rafmacalaba/data-use-mentions-extended huggingface dataset:
10 studies of growth, and also by Panizza (1999).<sup>10</sup> For Russian regions this measure has not been readily available and was constructed using the data from the 1989 census.<sup>11</sup> Urbanization [URBAN] is conventionally measured by the percentage of the urban population.

The most challenging task is to construct a region-specific measure of democracy [DEM]. Most cross-country studies (including Panizza, 1999) use the Gastil (1990) index of democracy and political rights or similar survey-based indices. Unfortunately such indices are unavailable for Russian regions. Instead, two proxies for the regional level of democracy are considered.

The first proxy is based on electoral statistics and equals the spread between the percentage of votes for the winner and for the runner-up in the first round of the elections of regional executive.<sup>12</sup> Certainly, tense electoral competition is neither a necessary nor sufficient condition for developed democracy; however, on average one would expect a high positive correlation between the competitiveness of elections and the quality of democratic institutions. For the sample of 38 presidential democracies the correlation between the percentage of votes that the head of state got in the first round of the last elections prior to 2000 and the Gastil index of democracy in 2000 is 0.53.<sup>13</sup> The second proxy is based on the only currently available survey-based study of the institutional environment in Russian regions carried out by an independent international organization, namely the corruption perception index compiled by Transparency International for 40 Russian regions in 2001-02. Once again, the perception of corruption and the perception of

> 10 Earlier studies (e.g., Oates, 1972) distinguished between heterogeneous and homogeneous states by using a dummy variable.

> 11 Ethnic fractionalization may have plausibly increased during the 1990s as a result of significant external migration and various shocks. However, we did not have access to the data from the latest (2002) Russian census.
IBGE, 2020)) compared to the role of local economic cycles. Formal employment has been closely following economic cycles, with a variation of about 12% between the lowest and highest levels in the same period. In fact, the Brazilian economy had more formal wage jobs in 2012 than in 2019 (MTE, 2019). In addition, large parts of the employed population in Brazil work informally (IBGE, 2019), so excess population growth, in the absence

> 18The dummy dependent variable _having a family member in formal dependent employment_ takes the value of 1 if a family member is in formal dependent employment and 0 if no family member is in formal dependent employment.

> 19The dummy dependent variable (family _i_ leaves BF) takes the value of 1 if the family leaves the program – based on the official payroll information of BF containing information on monthly benefit payouts of the benefit –, 0 if it stays, and is missing if the family is not in BF in the time-point.

> 20A conditional fixed-effect model could be used as well, but the linear probability model makes an IV approach and the interpretation of the results more straightforward.

> 21To obtain this information, we use RAIS data, which reports the number of job links, which slightly differs from the number of workers since a small share has more than one formal job.

> 22In the case that families moved between municipalities we consider the employment growth of the origin municipality as common in the migration literature (Jaeger _et al._ , 2018).

> 23In order to not have our results driven by extreme outliers, we winsorize employment growth at the 99.5 percentile. By doing so we change less than 1% of the municipalities’ employment growth.

> 24The CadUnico data provides only a few variables that vary over time.

14
.<br>Neighbourhood|.<br>HH|.<br>Country|.<br>Neighbourhood|

Notes: All specifications consider the full cross-country sample of host respondents and include the full set of control variables described in Table A.3 in appendix. We consider three sets of fixed effects: country fixed effects (Columns 1 and 4), neighbourhood fixed effects (Columns 2 and 5), and households fixed effects (Columns 3). To show the association between perceptions and context-specific indicators such as living in an urban context or within a Somali community, Columns 4 and 5 do not control for the perceptions of neighbours. The perception and interaction indices are standardized to facilitate interpretation. Standard errors in parentheses are clustered at the highest level of sampling, which is neighbourhoods in contexts with two-stage cluster sampling and households in context with simple random sampling. Significance levels are indicated by * _p <_ 0 _._ 1, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01.

42
it follows that conditional on the residuals observed in the survey data, the error _eaj_ is normally distributed with mean and variance given by (see e.g. van der Weide, 2014): where _γa_ = _σv_<sup>2</sup><sup>_/_(</sup><sup>_σ_</sup> _v_<sup>2+</sup><sup>_σ_</sup> _ε_<sup>2</sup><sup>_/ma_),with</sup><sup>_ma_equaltothenumberofsurveyhouseholdssam-</sup> pled into the area _a_ , and where: If there are sampling weights these will be absorbed by (and modify) the weights _αah_ (see van der Weide, 2014). Conditioning on the survey data when drawing the errors is known as Empirical Bayes (EB) prediction (see e.g. Molina and Rao, 2010). Note that EB prediction only concerns the drawing of the area errors, and only those areas that have been sampled into the survey. The gain in precision resulting from conditioning on the survey data for these areas is most notable when the variance _σv_<sup>2and the number</sup> of sampled households _ma_ are relatively large.

