rafmacalaba/datause-encoder-data huggingface dataset:
of substitution estimated for the GTAP model (Hertel _et al_ 2004). The sectoral CES and CET elasticities in the model in turn are assumed to be the same as the import-domestic Armington elasticities in the GTAP model. Total exports in 1994 were composed of 6.1 percent agriculture exports, 63.1 percent industrial exports, and 30.8 percent service sector exports. The principal industrial exports are semi-conductors, and textiles and garments. The semi-conductor industry is highly export intensive, followed by coconut processing, bananas and the textile-garment sector. 98.5 percent of total imports are industrial. The sectors which are most import-intensive are mining (75.3 percent; mainly due to crude oil imports), semi-conductors, machinery, and fertilizer<sup>4</sup> . While agriculture generally has a higher value-added ratio compared to industry, its contribution to domestic value added is smaller, 19.9 percent compared with industry’s 31.5 percent and services’ 48.5 percent. Labor intensity is uniformly higher in the agricultural sectors, with the exception of fishing and "other livestock".

# **3. Definition of the Scenarios**

In all Philippine simulation experiments, the calibrated tariff rates in the Philippine model, which are initially set at 1994 levels, are re-calibrated to the 2001 tariff rates used in the GTAP model for the Philippines. The solution of the model using the re-calibrated tariff rates serves as the base model to which all subsequent policy simulations are compared. For all but the last scenario, the GTAP world model is run separately to generate estimates of the resulting changes in world prices for Philippine exports and imports, demand for Philippine exports, and,

> 4 The Philippines does not produce all items in the semi-conductor sector, but instead imports these items. For example, it does not have the facilities to produce wafers (motherboards) and monitors, which are major parts of computers. Domestic production focuses on hard disks, disk drives, processors, and some chips. Thus, while there is substantial domestic production and exports in the semi-conductor sector, there are also substantial imports.

13
procedure is described in Betti et al. (2013). Their propensity score approach also aims at obtaining a covariate distribution in the census as if it was collected at the time of the recent survey. However, the method requires further modelling, including additional assumptions and uncertainty, and a survey collected at the time of the census.

In the remainder of this paper, we show that our proposed method has comparably low data requirements and weak assumptions. Although our outcome variables will be measures of welfare, our method is applicable to a wide range of outcome measures and research questions beyond poverty mapping. Section 2 presents the idea of the approach in detail. Section 3 describes the properties of the resulting poverty estimator. Simulation studies on artificial and real data are presented in Sections 4 and 5, respectively. Section 6 concludes.

# **2 Estimating poverty measures under structural change**

Assume that the target population is a village _v_ . While the proposed method is applicable to essentially all measures which can be derived from consumption (or any other dependent variable measuring welfare), for instance inequality measures such as the Gini coefficient, assume for now that the measures of interest are poverty measures of the Foster–Greer–Thorbecke (FGT) family (Foster et al., 1984): with Here, _Nv_ is the size of the village population, _yvj_ is the consumption for individual _j_ in village _v_ , _z_ is the poverty line and _I_ ( _yvj < z_ ) is an indicator function which equals one if the consumption of an individual is below the poverty line and zero otherwise. Poverty headcount ratio, poverty gap and poverty severity are obtained for _α_ = 0 _,_ 1 and 2, respectively.

