rafmacalaba/data-use-mentions-extended huggingface dataset:
market are limited; for the most part they are limited to the informal economy, mainly carrying out activities such as tailoring, cleaning houses, and cooking for neighbors and family (UN Women, 2018).

The limited availability of employment opportunities for women is strongly associated with social norms that bind women to specific roles in society (Kelberer, 2017; Felicio et al., 2018). Data from the World Development Indicators (WDI) show that in 2019 only about 13 percent of women over 15 participate in the labor market. Changing gender roles in the labor market can cause tensions and have been linked to increased rates of domestic violence in refugee households in Jordan (HagenZanker et al. 2017; Culcasi 2019).

The most common reason for low levels of female labor force participation is family responsibilities such as childcare and housework, reported by 44 percent of Syrian refugee women and Jordanian women of working age (Stave, Kebede, & Kattaa, 2021). This is tied to deeply engrained norms surrounding gender roles. A recent analysis of gender norms in Jordan found that women and girls hold more equitable gender role attitudes than their male counterparts and that there are no significant differences between Syrians and Jordanians (Krafft, Assaad, & Pastoor, 2021). Restrictive norms perpetuated by men continue to severely restrict women’s access to the formal labor market. Fortunately, the Jordanian government has worked in recent years to address this problem. For example, in the amendment made to the Jordanian labor law in 2019, the provision of childcare services was made mandatory in every business that has 15 or more employees, male or female, with children under 5 (World Bank, 2019). However, implementation is not consistent and low-income households report less access to childcare services (Weldali, 2022). Home-based businesses can also provide important access to economic opportunities for women, albeit within a limited number of sectors and the Government of Jordan’s 2017 amendments of the regulations governing the licensing of home-based businesses is particularly important
# _Dominican Republic_

According to national statistics, Dominican Republic is among the top 15 developing country recipients of remittances (and of remittances/GDP), receiving approximately $2.1 billion per year.

We use the Panel Survey of the Work Force (Encuesta de Panel de Fuerza de Trabajo) for 2000–2003. The survey is conducted biannually, is a nationally representative survey, and covers a three-month period (Jan-March or July-September). About 22,000 people are surveyed.

From 2000–2003, a number of detailed questions on remittances were added, including the channel by which they were received. During each three-month period, 1518 percent of households report receiving remittances. Table 6 shows the percentage of total remittances received via the various channels by residents of the Dominican Republic. The share of remittances that flow through the informal sector is declining, from nearly 10 percent in 2000 to about 41⁄2 percent in 2003. Money transfers have shifted to postal banks and money transfer providers. Of interest, banks account for a very small share of money transfer and their share did not increase. This is likely due to an intense distrust of the banking sector in this period: the withdrawal of funds from the country’s second largest private bank (Banco Intercontinental) in September 2002, ultimately culminated in the central bank bailing it out in April 2003, at a cost of roughly 15 percent of GDP to taxpayers (Hanke 2004). Despite nearly 50 percent of Dominicans residing in the U.S. reporting having a bank account, most choose not to use bank services for money transmission and report being satisfied with their current provider 24
||(0.25)|
|Household headship (ref: husband is head)||||||
|<br>Wife head of household|||0.012||0.048<sup>*</sup>|
||||(0.76)||(1.96)|
|Other male household head|||-0.014||-0.039<sup>*</sup>|
||||(-122)||(-174)|
|Other female household head|||.<br>-0.031<sup>***</sup>||.<br>-0.083<sup>***</sup>|
||||(-2.94)||(-3.79)|
|Constant|0.333|-0.851|-1.047|2.778<br>1.773|1.031|
||(0.25)|(-0.47)|(-0.65)|(1.51)<br>(1.03)|(0.62)|
|Observations|70,286|70,286|70,099|141,024<br>141,024|140,764|
|_R_<sup>2</sup>|0.03|0.04|0.07|0.07<br>0.10|0.12|

