimated to be SR 4.3 million (0.03 percent of GDP or 0.08 of the recurrent budget). According to information recorded in the PDNA utilization of the government’s budget framework accommodated the increased expenditures; no allocation for natural disasters was made to the contingency fund of SR 50 million. The SR4.3 million was spent on those areas identified with the greatest need. These being the provision of relief goods by the Agency for Social Protection SR3.3 million; emergency assistance provided by the Ministry of Land and Housing SR480,000 and the District Administrators have incurred expensed SR 0.6 million for similar activities. **Source: Government of Seychelles, 2013.**

# **Pakistan**

Following the 2011 floods in Pakistan the additional expenditure needed to fund the necessary relief, rehabilitation and reconstruction was estimated to cause equivalent to 0.3 percent of GDP. Consequently, the already stretched target of running a budget deficit equivalent to 4 percent of GDP was forecast to reach 6 percent of GDP as a result of the increased expenditures. **Source: Government of Pakistan, 2011.**

## **Fiji**

Post disaster expenditures will first and foremost be driven by the amount of cash readily available to the government. This makes the time that a disaster occurs a key issue; different amounts of funding will be available depending on whether the disaster occurs at the beginning or towards the end of the budget cycle. In December 2012 Fiji was struck by tropical cyclone Evan causing damage on the northern island of Vanua Levu and the western part of the main island of Viti Levu. The fiscal year in Fiji coincides with the calendar year so the government had a good idea what the full year expenditures had been and as a result they were able to swiftly reallocate FJ$7 million (US$3.7 million) from the national budget in 2012, equivalent to approximately 0.3% of the total budget. This money was used to finance the initial disaster response for TC Evan. A further FJ$17 million (US$9 million) was reallocated from the 2013 budget to finance housing rehabilitation, equivalent to 0.7 percent of total expenditures. **Source: Government of Fiji, 2012.**

# _A.4 Uptake of DRFI instruments_

The HFA national progress reports for 2009-2011 indicate that 60 out of 95 countries hold some form of national contingency fund for disaster response. In addition, 37 out of 95 countries indicated that they have some form of catastrophe insurance facility in place. The supporting narrative indicates that these figures include countries where private property and crop insurance schemes, as well as sovereign insurance mechanisms, are available (Benson, 2011). This number will have increased slightly following the launch of the Pacific Catastrophe Risk Insurance pilot in January 2013, which saw five, now six following the expansion in the second season, Pacific island countries (Cook Islands, Marshall Islands, Samoa, Solomon 20
|JSDF-Thailand: Legal Aid Services for Poor and Vulnerable People|Minneh MaryKane|
|---|---|
|JSDF-Thailand: Legal Aid Services for Poor and Vulnerable People|Minneh MaryKane|
|JSDF-Thailand: Legal Aid Services for Poor and Vulnerable People|Minneh MaryKane|
|JSDF-Thailand: Legal Aid Services for Poor and Vulnerable People|Minneh MaryKane|
|JSDF-Thailand: Legal Aid Services for Poor and Vulnerable People|Minneh MaryKane|
|JSDF-Thailand: Legal Aid Services for Poor and Vulnerable People|Minneh MaryKane|
|JSDF-Thailand: Legal Aid Services for Poor and Vulnerable People|Minneh MaryKane|
|EmergencyInfrastructure Project|Kulwinder Singh Rao|
|EmergencyInfrastructure Project|Kulwinder Singh Rao|
|EmergencyInfrastructure Project|Kulwinder Singh Rao|
|EmergencyInfrastructure Project|Kulwinder Singh Rao|
|EmergencyInfrastructure Project|Kulwinder Singh Rao|
|EmergencyInfrastructure Project|Kulwinder Singh Rao|
|EmergencyInfrastructure Project|Kulwinder Singh Rao|
|EmergencyInfrastructure Project|Kulwinder Singh Rao|
|EmergencyInfrastructure Project|Kulwinder Singh Rao|
|EmergencyInfrastructure Project|Kulwinder Singh Rao|
|EmergencyInfrastructure Project|Kulwinder Singh Rao|
|EmergencyInfrastructure Project|Kulwinder Singh Rao|
|Puntland PrimaryHealth Services|Carla Bertoncino|
|Puntland PrimaryHealth Services|Carla Bertoncino|
|Puntland PrimaryHealth Services|Carla Bertoncino|
|Djibouti CapacityBuildingfor the National Statistical Agency|Ingrid B. Ivins|
|Djibouti CapacityBuildingfor the National Statistical Agency|Ingrid B. Ivins|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|Critical Ecosystem PartnershipFund 2|Andrea Kutter|
|ID-Aceh-Infra. Reconstr EnablingProgram(IREP)|KhairyAl-Jamal|
|ID-Aceh-Infra. Reconstr EnablingProgram(IREP)|KhairyAl-Jamal|
|ID-Aceh-Infra. Reconstr EnablingProgram(IREP)|KhairyAl-Jamal|
|ID-Aceh-Infra. Reconstr EnablingProgram(IREP)|KhairyAl-Jamal|
|ID-Aceh-Infra. Reconstr EnablingProgram(IREP)|KhairyAl-Jamal|
|ID-Aceh-Infra. Reconstr EnablingProgram(IREP)|KhairyAl-Jamal|