The small area estimates of poverty and corresponding standard errors are obtained by means of bootstrapping. Let _R_ denote the number of simulations. The estimator for poverty then takes the form: where _h_ ( _y_ ) is a function that converts the vector _y_ with (log) consumption expenditures for all households into the corresponding poverty rate, and where _y_ ̃<sup>(</sup><sup>_r_)</sup> denotes the _r_ -th simulated vector with elements: ̃ ̃ ̃ ̃ where the model parameters _β_<sup> ̃(</sup><sup>_r_)</sup> _, σ_<sup>(</sup> _v_<sup>_r_
Nevertheless, this result is consistent with related research investigating the global proliferation of antidumping in particular. First, the United States is not only one of the largest users of trade remedies worldwide. Zanardi (2004) notes that between 1991 and 2002, exporting firms from the United States were the third-most _investigated_ producers worldwide in foreign antidumping investigations, as well as ranking third in number of instances of being _hit_ with trade remedy measures (antidumping duties and price undertakings) worldwide, behind only China and Korea. Finally, Prusa and Skeath (forthcoming) examine the pattern of antidumping filings across the world and find evidence at the bilateral level of retaliation activity that is consistent with the indirect evidence described here, while Feinberg and Olson (2004) use the worldwide antidumping filing data to find evidence of such a relationship across 2-digit industries as well.<sup>28</sup>

# **5.2.3 Estimates for inclusion of countervailing duties, the injury determinants and without the**

# **control for selection bias**

The rest of table 6b presents a number of initial robustness checks to our analysis. First, specification (2) adds in the additional observations for U.S. countervailing duty measures and their WTO challenges. Even after including these trade remedy observations as well, the basic pattern of qualitative results is unchanged.

In specification (3) of table 6b, we assess the importance of determinants expected to affect the injury decision in the U.S. trade remedy investigation (as described in table 6a) to check the robustness of our results. Inclusion of these variables in the second stage does not affect the qualitative pattern of results, and these variables are not of the expected sign and are not statistically significant. Part of the explanation for their poor performance is likely due to the fact that some countries are not challenging the injury investigation that took place in year _t_ , but instead may be challenging a sunset or administrative review or a dumping or subsidy determination.

Finally, in
number of firms. In particular, during the 2000s, Chinese SOEs captured on average about 57 percent of the equity volume raised through capital markets, while Indian SOEs captured on average about 21 percent of the equity amount raised. This corresponds to about 21 percent of the Chinese firms and only 6 percent of the Indian firms raising equity capital. With regards to bond markets, SOEs captured not only a more sizeable share of the volume raised (especially in India), but also represented a larger share of the number of firms raising capital. For example, SOEs accounted for 63 and 50 percent of the total amount raised through bonds in China and India, respectively, which corresponds to 60 (26) percent of the Chinese (Indian) firms issuing bonds.

In sum, the results based on firm-level data on the use of capital markets suggest that the expansion of financing to the private sector in China and India has been much more subdued than what the aggregate numbers suggest. The expansion of equity and bond market capitalization has not been associated with a more widespread use of capital markets by corporations. In fact, financing through capital markets has been channeled to relatively few firms and markets have remained highly concentrated, with few firms capturing an increasing amount of equity and bond financing. In other words, the deepening of equity and bond markets (measured by their expansion in absolute and relative size) has not led to a greater breadth of markets. Moreover, not only do few firms have used equity and bond markets on a recurrent basis, but also the bulk of capital market financing in China and India has been concentrated around few firms. These patterns suggest that capital markets have not provided a stable source of firm financing, which contrasts sharply with the perception in the existing literature that equity markets, particularly in India, are well-developed. Moreover, the patterns are particularly interesting if one considers that part of the financing in capital markets has gone to the SOEs and 18
28 pillar strategy: First, adopting a dual-track approach to reforms—giving transitory protections to old comparative-advantage-defying, capital-intensive sectors and liberalizing entry to comparative-advantage-conforming, labor-intensive sectors, and thereby simultaneously achieving both stability and dynamic transformation. Second, as a latecomer, choosing an economic development strategy that taps the potential of the advantage of backwardness along the lines of the flying-geese pattern.<sup>20</sup> Looking forward, China can still rely on the advantage of backwardness and has the potential to maintain dynamic growth for another 20 years or more.<sup>21</sup> Behind China‘s growth over the past three decades has been a dramatic structural transformation—in particular, rapid urbanization and industrialization. At the start of economic reforms in the 1980s China was primarily an agrarian economy. Even in 1990, 73.6 percent of its population lived in rural areas, and primary products accounted for 27.1 percent of GDP. In 2009 these shares had declined to 27.1 percent and 11.3 percent. A similar change occurred in the composition of China‘s exports. In 1990 primary products made up an important share of merchandise exports. Today almost all of China‘s exports are manufactures (figure 16).

