rafmacalaba/datause-encoder-data huggingface dataset:
|At times I feel I have gotten a raw deal out of|||||
|---|---|---|---|---|
|life.|(1.12)|(1.57)|(1.93)||
|There are people who have pushed me so far<br>|9.46|24.46|15.00|6849|
|that we have come to blows.|(0.53)|(1.38)|(1.48)||
|I can't help getting into arguments when|21.09|31.31|10.22|6847|
|people disagree with me.|(0.88)|(1.49)|(1.73)||
|Sometimes I fly off the handle for no good|28.05|27.26|-0.78|6848|
|reason.|(1.02)|(1.43)|(1.76)||
|Other people always seem to get the breaks.|21.89|44.55|22.66|6848|
||(0.95)|(1.59)|(1.86)||
|I have threatened people I know.|4.54|6.42|1.88|6841|
||(0.41)|(0.79)|(0.89)||
|My friends say I am somewhat|11.91|22.71|10.80|6849|
|argumentative.|(0.75)|(1.34)|(1.54)||
|I have trouble controlling my anger.|25.99|29.39|3.40|6839|
||(1.00)|(1.47)|(1.78)||
|I wonder why sometimes I feel so bitter|26.68|44.13|17.45|6839|
|about things.|(1.13)|(1.60)|(1.96)||
|I sometimes feel like a powder keg ready to|26.16|51.91|25.75|6845|
|explode.|(1.15)|(1.61)|(1.97)||
|_Average _|20.49|33.19|12.70|6847|

Notes: Responses indicating more severe outcomes are operationalized in this module as responses of “somewhat like me” and “very like me”. Estimates are unweighted, and clustered standard errors are reported in parentheses.

|**Risk for me**<br>**Question**|**ntal healthpr **<br>**Share of r**<br>**choosing**<br>**that indi**<br>**severe ou**<br>_PPCS_|**oblems**<br>**espondents**<br>**responses**<br>**cate more**<br>**tcomes (%)**<br>_Facebook_|**Difference**<br>**(Percentage**<br>**point)**|**N**<br>**(combined)**|
|---|---|---|---|---|
|Can you focus on your work as usual?|20.34|31.17|10.84|6848|
||(0.80)|(1.49)|(1.69)||
|Do you find it difficult to sleep because you|32.03|47.36|15.33|6862|
|are nervous or preoccupied?|(1.04)|(1.59)|(1.90)||
|Do you feel you are playing a useful role|8.66|10.92|2.26|6856|
|toward the people around you?|(0.49)|(1.00)|(1.11)||
|Can you make decisions as usual?|10.30|18.50|8.20|6849|
||(0.52)|(1.25)|(1.35)||
|Do you feel under continuous pressure?|29.59|56.47|26.87|6850|
||(1.01)|(1.59)|(1.88)||
|Do you feel incapable of overcoming your|16.59|68.14|51.54|6852|
|problems?|(0.74)|(1.49)|(1.66)||
||10.69|29.48|18.80|6846|

22
attainment of education. This includes the large number of deaths of teachers as a result of HIV/AIDS (Sany, 2010). Third, NGO-run primary and secondary schools stepped in to fill the education gap in the North in 2004. This could also produce a downward bias in impact of war on education.

All these factors could potentially generate selection bias and confounding errors, thus invalidating the exchangeability assumption. In the presence of these possibilities, it is unrealistic to assume that the incidence of war is randomly assigned and the confounding factors may generate bias in the causal effect of war on education. Since a direct comparison of two groups of individuals may not overcome the problem of identification, we go one step further and employ propensity score matching (Rosenbaum and Rubin, 1983). This means pairing individuals who are identical based on all observable characteristics (including department of birth, other households characteristics and the relevant socio-economic factors) that the rich ENV-2008 survey data offers, except variables that measure war victimization. We discuss it more formally below. Let us denote the binary victimization indicator equals to one if individual _i_ is a war victim and zero otherwise. We are particularly interested in estimating the average treatment effect on the treated (ATT). This can be written as equation (2) below:

