rafmacalaba/data-use-mentions-extended huggingface dataset:
both humanitarian agencies and governments are presented soon after the second cycle of the Harmonized Framework (or ‘ _Cadre Harmonisé_ ’) in March. Yet some form of seasonal variation affects household welfare, even in relatively benign years, like the 2018/19 agricultural and pastoral cycle. Deprivation and food insecurity could therefore be better tackled by expanding coverage of regular cash transfers to poor and vulnerable households, especially in rural areas.

Second, and relatedly, temporary social safety nets that are designed to provide relief to poor and vulnerable households during the lean season could be triggered earlier. The month-by-month results suggest that households face welfare losses as early as April each year, and that these losses get worse as the lean season draws on. Pre-empting the lean season welfare losses and responding earlier could have at least two key benefits. First, responding earlier would be more efficient as households would be able to buy food more cheaply; during the lean season itself, food prices tend to be higher (see Figure 9 and Figure 10. **Seasonal variation in staple crop prices in Sahelian countries according to World Food Programme data** 23
|0.087***|
||(0.020)|(0.007)|
|**Baseline probability**|0.009***|0.010***|
||(0.001)|(0.001)|
|**N (Obs.)**|4131|14981|

Source: Authors’ calculations based on TLMPS 2014, ELMPS 2018 Notes: *p<0.05; **p<0.01; ***p<0.001. Standard errors in parentheses. “Market” denotes an outcome of being reclassified from not 3-month market employed to added market employed (compared to either remaining non-employed or already being detected as 3-month market employed). “Subsistence” is a covariate for having been classified as in subsistence work only in the 3-month period.

We now focus on which individual characteristics predict under-detection; which women are we missing in traditional approaches to employment detection? We present marginal effects for the outcomes of being reclassified into market employment and extended work going from the 3- month to enterprise-added definitions in Table 4. These analyses test H3. In the appendix, Table 5 presents employment rates for women by individual characteristics and Table 6 presents these same outcomes descriptively by individual characteristics, as well as separately for urban and rural areas.

The reference, baseline probability for an urban, 15–19-year-old, single, higher educated woman is quite low (less than 0.1% to 0.3%). However, the marginal effect for rural is large and significant, ranging from 1.8 percentage points to 3.3 percentage points increase across countries and market employment/extended work. Compared to the reference 15–19-year-old, older age 19
99 percent level and the R-square of the regressions is 0.67. In column (2), we compute the share of employment at risk of automation based on educational attainments. Also in this case, the coefficient is positive and significant at the 1 percent level and the R-square is 0.65.

The main data source for the labor market analysis is the Indonesia’s labor force survey (Survei Tenaga Kerja Nasional - Sakernas), which is published by Indonesia National Statistics Bureau (BPS). The Sakernas is a cross-sectional dataset with wide national representation undertaken twice a year. The August waves of the survey since 2008 are representative at the regency level, which allows us to construct regency-level labor market measures.<sup>34</sup> Besides information on wages, employment status, sector and work location, the survey also includes information on the occupation of the worker identified according to the Indonesian classification (Klasifikasi Baku Jenis Pekerjaan Indonesia - KBJI). This is compatible with the International Standard Classification of Occupations (ISCO), allowing us to construct exposure to robots measures at the regency level as explained above. We focus on wage employees as those are the workers more directly affected by firms’ adoption of robots. This focus is also relevant from a policy perspective, as wage employment typically provides a more reliable and higher incomes than self-employment in a developing country like Indonesia.

Summary statistics for the labor market data are presented in Table 11. To facilitate the interpretation of the results, we normalize the change of regency ETR to have zero mean and unitary standard deviation in the sample.

