rafmacalaba/datause-encoder-data huggingface dataset:
food insecurity, because adequate diets and nutritious foods are crucial for child development. In our review, we found twelve studies that reported the effects of humanitarian assistance programs on household-level food security outcomes, nine of which compared the intervention to a pure control group. These studies used various ways to measure food security, ranging from its simplest form (e.g., number of meals eaten per day) to standard indicators (e.g., Household Dietary Diversity Score, Food Consumption Score, Food Insecurity Experience Scale).<sup>11</sup> Most of the studies in our review find positive and statistically significant effects of humanitarian assistance programs on food security outcomes (Gilligan and Hoddinott 2007; Tusiime et al. 2013; Hidrobo et al. 2014; Bonilla et al. 2017; Kurdi et al. 2019; Tranchant et al. 2019; Chaaban et al. 2020; Quattrochi et al. 2020). Four of these studies find improvements in Household Dietary Diversity Score (HDDS), although some of the coefficients are not statistically significant. The only exception is Lombardini and Mager (2019), who find that a cash-for-work intervention in a refugee camp in Jordan did not have any positive effect on food security.

Overall, these results suggest that most types of humanitarian assistance interventions have large impacts on food security outcomes in fragile and conflict-affected settings, not only through increased consumption but also through improved quality of diets and less severe experiences of food security. One limitation of the current evidence is that food security measures are mainly measured at the household level, which leaves a gap of knowledge regarding the intrahousehold distribution of food consumed and especially the food security situation among children.

# **Food expenditure**

While food security is typically measured by looking at consumption and dietary patterns in food groups, one can look at the dollar expenditure on food purchases as a complementary measure of food security. Food expenditure can be defined as the amount of money spent on food in any given period. This measure can be particularly useful for people who purchase most of their food rather than produce it themselves. In our review, seven studies look at the impact of humanitarian assistance programs on food expenditure, four of which compared the intervention

> 11 For detailed descriptions of different indicators, see “Data4Diets: Food Security Indicators” from Tufts University Friedman School of Nutrition Science and Policy.

14
Being the recipient of FDI does not correlate with the decision/intensity of innovation (Table 3) but it does with innovation outputs, especially product innovation, suggesting that firms’ innovation outcome may benefit from technology developed abroad. The level of fixed investments and the size of the firm are positively correlated with innovation outputs, be it innovation in product or process.

Some results are counterintuitive. Firms innovate less when they have more educated workers, they are part of a large group, their manager has more experience in the sector, they face more cooperation in innovation, they locate at a large city, and they use technology licensed by a foreignowned company (licenses). These results appear not to be driven by innovation in product or process. Yet, some of these counterintuitive findings are not peculiar to East Asia. Crespi et al. (2016) document similar findings regarding human capital for firms in Latin America. They argue that firms with a higher share of skilled workers may be operating in more complex markets and may lack the capabilities needed to introduce new products or processes in these markets. Similarly, one could argue that firms may be cooperating in product innovation precisely because it is harder to succeed in that particular field.

_Table 4 The determinants of innovation outputs in China and ASEAN5_

||**(1) Innovation**|**(2) Product innovation**|**(3) Process innovation**|
|---|---|---|---|
|R&D per worker (predicted)|0.485***|0.203*|0.217*|
||(0.116)|(0.104)|(0.111)|
|Firm age|0.0126**|0.00482|-0.000401|
||(0.00600)|(0.00625)|(0.00570)|
|Human capital|-0.0604***|-0.0273*|-0.0208|
||(0.0188)|(0.0155)|(0.0166)|
|Group|-0.461***|-0.0629|-0.169|
||(0.164)|(0.157)|(0.155)|
|FDI|0.441***|0.284**|0.101|
||(0.111)|(0.113)|(0.107)|
|Diversification|0.00483|-0.00209|-0.000554|
||(0.00332)|(0.00257)|(0.00319)|
|Manager experience|-0.00716*|-0.00403|0.000918|
||(0.00375)|(0.00359)|(0.00385)|
|Cooperation|-0.00625**|-0.00837***|0.00103|
||(0.00300)|(0.00292)|(0.00276)|
|Large city|-0.285*|-0.157*|-0.107|
||(0.160)|(0.0927)|(0.160)|
|Licenses|-0.182*|-0.0289|0.107|
||(0.110)|(0.129)|(0.0940)|
|Fixed invest|0.0494***|0.0305***|0.0438***|
||(0.0104)|(0.00917)|(0.00954)|
|Employment|0.0509***|0.0278***|0.0707***|
||(0.0127)|(0.00966)|(0.0151)|
|N|2,716|2,718|2,716|