# **(2)**

where denotes the potential education outcome (years of education in our case) for each individual _i_ . As the average education level of the counterfactual comparison group - - is not observed, we generate propensity scores to choose a proper substitute from the matched pairs based on propensity scores. Propensity scores are generated by simple probit regression. Individuals are paired and chosen from the war victims (treatment group) and the rest (control group) based on similar propensity scores. Then we calculate the average difference in years of schooling across them. There exists a range of possibilities for matching algorithms; however, the performance of different matching estimators depends largely on the data structure (Zhao, 2000). For our purpose, we use the straightforward nearest neighbor matching as a baseline strategy. This method first categorized both the treatment and the control 16
3

# Introduction

Since 1982, public and private investment rates have declined dramatically in moss debtor countries. For the group of 15 heavily indebted countries, the investment to GDP ratio dropped on average by 6% between 197181 and 1982-88 when, for non-crisis countries, this ratio increased in average by 15%2. On the basis of this empirical evidence, the poor investment and, consequently, growth performance in highly indebted countries is frequently attributed, at least to some extent, to the burden of their foreign debt.

Basically, the decline in public investment is explained through the cutoff in external financing. The public debt-service payments could not be financed by any new foreign borrowing so that adjustment efforts were concentrated on public investment. Easterly (1989) emphasizes "the adjustment burden in the crisis countries was on the public expenditure side. Capital spending was the most severely cut, falling sharply in Argentina, Brazil, Mexico, Morocco, and the Philippines". Furthermore, the public sector situation worsened, since the governments of debtor countries often felt compelled to assume external liabilities of the private sector. In order to explain the sharp reduction in private investment, the recent economic literature has identified several direct and indirect channels. If the private sector has been also credit rationed, most authors argued that a large foreign debt affects productive investment through a disincentive effect<sup>3</sup> . Since the government in most debtor countries appeared unable (or unwilling) to meet its increasing debt-service payments, private investors anticipated higher rates of taxation on real and financial assets as well as more instability in the economic environment. These changes affected private investment negatively through the debt-overhang effect, which refers to the reduced incentives to invest. In addition, as foreign assets became more attractive relative to domestic assets, this often led to an increase in domestic interest rates, reducing private investment further.

An impressive number of theoretical papers has been carried out during recent years in order to challenge or to assess the validity of the debt overhang hypothesis'. There has been, however, relatively few attempts to rest empirically this hypothesis. The purpose of this paper is to confront the actual strategy - debt-reduction - with a macroeconomic and dynamic framework that integrates the liquidity and incentive effects. The use of a simultaneous equations model allows to take into account various direct and indirect 2Source : IMF World Economic Outlook, supplementary note 1, 1989.

3A disincentive effect may also influence public investment. For instance, a large external debt might discourage the government adjustment efforts, since much or the benefit of adjustment will go to the creditors. We consider, however, that the credit rationing effect is stronger than the disincentive effect on public investment.

4see for example Sachs (1989), Krugman (1988), Corden (1989) or Diwan '1990).
**Table 3: IV estimations of labor productivity and exports**

||First-stage estimation|Reduced-form estimation|IV estimation|
|---|---|---|---|
||(1)|(2)|(3)|
||Panel A: Weighting bilate|ral routes by the interaction<br>trade in industry|of exporter and importer|
||DV: Δ Log country-|DV: Δ Log bilateral|DV: Δ Log bilateral|
||industry labor productivity|exports|exports|
|Δ Log estimator for technology|0.589**|0.938***||
|flows from the United States|(0.272)|(0.298)||
|Δ Log country-industry labor|||1.592**|
|productivity|||(0.637)|
|Observations|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|
||<br>Pan|<br>el B: Excluding sample weig|<br>hts|
||DV: Δ Log country-|DV: Δ Log bilateral|DV: Δ Log bilateral|
||industry labor productivity|exports|exports|
|Δ Log estimator for technology|0.267***|0.648***||
|flows from the United States|(0.078)|(0.112)||
|Δ Log country-industry labor|||2.429***|
|productivity|||(0.791)|
|Observations|103,839|103,839|103,839|
|Importer-Industry-Yr FE|Yes|Yes|Yes|
|Exporter-Importer-Yr FE|Yes|Yes|Yes|

