rafmacalaba/data-use-mentions-extended huggingface dataset:
121)|<br>(0.0419)|<br>(0.123)|<br>(0.121)|<br>(0.126)|<br>(0.124)|
|Observations|311,102,742|311,102,742|311,102,742|52,821|304,379,241|313,731,062|140,524,661|141,199,949|
|R-squared||||0.230<br>|||||
|||Ro|bust standard erro<br>*** p<0.01, ** p<|rs in parentheses<br>0.05, * p<0.1|||||

Source: Source: Authors’ estimates based on GHS Waves 1-3 Note: (1) – (3) differ in terms of standard errors, (1) uses robust standard errors, (2) clusters standard errors at the household level and (3) clusters standard errors at the LGA level. (4)-(8) all use clustered standard errors at the LGA level. The highest education level completed and education deprivation status among household members above 18 years of the respective gender is used to proxy for the parent’s education deprivation and highest completed education level of existing household members in waves 2 and 3, as the GHS only asks in wave 1 about the parent’s education of all individuals, but in the later waves only for new household member. As the GHS data is unfortunately not an individual level panel, it is not possible to merge information from wave 1 to the respective individuals in wave 2 and 3. Controls including the proxy information are marked with an *.

55
provide a wide range of information on firms’ production capacity and their BE. While the firms were requested to provide information as of 2004, for many accounting measures, information from up to three years prior was also collected.

The fifth data source concerns intellectual property (IP) cases in China. The data set comes from a collection of nation-wide online IP cases from 2005 to 2013. We use it to construct several measures on IP protection in China at different levels. In particular, we construct a measure on the enforcement quality of IP rights by using the proportion of IP infringement cases won by the plaintiff in the local courts (Ang et al., 2014).<sup>10</sup> To construct provincial level indicators on entrepreneurship and BE, we match most of the data at the provincial level and then average firm-level indicators in the same region.<sup>11</sup> **4. Main results**

# **Evolution of the role of the state and the BE**

Throughout the last two decades, the government has remained crucially important for both setting up the rules and keeping control over resource allocations despite the rapid expansion of market institutions in China, implying a dual-track feature that characterizes China’s economic reforms **.** As shown in Figure 1a, both the shares of state ownership in industrial output and industrial assets have declined substantially since the mid-1990s, thanks to ownership reforms in the state sector (Xu et al. 2005). Nevertheless, state ownership still maintains a considerable share in industrial sector until 2012, accounting for about 30% of industrial output and 40% of industrial assets. Moreover, both the share of foreign ownership in industrial output and industrial assets rose, especially after China's entry into the WTO, and dropped slightly after the global financial crisis in 2008 (Figure 1b). The dual-track development pattern also emerges in the regional distribution of different types of ownership. Compared with more marketized coastal regions, state
subsidy of this kind is a very imperfect method of targeting subsidy funds towards the poor. In the first place, the level of Type I error is extremely high at 94%; which is to say that only 6% of households in extreme poverty (1,000 out of the total 17,200) actually receive the subsidy. The level of Type II error is also substantial, with 31% of subsidies going to households above the extreme poverty line. Changing the eligibility criteria by reducing the telephone coverage limitation or the percentage of households in the zone living below the extreme poverty line did not change the general conclusions. More vulnerable households could be targeted, but more resources would at the same time be filtered to non-deserving households.

4Owing to the constraints of data availability, the zones used for the simulation were not, in fact, neighbourhoods, but sampling units from the national Living Standards Measurement Survey. These sampling units were actually smaller than neighbourhoods, numbering less than 15 households. Consequently, the targeting properties for sampling units might be expected to be better than those that would have been obtained using neighbourhoods.
rest of this note is organized as follows. Section II covers the analytical framework from the viewpoint of households and individual workers. It covers direct, first-order impacts of trade shocks on household welfare, second order or behavioral impacts, and finishes with a discussion of important policy issues, namely the role of adjustment costs, skills and gender. In the process it also discusses data than can be used to empirically implement the framework. Section III provides the analysis of firm behavior. It begins with a discussion of first order effects of trade-driven price changes on profits, and then discusses how different types of firms distinguished by the extent to which they are integrated into the global economy via trade in final and intermediate goods and foreign ownership would be affected through second-order, productivity and changes in output structure. Mimicking the sections on households and workers, the discussion of firm profits includes a cursory evaluation of relevant data that is readily available to busy public policy specialists. Section IV concludes.