## **2.1 The consumption model**

Usually, consumption values are observed at the level of the household, not the level of the individual. As most household consumption values are unobserved in a village, one needs a model which predicts those values for all households. Let _ycht_ be the consumption of household _h_ in cluster _c_ at time _t_ . Then, the model of consideration is 5
29 Another micro study is one that analyzes the effects of special programs that are implemented to counter the short-run costs of stabilization policies. For example, Newman, Jorgensen, and Pradhan (1990) examine the Bolivian Emergency Social Fund (ESF), a temporary employment creation program. They use two microsurveys, a survey of the general population and a survey of ESF workers, to compare characteristics and incomes of ESF workers to other workers. They also use these surveys to simulate what would have been the position of the ESF workers without the ESF program. They are not able to consider spillover benefits of the ESF program on other workers who did not participate in the program, implying a partial equilibrium analysis. They use a microeconometric analysis of wages, hours of work, and the decision to participate in the ESF, to estimate the probabilit" that the ESF workers would work, and the expected wages and earnings they would receive, in the absence of the ESF program. They find that the average ESF worker experiences a 27 percent increase in wages, an increase of 15.5 hours of work a week, and a 42 percent increase in weekly earnings over what would be expected (taking account of the probability of being unemployed) if there were no ESF program.

Several papers that examine the poverty impacts of price policLes recommended bv adjustment programs, incorporate the responses from other markets and from consumer behaviour. One such paper by Ravallion (1989) looks at labor market responses to price changes of a staple food. Many rural households are net demanders of the staple food, supplementing their own farm incomes with agricultural labor earnings. A price increase will hurt them, unless the price increase stimulates food production and the demand for agricultural labor, which then leads to higher wages. Ravallion identifies the critical value for the wage elasticity with respect to food price that ensures welfare improvement following
<mark>CPTPP as more countries request to join, and the launching of the African Continental Free Trade Area (AfCFTA) that aims to boost intra-African trade, are examples of a trend towards more regional or – in the case of the CPTPP – plurilateral integration. Along the same lines, Fajgelbaum at al (2022) show that while the trade war between the US and China reduced trade in the targeted product categories between these two countries, it boosted trade between third countries and the rest of the world in these products. The countries that exhibited the largest export growth to the rest of the world were countries with expansive trade agreements. So, the trade conflict between the world’s two largest economies did not simply cause reallocation of global trade flows, it also generated new trade opportunities for other countries. Therefore, it seems that outside the US, the picture is more nuanced with many countries striving to take advantage of the new opportunities potentially created by the reversal of US policy.</mark> <mark>Note however, that one of the most assertive recent moves of the US, the export restrictions in the semiconductor sector targeting China, require the cooperation of other countries to succeed. Because most of the semiconductor chip manufacturing takes place in countries other than the US, using however to a large extent US software and US machinery, the new export controls require third country chip manufacturers to obtain export licenses from the US in order to export their products to China. The US implemented this policy change unilaterally, , putting allies in a difficult position as they faced a choice between going along with the export restrictions or--- if they wished to continue exporting to China---foregoing access to US technology. This use by the US of economic interdependence to force other countries to go along with its policies has been termed “weaponizing interdependence.”</mark><sup>8</sup> <mark>It suggests that no matter what other countries’ preferences and intentions are, ultimately it is the priorities of the largest economy of the world that may shape the future of the global economic system. Seen in this light, US’s turning inward may have important implications for the future of globalization.</mark>

# **I.C. Public Discourse**

Just as economic variables respond with a lag to policy, policy responds with a lag to public sentiment and attitudes. Therefore, we now turn to an investigation of such attitudes regarding trade and globalization. Data from the PEW Global Attitudes Surveys show that the

> 8 The term was coined by H. Farrell and A. Newman (2019).

10
The NLSS is the first and only data source to provide statistically accurate estimates of levels of and trends in international work-related migration from Nepal and on the amount of money sent home in remittances. According to NLSS, 23 percent of households in Nepal received remittances in 1995, and that proportion climbed to about 32 percent in 2004. Further, the share of households with remittances from abroad grew from 10 to 17 percent between the survey’s two rounds. The amount of remittances also increased from about NPR 22,000, or 36 percent of mean household yearly consumption expenditure in 1995, to NPR 35,000 or 44 percent of mean expenditure in 2004.

