rafmacalaba/data-use-mentions-extended huggingface dataset:
according to constant-elasticity demand curves. Therefore the terms of trade for China are endogenous in our simulation. The values of export demand and Armington elasticities, which are reported in Table 1, are based on the estimation by Hertel et al (2003). Table 1 also lists some a variety of useful information on production and trade patterns of the Chinese economy, based on the 1997 Chinese social accounting matrix (SAM).

Production in each of the sectors of the economy is modeled using nested constant elasticity of substitution (CES) functions, and constant returns to scale is assumed. In the top level of the nest, value-added and a composite of intermediate inputs produce outputs. Then a further CES function disaggregates the value-added into a capital-labor composite and agricultural land. The capital-labor composite is further split into the capital-skilled labor composite and the aggregated less-skilled labor. The capital-skilled labor composite consists of capital and skilled labor, while aggregated less-skilled labor is composed of semi-skilled labor and unskilled labor. The values of substitution elasticities in production functions are listed in Table 2. A low substitution elasticity of 0.3 between capital and skilled labor is assumed here to introduce capital-skill complementarity. The elasticity of substitution between semi-skilled labor and unskilled labor is set to 1.5, based on estimates for the U.S. by Katz and Murphy (1992) and Heckman and Lochner (1998).

Within each labor category (i.e., unskilled, semi-skilled and skilled), we distinguish rural and urban workers. These two categories of workers substitute imperfectly in production. This is an indirect means of building into the model a geographic flavor – since some sectors will be located largely in urban areas, while others will be predominantly in rural areas. By limiting the substitutability of rural and urban labor in each sector, we are able to proxy the economic effect of geographically distributed activity. This is particularly important in China where significant 8
# **3. Data characteristics**

The panel data used in this paper, collected by the Bangladesh Institute of Development Studies (BIDS), are based on household and community surveys prior to, and following, implementation of two World Bank funded projects that allowed identification of control and treatment villages. The data are used here to calculate economic returns to roads and its impact on poverty, and overcome some of the pitfalls of past road evaluations that have relied mostly on cross-sectional household survey data. The datasets have a true before/after and with/without structure and are reasonably large, allowing a study of household-level impacts, especially with reference to households above and below the poverty line. They cover not just the standard road project outputs such as trip frequency and duration, but also key outcomes (consumption, employment) and a broad range of market interactions.

The data collection was financed under the World Bank-funded projects and conducted by BIDS as part of the government’s efforts to analyze and quantify both the short-term and long-term impacts of rural road improvements. The first survey covered the Rural Roads and Markets Improvement and Maintenance Project – I (RRMIMP-I), which was a component of the Rural Development Project-7 (RDP-7). For the purposes of this paper, we will define this project as RDP. The RDP initiative entailed improvement of 47 FRBs to bitumen surfaced standard, upgrading of 65 secondary markets and construction of 3700 meters of culverts and small bridges. Its physical works were completed during 1995-96. The first phase of the survey collected benchmark information in the study area prior to the project work, and the second phase collected the same information during the first half of 2000. The next phase included 24 project and 18 control villages, and 1,260 households.

The second survey studied the Rural Roads and Markets Improvement and Maintenance Project – II (RRMIMP-II), which included improvement of 574 kilometers of FRBs to bitumen-surfaced standard, construction of 1,900 meters of culverts, 1,750 meters of bridges and
the property value; as such, the detection capacity channel is muted. Using a modified version of the Allingham and Sandmo (1972) tax compliance framework, the paper shows that taxpayers’ decision to pay a given tax liability depends not only on their perceived probability of their property being discovered by the tax authority and the associated penalties, but also on the perceived probability that the government will enforce the law and collect the taxes and penalties they owe.

In the first phase of the project, I worked closely with the LRA to conduct a door-to-door property enumeration and develop a new electronic property database. Prior to this exercise, only 5 percent of properties in the city were on the tax register, and many records were not digitized. This exercise thus represented a significant technology investment for the LRA that made the tax base more legible.

Experiment one examines the impact of signaling this newly acquired identification capacity and providing information on penalties to property owners who had not previously paid real estate taxes. The control group received a generic tax notice addressed “Dear Property Owners.” The notice provided information on the procedure for tax payment and requested compliance. A treatment group received a tax notice that, in addition, included identification information from the new database: the name of the property owner and a photograph of the property. This notice also included information on legal penalties for noncompliance. Two additional treatment groups received notices with either the identification information alone or the penalty information alone.