# **II. Trade and Inclusive Growth: Households**

Although the framework for tracing the effects of trade shocks on household welfare is credited to the literature on rural household behavior, it has broader applications that go beyond rural settings. In fact, the most basic setup was also used by Winters et al. (2004) in their review of the literature on trade and poverty. The starting point is a household with a given consumption basket and various sources of income. The so-called first order effects of a trade shock can be seen as a price change that affect some 3
hereof are shown in table 3.3 and 3.4, where the numbers represent the share of modelled flood area that would be flooded considering the existing protection infrastructure. Therefore, it decreases the flood extent in those places where dike systems and other protection infrastructure are in place. With this, knowledge based on a detailed qualitative protection infrastructure assessment is converted into a numerical offset. To illustrate this, consider 100 square kilometers of rice paddy in the province of Nghe An that are marked as flooded by a 5-year coastal flood according to the original flood maps that do not take into account protection infrastructure. The expert assessment here states that only 20 percent of the modelled coastal 5-year flood extent in this province would occur due to the protection offered by a dike along one part of the coast, reducing the actual area flooded to 20 square km. In the asset exposure analysis of this report, exposure estimates derived from the unprotected flood maps are treated this way to incorporate flood protection measures.

These protection offsets are applied uniformly per province and event after the flood exposure is estimated. The strongest assumption in the methodology lies in this uniformity – it assumes that the existing protection infrastructure protects all assets to the same degree, irrespective of their location within the province. Given this assumption this methodology cannot be used to identify affected areas within a province. At the national level, however, the methodology produces estimates that hold considerable advantages over the absence of any estimates, as well as over unprotected figures that dramatically overestimate the existing risk.

17
advantage in the public sector in the 2000s. In the 2000s, the labor market for young workers appears competitive, with no significant difference in the public and private sectors' pay (α= 1 and η= 0). This finding suggests that public sector rents only benefit older workers.

|TABLE7. Exten|ded Model: In<br>|teraction wit<br>1993<br>|h Experi<br>|ence<br>|2006<br>||
|---|---|---|---|---|---|---|
||Coef.|Std. Err.|P>t|Coef.|Std. Err.|P>t|
|Relative returnsα|0.381|0.271||1.013|0.591|*|
|Publicpremiumηt|−0.288|0.220||−0.029|0.078||
|Publicpremiumηt+1|−0.292|0.227||0.107|0.078||
|Old workers * Returnsγ|0.080|0.206||0.976|0.756||
|Old workers * Publicpremiumδt|0.118|0.083||0.053|0.117||
|Old workers * Publicpremiumδt+1|0.202|0.076|***|−0.026|0.119||
|Time dummy τt|0.118|0.083||1.294|0.037|***|
|Time dummy τt+1|0.202|0.076|***|1.538|0.035|***|
|Sample size||1750|||7786||

_Source:_ Author's calculations based on VLSS and VHLSS data.

# _Returns
. This partly links with Africa’s commodity boom during the 2000s, which fueled economic growth in many countries as well as urbanization, in particular the emergence of consumption cities, characterized by higher shares of imports (including of food) and employment in non-tradable services, as opposed to tradable manufacturing or services (Gollin, Jedwab and Vollrath 2016). While inconsequential regarding the level of urbanization (and also growth and aggregate income in the short run), such resource driven structural transformation, urbanization and

> 1 This and all subsequent ‘World Bank data’ come from the World Development Indicators data

> (http://wdi.worldbank.org/), unless otherwise indicated.

> 2 Careful scrutiny of the data suggests that the number may be somewhat lower, but even under the most optimistic data scenario, more than 330 million people were estimated to be extreme poor in 2012 (Beegle et al., 2016).

> 3 <u>http://iresearch.worldbank.org/PovcalNet/home.aspx</u> (read on 15 November 2016).