Figure 1 shows the incidence and the amount of remittances by household size for 1995 and 2004.<sup>4</sup> Focusing first on the top panel of the graph, the proportion of households receiving remittances grows monotonically with household size. For example, in 2004 only about 10 percent of households with two or three members received money from abroad, while that proportion is more than three times higher for households with 11 or more members. The changes in the amounts of remittances by household size are shown on the lower panel of the graph. The plot indicates that in 1995 households with different sizes received almost the same amount of money, while the 2004 data show that remittances increase with household size.

The incidence of remittances is higher in rural than in urban Nepal. The proportion of households receiving remittances from within the country increased only marginally between 1995 and 2004, and even declined in Kathmandu (top section of Table 1). At the same time, the share of households receiving money from abroad increased uniformly across the country. For example, the rural eastern hills––the poorest region in Nepal––registered a fourfold increase in the number of households receiving money from abroad; that proportion more than doubled in “other urban areas” of Nepal. Thus, the overall increase in the proportion of households with remittances could almost entirely be attributed to the growth of remittances from abroad. There is no clear pattern in the distribution of the household recipients of the remittances by the size of landholdings. The largest increase in the incidence of both domestic and international remittances is registered among households with two and more hectares of land.

> 4  The size of households with migrants was adjusted for the missing migrant members.

8
Figure 7 highlights that significant gains in productivity are achievable if full relaxation of business environment constraints were possible. The gains in productivity are calculated based on existing resource inputs and technology. We calculate that these potential levels of output could alternatively be achieved in 15 to 20 years if the economy grew at eight percent per annum from its current levels of output and value added. Our productivity frontier analysis therefore implies, under the assumption of a smooth transition in policies and constant inputs and technology, that policies which incrementally relax business environment constraints over a period of 15 to 20 years could be expected to induce eight percent growth in manufacturing output and value added per annum (Table 6).

# **VI. Firm-level TFP Analysis**

# **Data sources**

A 2011 World Bank Enterprise Survey (WBES), covering data from fiscal year 2010, was conducted as a follow-up to the original 2004 WBES (which covered fiscal year 2003). For comparability across both years, and to allow calculation of TFP based on reliable capital asset estimates, we restrict our focus here to manufacturing establishments (the 2003 sample consisted exclusively of manufacturing establishments). The 2010 manufacturing subset of the survey is comprised of 330 establishments.

Summary statistics of the 2010 manufacturing data, and the comparable 2003 data, are presented in the second through fourth columns of Table 3. The second column presents means based on all available manufacturing establishments included in the 2010 sample. There are stark differences between these 330 establishments and the 408 establishments sampled in 2003:<sup>15</sup> only 3 percent of the 2010 establishments have more than 10% foreign ownership, while 19 percent of the 2003 establishments had this level of foreign ownership; conversely, less than 22 percent of 2010 establishments export while 68 percent of 2003 establishments exported. These differences are no doubt related to underlying differences in establishment characteristics: while the 2003 sample contained mainly larger firms with only 6 percent of establishments being micro-sized (10 employees or less), 41 percent of manufacturing establishments in 2010 are micro-sized. And while 20 percent of 2003 establishments were in the textiles sector, only 3 percent of 2010 establishments are in textiles. So differences in sample industry and size distributions, among others, account for differences in business environment indicators. While we control for these differences in the regressions with industry, size and location fixed effects, we report, in columns 3 and 4 of Table 3, how the business environment indicator means would change if the 2010 sample were more similar to the 2003 sample – by respectively restricting the sample to overlapping industries, and then also weighting the means by the 2003 sample size distribution.

> 15 For the firm-level TFP analysis, the 2004 WBES sample is reduced from 452 to 408 surveyed establishments due to missing information on output (sales). All summary statistics reflect this sample adjustment.

