rafmacalaba/datause-encoder-data huggingface dataset:
_For labor or for divorce?_ gender inequalities to macroeconomic growth and development outcomes (Seguino, 2010; 2013; 2020; Santos Silva and Klasen, 2021).

Women face several barriers to enter the labor force, including discriminatory labor laws, inadequate maternity leave policies, and very limited legal social protection against workplace harassment and gender-based violence (World Bank, 2021). Cultural factors also play a significant role, as traditional gender roles and societal expectations often discourage women from pursuing careers and limit their mobility (Jayachandran, 2015; Heath and Tan, 2020; Field et al., 2021). As such, deeply ingrained stereotypes, and biases, still highly present in the MENA region, perpetuate the belief that women are primarily responsible for household chores and child-rearing, creating an unequal burden of unpaid work and limiting their ability to engage in paid employment. On a macroeconomic level, and throughout the economic literature, the U-shaped labor force participation curve hypothesis is a stylized depiction of the relationship between female labor force participation and economic development (Goldin, 1995). Although this hypothesis is widely spread in cross-country analysis of the subject, some studies such as Gaddis et. al (2013) or Eastin & Prakash (2013) find no or weak evidence of this relationship. They conclude that an S-shaped relationship is more likely to exist and that through this paradigm, the “dangers of the middle phase” should urge to put in place new norms and new institutions promoting women in the workforce (Eastin & Prakash, 2013). Thus, at a macro-level, economic and institutional barriers combine to create a complex web of challenges that impede female labor force participation, exacerbating gender inequalities and hindering economic growth and development as whole.

At a micro-level (individuals and households), women’s labor force participation is found to have an inverse U-shaped function of bargaining power (Moeeni, 2019). Accordingly, the importance of intra-household bargaining power dynamics to female labor force participation decisions can be explained by the not-so-rare men’s opposition to the labor participation of their spouses. Asaad and al. (2017) show that women’s employment in the MENA region is affected by early marriage and find that marriage by the median age reduces the probability of working for women. In this regard, the reform of legislation on divorce can constitute a strong shock to the risk of divorce making it easier and, subsequently, altering the perceived risk of marital dissolution which alters intrahousehold relations and decisions (Stevenson and Wolfers, 2006; Stevenson, 2007; Alesina and Giuliano, 2007). Literature suggests that enhancing the overall legal framework potentially increases women’s bargaining power within the household, 4
discuss how omitted variables, especially caused by measurement error in the tariff data and omitting controls for general trends, biases the NTB effect upwards.

# **2.1 Identification Strategy**

To answer the question whether regional trade agreements (RTAs) lower non-tariff barriers (NTBs) we follow the empirical strategy put forward by the literature, i.e., Aichele et al. (2016), Dhingra et al. (2021), and Felbermayr, Gröschl, et al. (2018). The basic idea of the _indirect approach_ is to estimate RTA effects using the gravity model while controlling for changes in tariffs to disentangle between the effect of tariffs and NTBs on trade. In this fashion, NTBs can be interpreted as _residual_ policy measures that explain the observed pattern of bilateral trade flows given tariffs. A key assumption is that RTAs do not change bilateral preferences between trade partners for their respective goods and services. Using structural gravity, changes in trade costs due to RTAs can, thus, be indirectly identified from trade data.

Next, we formally derive the main estimation equation starting with a structural gravity equation for bilateral trade flows that explicitly models changes in tariffs and NTBs due to RTAs. Head and Mayer (2014) show that a broad class of general equilibrium trade models with constant elasticity of substitution aggregation leads to a gravity equation of the following form: Here, _Xijst_ equals the value of exports of country _i_ to country _j_ in sector _s_ at time _t_ ; _Yist_ is country _i_ ’s value of production in sector _s_ ; _Ejkt_ is country _j_ ’s expenditure in sector _s_ ; _Yst_ is the value of global output. The terms Ω _ist_ and Ω _jst_ are the multilateral resistance terms and account for the effects of third countries’ trade costs on _i_ ’s exporting and _j_ ’s importing behavior (Anderson and Van Wincoop 2003); _σ >_ 1 is the elasticity of substitution among goods from different countries. Sectors _s_ are defined as broad sectors, i.e., agriculture, manufacturing, and services. For some specifications, we also do the analysis at a finer level of aggregation.