> 4 Calculations based on the I2D2 database using 12 African countries with at least two points of sectoral employment data during 1995-2013 separated by at least 5 years (excluding Nigeria) suggest a population weighted 10-year decline of about 10 percentage points. The numbers are similar in magnitude to those reported by MacMillan and Harttgen (2014) using the Demographic and Health Surveys and de Vries et al. (2014). For the 14 and 9 African countries studied, they also report about a 1 percentage point decline in the share of agricultural employment per year over the past 10 (2000-2005 and 2006-2012) and 20 years (1990-2010), respectively.
the HFPS, the occupation variables had not been codified before. The responses to the occupation questions are open, and hence difficult to assign to a few categories.

We find 9,223 different answers in all the occupation variables included in the Latin American HFPS. In this study we make an effort to codify these answers in a few categories. To that aim we map the open individual responses to the occupation questions in the HFPS to the 41 groups in the 2-digit International Standard Classification of Occupations version 08 (ISCO-08). The ISCO categorization, proposed by the International Labour Organization (ILO), is currently adopted by most countries in the world, including many in Latin America, to classify the data on occupations collected in surveys, census and administrative records. We select the 2-digit grouping since it provides enough heterogeneity for the analysis, and at the same time avoids problems of lack of observations (Vosters 2018).

The codifying of the occupation variables in the HFPS implies a painstaking process of assigning each answer to an ISCO group, following the guidelines of the ISCO codebook, which includes specific information of the names, characteristics and activities of each occupation. In particular, we use the Spanish version of the codebook, consistent with the official language of the 13 Latin American countries included in the analysis. We were able to match 98 percent of the answers to the HFPS surveys to an ISCO group.<sup>6</sup> The orange bars in Figure 1 show the share of respondents in each of the ten occupation groups in the 1-digit ISCO classification before the start of the pandemic. On average, for all countries, the group with the largest share is Services and Sales Workers (21 percent) followed by Professionals (19 percent), Elementary Workers (14 percent), and Craft and Related Trades Workers (12 percent). There are some groups with a share between 6 and 9 percent (Technicians and Associate Professionals; Clerical Support Workers; Skilled Agricultural, Forestry and Fishery Workers; and
number of observations in the cluster. Within cluster covariance is given by _σ_ ˆ _η_<sup>2.Onthediagonal</sup><sup>_σ_ˆ</sup> _e,ch_<sup>2is</sup> added to obtain the observation specific disturbance varaince. The resulting **cluster block** of this _N × N_ matrix is equal to: where **0** is a block of zeroes; the number of columns equals the Ω<sup>ˆ</sup> _c_ from above, and the number of rows to the Ω<sup>ˆ</sup> _c_ next to it.

Ωis the variance-covariance matrix of the error vector necessary to estimate equation (1) via GLS and obtain ˆ ˆ _βGLS_ and Var _βGLS_ . The estimates for the GLS detailed by ELL (2003) are: � � and where _W_ is a _N × N_ diagonal matrix of sampling weights. Because _W_ Ω<sup>_−_1</sup> is usually not symmetric,<sup>23</sup> as noted by Haslett et al. (2010), the variance covariance matrix must be adjusted to obtain a symmetric matrix. This is done by obtaining the average of the variance covariance matrix and its transpose (Haslett et al., 2010).

Newer versions of the PovMap software no longer obtain _GLS_ estimates using this approach. Adjustments have been made in order to better incorporate the survey weights into Ω. The newest version of PovMap uses the _GLS_ estimator presented by Huang and Hidiroglou (2003), which is discussed below.

# **3.2 Henderson’s Method III decomposition**

After a decade of use of PovMap, and numerous poverty maps completed by the World Bank using the original ELL small area methodology; the software was updated with Henderson’s Method III (H3) decomposition of the variance components and an update of the _GLS_ with a modification of Huang and Hidiroglou (2003).<sup>24
if you want to succeed as a small entrepreneur. I started with a small capital, but I was constantly looking for important information and opportunities that could help me succeed. When I joined MSEDA, it was like God opening the door for me. I met several entrepreneurs and we exchanged ideas and also got training from several sources. I can go so far as to say that I am one of few women who, when you come back here a few years from now, will be owning a factory, and in my case, my own cashew nut processing factory.” _Source: Narratives from the FGD report, 2009._