Employment increased substantially during the period with a 12 percent mean increase across regencies. Employment growth has been particularly high in services, construction and utility sectors, which were the sectors where most jobs were created as a result of the commodity-driven growth of the 2000s in Indonesia (World Bank (2015)). At the same time, wages doubled in the average regency, partly driven by minimum wage growth, particularly since
.90|0.32|O.42|
|Pas0|Binomial|86.88|90.53|92.30|
||Schwartz|86.57|90.02|92.35|
||Difference_(S)_|0.36|0.57|-0.05|
|P-50|Binomial|69.30|70.22|70.59|
||Schwartz|68.89|70.14|70.58|
||Difference<br>(5)|0.60|0.11|0.01|

Fire Value, v-1000

|||P0.0|0.35|0.70|Dsf. Free|
|---|---|---|---|---|---|
|P-100|Binomial|107.95|<br>109.06|109.58|<br>109.41|
||Schwartz|107.15|100.92|109.40|109.41|
||Difference<br>(1)|0.75|<br>0.15|0.16|<br>0.00|
|P.o0|Blnomial|91.59|<br>92.50|<br>92.42|<br>92.42|
||Schwartz|91.45|92.41|92.60|92.60|
||Difference**()**|0.15|<br>0.10|0.20|<br>-0.19|
|P-50|Binomial|70.64|<br>70.58|<br>70.61|<br>70.61|
||Schwartz|70.39|<br>70.61|<br>70.64|
men on average, and are more likely to be in commerce. De Mel et al. (2009) argue that women see lower returns to capital than men because many of them are operating in industries where the efficient scale is low. Hardy and Kagy (2020) note further that women tend to concentrate in a small number of industries, so that demand is more of a constraint on their growth than access to inputs. Sectors like commerce and hairdressing may have less scope for growth through capital accumulation alone, but face difficulties consolidating into a smaller number of larger firms (Hardy et al, 2024). Even though all our surveys took place at times when markets were open, a further sectorial reason for differences could be if the COVID-19 pandemic hit women-dominated sectors harder, causing 13
10 Finally, we divide the communities based on land scarcity before the 1993 conflict in order to explore the possible role of posterior rules regarding land provision to returnees. Respondents in communities that had more and less pre-war land available have broadly similar attitudes towards return.

# **4. Research design**

# **4.1 The survey**

We collected the data for this project during January to March 2015 as part of a nationwide survey on issues related to migration for the Labour Market Impacts of Forced Migration (LAMFOR) project. The survey had two components. First, a household survey in which 15 households were interviewed in 100 communities (i.e. sous-collines) across the 17 provinces of the country. Second, a community survey in which a local leader was interviewed in each of the 100 communities. The number of communities selected in each province was based on information from the 2008 Census. Figure 4 indicates the location of the communities surveyed.

Figure 4 – Location of communities surveyed in Burundi Note: Geolocation of the 100 communities (i.e. _sous-collines_ ) sampled in the survey. Each community corresponds to a dot. Fifteen households and a local leader were interviewed in each community. The number of communities selected in each province was based on information from the 2008 Census.

In the analysis below we focus on rural areas. In particular, we exclude Bujumbura from the analysis which is the largest city of the country and the centre of commercial activity. Until
This formnulation is useful in tlree ways.

First, it clarifies the role of nutritional standards in poverty line calculations.

Some might believe that nutritional standards eliminate the arbitrariness in settling on a social convention for. what (CE]D poverty ought to be and do so by introducing a technical, physiological given "necessary" level of consumption. This while a convenient function, is a fiction. Rather nutritional standards merely give us a way to discuss and settle on a level of utility below which a household is poor.

Second, as we have shown in on earlier pages (Suryahadi, Sumarto, and Pritchett, 2000) the expenditure function is convenient in thinking about the inter temporal comparisons of poverty as there is a well developed literature on price deflation using the expenditures function.

Third, as we show below, this is a useful way to approach extensions to the definition of poverty.