**Figure 16. The Structural Transformation of China’s Exports**

<!-- Start of picture text --> Composition of Chinese Exports, Composition of Chinese Exports,<br>1990 2009<br>Primary<br>products and<br>chemicals:<br>Primary<br>products<br>and<br>chemicals:<br>Manufactured Manufactured<br>Goods, (including Goods, (including<br>machinery and machinery and<br>transport transport<br>equipment):<br><!-- End of picture text --> Source: World Bank, WITS database.

China is unquestionably the dragon in the global marketplace for low-tech products today. In the four
is equal to the difference between the average comparative advantage of the switchers to private and the switchers to public: �E�yit+1|Hi=10�−E�yit|Hi=01��−�E�yit+1|Hi=00�−E�yit|Hi=00��= m10 −m01.

Finally, it is important to note that our model allows for the free sorting of workers into employment sectors as long as the sorting is driven by their comparative advantage. For example, the probability of working in the public sector could be an increasing function of θi: workers with a low (high) level of comparative advantage would always work in the private (public) sector whereas the sector switchers would be found at the intermediate levels of θi. However, we do not allow the past idiosyncratic shocks (i.e., the lagged values of uit) to influence the sectoral choice. The reason that we need this assumption is straightforward: we want to use data on the workers who switch sectors between the two surveys to disentangle the effect of the workers' fixed characteristics and the effect of the sector-specific wage settings. However, the sector switchers’ earnings are informative about the entire workforce if and only if, at any given date, they are paid the same as the nonswitchers who are working in the same sector with the same level of comparative advantage (the same θi).

# _Estimation_

Drawing on the identification results presented in the previous section, we can now estimate the model described in equation (1) even though the index of the workers' productive characteristics θi is not observed. We regress the log earnings on a set of dummies for the public and private workers and for the four groups of workers with different employment histories. The estimation yields the model parameters and the four 14
WPS3754

# **Reaching out:**

# **Access to and use of banking services across countries**

## **Thorsten Beck, Asli Demirguc-Kunt and Maria Soledad Martinez Peria**

**Abstract:** This paper (i) presents new indicators of banking sector penetration across 99 countries, based on a survey of bank regulatory authorities, (ii) shows that these indicators predict household and firm use of banking services, (iii) explores the association between the outreach indicators and measures of financial, institutional, and infrastructure development across countries, and (iv) relates these banking outreach indicators to measures of firms’ financing constraints.  In particular, we find that greater outreach is correlated with standard measures of financial development, as well as with economic activity.  Controlling for these factors, we find that better communication and transport infrastructure and better governance are also associated with greater outreach.  Government ownership of financial institutions translates into lower access, while more concentrated banking systems are associated with greater outreach. Finally, firms in countries with higher branch and ATM penetration and higher use of loan services report lower financing obstacles, thus linking banking sector outreach to the alleviation of firms’ financing constraints.

### World Bank Policy Research Working Paper 3754, October 2005

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

* The authors are with the World Bank’s research department.  We would like to thank Ross Levine for insightful
**Table 3:  Percent of Urban Population with Access to Services and Housing Conditions by Reference**

**Group**

|**Reference**<br>**Group**<br>**(Percentile**<br>**Range)**|**Threshold**|**Housing**<br>**Unaffordability**<br>**rate**|**Households**<br>**with access to**<br>**safely managed**<br>**sanitation**|**Households with**<br>**access to safely**<br>**managed drinking**<br>**water**|**Households with**<br>**access to roof, floor &**<br>**walls that it is**<br>**constructed of low-**<br>**quality material**|
|---|---|---|---|---|---|
|10 to 40|3716|25.2|69.1|84.3|37.5|
|20 to 40|3911|29.1|71.1|84.2|37.8|
|0 to 60|4049|31.9|69.2|83.3|35.0|
|20 to 50|4156|33.9|72.3|83.9|36.2|

Source: Author calculations based on HIES 2018-19 data

**<mark>Comparability of expenditure and income data.</mark>** <mark>In most developing countries, consumption expenditure is used as a proxy for welfare. Theoretically, consumption is likely to remain more stable over time than income, which can be affected
in LSMS-ISA survey data. For those households with trees on their farms the average contribution across the study countries was almost three times as much, i.e. 17% and about 32% as a share of agricultural gross income.<sup>12</sup> Surprisingly, the contribution of trees on farms to gross income among tree growers was highest in Nigeria, even though tree growing occurred least frequently there, suggesting a high degree of specialization among tree growing households. At 18.7% income from trees among tree growing households is also more important in Uganda. Clearly, income from tree growing can be quite important, with the larger share typically coming from tree cash crops (14% of gross income among tree growers). Nonetheless, even though much less commented upon, income among fruit trees still contributes 5% of

> 12 By comparison, a recent cross national study of households living in or near forests found that natural forests in Africa contributed 21% to household incomes and plantation forests less than 1% (Angelsen et al., 2014).