# **3.2 Analysis of Productivity Drivers for HE Earnings**

29. HE operators enter the business to gain income. Does running an HE pay off? Unfortunately, the ILFS data set does not include agricultural income so a comparison cannot be made between earnings from HEs and from the alternative for most of the labor force in rural areas -- agriculture. However, when comparing HE earnings with those from other nonagricultural employment options, HE is not a poor choice. Although public employment is obviously a better choice in Tanzania, 95 percent or more of HE operators do not have the qualifications for these jobs (see Table A1.9). Not surprisingly, if an HE is able to graduate to an MSE, reported earnings tend to rise. For women, the median hourly earnings appear to be higher than those from a wage job in the private sector, even though HE earnings tend to be undermeasured compared to wage earnings (Figure 3.3). Moreover, if agricultural wage earnings approximate the alternative of agriculture, both men and women do better in non-agricultural activities, including HEs.

30. To determine in more detail which of the various characteristics of HE operators support increased earnings, a multivariate analysis was done separately for men and women. Table 3.4 16
# **Tables**

Table 1: International Migrants in Libya (2000 and 2017)

|Num<br>internation<br>(thous|ber of<br>al migrants<br>ands)|International<br>(percentage<br>populat|migrants<br>of total<br>ion)|Women<br>interna<br>migr<br>(perce|among<br>tional<br>ants<br>ntage)|Median<br>internation<br>(ye|age of<br>al migrants<br>ars)|
|---|---|---|---|---|---|---|---|
|2000|2017|2000|2017|2000|2017|2000|2017|
|567|788|10.6|12.4|35.0|28.8|29.6|32.2|

Source: UN DESA (2017).

Table 2: Five Top Nationalities Registered at Flow Monitoring Points

|**2017**|**2018**|
|---|---|
|Egypt, Arab Rep.|Egypt, Arab Rep.|
|Niger|Niger|
|Nigeria|Sudan|
|Sudan|Nigeria|
|Chad|Chad|

Source: International Organization for Migration Displacement Tracking Matrix dataset. Note: Elaboration of the authors.

Table 3: Five Preferred Arrival Destinations Registered at Flow Monitoring Points

|**2017**|**2018**|
|---|---|
|Libya|Libya|
|Italy|Italy|
|Germany|France|
|France|Germany|
|Egypt, Arab Rep.|Egypt, Arab Rep.|

Source: International Organization for Migration Displacement Tracking Matrix dataset.

Note: Elaboration of the authors.

22
body of literature showing effects in both directions (Desai and Jain, 1994; Kishor and Parasuraman 1998; Glick 2002).

It is ironic that in India, as the burden of <u>childbearing</u> has reduced, the burden of <u>childrearing has increased over time (table 2). There could be two main reasons for this. First,</u> intergenerational co-habitation has declined, especially in urban areas, as families have become more nuclear. Women have fewer potential helpers in child care and domestic responsibilities and hence have fewer options (Tuli and Chaudhary 2010). According to NSS data, among those married women (aged 25–55) who reported not being in the labor force in 2011, a significant majority (70 percent) reported staying in the domestic realm, due to compulsion. While the responses did not vary greatly by women’s education level, about one-third of women attending domestic duties only said they would be willing to accept work outside the home, and most of them (75 percent) wanted regular part-time jobs (World Bank 2011).

Second, it is possible that while the number of children per woman has declined, families place greater emphasis on the quality of childrearing and make higher investments in children, both as a route to social mobility and in response to pressure from schools. It is for instance, possible that especially in recent years, mothers in urban areas spend more time tutoring and mentoring their young children, as pre-schools insist on greater involvement of parents in the child’s education. Palriwala and Neetha (2011) suggest that the elite and middle classes have higher expectations of quality from child care providers, while Datta and Konantambigi (2007) argue that while many middle and upper class mothers have few alternative care options, they are pressured to provide early stimulation and education for their children. In another study, Basu and Desai (2012) find children in one-child families to be advantaged: they are more likely to be
Asian countries (De Mel et al. 2008). Small firms finance a smaller share of their investment with formal sources of external finance (Beck, Demirg ̈u ̧c-Kunt, and Maksimovic 2008) and are more constrained in their operations and growth (Berger and Udell 1998; Galindo and Schiantarelli 2003). In line with this evidence, financial development exerts a disproportionately large positive effect on the growth of industries that are more dependent on small firms (Beck, Demirg ̈u ̧c-Kunt, Laeven, et al. 2006).

