rafmacalaba/datause-encoder-data huggingface dataset:
**Table 12: L2CU Life Satisfaction Transition Matrix**

||Not<br>Satisfied|2|3|4|Complete<br>Satisfied|Total|
|---|---|---|---|---|---|---|
|Not<br>Satisfied|20.51|25.64|44.44|5.98|3.42|100|
|2|1.32|26.64|68.59|1.81|1.64|100|
|3|0.43|3.56|86.11|6.81|3.08|100|
|4|0.13|0.76|54.45|33.59|11.07|100|
|Complete<br>Satisfied|0.47|1.18|52.88|16.33|29.14|100|
|Total|0.91|8.01|74.67|7.64|8.76|100|

These transitions can be analyzed using a similar fixed-effects framework to that of the concluding results presented in the preceding section. The goal is to estimate a regression of the following form: <mark>SWBit = αi + βOWBit + βIit + εit Eq. 2</mark> Where _SWBit_ is a measure of individual _i_ ’s well-being at time _t_ , _αi_ is the time-invariant individual-level component of subjective well-being, _OWBit_ is a set of “objective” welfare variables for individual _i_ at time _t_ , and _I_ is a set of time-varying individual- and household-level control variables, where appropriate. The results of these estimations are presented in Table (13). The table is split into two parts: the first three columns present results for which the dependent variable is the value of the respondent’s life satisfaction at time _t,_ with a response of 1 indicating the lowest value of life satisfaction, and 5 indicating the highest. Thus positive (negative) coefficients in the first three columns would suggest that a change in the variable of interest is associated with a rising (falling) level of life satisfaction. In contrast, columns 4-6 report results using a binary variable that is equal to one if the respondent provided a “dissatisfied” response (a value on the life satisfaction ladder of 1 or 2). Thus positive (negative) coefficients in the last three columns would suggest that an increase (decrease) in the variable of interest is associated with a falling (rising) level of life satisfaction.

Column (1) shows that households in which at least one person began to consider migration in the latest round of the survey have significantly lower life satisfaction on average, after controlling for month of interview and the household fixed effect. However, column (2) shows that those households that have moved on from _merely considering_ to _actually sending_ migrants abroad no longer exhibit significantly lower life satisfaction. Column (3) suggests that households that are in daily contact with migrants have significantly higher life satisfaction. Columns (4-6) report consistent results when focusing specifically on households with low life satisfaction: column (4) suggests that households with members considering migration are more often to fall into dissatisfaction, but that this is not the case for current migrants (column 5). Likewise, column (6) reports that households in frequent contact with migrants are significantly less likely to be dissatisfied.

Table (14) reports the association between sending remittances (among those with current migrants), demonstrating that shocks are not correlated with when remittances are sent (columns 3-7), but that they are strongly associated with subjective well-being indicators (columns 1-2).

27
7 impossible. Of course, going back to the original data preaents its own kind of problems.

For the comparison over tmne, households are classified into expenditure par capita quantiles (poorest 40%, middle 30% (40%), top 30% (20%) to match the earlier groupings) and by urban/rural and Java/Outer Islands area of residence. Utilization is recorded, aggregated across each subgroup, and the relevant shares compared to the 1978 outcomes. The paper first turns to a discussion of utilization incidence. In the next section, the utilization of education services are examined, while in the follcting the health facilities situation is explored.

# 3. Utilization of Education Services

# **_The_** _Picture in 1987_

The 1987 SUSENAS indicates an overall primary school attendance rate -- the proportion of children aged 7 to 12 attending school -- of 93 percent. For children aged 13 to 15 (ages correp-londing to the junior secondary schooling level) the rate is 75 percent. The drop out rate is then quite rapid -- to 49 percent in the 16 to 18 age bracket (senior secondary school), and 12 percent in the 19 to 25 group (university) (Table 1).8 However, these aggregates hide variation across consumption groups and regions. Table 1 presents the attendance rates stratified by subgroups defined by region of residence, schooling level, and by quantiles of per capita expenditures.