Source: Author's analysis based on data described in text. Notes: See Table 1. The instrument combines panel variation on the development of new technologies across US cities during the 1975-2000 period with historical settlement patterns for migrants and their ancestors from countries that are recorded in the 1980 Census of Populations. The F statistics in Panels A and B are 4.7 and 11.6, respectively.
purchases across other sectors.<sup>2</sup> The basic insight here is that institutions lower the transaction cost and hence benefit those sectors more which enter into a more complex web of transactions with the rest of the economy. The measure suggested by Blanchard and Kremer is acknowledged by the authors to be a “noisy” one and has a number of limitations. First, the measure does not include intra-sector transactions between firms largely due to data limitations. Second, it does not take into account inputs supplied by a sector although there is no reason to believe that high transaction costs do not affect the suppliers as well.

Nevertheless, an examination of the input-output matrices for a number of countries reveals that in general input supplies and purchases were much more evenly distributed for the services sectors relative to manufacturing. In the spirit of Blanchard and Kremer, it can be argued that the services sectors are more institutionally dependent than the other sectors. As an example, institutional dependence of various sectors calculated using India's input-output matrix is shown in Figure 1 where higher values indicate greater dependence.<sup>3</sup>

> 2 Blanchard and Kremer (1997) look at the decline in output of manufacturing sectors in the former Soviet Union countries between 1992 and 1994 and argue that the decline was more pronounced in the more institutionally dependent sectors.

> 3 The graph uses GTAP data for India. To construct the graph we first computed the Gini coefficient for the inputs supplied to and bought from a sector to all other sectors. We then calculated 100 minus Gini for each sector to arrive at the figures shown in the graph. This is slightly different from the approach of Blanchard and Kremer who use the inverse of the Herfindahl index instead. Both these measures are widely used in the literature.

3
compare between the two cities, and depending on the migration/relocation cost between the two cities, an increase in the urban school fee may result in the migrant worker sending the child back to the rural area (case (i)) or relocating to another city with or without the child (cases (ii) and (iii) respectively). It is easy to see that when the relocation cost is sufficiently large, both ��∗ ���∗∗ and ��∗ ���∗∗∗ hold, so that case (i) prevails. We thus have the following corollary.

# **Corollary 3.1. School fee increases and relocation decisions**

_If relocation costs are sufficiently large, an increase in the school fee will only affect the decision to bring the child as the worker will remain in the city._ **Proof:** Appendix 1, Part A _._ Corollary 3.1 is relevant in the case of China and helps explain that, given prohibitive relocation costs across Chinese cities, migrants typically do not relocate after migrating to a city (see, for example, Chen et al., 2010). Put differently, under Corollary 3.1, ��, the set of migrants in city 1 defined in equation (3), is likely to remain the same after the change in school fee, which supports the internal validity of our empirical results using post-migration data in destination areas.

Moreover, note that higher school fees may place vulnerable households (i.e., poorer households or households with precarious jobs or without health insurance) at a particular disadvantage. These households are likely to expect lower (non-)schooling outcomes in the city, and would thus benefit most from social protection policies.<sup>21</sup> The following corollary to Proposition 3 helps shed light on their response to higher school fees.

# **Corollary 3.2. School fees and vulnerable households**

_Vulnerable migrant households are more likely to respond to an increase in the school fee by sending their children back to the rural area._ **Proof:** Appendix 1, Part A _._

> 21 Households with lower education levels, for example, may not know how to access information about the city’s services and, thus may not have access to these services.

13
18 Comparisons of the 2003 and 2008 DHS data show similar trends in ITN ownership across all malaria risk zones. This indicates that a substantial number of ITNs was acquired in areas of Kenya where malaria is not a major health issue and suggests that better targeting of ITN distribution primarily to malaria-endemic zones could increase the number of lives saved. In its National Malaria Strategy 2009-2017, Kenyan Ministry of Public Health and Sanitation announced a new goal of universal long-lasting insecticide-treated bednets (LLIN) coverage for populations at risk in malaria risk areas by 2013 (Ministry of Public Health and Sanitation 2009). PSI's activities on ITN distribution are still in progress. More recently, USAID promised to contribute 2.7 million ITNs in the campaign against malaria (USAID 2011).