The combination of identification and penalty information significantly increased property owners’ compliance: property owners were more than four times as likely to pay the tax. The payment rate increased from 2.2 percent of property owners in the control group to 9.7 percent in the treatment group. This effect persisted, as the treatment group continued to have a higher payment rate over the next three years of available data. The impact of the combined identification and penalty treatment is statistically different from the identificationonly and penalty-only treatments, neither of which is statistically significant. These
- de Walque, Damien. 2005. “Selective Mortality During the Khmer Rouge Period in Cambodia.” _Population and Development Review_ 31 (2): 351-368.

- de Walque, Damien. 2006. “The Socio-Demographic Legacy of the Khmer Rouge Period in Cambodia.” _Population Studies_ , 60 (2): 223-231.

- de Walque, Damien. 2007. “How Does the Impact of an HIV/AIDS Information Campaign Vary with Educational Attainment? Evidence from Rural Uganda.” _Journal of Development Economics_ , 84(2): 686-714.

- de Walque, Damien, and Philip Verwimp. 2009. “The Demographic and Socioeconomic Distribution of Excess Mortality during the 1994 Genocide in Rwanda .” _Journal of African Economies_ .  19: 141-162.

- Duam, Naihua. 1983. “Smearing Estimate: A Nonparametric Retransformation Method.” _Journal of the American Statistical Association._ 78(383): 605-610.

- Filmer, Deon. 2005. "Fever and its Treatment among the More and Less Poor in Sub-Saharan Africa." _Health Policy and Planning_ 20(6): 337-46.

- Fortson, Jane. G. 2008. “The Gradient in sub-Saharan Africa: Socioeconomic Status and HIV/AIDS.” _Demography_ 45(2): 303-22.

- Gakidou, Emmanuela, and Gary King. 2006. “Death by Survey: Estimating Adult Mortality Without Selection Bias from Sibling Survival Data.” _Demography_ 43:569-585.

- Gwatkin Davison, Shea Rutstein, Kiersten Johnson, Eldaw Suliman, Adam Wagstaff, and Agbessi Amouzou. 2007. _Socioeconomic Differences in Health, Nutrition, and Population Within Developing  Countries. An Overview._ Country Reports on HNP and Poverty. Health Nutrition and Population, The World Bank, Washington DC.

- Gwatkin Davidson R., Adam Wagstaff, and Abdo S. Yazbeck. 2005. _Reaching the Poor with Health, Nutrition and Population Services_ . The World Bank, Washington DC.

- Halvorsen, Robert and Raymond Palmquist. 1980. “The Interpretation of Dummy Variables in
)orunder</sup> the new AFP system. Finally, the initial conditions include two permanent characteristics: completed schooling levels of men and women ( _e_<sup>_j_</sup> ), and their birth cohorts ( _bc_<sup>_j_</sup> ). We denote the set of initial conditions for a single household by: The set of initial conditions for a couple is then: At ages _t_<sup>_f_</sup> _C_<sup>=60and</sup><sup>_tm_</sup> _C_<sup>=65yearsoldrespectively(orsooneriftheyqualifyforearly</sup> retirement), males and females begin to withdraw money from their pension savings accounts. For tractability, we did not incorporate the choice about whether to take retirement savings as an annuity or as a phased withdrawal. Rather, we assume phased withdrawal, because the formula is a simple function of age.<sup>24</sup> The level of pension benefits is calculated according

> 22 We use the terms husband and wife, but the model applies to cohabiting, non-married couples 23 Singles are assumed to remain single after age _t_ 0. Married couples are able to transition to being divorced or widowed, as further described below. We estimate the model for singles on people who remain single after age _t_ 0.

> 24In computing the programmed withdrawals, we used the life tables RV-2004 published by the _Superintendencia de Pensiones_ . The 2009, 2010 and 2011 rates of return were used for the corresponding years. To discount years more than 20 years in the future, the 20th discount rate was repeated. For years after 2011, the 2011 vector was used. For years before 2009, a single discount rate of 5 percent was used. If a couple qualifies for the PASIS pension, our simulation assigns the PASIS pension to the woman (only one member of the household can get the PASIS).

11
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was met with considerable success. However, a financial crisis broke out in 1982 and several brokerage houses went bankrupt. In 1983, the Central Bank took over the administration of some "problem" banks, put all the others uneer close supervision, and started to reregulate deposit interest rates. Overall, it can safely be said that the response of the financial sector to deregulation was worse than expected.