*** p<0.01, ** p<0.05, * p<0.1. Probit model. Marginal effects are reported, i.e., the coefficients predict the likelihood of introducing product or process innovation. The sample includes data from these surveys: China <u>(2012), Indonesia (2015), Malaysia (2015), Philippines (2015), Thailand (2016), Vietnam (2015).</u>

9
# 4 Model

We study an economy populated by a single household composed of a continuum of agents. These agents are heterogeneous with respect to their ability to operate a production technology and run a business. The head of the household makes an occupational choice on behalf of each agent, choosing either to assign her to entrepreneurship and earn a risky prot or make her participate in the labor force, in exchange for a xed wage. Each individual commits to participate in a risk-sharing agreement that insulates individual consumption from uctuations in idiosyncratic income. In addition to occupational choices, the head of household chooses aggregate consumption and investment to maximize lifetime utility.

There are endogenous and exogenous forces for rm dynamics and resource reallocation. The endogenous component stems from entrepreneurs' investments in a risky innovation technology that controls the expected evolution of entrepreneurial ability over time, and their entry and exit decisions. The exogenous element results from idiosyncratic productivity shocks around the expected path. It is the endogenous decision of entrepreneurs to innovate together with the decision to enter and exit entrepreneurship that connects the life cycle and the size distribution of rms with policies and distortions to factor allocation.

We rst present the details of the frictionless economy, which we take as a reference for the calibration of preferences and technological parameter values which are kept constants across countries. These parameters are calibrated to match data on the dynamics of rms and income inequality in the US, a relatively undistorted economy. Then we introduce an extension with distortions and calibrate it using information from growth accelerations.

## 4.1 Consumption and Savings Problem

The assumption of perfect sharing of idiosyncratic risk allows us to separate the consumption/investment decision from the occupational choices.

Taking wages and occupational choices as given, the household chooses consumption and investment in order to solve the following problem: 12
Table 8: Treatment Effect for Target Child on Literacy and Numeracy Test Scores: Pre- and PostCovid Samples

|||_Literacy_|_Skills_||_Numer_|_acy Skills_|
|---|---|---|---|---|---|---|
||Prop.|Zero|Z-s|core|Z-s|core|
||(1)|(2)|(3)|(4)|(5)|(6)|
||_March 20_|_Sam_<br>_20 & Sept-_|_ple 2 Pre-_<br>_Dec with_|_and Post-C_<br>_less than 1_|_ovid_<br>_0% of scho_|_ol closures_|
|Any Treatment (T1 or T2)|-0.094*<br>(0.053)||0.274**<br>(0.133)||0.439**<br>(0.178)||
|Videos (T1)||0.121*||-0.129||-0.328|
|||(0.071)||(0.127)||(0.207)|
|Videos + Mobile apps (T2)||-0.218***<br>(0.048)||0.507***<br>(0.160)||0.880***<br>(0.191)|
|Observations|5,077|5,077|5,077|5,077|5,077|5,077|
|R-squared|0.200|0.243|0.296|0.302|0.285|0.312|
|Covariates|YES|YES|YES|YES|YES|YES|
|Enumerator FE|YES|YES|YES|YES|YES|YES|
|Baseline Mean|0.977|0.977|0.000|0.000|0.000|0.000|
|Control Mean in F-up|0.453|0.453|-0.684|-0.684|1.101|1.101|
|T1 vs T2(p-value)||0.000||0.000||0.000|
|||_S_|_l 3 P_|_d P_|_id_||
||_Marc_|_am_<br>_h and Nov-_|_pe re-_<br>_Dec 2020_|_an ost-_<br>_(most of th_|_ov_<br>_e schools o_|_pened)_|
|Any Treatment (T1 or T2)|-0.075<br>(0.052)||0.105<br>(0.128)||0.249<br>(0.164)||
|Videos (T1)||0.084||-0.161||-0.295|
|||(0.062)||(0.126)||(0.192)|
|Videos + Mobile apps (T2)||-0.228***||0.361**||0.773***|
|||(0.050)||(0.172)||(0.203)|
|Observations|4,863|4,863|4,863|4,863|4,863|4,863|
|R-squared|0.203|0.242|0.285|0.289|0.287|0.311|
|Covariates|YES|YES|YES|YES|YES|YES|
|Enumerator FE|YES|YES|YES|YES|YES|YES|
|Baseline Mean|0.974|0.974|0.000|0.000|0.000|0.000|
|Control Mean in F-up|0.479|0.479|-0.753|-0.753|1.013|1.013|
|<br>T1 vs T2(p-value)||0.000||0.002||0.000|
|Note: Columns 1 and 2 show<br>of children scoring ”zero” i<br>skills and Column 5-6 z-sco|the resu<br>n letter re<br>res of nu|lts of linea<br>cognition. <br>meracy sk|r probabi<br> Column<br>ills. Test|lity model<br>3-4 show<br>scores are|s of the pr<br>z-scores o<br>normaliz|oportion<br>f literacy<br>ed using|
|the mean and standard dev<br>have clustered standard er<br>considered in all regression|iation fo<br>rors at th<br>s are age|r the contr<br>e school c<br> of child,|ol group <br>atchmen<br>gender o|at baselin<br>t area leve<br>f child, ed|e. All re<br>l. The c<br>ucation|gressions<br>ovariates<br>of house-|
|hold head (baseline), availa<br>p_<_0.01, ** p_<_0.05, * p_<_0.1|bility of f|ood (basel<br>34|ine) and|enumerat|or fxed ef|fects. ***|
10 14. As a result, financlal data often present rosy scenarios wnieo In absence of adequate provisions for loan losses, the SACIs' financial statements are likely to mislead the analyst. The overall cost of maintalning the SACI afloat Is almost never presented. Many of the subsidies Involved In SACIs' operations, such as concessional financial resources that were made available by the Central Bank, or state repayment of forolgn exchange losses on strong currency denominated loans, are not adequately captured In the SACIs' fInancial statements when the Issue Is the overall financial cost of malntaining the SACI afloat. Deficlent fInancial reporting has contrlbuted to lack of clarity on the SACls cost side. In many Instances, the subsidles that benefitted the SACis were not transparent, not funded from the budget, so as to allow a public debate on their costs, benefits and social desirability.