Finally, _T ijst ≥_ 1 denotes bilateral trade costs between partners _i_ and _j_ . The total trade costs _T ijst_ can be decomposed into two parts: _i)_ ad valorem tariffs denoted by the tariff factor (1 + _τijst_ ) and _ii)_ other bilateral non-tariff trade costs Φ _ijst ≥_ 1.

5
# **A.3.2 Delineating Daesh territorial control**

Daesh territorial control spanned from the east of Fallujah to the north of the city of Aleppo [26]. Most of the oil assets in Syria under Daesh control are in the eastern part of the country, i.e. in the governorates of Deir Ezzour and Hassaka, and to a lesser extent Raqqa. In Iraq, Daesh controls the Ajil field and oil wells in the Hamrin Mountains, the Qayara and Najma fields, and had access to parts of the Baiji refinery until October 2015. We assign control of fields by determining the date at which a site is taken by or away from Daesh using news reports in both English and Arabic and verifying this assignment with maps published by the U.S. Department of Defense, the New York Times, and the Institute for the Study of War.

**Deir Ezzour:** In this governorate, Daesh consolidated its control of the western part of the governorate within a few days. CNN reports the seizure of Al Omar field on July 3rd 2014 [13], while Al Arabiya indicates that Tanak field fell under Daesh control on July 4th [4]. The rest of the western fields are not covered individually by news reports. Asharq Al Awsat announced that major fields in the governorate had been seized by July 11th [12]. The hegemony of Daesh over that part of the governorate is confirmed around that time also; we thus assign the fields of Jaffra, Izba, Sijan, Abu Hardan, and others using the date of control of either the closest major field or the district capital. Al Arabiya reports that Al Mayadin city fell under Daesh control by the end of June, while Al Hayat and other sources report that Abu Kamal did so on July 1st [1]. Finally the city of Deir Ezzour was contested for a while, and Al Jazeera announced on July 14th that the group seized control of a number of neighborhoods in this city, chasing out the opposition and other Islamist groups [6]. On July 15th, the Syrian Observatory for Human Rights reported that 90 percent of the governorate had gone under Daesh control [7] and Al Jazeera announced that the group had seized all the fields in the governorate [22]. On the Eastern side, there are only two major fields: local websites and social media feeds report

31
# **Figure B15: TSP**

<!-- Start of picture text --> Log scale<br>7.5<br>Log price Permanent component<br>7.0<br>6.5<br>6.0<br>5.5<br>5.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>1.5<br>Medium-term cycle Transitory component<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 56 —_
Policy Research Working Paper 7696

# **Abstract**

Little is known about innovation in developing countries, partly because of the lack of comparable and reliable data. Collecting data on firm-level innovation is challenging because of the subjective definition of what determines an innovation, a problem that is exacerbated in developing countries where innovation is likely to be more incremental and less radical. This paper contributes to the literature by presenting the results of an experiment aiming to identify the survey instrument that better captures firm-level innovation in developing countries. The paper shows that a small set of questions included in a multi-topic, firm-level survey does not provide an accurate picture of firm-level innovation and tends to overestimate innovation rates. Issues related to framing explain some of the unreliability of innovation responses, while cognitive problems do not appear to play a significant role.

This paper is a product of the Trade and Competitiveness Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at xcirera@worldbank.org and smuzi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
O.A.M.D.G.

Given the data we have available, i.e. the original SISBEN database from 1993, and the later _Familias_ survey from 2001, we can attempt a correction for sample selection in some of our specifications. We have the original household population from which the _Familias_ sample was drawn: the SISBEN register. This is a c20,000 household register of all beneficiaries of the national welfare system, which are in the lowest income stratum, ‘SISBEN 1’. The dataset identifies some basic characteristics, such as age, gender and education. However, information on these is missing for about half of the households. We can identify, which people stayed in and which left the original SISBEN population by comparing the households in the SISBEN sample with those in the _Familias_ sample. Although _Familias_ and SISBEN do not share the same household identifier, we can link the households up via the National ID number of the head. (A National ID number is unique and stays with the bearer for life, no matter where they move. That means, those SISBEN households which we no longer find in _Familias_ did not just move from one place to another within the same sample. They are definitely not picked up anywhere in the _Familias_ sample.) The SISBEN register can be merged at the municipality level with some municipal variables from before 2000. This includes violence data on armed group and common delinquency actions. It also contains some municipality level wealth indicators for various years. We include the sum of all bank assets per head in the municipality. Although the above data is obviously limited, we can use it to attempt a sample selection correction. We can model the selection process according to Heckman and calculate the IMRs. This will allow us to correct the linear approaches and linear approximations in our work. However, we cannot control for selection in the non-linear specifications. This is because we cannot calculate the stratification weights in the original population which are needed 23
**Table 2: Definition of Scenarios**