The objective of this paper is to present an overview of the events that culminated in the crisis. This will be done in two steps. First (Section 2) the events in the banking sector will be summarized. In the banking sector, shocks to the corporate sector made portions of banks' assets nonperforming. This was true especially for some smaller banks. Rather than forcing liquidation of their clients, these banks engaged in a fierce competition to collect funds, to raise resources both to meet their liabilities and to refinance non-performing loans. Interest rates soared. Some banks were thus able to survive even though they were insolvent, until the government finally intervened.

To get some clues about why bank loans might have become non-performing, Section 3 looks at the private corporate sector. Analysis of a panel data set of corporations reveals that in this period firms were subject not only to an interest rate shock, but also to a gross earnings shock (increase in costs relative to sales income). While one would expect that higher interest rates on loans would make firms reduce their indebtedness, debt to assets ratios of profitable firms did not change much during 1980-82, and those of firms under distress actually increased.

These observations suggest that financial liberalization may not generate desired responses if it is carried out when there are major changes in the
Other studies found supporting evidence that gender role expectations make business accomplishments harder for women, especially through lower access to business capital and fewer social networks (Gupta et al. 2019; Mitra and Basit 2019). In the CBFW (2021) report, half of the women entrepreneurs surveyed reported that family members and friends encouraged them to devote more effort to domestic and care work in their homes. Where women do run their own businesses, they often align with societal expectations that these should be small-scale and entail minimal risk (Adom, 2015; Croson and Gneezy, 2009).

In Madagascar, Nordman and Vaillant (2014) investigated whether genderdifferentiated allocations of time within the household, based on gender norms, cause women to allocate their time sub-optimally to their businesses. They found that particularly for homebased businesses, domestic chores negatively affect business efficiency. Another notable exception is a recent work by Anambane and Adom (2019) which explores the role of culture and gender stereotypes on growth of women operated businesses in Ghana. Using qualitative data from 20 women entrepreneurs from the Upper East region of the country, the authors find that entry into entrepreneurship as well as the kinds of businesses pursued are affected by gender stereotypes. Gender stereotyping influences the size and sectors of women’s businesses, with women operating small businesses in low value-added sectors like retail and services. The authors explain this as a cultural expectation of women showing modesty and humility in all endeavors, including business. It should be noted, however, that entry into other sectors like manufacturing may likely be affected by underlying skill sets and financial capabilities.

In addition to the effects of gender and social norms, there is a growing body of economics literature on the role of personal initiative on business outcomes (Dal Bo et al., 2013; Callen et al., 2015), a subject that has been explored for much longer in the field of industrial psychology. Burger (2010) defines personality traits as characteristics originating from within a person that define their individuality. Zhao
Policy Research Working Paper 9222

# **Abstract**

Household consumption surveys do not typically cover refugee populations, and poverty estimates for refugees are rare. This paper tests the performance of cross-survey imputation methods to estimate poverty for a sample of refugees in Chad, by combining United Nations High Commissioner for Refugees survey and administrative data. The proposed method offers poverty estimates based on administrative data that fall within a 95 percent margin of poverty estimates based on survey consumption data. This result is robust to different poverty lines, sets of regressors, and modeling assumptions of the error term. The method outperforms common targeting methods, such as proxy means tests and the targeting method currently used by humanitarian organizations in Chad.

This paper is a product of the Fragility, Conflict and Violence Global Theme and the Development Data Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ prwp. The authors may be contacted at pverme@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 3. Distribution framework for government securities before and after the Expanded Small** **<u>Investors Program</u>**

|**Before**|**After**|
|---|---|
|_Primary market_||
|•Only authorized securities dealers (banks) and large,<br>tax-exempt institutional investors (such as pension<br>funds) were allowed to participate in auctions of<br>securities|•Noncompetitive pricing; retail investors<br>acquire securities for the most recent<br>average auction price less 50 basis points<br>(0.5 percent)|
|•Dealers distributed securities through a subset of<br>their branch networks|•Nationwide|
|•Only large market participants (institutional<br>investors and dealers (GSEDs)) had accounts in the<br>Registry of Scripless Securities|•Retail investors open individual accounts<br>in the registry|
|•Minimum investment through dealers (GSEDs) is<br>100,000pesos, and not in theprimarymarkets.|•Minimum investment is 5,000 pesos|
||•Unlimited supply of securities|
|_Secondary market_||
|•Dealer and customer accounts were combined||
|•Minimum investment was 100,000 pesos through<br>dealers in secondarymarkets||
|•Supply of securities for distribution in secondary<br>markets depended on availability||
|•Prices varied according to the spreads charged by<br>dealers||

Although the new process does not eliminate dealers, they are no longer able to determine spreads or control access to government securities. Under the old arrangements dealers chose their technology and determined their workflows, then imposed these on the Treasury. But with the Expanded Small Investors Program, the Development Bank’s Data Center has been transformed into a shared services provider that connects investors, dealers, and the Treasury. Thus the power for determining efficiencies
the purchase of housing (Table 2.5); this is much less than the proportion of households having such plans. Surveys conducted in 1994 (Urban Institute, 1995; Guzanova, 1994) indicate that the share of such households was noticeably higher then (for instance, 27.5% in Bamaul and 19.3% in Nizlmy Novgorod) and the range of sources was broader, reflecting the fact that the financial situation of many households has deteriorated in the past few years.