12
Research Working Paper 6436.

Gaddis, Isis, Gbemisola Oseni, Amparo Palacios-Lopez, and Janneke Pieters. 2019. “Measuring Farm Labor: Survey Experimental Evidence from Ghana.” World Bank Policy Research working paper no. WPS 8717.

Gaddis, Isis, and Amparo Palacios-Lopez. 2018. “Counting Women’s Work and Employment: Recent Methodological Advances and Tracing the Links Between Data and Policy.” Presentations made at the 20<sup>th</sup> ICLS, October 2018, Geneva; and at the workshop on Improving Data Collection on Women and Youth Employment, World Bank and IFAD, November 2018, Rome.

Giné, Xavier and Ghazala Mansuri. 2016. “Money or Management? A Field Experiment on Constraints to Entrepreneurship in Rural Pakistan”, Mimeo. World Bank.

Global Strategy to improve Agricultural and Rural Statistics (GSARS). 2017. “Intra-household decisionmaking in agriculture: The value added of asking who makes the decisions around specific agricultural activities.” Working Paper No. 18. GSARS Working Paper: Rome.

25
by distance between Pakistan and the partner country.<sup>22</sup> The underlying idea is that changes in trade costs in further away countries should have less of an impact on the export decisions of Pakistani firms than corresponding changes in neighboring countries. We assume that a reduction in the number of days needed for clearing merchandise at the customs is correlated with a more general effort of a country to simplify import procedures (and/or an effort from the Pakistani authorities to expedite the movement of products across borders) and thereby reduce the fixed costs of entry to world markets. We obtain country specific data on the number of calendar days to import (export) from the World Bank Doing Business database.<sup>23</sup> Finally we add time-varying 2-digit HS fixed effects to the specification (φk2t). These act as sectoral deflators, controlling for any time-varying sectoral determinants, including any sector

> 21 Source for the tariff data: Market Access Map, Market Analysis Tools, International Trade Centre, www.intracen.org/marketanalysis.

> 22 Specifically, our measure of fixed cost to export between Pakistan and country c between year t+1 and year t is defined as ∆(customs_daysct) = ln�days_expmkd,t+1+days_impc,t+1�−ln (days_expmkd,t+days_impc,t) ln�distancemkd,c� ln (distmkd,c)

23 The time for exporting (importing) is recorded in calendar days. The trading across borders methodology defines this variable as _“The time necessarily to comply with all procedures required to export (import) goods. The time calculation for a procedure starts from the moment it is initiated and runs until it is completed. If a procedure can be accelerated for an additional cost and is available to all trading companies, the fastest legal procedure is chosen. Fast-track procedures applying to firms located in an export processing zone are not taken into account because they are not available to all trading companies.
Domestically, the focus has been on rural-urban migration, without much distinction between urban areas. Yet, incipient evidence suggests that secondary towns may be more within the reach of the poorer segments of the population than large cities, and that secondary town development may hold more promise to improve job opportunities and for poverty reduction than metropolitanization. This is an area for further investigation with important policy implications for investment (Gibson, et al., 2017; Christiaensen and Kanbur, 2017). More broadly, a systematic documentation and incorporation of internal migration patterns in national development strategies (rural-urban, but also urban-rural, urban-urban and rural-rural, and return migration) remains lacking. More systematic use of the census data could help address this lacuna.

# **3 ECONOMICS OF MIGRATION AND JOBS**

The policy framework that we develop in this section is based on the spatial equilibrium model developed in Rosen (1979) and Roback (1982). The model assumes that workers move in geographic space to equalize “livelihoods” over time. Labor income is a key determinant of these livelihoods and therefore migration decisions are affected by the geographic distribution of job opportunities.<sup>16</sup> At any time, three key factors enter in the individual decision to migrate: 1) their expectations about job opportunities in the area(s) of destination; 2) job opportunities in the area(s) of origin; and 3) the costs associated with moving and realizing the prospects in the destination. If the expected gains in labor income net of migration costs are positive, people are expected to migrate.<sup>17</sup> This continues until a spatial equilibrium is established.<sup>18</sup> This model provides a benchmark to assess the efficiency of the distribution of workers across space and, by extension, the efficiency of the policies and regulations shaping this distribution.<sup>19</sup> In the absence of market or regulatory failures, there would not be a