Proportions in rural areas are consistently lower than those in urban areas. Incidence in the rural Outer Islands tends to exceed that of rural Java. This is also true for the urban Outer Islands at levels beyond

> 8 There -are significant numbers of over and underage students at each schooling level. Unfortunately, it was not possible to determine the net enrollment rates, defined as the proportion of the relevant school age group going to the appropriate schooling level for that age, from the SUSENAS data for 1987. The data given here refers to the proportion of each age group enrolled at any level of schooling.
# **Two Decades of Top Income Shares in Honduras**<sup>*</sup>

Giselle Del Carmen Santiago Garriga Enodo S.A. CEFIP, IIE-FCE, Universidad Nacional de La Plata Wilman Nu ̃nez Thiago Scot Servicio de Administraci ́on de Rentas (SAR) DIME, World Bank JEL CODES: H23, H31, H71, I38, J31, J32, J33 KEYWORDS: inequality, top income, administrative registries, Honduras

> *Del Carmen: gdelcarmen@enodohn.com; Garriga: santiago.garriga@econo.unlp.edu.ar; Nu ̃nez: wponce@sar.gob.hn; Scot: tscot@worldbank.org. This project was made possible by extensive collaboration with SAR, the tax authority in Honduras. We extend special thanks to Christian Duarte, Oziel Fern ́andez, Edgardo Enrique Hernandez, Milton Maldonado, Marlon Ochoa, Mario Enrique Pineda, David Pinto, Jose Carlo Berm ́udez Sanchez and Alesandra D ́ıaz Tovar from SAR for their collaboration. Gabriel Oqueli, Malena Dolcet and Santiago Rodrigo Cesteros provided invaluable research assistance. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.
<mark>Jain, Ritika (2021). "Information and Communication Technology Adoption and the Demand for Female Labor: The Case of Indian Industry"</mark> _<mark>The B.E. Journal of Economic Analysis & Policy</mark>_ <mark>, 21(2): 695-722. https://doi.org/10.1515/bejeap-2020-0295</mark> <mark>Jayachandran, Seema, (2021), Social Norms as a Barrier to Women’s Employment in Developing Countries,</mark> _<mark>IMF Economic Review</mark>_ <mark>, 69, issue 3, p. 576-595.</mark> Juhn, Chinhui, Gergely Ujhelyi, Carolina Villegas-Sanchez (2014), “Men, Women, and Machines: How Trade Impacts Gender Inequality,” _Journal of Development Economics,_ 106: 179-193.

Karshenas, M., Moghadam, V.M., & Alami, R. (2014). Social policy after the Arab Spring: States and social rights in the MENA Region. World Development, 64, 726–739.

<mark>Klasen, Stephan (2019), What Explains Uneven Female Labor Force Participation Levels and Trends in Developing Countries?,</mark> _<mark>The World Bank Research Observer</mark>_ <mark>, 34(2): 161–197.</mark> <u><mark>https://doi.org/10.1093/wbro/lkz005</mark></u> <mark>Klasen, Stephan, TU THI NGOC Le, Janneke Pieters, and Manuel Santos Silva (2021) What Drives Female Labour Force Participation? Comparable Micro-level Evidence from Eight Developing and Emerging Economies,</mark> _<mark>The Journal of Development Studies</mark>_ <mark>, 57(3): 417442, DOI: 10.1080/00220388.2020.1790533</mark> <mark>Kodama, N, Javorcik, BS, Abe, Y. (2018), Transplanting corporate culture across international borders: Foreign direct investment and female employment in Japan.</mark> _<mark>World Economy</mark>_ <mark>41: 1148– 1165. https://doi.org/10.1111/twec.12612</mark> <mark>Lee, Kye Woo and Cho, Kisuk, (2005), Female labour force participation during economic crises in Argentina and the Republic of Korea,</mark> _<mark>International Labour Review</mark>_ <mark>, 144, issue 4, p. 423-450.</mark> <mark>Mammen K, Paxson C (2000) Women’s work and economic development. J Econ Perspect 14(4):141–164.</mark> Macmillan, R., Shofia, N., & Sigle, W. (2018). Gender and the politics of death: Female representation, political and developmental context, and population health in a cross-national panel. _Demography_ , _55_ (5), 1905–1934.