|**Scenario**|**Sub-Scenario**|**Definition**|
|---|---|---|
|**0**||Baseline scenario|
|**1**||Command & control approach: carbon emission target (i.e., NDC target) is<br>exogenously set. The model calculates the shadow carbon prices to meet the<br>emission target|
|**2**||Carbon tax with tax revenues allocated in the same way the government<br>allocates its total revenues in the baseline|
|**3**||Carbon tax with tax revenues used to subsidize wind and solar power<br>generations. China does not provide direct subsidies to renewables.<br>However, to expedite the penetration of renewables, they might need<br>subsidies.|
|**4**|**4a**|Carbon tax with tax revenues recycled to cut labor taxes in sectors excluding<br>fossil fuels and thermal electricity generation sectors|
||**4b**|Carbon tax with tax revenues recycled to cut capital taxes in sectors<br>excludingfossil fuels and thermal electricity generation sectors|
|**5**||Carbon tax with tax revenue recycled to households as a lump-sum rebate<br>(cash transfer)|
|**6**|**6a**|Carbon tax with tax revenues recycled to cut corporate income taxes in<br>sectors except for fossil fuels and thermal electricity generation sectors; tax<br>revenues are reallocated to a sector in proportion to the amount of income<br>tax paid by it (a sector that pays a higher amount of corporate taxes gets<br>higher rebate)|
||**6b**|Similar to Scenario S6a, but tax revenues are reallocated to a sector in<br>proportion to its income tax rate (a sector with a higher income tax rate gets<br>a higher rebate)|
||**6c**|Similar to Scenario S6a, but tax revenues are reallocated to a sector based on<br>its CO2intensity ( a sector with a higher emission-intensity gets a lower<br>carbon tax revenues as the rebate)|
||**6d**|Similar to Scenario S6a, but tax revenues are reallocated to a sector based on<br>its export intensity (a sector that exports a higher volume of outputs gets a<br>higher carbon tax rebate). Fossil fuel sectors including fossil fuel-based<br>electricity generation are excluded from receivingthe tax rebate.|

The baseline scenario is constructed based on assumptions about GDP growth and population growth and the projected primary energy supply mix. The GDP growth rate for 2018-2021 is based on the National Bureau of Statistics of China (CNBS, 2022). The GDP growth rate for 2022-2035 is from ICCSD (2021). The annual average GDP growth rate is about 5.1% for the period 2017–2035. Although China's population is declining, labor 9
_ANDR EE_<sup>_ ́_</sup> _AND PAPE_ _SEPTEMBER 5, 2023_ _22_ curate suggests that the approach may be viable in other settings where markets are only weakly interlinked. It is important to note that the individual Pacific Islands share common trade links and are heavily reliant on the same imported foods (Snowdon et al., 2013). Thow et al. (2011) analyze the effects of trade policies in the region and observes that from 1960 to 2005, liberalization policies, export promotion, protection of the domestic meat industry and support for foreign direct investment, have contributed to a reduced availability of traditional staples and increased availability refined and processed foods. Such commonalities suggest that prices across islands may be weakly interlinked, and so a system that monitors prices through the combination of surveying and imputing may work across islands. A joint monitoring system would be economically more attractive to maintain for an international institution than separate systems for each islands, which may quickly be cost-prohibitive on a per-capita basis.

Third, the low correlation in prices between markets suggest that local PNG markets are not basic price-taking components of a wider market. This provides a unique opportunity to study the impacts of local supply shocks on prices. In the more common situation in which a country imports food and is price-taking, local supply does not affect prices as shortfall is offset by increased imports. The presence of strong local price-supply correlations may provide an opportunity to validate other models that seek to estimate dynamics in agricultural yield.

Finally, the extremely high item-specific price volatility suggested that future price monitoring efforts should prioritize high primary data coverage in staples that are economically and nutritiously important. Charlton et al. (2016) review 29 studies on primary food sources for Pacific Islanders and emphasizes that Pacific Islanders depend for large parts of their household income and direct food consumption on fishing, a practice that has come increasingly under threat by climate change (Barnett, 2011; Connell, 2015). From this perspective, tracking catch prices or daily labor rates in the fishing industry may be useful to consider in the future. For all areas, tracking processed foods, that are generally imported from shared origins, would be helpful.