15. Specialized Agricultural Credit Institutions which depend almost exclusively on external funds have to follow goverrdnent directives when allocating thelr funds. As these directives frequently Include administrative allocation of funds to certain target groups And exclusive lending for agriculture, the financial Intermediarles have limited ability to diverslfy their loan portfolio, to use creditworthinoss criteria, and to Implement adequate risk management policy.

18. High lending and borrowing costs. In their attempt to ensur ellgibillty and avold diverslon of funds, specialized formal Institutions Incur high costs and also kmpose high transactlon costs on borrowers. Typically, farmers are obliged to spend slgnificant amounts of both money and tkne to obtain loans *ron specialized Institutions. In fact, small borrowers' transactlon costs are often so hlgh tnat the effective cost of a loan (including transaction costs and Interest
|Perez-Reyes<br>and Tovar<br>(2009)|non-<br>parametric|Peru|14 distribution<br>companies for the<br>period 1996-2006|DEA|reforms led to<br>improvements in<br>efficiency and<br>productivity|
|---|---|---|---|---|---|
|Perez-Reyes<br>and Tovar<br>(2010)|parametric|Peru|14 distribution<br>companies<br>between 1996 and<br>2006|distance<br>function|incentives lead by<br>the reform process<br>made firms more<br>efficient|
|Pombo and<br>Ramirez<br>(2002a)|non-<br>parametric|Colombia|panel of 33<br>distribution<br>companies from<br>1988-2000|DEA with 2<sup>nd</sup><br>stage<br>regression|reforms improved<br>average efficiency<br>levels|
|Pombo and<br>Ramirez<br>(2002b)|non-<br>parametric|Colombia|panel of 33<br>generation and 12<br>distribution<br>companies 1988-<br>2000|DEA with 2<sup>nd</sup><br>stage<br>regression|technology<br>improvements and<br>regulatory policy<br>had positive effect<br>on average<br>efficiency levels|
|Mello and<br>Espinoza<br>(2004)|parametric|Colombia|panel of 20<br>distribution<br>companies 1999-<br>2003|Free Disposal<br>Hull (FDH)|environmental<br>variables mattered<br>significantly|
|Galan and<br>Pollitt<br>(2014)|parametric|Colombia|panel of 21<br>electricity<br>distribution firms<br>for the period<br>1998-2012|dynamic SFA<br>model|increases in<br>efficiency among<br>rural firms only<br>manifested during<br>the last five years<br>driven by<br>improvements in<br>service quality and<br>energy losses<br>occured|
|Plane (1999)|parametric|Côte<br>d’Ivoire|time-series from<br>1959-1995|stochastic<br>production<br>function|significant but<br>irregular gains<br>from the<br>privatization of<br>management|
|Estache,<br>Tovar and<br>Trujillo<br>(2008)|non-<br>parametric|Southern<br>African<br>countries|12 operators of 12<br>different<br>countries, 1998-<br>2005|DEA|no clear<br>correlation<br>between between<br>adoption of<br>reforms and<br>improvements in<br>efficiency|
|Berg, Lin<br>and Tsaplin<br>(2005)|parametric/<br>non-<br>parametric|Ukraine|24 distribution<br>companies, 1998-<br>2002|stochastic<br>production<br>frontier/DEA|private operators<br>responded well to<br>incentives than<br>public operators,<br>perverse|