18
Table 5: Logit Model on the probability of being risk-averse (0-risk-tolerant 1-risk-averse)

|VARIABLES|(1)<br>(2)<br>coef<br>se|
|---|---|
|**Social and Economic Variab**|**les**|
|Per Capita Expenditure|-1.79e-06**<br>(8.92e-07)|
|Household Size|0.00192<br>(0.0180)|
|Share of Dependent Members|-0.116<br>(0.174)|
|State Fixed Efect|yes|
|Type of Settlement (Rural=1)|0.0186<br>(0.120)|
|HH Gender (1=Male)|-0.458**<br>(0.205)|
|HH Age|-0.00376<br>(0.00306)|
|HH Years of Education|0.000545<br>(0.0107)|
|HH Employed|-0.558***<br>(0.153)|
|HH Married|-0.669<br>(0.428)|
|HH Gender * HH Married|0.871**<br>(0.437)|
|HH Language||
|(baseline= Hausa)||
|Igbo|-0.33<br>(0.285)|
|Yoruba|-0.0613<br>(0.258)|
|Other|-0.0399<br>(0.162)|
|**Quality of Dwelling**||
|Number of Rooms|-0.036<br>(0.0232)|
|Quality of Roof||
|(baseline=Low)||
|Medium|-0.102<br>(0.142)|
|High|-0.463<br>(0.344)|
|Quality of Wall||
|(baseline=Low)||
|Medium|0.294*<br>(0.173)|
|High|-0.00575<br>(0.137)|
|Quality of Wall||
|(baseline=Low)||
|Medium|0.0472<br>(0.168)|
|High|0.098<br>(0.165)|
|Type of Toilet||
|(baseline=No Facility)||
|Flush Toilet|-0.147<br>(0.207)|
|Improved Pit Latrine|0.0731<br>(0.244)|
|Uncovered Pit Latrine|-0.19<br>(0.139)|

33
must be met before shelters can be considered “adequate”.<sup>2</sup> These conditions can be subsumed as follows: security of tenure, affordability, habitability, availability of services and infrastructure, accessibility, location, and cultural adequacy. Since then, most literature on housing adequacy revert to UN-Habitats (2016) five factors of defining a slum when assessing housing adequacy: (1) access to improved water, (2) access to improved sanitation facilities, (3) sufficient living area, (4) structural quality / durability of the housing unit, and (5) security of tenure.

These housing adequacy criteria have triggered a myriad of scholarly research examining how housing adequacy relates to a series of socio-economic outcomes, including health, emotional well-being, social belonging, education, economic attainment, political participation, or job creation (e.g., Thomson et al. 2001& 2009; or WHO 2018 for an overview). These usually microfounded analyses provide adequacy assessments of existing housing units on one or two of these dimensions within or across countries.

Many articles establish a positive link between piped water into households or plots and reduced diarrhea prevalence and mortality in young children (e.g., Galiani et al. 2005; Jalan & Ravallion 2003; Jamison et al. 1993; Nabassaga et al. 2019). Further, positive links are established between piped water and improvements in overall well-being and life satisfaction through significant time gains as households spend less time in fetching water (e.g., Devoto et al. 2012; Kremer et al. 2011).

Improved sanitation is a closely related housing adequacy component which has been amply demonstrated to improve health outcomes, particularly in children (e.g., Duflo et al. 2015; Wolf et al. 2014). Given the complementarities across water and sanitation, especially when considering the multiple pathways for transmission of water-borne diseases, some studies point to the fact that water and sanitation improvements are _jointly_ responsible for most of the decline in child mortality rates and for health improvements (e.g., Cutler and Miller 2005; Watson 2006).

A series of studies also point to the beneficial effects of durable structural quality such as replacing dirt floors with cement floors. These improvements are highly correlated with reduction of child mortality rates, improvement of overall health, and adult welfare (e.g., Cattaneo et al. 2009; Bah et al. 2018).