_Insecurity poor or vulnerability._ A first additional dimension of poverty is that people who may enjoy current expenditures above the poverty line but have a high likelihood of experiencing episodes of poverty. Both quantitative data and people's responses in focus groups of participatory, open-ended approaches indicate that the dynamic of poverty vulnerability is a crucial aspect of how many people experience poverty. The panel data sets indicate a very high variability of the poverty level at the individual level. A recent study on the 100 villages data indicates that of the poor 27
use and production activities as described in equation (1) below: (Eq. 5)

- E  = PM10 emissions

- Ci,j  = Demand for fuel products j in Sector i

- i = Sector (firm, household, government)

- _j_ = Energy (coal, crude oil, refined oil and coal products, natural gas, and electricity).

- αi,j  = Emission coefficient associated with the consumption of one unit of energy product j by the Sector i

- XPi = Production activity and process of sector i βi  = Emission coefficient associated with one unit of output in sector i.

23. Both the consumer demand for energy products (C _i,j_ ) and sectoral economic activity (XP _i_ ) up to 2030 were estimated by the CGE model.

24. First, PM10 emission coefficients are taken from the Garbaccio et al. (2000) study for China. This is presently the only source for these coefficients being mapped

> 20 Emission coefficients vary through time to reflect technological change, modernization of power plants, improved energy efficiency and India‘s emission abatement levels (on the basis of  1% annual increase on average in BAU  reported in WDI statistics).

12
in working there as a researcher.<sup>6</sup> However, the same experiment shows that the short-run impacts on attitudes and knowledge are relatively weak. While we detect some changes in attitude among predetermined sub-groups of interest, we find no evidence of impact on the full sample. On knowledge, reading the new blog seems to improve awareness of findings from recent studies for the average reader, but not for the marginal reader who only reads because of encouragement. To our knowledge, this paper provides the first quantitative evidence on the impacts of economics blogs and covers a number of the more important likely channels of influence.

The rest of the paper is as follows. Section 2 presents the impacts of blogs on dissemination of economics research using event study analysis, and Section 3 provides evidence on the effect of blogs on the reputation of their creators. Section 4 uses our new survey data to describe how blogs are used by graduate students in economics, junior faculty, and field workers in development. Section 5 presents impacts of reading a new blog, exploiting the randomized encouragement design. Section 6 provides a discussion of whether economics blogs influence policy, while Section 7 concludes.

# **2. Event Study Analysis of Dissemination Impact**

One of the main purposes of economics blogs is to help better disseminate economic ideas and research: both to other economists and to the broader public. The impact of some of this effort is very hard to measure – for example, many economics blogs have devoted considerable space to discussing public policy issues in the U.S. such as different plans for dealing with budget deficits, but it is difficult to assess how much any one particular blog post has influenced this debate. However, one area of research dissemination that is potentially important

> 6 McKenzie and Özler are two of the four researchers who produce the “Development Impact” blog.

5
variation in the _ex-ante_ costs and returns to migration but does not otherwise affect the household’s educational investment decision.

Our paper is, to our knowledge, the first to document a negative causal effect of parental migration on such informal payments to teachers. We show that the reduction in petty corruption occurs even though migrant households are, on average, wealthier than their non-migrant counterparts. This suggests that the income effect is overcompensated by other channels. Schoollevel variation indicates strong spillovers within schools, that could partly be due to social remittances (i.e., migrants affecting the opinions of those left behind) and partly due to migrant families’ behavior leading to a breakdown of the social norm of taking part in petty corruption. The results are neither explained by differences in public school funding nor by differences in the share of migrant children across schools. The money saved on informal payments to teachers does not translate into higher spending on out-of-school tutoring (henceforth: tutoring), which is an alternative way of teachers to make up for lost informal wage supplements. Rather, we find some evidence that main caregivers allocate more time to educational and school-related activities in migrant households. The reduction in informal payments might be explained by access to information or value change due to migration - a literature which finds that the migration experience can alter migrants’ and their left behind families’ political values, social norms, and behavior in general.<sup>3</sup> Since the underlying preferences and beliefs about the spread of corruption are unobserved, this remains a tentative hypothesis. We are, however, able to rule out several alternative explanations: income-effects, the valuation of education, non-parental caregivers, and several supply side factors, which we measure using matched school budget data, community level data as well as additional parts of the survey.