There is some evidence that ITN ownership may have fallen recently. The report from the 2010 Kenya Malaria Indicator Survey (KMIS) finds 47.9 of households owning at least one ITN, compared to 55.7 percent of households in the 2008 DHS, and the KMIS report suggests that this is because a planned distribution to replace aging ITNs did not take place in 2009. The KMIS report also shows an increase in reported malaria rates between 2007 and 2010, although changes in the geographical coverage and age window of the KMIS between the two years makes it difficult to interpret this change, and the data from the survey is not publicly available. In the light of the evidence on the benefits of ITNs‘ increased availability for infant health in high malaria risk zones of Kenya and given the cost effectiveness of ITN delivery campaigns, continuous interventions and further ITN scale-up and keep-up campaigns are likely be desirable because they are both beneficial to infant health and available at an acceptable cost.

# **6 Conclusions**

Infant mortality has fallen substantially in many countries in Sub-Saharan Africa in recent years. The drop shown for Kenya in the DHS data from 2003 and 2008 is the most rapid in the region for countries for which recent data is available. In this paper, we closely analyze the possible factors for that decline. The Oaxaca-Blinder decomposition analysis implies a substantial role for the increased ownership of insecticide-treated nets, which can account statistically for 58 percent of the overall decline in infant mortality. Our analysis does not find evidence that other
data set corresponds to the first wave of COVID-19 infections and deaths which were milder in India (Figure 2). The vertical lines in Figure 2 show the start and end of the lockdown period (red and green lines respectively), and the end date for our data set (dark orange line). As clear from Figure 2, fatality rates were quite low by February 24, 2021, and many observers reas1d (and perhaps hoped) that India escaped much of the scourge of COVID-19. That hope was dashed soon with the arrival of the next wave (Delta variant) in March 2021 which caused a serious rise in infections and deaths in the following months.

1 caveat with the data set is that in October 2020, the logistics company (JDL) completed a reorganization in which it restricted access to its platform only to its own trucks. This led to a very large reduction in the number of transactions recorded on the platform. We restrict our sample to JDL trucks for the analysis of freight rate behavior before, during, and after the lockdown period. There are 7,344 trips after March 24, 2020, in our data set. To avoid the uncertainty during re-organization, we define a pre-lockdown sample from November 1, 2019. There are 9,220 trips during the pre-lockdown sample period. To show trends in freight rate, we collapse data by date and fit a time trend to both pre-and postlockdown periods. Figure 3 plots the observed trend in freight rate from November 1, 2019, to February 24, 2021, and the red vertical line indicates the date when the nationwide lockdown was imposed. Before the lockdown in March 2020, the freight rate had been completely flat. The freight rate doubled during the first week of lockdown and started to come down quickly though it was about 37 percent higher even in February 2021.<sup>10</sup> The negative shocks to production and trading due to lockdown and COVID-19 reduced demand for transport services. Similarly, the price of crude oil dropped substantially during much of 2020 compared with the pre-pandemic period. Both of these changes should in principle result in a lower freight rate. Two factors -directly relevant to the transport sector itself could potentially explain the

> 10 This rise in trucking prices during the pandemic period is confirmed in the month-to-month comparison as well (Figure A.1 in the online appendix). This pattern in India is quite different from those observed in developed countries over the same period. Trucking costs increased only for a short while during early 2021 in the US due to the shipping backlog during the Christmas season. For the rest of the time, it remained stable with no disruptions in the internal supply chain, though supply chain issues reemerged during the latter half of 2021.