_Note:_ Data for 39 countries from 2010-18. _t_ statistics in parentheses. Dependent variable is wife's land ownership (sole and joint). Coefficients after OLS estimation. Standard errors clustered at the country level.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0
increase in attendance. In addition, for studies that report impact on learning, we start with impact estimates that can be expressed in terms of standard deviations. The list of studies included in our analysis is illustrative rather than exhaustive, and in the future, we aim to continue adding more studies and build as comprehensive a database of education interventions as possible. In total, after applying our inclusion criteria, we analyze data from over 150 impact estimates across 46 low- and middle-income countries.<sup>10</sup> In this analysis, we make several choices for parameters and data inputs. First, we assume that the intervention’s effects ( _t_ ) last only for the duration of the evaluation since this is a known quantity and requires no further assumptions. In Appendix Figure B1 we explore the alternative assumption that the impacts last just one year ( _t_ = 1). A second choice that we make is to set the high-quality benchmark learning rate ( _δh,n_ ) equal to 0.8 standard deviations per year. This is a conservative estimate for high rates of learning, drawn from year-on-year learning gains in high-performing education systems, policy-relevant differences across education systems, and standard benchmarks. We choose an artificial benchmark for this analysis, because unlike the actual learning rates of high performers, such as Finland or Singapore, it has the advantage of being stable over time and of being apolitical. This approach to defining high-quality learning rates is similar to the one used to define the high-performance benchmark learning level in the World Bank Human Capital Index, which sets the benchmark at 625 on the scale of TIMSS and PISA. In the robustness section, we explore four plausible approaches to validate this 0.8-standard-deviation high-performance benchmark.

We calculate how much to adjust improvements in access for the level of learning (i.e., the learning adjustment rate _L_<sup>_h_</sup> _i_<sup>)usingHarmonizedLearningOutcomes(HLO),whichareglobal</sup> measures of learning introduced by Angrist
series, including the data reported in Figure 2, contain all forms of storage: speculative storage, public storage, and pipeline storage that is needed to keep shipping and processing operations running smoothly. It is impossible to isolate speculative storage in the data; however, it is speculative storage that is key to the model’s pricing mechanics. Conceptually, when one country is a perpetual importer, speculative storage will always take place in the exporting country, absent physical impediments to trade, large differences in physical storage costs or changing transfer costs (Gouel and Jean, 2012). This is because an importing speculator pays interest on the costs of importing in addition to interest on the price of the commodity, a cost that a speculator in an exporting country does not have to pay. Unless there is an offsetting benefit to storing locally, competition will result in adjustments in trade rather than storage in response to changes in availability in the importing country.<sup>17</sup> This general finding applies to our model outcomes as well, and speculative private storage always takes place in RoW.

> 17 The result also depends on the assumption that trade is instantaneous in our model. If trade takes time, it is rational to store while importing in order to be able to wait for the next shipment (Coleman, 2009). In a related way, variations in production generate greater price variability in landlocked and isolated countries (Byerlee, Jayne and Myers, 2006).

12
.067)|-0.038||(0.070)|
|ln_MINCSB_|0.001|(0.025)|-0.010|<br>(0.024)|0.029||(0.031)|
|ln_ROOM_|0.184|(0.066) ***|0.126|<br>(0.067) *|-0.167||(0.066) **|
|_HOME_|-0.039|(0.065)|-0.041|<br>(0.064)|-0.116||(0.073)|
|_POWR_|-0.018|(0.125)|0.229|<br>(0.102) **|-0.124||(0.116)|
|_TAPW_|0.279|(0.106) ***|0.224|<br>(0.098) **|-0.128||(0.097)|
|_TOIL_|0.146|(0.078) *|0.064|<br>(0.070)|0.094||(0.075)|
|Constant|10.216|(0.578)***|10.307|<br>(0.470)***|0.289||(0.434)|
|Obs.|858||2883|||||
|No. of district|8||8|||||
|dummies|||||8|||
|R squared|0.4346|||||||
|F stat.|26.38|||||||
|Wald chi2|||659.15||||
24|3|49.44%|Mar-07|
|Mali|XOF|77/126|6|58.37%|Jan-07|
|Mozambique|MZN|25/52|7|63.13%|Jan-07|
|Myanmar|MMK|36/165|3|43.00%|Apr-07|
|Niger|XOF|68/79|4|79.60%|Jan-07|
|Nigeria|NGN|33/35|16|27.59%|May-12|
|Somalia|SOS|18/28|4|55.49%|Jan-07|
|South Sudan|SSP|9/20|8|52.35%|Jan-07|
|Sudan|SDG|14/14|3|58.01%|Jan-07|
|Syrian Arab Rep.|SYP|36/91|15|56.99%|Aug-11|
|Yemen, Rep.|YER|24/24|12|45.48%|Nov-08|
|Average||27/49|7|51.47%||