Maoz, Z., & Henderson, E. A. (2013). The world religion dataset, 1945-2010: Logic, estimates, and trends. International Interactions, 39(3), 265–291.

<mark>Mishra, Ankita, Mishra, Vinod and Parasnis, Jaai, (2021), The asymmetric role of crime in women's and men's labour force participation: Evidence from India,</mark> _<mark>Journal of Economic Behavior & Organization</mark>_ <mark>, 188, issue C, p. 933-961.</mark> Nelson, Robert L. and William P. Bridges (1999), Legalizing Gender Inequality: Courts, Markets, and Unequal Pay for Women in America, Cambridge University Press.

45
The result is robust to a variety of empirical strategies - difference-in-difference (DID) and event study design accounting for staggered treatment (following Callaway and Sant’Anna 2021) - where sanctioned firms are matched to non-sanctioned firms with similar initial firm characteristics and patent portfolios, by applying propensity score weights. The event study plots suggest common (pre-sanction) trends between our treatment and matched control group firms, and we show balance also in covariate levels.

To investigate the underlying mechanism, we draw upon existing literature on the importance of U.S. collaborations for Chinese research (Veugelers 2017; Aghion et al. 2023). We first show that Chinese firms’ innovation output is positively correlated with collaboration with U.S. inventors, more so than collaboration with inventors in Europe or advanced East Asia. Secondly, inclusion in the Entity List reduces Chinese firms’ U.S. collaborations in their subsequent patenting. Finally, the decline in patent output due to inclusion on the Entity List is driven entirely by Chinese firms with prior U.S. collaborators.

We also examine whether domestic innovation capacity can mitigate the effect of sanctions and find some evidence in support of this hypothesis. Specifically, more innovative firms, i.e., those with a higher initial patent stock, suffer a smaller decrease in patent output following the imposition of U.S. sanctions. We also examine the role of technological distance from the U.S., by the stock of Chinese patents relative to the U.S., as done by Akcigit et al. 2024. Firms in sectors with a smaller technology distance to the U.S. experienced a smaller decline in patent output compared to firms in other sectors.

After examining the impact on sanctioned firms, we consider spillovers on non-sanctioned firms in China and the U.S. that operate in the same primary technology fields as sanctioned firms.<sup>2</sup> We find that the influence of the U.S. Entity List extends beyond the directly targeted firms. The treatment group consists of firms operating in the sanctioned field, while the control group includes similar firms in non-sanctioned fields matched using propensity score

> 2To estimate these spillover effects, we assign firms to a single technology field using the most common field of their patents. Sanctioned technology fields are defined using the sanctioned firms’ primary technology fields.

2
km from a highway, and districts over 50 km from a highway. In an alternative setup, the last distance band is further broken down into three bands: districts located 50-125 km from a highway, districts located 125-200 km from a highway, and districts over 200 km from a highway.

In our empirical work, our core focus is on the non-nodal districts of a highway. We measure effects for nodal districts, but the interpretation of these results will always be challenging as the highway projects are intended to improve the connectivity of the nodal districts. For the GQ network, we follow Datta (2011) in defining the nodal districts as Delhi, Mumbai, Chennai, and Kolkata. In addition, Datta (2011) describes several contiguous suburbs (Gurgaon, Faridabad, Ghaziabad, and NOIDA for Delhi; Thane for Mumbai) as being on the GQ network as “a matter of design rather than fortuitousness”. We include these suburbs in the nodal districts.

We finally consider distance from the nodal points of the GQ system to each district. We measure these distances from the four main nodal districts using a straight line formulation. We consider the contiguous suburbs to have zero distance for this exercise. Roughly half of the nonnodal districts that lie with 0-10 km of the GQ highway system are also within 350 km of a nodal point. The remaining districts are more remote, and we use this point for our demarcation.