11
Chart 4 Gross Financing Requirements of Sub-Saharan Africa, 1989-2000 (Percent of GDP) <!-- Start of picture text --> 19<br>17<br>15<br>14<br>13<br>12<br>010<br>L.J~~ 9<br>6<br>5<br>4 2 - t~ ~ ~ ~ ~ ~ ~~~~~~~~~~-<br>O- I I IIIII I<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000<br><!-- End of picture text -->

- Gross financing requirements before debt relief * Gross finanLing requirements after debt relief 4 Debt service obligations before debt relief A Debt service obligations after debt relief

- X Trade deficit including private transfers v Change in reserves
always adapting to varying levels of uncertainty and opportunity, adding a layer of complexity to understanding the association between behavior change intentions and actions. For African women, Johnson-Hanks (2007) argues that the ability to reproduce is a resource in that it provides opportunities that might otherwise not have arisen, and therefore whether or not a woman has a child is dependent on many more things than simply her intentions to do so. Similarly, sex could be considered such a resource, and uncertainty faced in the daily lives of those at risk for HIV infection may influence how intentions are formed and acted upon and how pursuit of one life goal is selected over pursuit of another. Characterizing this uncertainty by gaining a clearer understanding of the reasons for discrepancies between intentions and actions in a particular cultural context may guide design of interventions that are perhaps more effective in reducing risky sexual behavior.

To better understand what factors might intervene between intent to change risky sexual behavior and actual behavior change, we use panel data from the baseline and 4-month follow-up visits of participants enrolled in the Tanzania RESPECT trial. These data provide an opportunity to explore how a cash incentive might influence the consistency or lack thereof between reported intent to change sexual behavior at baseline and reported actual behavior change at follow-up. To our knowledge, this study is unique in its ability to characterize both stated intention and realized actions in panel data before and after a behavioral change intervention such as this.

# _Conceptual Framework and Hypotheses_

Previous research can lend guidance in developing a conceptual frame and corresponding hypotheses relating to sexual behavior change intentions and actions for study enrollees. To understand factors associated with behavior change, authors have studied the reciprocal relationship between selfefficacy, risk perception and behavior change (Bandura, 1977; Ajzen & Fishbein, 1980; Weinstein & Nicholich, 1993; Smith & Watkins, 2005), the effects of receiving STI and HIV test results on subsequent behavior change (Kamali et al., 2003; Thornton, 2006; Wolitski et al., 1997; Boozer & Phillipson, 2000; Gong, 2010), women’s economic dependence on men, transactional sex (Hunter, 2002; Luke, 2003; 3
during 2009—an upward revision of salary scales could have a significant impact on private consumption demand in the short run.

# **Box 2 How Large Is the Fiscal Multiplier?**

The effect of fiscal expansion on output is referred to as the _fiscal multiplier_ . It is measured by the unit change in output or GDP per unit change in government expenditure, other determinants of output remaining unchanged: Where _F_ = fiscal multiplier, _Y_ = income or GDP, and _G_ = government expenditure. Large standard errors of estimation have prevented researchers from pinning down precise estimates of the value of _F_ . Recent estimates range from negative to more than one (Spilimbergo and others 2008). Calculating the fiscal multiplier is difficult because there is no simple way to control the “fiscal experiment” (Barro and Redlick 2011; Christiano, Eichenbaum, and Rebelo 2009)—that is, to separate the impact of changes in government spending from changes in other variables that simultaneously affect changes in income, through various channels.

Estimates of fiscal multipliers depend on the methodology, period, initial conditions, and controls applied. Study of the experience in the United States suggests that deficit-financed state government spending has a lower multiplier than windfall-financed spending, because deficit-financed state spending tends to crowd out private consumption and investment (referred to as _Ricardian equivalence_ ). However, in periods of stagnant or declining consumption and investment demand, deficit-financed government spending does not crowd out private demand. The multiplier is therefore larger than during boom times.

A survey of empirical evidence suggests that for the 2008–09 stimuli by the G20, the low set of multipliers was 0.3 for revenue, 0.5 for capital spending, and 0.3 for other spending; the high set of multipliers was 0.6 for revenue, 1.8 for capital spending, and 1.0 for other spending (Spilimbergo, Symansky, and Schindler 2009). For a sample of 102 developing countries, the one-year government spending multiplier resulting from borrowing from official creditors is estimated to be about 0.4 (Kraay 2012). In emerging markets, where fiscal space is large, the safety net shallow, and infrastructure deficits great, the effects of 7