_Note:_ The first column reports the local currency in which prices are measured, the second column reports the number of markets from which data is used as a fraction of all known market location for which predictions are made, the third columns reports the number of food items for which data is used to construct the food price index, the fourth columns reports the total number of price observations as a share of all _market × time_ combinations where _markets_ is the first number in the third column, the final column reports when the estimated price index starts. _Source:_ The statistics have been prepared by the author for this paper based on end-of-August (2021) food price data from World Food Program.
Moreover, it might also lower producer price inflation by increasing firms’ abilities to substitute domestic for foreign inputs.

Table 5 studies this link using gross trade data in column (1) and value added trade data in column (2). Results show that, controlling for each country’s average level of inflation as well as global changes in inflation for each time window, an increase in trade in both final goods and intermediate goods re-exported further (forward linkages) is indeed associated with a downward pressure on the level of the GDP deflator inflation. This is in line with global inflation trend observed in figure 1. Column (2) confirms these results when using trade in value-added terms. Turning to columns (3) and (4), we use more “standard” GVC participation indicators to analyze the link between GVC and inflation level. The negative coefficients of these indices suggest that greater GVC integration contributed to the decrease in inflation levels. Again, those results also hold when using CPI inflation as the dependent variable, as shown in table 8 in appendix.

# **Graphical representation of some results**

In an effort to make our results more accessible for a wide audience, we present our main insights in figures 4 and 5.

First, focusing on backward GVC participation, figure 4 shows the strong association between an increase in bilateral inflation correlation using the GDP delfator and bilateral backward participation. In this graph, each dot is a pair of World Bank Region, with the vertical axis representing the change in inflation correlation between domestic CPI inflation and World inflation between 2001 and 2011, while the horizontal axis represents the change in backward GVC participation for the same period. This representation, in changes, allows to control for country fixed effects in an intuitive way since the only variation that is used is the _within region pair_ change in both inflation correlation and GVC participation.

Second, figure 5 shows that stronger backward GVC participation (in the horizontal axis) is associated with an increase in CPI inflation correlation
2 FERNANDES-KLENOW-MELESHCHUK-PIEROLA-RODR ́ıGUEZ-CLARE excluding country pairs with few exporters or tiny exporters, and looking within industries.

We interpret the finding that up to 60 percent of the variation in bilateral trade flows are explained by the extensive margin as providing support for the Melitz (2003) model. But the finding that at least 40 percent of that variation is explained by the intensive margin — even while allowing for origin-year and destination-year fixed effects — contradicts an important special case of the Melitz model, namely the one with Pareto-distributed firm productivity and fixed trade costs that vary only because of separate origin and destination components. In this case all variation in exports across trading partners should occur through the number of exporters (the extensive margin). Lower variable trade costs should stimulate sales of a given exporting firm, but draw in marginal exporting firms to the point that average exports per exporter (the intensive margin) is unchanged. This exact offset is a special property of the Pareto distribution.<sup>3</sup> We explore several potential explanations for the prominent intensive margin in the EDD data while retaining a Melitz-Pareto core. We do this because Melitz-Pareto has become an important benchmark model in international trade. It is consistent with many firm-level facts (Eaton et al., 2011), generates a gravity equation (Chaney, 2008), and yields a simple summary statistic for the welfare gains from trade (Arkolakis, Costinot and Rodr ́ıguez-Clare, 2012). We want to make sure our empirical finding cannot be made compatible with Melitz-Pareto before exploring an alternative productivity distribution.