Likewise, over-crowded houses, which are often classified as housing units with <mark>less than nine square meters per person or with more than three people occupying a room (</mark> e.g., Blau et al. 2019; Gove et al. 1983; UN-Habitat 2007), are often associated with adverse health outcomes (e.g., Bashir 2002; Cattaneo et al. 2009; Irfan et al. 2017; <mark>Krieger & Higgins 2002),</mark> violence (e.g., World

> 2  Committee’s general comments No. 4 (1991) on the right to adequate housing and No. 7 (1997) on forced evictions. General comments are adopted by the treaty bodies offering guidance to states on their obligations under the respective treaty.
2005 and 2006-2016. For the overall period, we see that the mean Capital regulation for loans provided through subsidiaries in borrower countries exceeded the mean value for parentcountry loans, while it was lower (higher) in the first (second) subperiod (column 8). These results suggest that foreign subsidiaries located in borrower countries were used relatively heavily to avoid burdensome parent-country capital regulation in the first half of the sample period, in line with figure 5.<sup>17</sup> On average, subsidiaries located in third countries were subject to more stringent capital regulation than banking establishments in parent countries throughout the sample period (column 9), which suggests that on average subsidiaries in third countries were not used to evade parent-country capital regulations. However, subsidiaries located in third countries were subject to less stringent capital regulation than subsidiaries located in borrower countries during the overall sample period, and also during 2006-2016 (column 10).

Panel B provides analogous mean values of the activities restrictions variable for loans provided through foreign subsidiaries and parent banks. Subsidiaries located in borrower countries have been subject to more stringent activities restrictions than banks located in parent countries throughout (column 8), while subsidiaries located in third countries had fewer restricted activities relative to establishments in parent countries (column 9) and subsidiary banks in borrower countries (column 10). From Panel C, we see that on average subsidiaries in both borrower and third countries were subject to greater official supervisory power than banks in parent countries (columns 8 and 9), but that subsidiaries in third countries were subject to less powerful official supervision compared to subsidiaries in borrower countries for the overall period and during 1996-2005 (column 10). Finally, Panel D shows that subsidiaries in both borrower and third countries were subject to greater

> 17 <mark>For the loans provided through a subsidiary in the borrower country in Figure 5, the difference between the mean capital regulation variable in the borrower country and mean capital regulation in the pertinent parent country is significantly positive for the overall sample. It is significantly negative during 1996-2005 and significantly positive during 2006-2016.</mark>

15
17 Ultimately the goal for universal access to clean energy is abandonment of polluting energy. This study therefore also examined if, in the countries selected for abandonment, there were other households who had not used LPG and used only electricity. The selection criterion—only those areas where one-fifth of the population cited LPG as their primary cooking fuel—meant exclusion from the sample of Botswana, Eswatini, Namibia, South Africa, and Zambia, where few cooked with LPG and most households reliant on clean energy used electricity. As a result, there were few households that had used electricity to meet all their household needs (Figure 10).

**Figure 10  Share of population abandoning polluting fuels**

<!-- Start of picture text --> Electricity only<br><!-- End of picture text --> _Source_ : World Bank staff calculations using household survey data. _Note_ : * = urban areas only.

Another question is the extent to which adoption of LPG as the primary cooking fuel is correlated with abandonment of polluting fuels (Figure 11). Virtually all households citing LPG as their primary cooking fuel kept using polluting fuels in urban Burkina Faso. In Cameroon, Ghana, urban Kenya, and Lesotho, more than three-fifths of the population continued to use polluting fuels. By contrast, virtually all households in Mauritius had abandoned polluting energy for all activities, closely followed by Gabon. In Côte d’Ivoire, Mauritania, and urban Nigeria, once a household adopted LPG as the primary cooking fuel, more than three-fifths abandoned all other fuels. When the results were broken down by expenditure quintile, there were no marked differences between lower and higher quintiles. Such findings about expenditure quintiles, however, should be interpreted with caution because the sample size of those citing LPG as the primary cooking fuel is small in lower quintiles in many countries. For example, the percentage of the population living in households in the bottom quintile citing LPG as the primary cooking fuel exceeded 10 percent only in Gabon, Mauritius, and Sudan, with Cameroon and Ghana reporting less than 1 percent. In such cases, the lower quintile figures are not statistically meaningful.