# _Urban and Rural Regions of Districts_

The ASI and NSS surveys identify for each establishment whether it is in an urban or rural location. Our study considers changes in urbanization over time, and thus the stability and comparability of this survey question across rounds are very important. The statutory definition of an urban setting during our period of study is:

- (a) All statutory places with a municipality, corporation, cantonment board or notified town area committee, etc., or

- (b) A place satisfying the following three criteria simultaneously:

- i) A minimum population of 5,000; ii) At least 75% of male working population engaged in non-agricultural pursuits; and

- iii) A density of population of at least 400 per sq. km. (1,000 per sq. mile).

This definition is stable through our time period. Our 1994 and 2000 surveys follow a 1991 classification of places based upon these criteria, while the 2005 and 2007 surveys follow 10
periods of school enrollment data before the departure, the baseline (2002) and the year before (collected retrospectively at the baseline). We cannot reject the possibility that schooling trends are the same in both types of households, as indicated by the statistically insignificant coefficient of the interaction between the type of household (“absence”) and the year dummy in the upper panel of table 3. As a second check for common trends, we compare trends in household per capita income in both types of households before the father departed. We have three periods of income data before the departure, all collected retrospectively at the baseline. The evolution of per capita household labor income in the years 1999, 2000, and 2001 is similar across both types of households prior to the father's departure (lower panel of table 3). This finding gives us no reason to believe that they would have differed if departure had not occurred.

It is also notable that the signs of the estimates in table 3 point to a positive selection of families with absent fathers in terms of schooling and income trends (although the selection is not statistically significant).<sup>13</sup> In this worst-case scenario, we would underestimate the magnitude of the “true” impacts of departure. To anticipate our main results, the detrimental impacts of the permanent absence of the father on school enrollment and work would be even more pronounced than what we find in our study.

As an additional exercise to build confidence in the quasi-random nature of the father’s absence, we determine whether current child activities are correlated with the future\e absence of the father; that is, _future_ absence should not lead to a significant effect on current activities if departure is random. To do so, we regress current children’s activities (schooling/work at time _t_ for _t_ = 1,2) on future absence (at time _t_ + 1).

13
# **Table 8.  Effects for manufacturing and services firms**

||**Lgrowth**<br>||**LPgr**<br>|**owth**|
|---|---|---|---|---|
||**Coef**|**se**|**coef**|**se**|
|Corruption obstacle|**-0.010**|0.007|**0.014**|0.015|
|Corruption obstacle * service|**-0.012***|0.006|**-0.003**|0.012|
|Outage|**-0.002**|0.007|**0.014**|0.015|
|Outage * service|**0.001**|0.006|**-0.039****|0.014|
|Land access obstacle|**0.003**|0.008|**-0.006**|0.016|
|Land access obstacle * service|**-0.023***|0.009|**0.005**|0.019|
|Overdraft facility|**0.009**|0.006|**0.041****|0.014|
|Overdraft facility * service|**0.012**|0.006|**-0.029***|0.013|
|Trade credit|**-0.006**|0.009|**0.009**|0.016|
|Trade credit * service|**0.000**|0.009|**0.059****|0.017|
|Labor regulation obstacle|**-0.010**|0.008|**0.011**|0.016|
|Labor regulation obstacle * service|**0.029****|0.009|**-0.026**|0.018|
|In Big City|**0.002**|0.003|**-0.005**|0.005|
|In Big City * service|**0.009****|0.003|**0.014***|0.006|
|Other controls as in Table 4 panel B|Yes||Y|es|
|N|51,685||38,|120|
|Country, industry, year FEs|Yes||Y|es|
|adjusted R-squared|0.117||0.2|71|

*, ** represent statistical significance at the 5 and 1 percent levels.

Heteroskedasticity-corrected standard errors clustered at the country level in columns indicated by “se”.

Those variables without significant interaction terms with the two income dummies have similar coefficients as in Table 5 and are not reported.