First, we consider the possibility that fixed trade costs vary by origin-destination pair. Higher fixed trade costs raise average exports per exporter, but _lower_ total exports. For average exports to be increasing in total exports, therefore, one needs variable trade costs to be very negatively correlated with fixed trade costs. A corollary is that, whereas variable trade costs rise decisively with distance between trade partners, fixed trade costs would need to fall with distance. We find this rather
suggests two main reasons why survey-based investigations of health may be of value in some countries. Fir-t they could provide information on health status and service use for the whole population. Most countries collect some information on mortality of this type from existing sources. Fewer have reliable data or. other aspects of health. Second they could provide person-based rather than episodebased estimates of the extent of ill-health. Such information would be valuable in identifyins demographic and socio-economic differentials in health and thus to identify priorities based on need and to target future interventions. Data could be collected on the coverage and use of health facilities and prograines to identify groups of the population who lack adequate provision. By relating measures of health status to data on service use, survey-based estimates could be used to identify unmet needs and to clarify the biases affecting statistics derived from facility and PHC-based reporting systems. In addition, because survey investigations are independent of the provision of services they are particularly useful for examination of attitudes to and satisfaction with the services available.

Not every country will perceive the need for such a survey. M4any previous surveys have been slow to produce results and, when they have appeared, have not addressed the questions that policy makers need answering. Administrators engulfed with routine work may be unable to take a more holistic view of problems and priorities. In section 4 we describe the methodologies that are emerging for collecting useful information by means of health interview surveys. Nevertheless there are severe limitations to what can be learnt from them. In particular, respondents' assessments of their own health should not be expected to provide information comparable to that obtained from medical examinations. Even in developed countries, population surveys have tended to concentrate on examining ill-health in relation to restrictions on activity and patterns of service use.

Nevertheless many developing countries do see a role for national 9
force survey ( _Survei Tenaga Kerja Nasional,_ SAKERNAS) for the years 2013 – 2015.<sup>12</sup> In doing so, we measure the commuting flow from a given origin district _i_ to a destination district _j_ as the share of workers who live in _i_ but commute daily to work in _j_ , where – following SAKERNAS – workers are defined to include all employed wage workers including casual workers, selfemployed workers, and unpaid family workers, where anyone who worked for at least one hour consecutively in the previous week, including temporary non-workers who normally meet the condition, is considered employed.

Both the AI and the two cluster algorithms require a gridded population data set. We use the _Landscan-2012_ gridded population data set produced by Oak Ridge National Laboratory. This population grid has a resolution of 30 arc-seconds. An earlier version of the same population grid was used by Uchida and Nelson (2009) in their original application of the AI. More generally, the _Landscan_ population grid is the most established global gridded population data set and has been widely used in social scientific research.<sup>13</sup> This includes the paper by Henderson _et al_ . (2018), who use the same _Landscan-2012_ data in identifying urban areas and for constructing measures of population, and economic, density for six African countries. Importantly, Henderson _et al_ . (2018) “ground-truth” the _Landscan_ data, reaching the conclusion that the data do good job in estimating population at a fine spatial scale. The population grid is derived through distributing population data for sub-national administrative units across grid cells using a modeling process that relies on other geo-spatial data sources and high-resolution satellite imagery analysis.<sup>14</sup> In addition to gridded population data, the AI also requires data on estimated travel times. The travel time data originally used by Uchida and Nelson (2009) for the AI were based on “... _estimates of the time required to travel 1 km over different road and off-road
# **Figure 5:** Effects on Public Goods Provision (Administrative Data)

**(a)** Administrative data, 2021

<!-- Start of picture text -->
-.2 -.1 0 .1 .2<br>Margin of victory of best-ranked challenger<br>1.3<br>0.5<br>-0.4<br>-1.2<br>Local public service provision, 2021 (z-score)<br>-2.0<br><!-- End of picture text -->