Credit constraints are particularly salient for entrepreneurship loans, for two main reasons. First, income from self-employment is hard to verify as opposed to income from wage work stated in an employment contract, increasing information asymmetries between lenders and borrowers. Second, lending for self-employment is particularly risky given the low rates of survival of small enterprises in developing economies (McKenzie and Paffhausen 2019). To mitigate this risk, lenders limit the amount of credit provided and require high levels of collateral. In this context, micro-finance institutions emerged to address low access to formal lending opportunities by low-income borrowers. However, the conditions of entrepreneurship loans provided by microfinance institutions, which are now the main source of entrepreneurship loans in Bangladesh, are also restrictive. BRAC microfinance, the main provider of microfinance in Bangladesh, requires aspiring entrepreneurs to provide collateral assets that are at least equal to the nominal amount of their loan (Battaglia et al. 2021). Microfinance and NGO institutions, charge high interest rates, averaging 22 percent, on entrepreneurship loans (A. Islam et al. 2015; Battaglia et al. 2021). Interest rates charged by informal money lenders, to which many borrowers still resort, are significantly higher than these levels (Mallick 2012).

Only a small share of entrepreneurs in Bangladesh and other developing countries finance their initial investment through credit by formal lending institutions (C. M. Woodruff and Zenteno 2001; Banerjee and Duflo 2014). For Bangladesh specifically, international surveys and prior studies
percent. This pattern suggests, albeit informally, that any further omitted variable bias is not likely to be too serious (see Altonji, Edler, and Taber, 2005).<sup>20</sup> In other (unreported) specifications, we include quadratic terms in temperature and rainfall and we also separate climate variables into aggregates over the two main growing seasons, kharif and rabi, none of which leads to appreciable improvements in fit. What is found, though, to be statistically significant and positive in the second stage is the interaction be-

> 18Variability could influence land values through at least three mechanisms: (1) rainfall affects contemporaneous cash flow from land in a nonlinear fashion so that expected cash flow is a function of the rainfall variance as well as the mean; (2) in making investments in agricultural assets, risk-averse farmers trade off high expected returns for safety so that productivity per acre is lower where weather variability is greater (see Rosenzweig and Binswanger, 1993, for some evidence from India); and, (3) under risk-aversion, land prices exceed the expected present discounted cash flow from land by a risk premium that is increasing in weather variability.

> 19See Table 1. Data on soils come from the Food and Agriculture Organization’s Soil Map of the World (SMW), which provides detailed data for each soil mapping unit, defined as a geographical area with similar soil classes. Dummies are created for the five dominant soil types in India (acrisols, cambisols, luvisols, nitosols, and vertisols). In addition, there are three textural classes (coarse, medium, and fine), which reflect the relative proportions of clay, silt, and sand in the soil, and three slopes classes (level to gently undulating, rolling to hilly, and steeply dissected to mountainous). 20Sanghi, Mendelsohn, and Dinar (1998), applying a cross-sectional Ricardian approach to pooled districtlevel panel data from India for the period 1966-86, find a comparable temperature effect on
was 59 percent. This suggests that the constraint of having to charge the Nokia phone using an electrical outlet did not influence survey completion.

Three probit regression models were used to explore in more detail factors associated with successful completion of the survey. Model 1 in Table 5 assesses the likelihood that a respondent completed the questionnaire given personal characteristics and SSEPS design features. Models 2 and 3 in Table 5 assess the association of these same factors with the likelihood a participant completed all four surveys and with the likelihood a participant completed none.

The decline in response over the course of the four month implementation period is evident in the regression results. Controlling for other variables, respondents were 7, 16, and 20 percent less likely to have completed a questionnaire in January, February, and March relative to December. Operational data tracked by the Horizon Contact Center suggests that this result was in part due to declining network availability over the four month period.

12