42
Relying on detailed farm-level data on shocks (both related and not directly related to agriculture) and the use of extension services from Sub-Saharan Africa, we then demonstrate that empirical observations are consistent with the predictions of our model.<sup>5</sup> In particular, we show that there is statistically strong and robust evidence that, in line with the model, non-agricultural shocks are indeed associated with increased demand for extension. For this purpose, we split shocks into two categories, namely shocks that are directly related to agriculture (e.g., pest outbreaks and irregular rainfalls) and shocks that are not directly related to agriculture (e.g., shocks to farmers’ wealth or health). In addition, our data allow us to separately identify extension services that are actively requested by farmers from unsolicited extension services of which the household is merely a passive recipient.

Given that the agricultural and non-agricultural domains are likely not separable in the context we study (see Benjamin, 1992; LaFave and Thomas, 2016), one should be worried that there may be other channels than the one implied by our model that account for the observed link between non-agricultural shocks and demand for extension. Our data allow us to assess the empirical plausibility of several other potential channels in this context, but we do not find strong evidence in favor of those alternative explanations. In particular, there is evidence that suggests that the effect of non-agricultural shocks is not due to an increased need for credit (e.g., driven by shocks to non-farm income and remittances) or because non-agricultural shocks (some of which may affect household labor supply) work through a demand for advice on labor inputs. Overall, our modeling insights are more broadly applicable and the empirical support for the model provided in this paper suggests that scarce attention may be part of explanations for phenomena in economic development, including beyond the specific application studied here.

The remainder of the paper is organized as follows. Section 2 discusses related literature and provides further background on agricultural extension services. Section 3 presents the model and derives predictions about farmers’ demand for agricultural advice. Section 4 provides empirical evidence on the predictions of the model and addresses alternative explanations. Section 5 concludes.

# **2 Related literature and background on extension services**

Our paper contributes to several different literatures. First, we add to the recent literature in development economics that tries to explain low levels of technology adoption and low input use in agriculture, in particular in Sub-Saharan Africa. Various channels have

> 5 The empirical work is based on survey data from the Living Standard Measurement Study - Integrated Surveys on Agriculture (LSMS-ISA) database (World Bank, 2018).

4
# 2. Data and methodology

There now exists a large literature on the measurement of multidimensional poverty offering a variety of measurement methodologies; see Alkire et al. (2015) for a recent survey. Cutting across these different approaches, however, there are broadly three sets of issues that need to be addressed in any measurement framework for multidimensional poverty. These relate to:

- 1) _Dimensions_ ?  Which indicators should be included in a multidimensional poverty measure?  What sort of dimensional cut-offs should be used to define deprivation in different dimensions?

- 2) _Weights_ ?  How should the different dimensions be weighted?

- 3) _Identification and aggregation_ ?  How should dimensional deprivations of individuals be aggregated into an overall measure of multidimensional poverty?  This includes the issue of how the multidimensionally-poor should be identified, in particular the use of a cross-dimensional (or poverty) cut-off as for instance in the counting approach of Alkire and Foster (2011), as well as the issue of how the multiple deprivations of those identified as poor should be aggregated over the population (or a sub-population).

We discuss our approach to considering different options for each of these issues below, though, as mentioned in the Introduction, a key focus will be on alternative approaches to assigning weights to different dimensions and on the use of alternative poverty measures.<sup>6</sup>

## 2.1 Dimensions and indicators

Our choice of dimensions and indicators to be included in the measurement of multidimensional poverty is guided both by common empirical practice as well as particular data considerations for the Philippines. Since multidimensional poverty is concerned with the joint distribution of deprivations in different dimensions, a key data consideration is the ability to observe deprivations across these dimensions for the _same_ set of households or individuals. This limits the range of data sources that can be used for such analysis, and conversely, a given data source may limit the set of includable dimensions to those that can be readily measured from that source. In this paper, we use data from six rounds of the Annual Poverty Indicators Survey (APIS). With the exception of anthropometric information which it does not have, the APIS is quite well-suited to the task of multidimensional poverty measurement. As

> 6 Most of the literature has concentrated on the identification and aggregation issues with the choice of dimensions and weights having remained relatively under-explored.

5