# **(b)** Administrative data, 2014 (balance)

<!-- Start of picture text -->
-.2 -.1 0 .1 .2<br>Margin of victory of best-ranked challenger<br>1.2<br>0.6<br>0.0<br>-0.6<br>Local public service provision, 2014 (z-score)<br>-1.2<br><!-- End of picture text -->

_Notes_ : In panel (a), the dependent variable is a standardized index of local service provision constructed using the 2021 _Podes_ survey. The index has the following 10 components: drinking water, sewage, garbage collection, street lighting, kindergartens, primary schools, village maternities ( _polindes_ ), community health centers ( _puskesmas_ ), paved roads, and public transit. We first standardize each individual component before taking the village-level average of all components. The sample includes all villages in our sample that conducted their last election before 2021. In panel (b), the dependent variable is a standardized index of local service provision constructed using the 2014 _Podes_ survey. The 2014 index has the same components except garbage collection, which was not collected in 2014, and a different coding for street lighting, which was categorized into two groups in 2014 ( _no lights_
changes of commodities and factors prices implied by GTAP simulations for China’s WTO accession to a sample of 84,000 households from China’s household survey to analyze the implications of WTO accession. Their study offers a rich analysis of the potential incidence of accession, but it abstracts from households’ responses to the ensuing price changes and it does not explicitly model the labor markets.

Our paper represents a compromise between the global CGE analysis and detailed postsimulation analysis of household incidence. Using a household-disaggregated national CGE model, we find that factor market reforms can go a long way toward addressing the maldistribution of income in China. Furthermore, openness to trade does not have to induce income inequality in China – if it is accompanied by these complementary reforms. Indeed, the combination of WTO accession and factor market reforms significantly improves both efficiency and equality.

This paper is organized as follows: the next section motivates the paper by discussing recent estimates of the size of the rural-urban wage gap induced by the _hukou_ system, as well as limitations in the land market that inhibit off-farm labor mobility. We also examine evidence on the current degree of labor mobility between the agriculture and non-farm sectors. We then turn to the specification of a CGE model that explicitly incorporates these labor market distortions. The baseline scenario to 2007 is developed next. This is the backdrop against which China’s labor market distortions and accession to the WTO will be evaluated. Section 5 assesses the impact of reducing labor market barriers, as well as China’s WTO accession, on rural-urban inequality. The final section offers conclusions and suggestions for future research.

3
reaching low-income and rural households.<sup>4</sup> Using private school census data from 2000, Andrabi et al. (2008b) find that there was an exponential increase in the number of private schools over the 1990s, with over 50% of existing private schools established in or after 1996. Furthermore, they find that while existing schools set up before 1990 were predominantly in urban areas, the distribution since then has become increasingly rural.

In line with this increase in institutions, the share of enrollment in private school has also increased. As Panel B in Table 1 shows, in 2004/05, 15.8% and 18.5% of children ages 6–15 years were enrolled in private schools in all-Pakistan and Punjab, respectively; these shares represent significant increases from 1998/99. The increases were particularly dramatic in rural areas and for households in the poorest expenditure quintile. Similar evidence is provided by Andrabi et al. (2008b) who find that the growth rate in private school enrollment over the 1990s was highest among low-income households nationally, and among middle-income households in rural areas, whereas the growth rate in public school enrollment over the same period was negative in both urban and rural areas and across the household income distribution. They also find that fees in private schools are generally low: median annual fees per student in 2000 in Pakistan were 960 (US$23.4<sup>5</sup> ) and 751 rupees (US$18.3) in urban and rural areas, respectively, and account for a small percentage of mean annual household expenditure. The corresponding statistics for Punjab were lower, at 828 rupees (US$20.2) and 600 rupees (US$14.6).

> 4 It is conceivable that much of the growth and metamorphosis in the private school system is in direct response to the rigidities and shortcomings in the public school system.

> 5 The exchange rate in 2000 was 41 rupees per US dollar.