The remainder of the paper proceeds as follows. Section I anchors the paper in the literature. Section II provides information on Moldova and corruption in education. Section III describes the data used, and
, a growing body of literature has shown that the relationship between intrahousehold distribution of bargaining power, social norms on gender roles, and labor supply is context-dependent, especially as far as developing countries are concerned (Duflo and Udry, 2004; Ashraf et al.,2009; 2014; 2016; Bau, 2016; Heath and Tan, 2020; Field et al., 2021). Moreover, by using micro data, we explore pathways between micro-level gender-based discrimination and inequalities and macro-level outcomes, adding to the growing micro-founded literature that relates

> 6 Marriage markets in countries of the Middle East and North Africa differ from other countries where most research on the canonical collective household labor supply is developed and can be characterized by underdevelopment and missing markets (Anukriti and Dasgupta, 2017). Marriage rates are substantially higher, mate selection often involves family arrangements, divorce legislation is limiting to women and social norms about gender roles tend to determine spousal relations.

3
# **2.5 Parental Support at School**

The publication of the bestselling book [Chua, 2011] “Battle Hymn of the Tiger Mother” in 2011 ignited a firestorm of controversy. The book gave prominence in popular culture to a vast academic literature regarding parenting styles and the perceived higher performance of children from Asian immigrant families in the US and other Western countries. One of the ways that parents influence their children’s educational outcome is through the interaction that parents have with their child’s teachers and others at school. The PISA data includes a question that tries to examine parental expectations towards schools. The question SC24 includes a statement “There is _constant pressure_ from many parents, who expect our school to set very high academic standards and to have our students achieve them.”<sup>6</sup> Table 5 indicates a higher level of PARPRESSURE (an index derived from SC24) for Vietnam, compared to Dev7. Another question (SC25) asks school principals about the proportion of parents that take part in a set of 12 activities. While the question does not specify which parent (or both) may be involved, the variables, that may contain more than one of these activities, have been named after the mother for ease of exposition.

**Table 5: Parental Support at School**

|||Dev7 co|untries|Vietn|am|
|---|---|---|---|---|---|
|Variable|Description|MS|Valid N|MS|Valid N|
|PARPRESSURE|Parental achievement<br>pressure|0.2665<br>(0.4421)|40372|0.3837<br>(0.4863)|4866|
|TIGERMOM|Parent initiates -<br>progress discussion|52.4472<br>(38.097)|41394|62.4183<br>(41.3743)
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.89*** 0.92*** 0.78***<br>H<br>Kendall tau-b 0.73*** 0.8*** 0.69***<br>Spearman 0.93*** 0.89*** 0.78***<br>A<br>Kendall tau-b 0.81*** 0.72*** 0.69***<br>Spearman 0.94*** 0.93*** 0.78***<br>M0<br>Kendall tau-b 0.81*** 0.81*** 0.68***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C7.1. FOSD: Counting Vector C7.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C7.3. SOSD: Social Sustainability Index C7.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k
sup> members, as well as an online panel respondents of gig workers. The data collection period is February 2022 – May 2022.

The online survey questionnaire is divided into four parts: (i) respondents’ demographic background, which asks details on age, gender, schooling and marital status; (ii) employment characteristics, which asks details on respondents’ current employment such as their status in employment, occupation, duration of employment, weekly pay, social security coverage, and other occupations if they are working more than one job; (iii) an experimental section on choosing between two hypothetical jobs; and lastly (iv) preferred work characteristics, which asks respondents’ preferences on different aspects of work characteristics using a 5-point Likert scale from “Very Important” to “Not important”. Some examples of work characteristics include earning good money, the ability to work in international settings/teams, insurance coverage for workplace injuries and pension income upon retirement.