29
spouse by cohort of entry (see table 8).<sup>45</sup> The only group for which we find a statistically important impact on employment (after accounting for multiple hypotheses testing) are spouses (who are females in 98% of the cases), who were not employed before 2002, and who (potentially) entered the program after 2004. We reject the null that the effects on early and later cohorts are the same (p-value is 0.02). This suggests that CS promotes the employment of married women. With an average participation of 30%, female labor force participation in Chile is low by Latin American standards, overall and among the poorest sections of the population. In this context, CS coupled with the support of employment programs could constitute an important avenue to improve female labor market participation. The expansion of access to child care might have enhanced these improvements, although there are no reasons to expect that the access to public day care centers exhibits a discontinuity at the effective cutoffs we estimate (Medrano, 2009).

**Other Outcomes** We also study program impacts on four other sets of outcomes: housing, education, health and variables associated with behaviors. The effects we find in most of these outcomes are not statistically significant (even without adjusting inference for multiple hypothesis testing).

There are seven variables related to housing conditions (collected from the CAS), related to connections to the water and sewage networks, ownership of the house, and quality of the walls and ceilings (see table A.9 in Appendix A). We also analyze nine variables (collected from the FPS) concerning school enrolment of children, health coverage of children and adults in the household, and participation in employment centers. The variables where we find a positive impact of participation in CS indicates that all adults aged 65 or above in the family have completed health check-ups (see table A.10 in Appendix A).

# **5.6 Additional Results and Sensitivity Analysis**

In this last section we discuss how robust our results are to changes in the functional form used to model the running variable in our RD and IV estimators, to changes in the window of data we use around each discontinuity threshold, and to explicitly accounting for some dynamics of program participation in our RD estimator. All results discussed here refer to reduced for estimates taken two years after (potential) program entry.

Panel A of Table 9 shows robustness to the choice of functional form of running variable. We present ITT estimates for several specifications: basic (column 1), using cubic in distance to cutoff as running (column 2), the specifications of columns 1 and 2, but without interacting eligibility with distance to cutoff (columns 3 and 4), and using quadratic of CAS as running variable (column

> 45We consider the overall sample (panel A), and also divide it depending on whether the individual was not employed before 2002 (panel B) or was employed (panel C) of table 8.

35
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# **4 Industrial employment and employment losses following technology shocks**

Several characteristics have been identified as being associated with the size and persistence of the impact of technology shocks on employment. Only the change in the share of industrial employment since 1990 is found to be a key determinant of the employment effect at all horizons. This section examines the SVAR and reduced-form evidence on the importance of the industrial employment share and its association with technology-driven employment losses. It then outlines a theoretical model to explain the empirical findings.

## **4.1 Pooled estimation of high and low industrial employment share economies**

This section examines the differences between economies that have experienced higher changes in industrial employment shares in recent decades, and how their employment impact has differed from economies experiencing falling industrial employment using separate panel VAR estimations.<sup>7</sup> All advanced economies in our sample have experienced a fall in the industrial share of employment since the 1990s (Figure 5). Those with a “high” employment share change can be regarded as having experienced the smallest fall in industrial employment shares in the advanced economy cohort. Those with a “high” employment share change include the United States, Australia, Finland, France, and Italy. Those that have experienced a “low” change in the industrial share of employment include Hong Kong, SAR, Singapore, Spain, Ireland, and Slovenia. These economies have more rapidly de-industrialized. In the EMDE sample, more than half of the 96 economies have increased in their share of industrial employment between 1990-99 and 2010-18. These include economies such as Vietnam, China, Thailand, Indonesia, and Oman. Those EMDEs with “low” or falling manufacturing shares include Bulgaria, the Russian Federation, South Africa, and Zimbabwe.

It is clear that the services sector has been the sector where the most new jobs have been created in both advanced economies and EMDEs. However, not all of this change has been generated by technological-displacement. There have been large flows in EMDEs from agriculture to services, and also a component of job flows due to changing income elasticities of demand for these products (Comin et al., 2015; Rodrik, 2016). This additional factor may be the reason that changes and the level of employment in agriculture and services are not found to be related to employment disruption changes following a technology shock; structural change has dominated employment flows in these sectors. This structural change has occurred regardless of whether increasing income has been driven by rising participation rates, employment-augmenting technologies, or employment displacing technologies.

> 7This approach has been used in different contexts to identify characteristic dependent fiscal multipliers (Ilzetzki et al., 2013).

20