# **Acknowledgments**

The authors thank Michael Wild for comments on an earlier version of this work, Emily Schmidt and Soonho Kim at IFPRI for data support, and Andres Chamorro, Lydia Kim, and Kamwoo Lee for project support.

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.

# **Funding statement**

The country application was prepared to support the Pacific Observatory, a wider initiative aimed at providing non-traditional data sources as complements
firms fail to invest enough.<sup>32</sup> Our finding in Tables 4 and 6 that as trade costs (as measured by the gravity variables) fall, average size and concentration at the top of the exporter size distribution rise, is also consistent with the allocative efficiency story. To the extent that higher trade costs impede competition, allowing a less efficient allocation of resources to persist, we would expect the largest and most productive firms to perform especially well when the distortions are reduced. In Section 5.1, we turn to time-series evidence as a further robustness test of the results.

The results on exporter dynamics also speak to a more efficient allocation of resources in more developed countries. Better firms grow and these high-productivity firms are less likely to exit immediately following entry into the export market. The relatively higher entry and exit in developing countries suggests that the distortions relating to uncertainty about export profitability are greater than entry cost distortions. Effectively, there is a lot of trial and error in exporting, especially in less developed countries where exit goes up precisely because there are more entrants. Indeed, this is consistent with high correlations between entry and exit at the country level and country-sector level.<sup>33</sup>

# **5.1 Average Exporter Size and Concentration and Allocative Efficiency**

While the results are consistent with high-productivity firms failing to invest enough, resulting in too few large firms and lower exports at an earlier stage of development, they could alternatively reflect systematic differences in the underlying productivity distributions and not be a result of resource allocation. To explore how changes in the allocation of resources affects the number of

> 32 Consistent with better resource allocation in richer countries, we also find in unreported results evidence of more diversification at the firm level as countries get richer.  This is consistent with models of multiproduct firms, such as Bernard, Redding and Schott (2011), where only higher-ability firms are able to generate variable profits to cover fixed costs of entry for more products and thus supply a wider range of products to each market.

> 33 For example, using the data for years 2004 to 2008, the simple within-country correlation between entry and exit is 0.77 and the within-country-sector correlation between entry and exit is 0.76.

24
search, relaxing these barriers will enrich a pool of potential matches for the firms, enabling them to match faster (Pissarides, 2000). Under certain conditions, this faster (and better) match may lead to increasing aggregate productivity. For example, Beerly et al. (2021) show that the increase in the available pool of workers due to easing of migration rules in Switzerland enabled firms to find workers who are better matches for available jobs and increased employment and wages.

We examine the link between ECED expansion and firm productivity in the context of Indonesia. This is a suitable laboratory for our analysis for several reasons. First, the female labor force participation (FLFP) rate in Indonesia is relatively low: 49% in 2002, the beginning of the period we study, compared to 70.6% in the rest of developing East Asia and Pacific (World Development Indicators, 2021). The FLFP rate remained low over our study period: by 2014, the FLFP rate in Indonesia only increased to 52.8% while the number declined to 66.47% in the rest of developing East Asia and Pacific. However, average rates conceal heterogeneous changes across the educational distribution. More educated women significantly increased their labor force participation, while less educated women at the lower end of income distribution withdrew from the labor market, as incomes grew (Schaner and Das, 2016). Second, starting in 2003, the Government of Indonesia adopted several pieces of legislation and increased public spending to facilitate public and private provision of ECED programs including preschools, which simultaneously provide early learning for children and serve as childcare. As a result, the gross pre-primary (age 3-6) enrollment rate more than doubled, jumping from less than 25 percent in 2004 to 60.3 percent by 2016 (World Development Indicators, 2021). Importantly, this growth has been geographically heterogeneous due to differential implementation across districts.

Such a dramatic increase in access to preschools enabled Indonesian women, who have relatively high levels of human capital, to join the labor market. Halim et al. (2022) show that an additional public preschool per 1,000 eligible children increased women’s employment probability by 9.1 percent. This increase in labor supply may be particularly salient for Indonesian firms, which are more likely to cite inadequate skills as a barrier for hiring skilled workers compared to other neighboring countries, including Malaysia and Thailand. According to World Bank Enterprise Survey data, 55% of Indonesian firms report this constraint compared to 39.5% and 46% of Malaysian and Thai firms, respectively.

3