# **4 Methodology**

## **a. Research Question**

This paper focuses on three inter-linked research questions:

> 4 The contingent valuation method directly asks people for valuations of different attributes, while in discrete choice experiments, people are given the choice of two or more scenarios, in which attributes and prices/values are randomly varied, and are asked to choose their preferred option. Mas and Pallais (2017) discuss some of the different considerations with regard to the two methods.

> 5 GLOW is a training program offered by MDEC for individuals interested in becoming a digital freelancer. During the peak of COVID-19, MDEC created a new program called GLOW _Penjana_ , a highly targeted program to help individuals whose livelihoods were affected by the COVID-19 pandemic and movement restrictions.

5
Third, the high consumer need for essential goods and services accelerated human pressure on the environment and its resources, especially for fuelwood and bamboo extraction. Prominently, the fuelwood demand for domestic household energy use contributed to forest area loss (Bdnews24.com, 2017; Paul, 2017; Tani & Rahman, 2018). According to Uddin and Khan’s (2007) study, the average family in the area consumes 6kg of fuelwood per day (Tani & Rahman, 2018). The annual per capita fuelwood consumption in Teknaf was estimated at 1,168 kg (Tani & Rahman, 2018). Before the introduction of the pilot phase of the liquefied petroleum gas (LPG) project in August of 2018, an estimated 6,800 tons (6,800,000 kg) of fuelwood was collected monthly by the Rohingya refugees (UNDP & UN Women, 2018). Scaled up in February 2019 to include more refugee and host population households, the LPG project is anticipated to reduce the local demand for firewood use for cooking. The LPG program, according to UNHCR, has resulted in an “80% reduction of demand for firewood in the Rohingya households in the camps, reducing deforestation to well within sustainable forestry rates, while the overall demand for firewood in the area has dropped to well below pre-influx levels” (UNHCR, 2020c). Additional research is needed to use high-resolution satellite imagery to precisely measure and evaluate the environmental impact of the LPG project in the region.

Besides fuelwood, both Rohingya refugees and the local population rely on bamboo sticks from the forests for shelter construction and other uses. Data from a recent shelter assessment survey shows that 38 percent of all assessed shelters are in “bad condition,” 59 percent are in “moderate condition,” and only 3 percent are in “good condition.” As the primary building material, nearly 99 percent of all refugee shelters currently required both “muli” and “borak” bamboo types for shelter improvement (UNHCR, 2020a).
Chinese imports, the value of this measure is 0, while if all the products face competition from Chinese imports and China’s import share in all these products is close to 1, this measure is close to 1.

_NTMExposurei_ 0 _,t_ : The measure of NTM exposure of the firm _i_ selling product _g_ in month _t_ . The measure creates an exposure variable by combining the initial weights of different HS-10 products in a firm’s import basket with product-level changes in NTM exposure over time at the HS-10 level. We combine the trade data with monthly data on NTMs applied by Indonesia on imports at HS-10 digit level. The NTM exposure for exporter _i_ is weighted using an invariant weight to reduce the potential endogeneity bias. It is computed as the share in value of HS-10 products that are subject to the NTM in question as a share of total imports on the first year of trading. Thus, the exposure is computed as follows: _j_ is the HS product, _NTMjt_ = 1 if an NTM on HS-10 product _j_ was in effect in month _t_ and 0 otherwise. _IMPij_ =<sup>�</sup><sup>_t_</sup> _t_<sup>=</sup> =<sup>_m_</sup> _m_<sup>12</sup> 1<sup>_Importsijt_andistheinvariantweightofproduct</sup><sup>_j_in</sup> exporter _i_ imports. _m_ 1 is the first month of the year that firm _i_ joined the export market and _m_ 12 is the last month of that year. By definition, this weight will only have values for exporting firms that also import directly. For the aggregate NTM measure in Section 5.1, the NTM dummy ( _NTMjt_ ) is 1 any of the 60+ NTMs in our data was in effect. For the specific, trade distorting NTMs discussed in Section 5.2, _NTMjt_ = 1 if that specific measure was in _TariffExposurei_ 0 _,t_ : Similar to the _NTMExposurei_ 0 _,