Table 2.5. Sources of financing for future improvement of housing conditions as identified by households

|Sources|Vladimir|Gorodets|
|---|---|---|
|Loans|4.3|2.6|
|Own firm, enterprise|3.2|1.3|
|Sale of apartment (house)|1.6|1.3|
|Help from relatives|1.0|2.0|
|Sale of property|1.0|0.3|
|Savings|0.8|0.7|
|Other|1.6|0.7|
|None|36.0|32.2|
|No need|52.6|59.9|

Source: Vladimir and Gorodets Longitudinal Household Survey, December 1996.

Despite this deterioration in household financial standing, 16.8% of Vladimir and 11.4% of Gorodets households would like to get long-term loans for the purchase or construction of housing. When the housing problem is very pressing, the proportion of such households increases sharply to reach 36% in
We turn these assumptions into predictions for China's net foreign asset position as follows. Our assumption on average per capita GDP growth allows us to project forward per capita GDP. Our assumption on future saving rates then gives us the gross change in wealth for each future year. We then estimate per capita wealth by cumulating these gross changes in wealth and subtracting 6 percent depreciation per year. This gives us per capita wealth in 2025 which, as we have seen in Sections 3 and 4 of the paper, is a key determinant of China's future net foreign asset position. In the calibration exercise the relevant variable is China's per capita wealth relative to that of the rest of the world. We assume that the growth rate of per capita wealth of the rest of the world is equal to its historical rate over the period 1980-2000. This gives us China's relative per capita wealth in 2025. In our first and third scenarios, which feature continued very high saving rates, we set the saving rate at 25 percent of GDP, which is roughly equal to the current saving rate in PPP terms. This is of course much lower than the rate in local currency terms, reflecting the relatively high cost of investment goods in China. We use the lower PPP saving rates since our estimates of wealth are measured in international prices. In the second scenario with lower saving we reduce the saving rate to its historical average between 1980 and 2005 of 20 percent. Our assumptions for per capita GDP growth for the three scenarios are 6, 5, and 4 percent respectively, where 6% is the historical growth rate of GDP per capita between 1980 and 2005 as reported in the Penn World Tables.<sup>10</sup> We are more eclectic in our approach to future productivity levels, which is the other key determinant of the net foreign asset position. For the calibration model, the relevant variable is π which measures ROW's productivity advantage. In our first two scenarios we assume
**Figure 5.3: Effect of Exports on Unemployment Rate** (Response of the unemployment rate after a 1% growth in exposure to exports, in percentage points)

<!-- Start of picture text --> 0<br>-.02<br>-.04<br>-.06<br>-.08<br>Confidence interval<br>Coefficient<br>-.1<br>-1 0 1 2 3 4 5 6<br>Years after foreign demand shock<br>Estimated Coefficient<br><!-- End of picture text --> Figure 5.4 shows the relative effect on district populations after a foreign demand shock. The effect is either zero or modest, likely smaller than percentage responses in employment and labor force. This differs from one of the assumptions in your theoretical model, that assumes free migration across regions. Any observed effect could be due to internal migration or to other drivers of population growth. For instance, we cannot distinguish whether changes in a district population are driven by relative changes in fertility rates or cross-district migration.<sup>5</sup> We could only do so with individual level data. Estimating the effect of trade shocks on population changes (or lack thereof) in a developing country like Indonesia with longitudinal data of individuals would be a fruitful avenue for future research.

> 5 In general, the Bartik shift-share coefficient will differ from the aggregate coefficient in the presence of spillovers or heterogeneous treatment effects and cannot be extrapolated to the aggregate effect unless under very strong assumptions of no spillovers. The effect on population suggests at least cross-region spillovers. How can we interpret the stable unit treatment values assumption (SUTVA) violations in our setting? Since migration is in the same direction of the employment treatment effect, if migrants take up some of the employment, we should interpret the effect as an upper bound to the (aggregate) effect without migration. Intuitively,