rafmacalaba/datause-encoder-data huggingface dataset:
levels (around 5 years of education) (see Ib ́a ̃nez and Moya, 2006; Garay, 2008; Carrillo, 2009).

Several studies have attempted to characterize the migration decision of forced migrants using surveys. Their findings suggest that forced displacement in Colombia mostly originates from rural areas, where the internal armed conflict has taken place. In that sense, the migration decision of forced migrants is mainly driven by safety concerns related to the presence and activities of illegal armed groups. In particular, data from RUV suggests that for the 59 percent of individuals for whom information is available on the cause of displacement, 54 percent of migration cases were attributed to the activities of illegal armed groups and 84 percent were attributed to a death threat (Unidad para la Atenci ́on y Reparaci ́on de V ́ıctimas, 2013). Most displaced individuals, consequently, had previously worked primarily in agriculture (Ib ́a ̃nez and Moya, 2006; Carrillo, 2009). Households that had access to basic public services, had better economic opportunities, or had private property showed a lower probability of migrating (Engel and Ib ́a ̃nez, 2007; Ib ́a ̃nez and Moya, 2010). In areas with extreme levels of violence, however, owning land increased the probability of displacement because these household were targeted for extortion by illegal armed groups (Engel and Ib ́a ̃nez, 2007).

Forced migrants in Colombia have moved to areas where they had friends or relatives and that were closer in distance to their municipalities of origin (Ib ́a ̃nez and Moya, 2006; Carrillo, 2009; Lozano-Gracia et al., 2010). Yet, in regions with extreme violence, individuals preferred to relocate to more distant locations and to cities that were more populated; they were attracted to the sense of anonymity both provide (Carrillo, 2009; Lozano-Gracia et al., 2010). Other criteria migrants take into account when choosing their destinations includes the provision of public goods and the population density of the destination municipality (Carrillo, 2009; Lozano-Gracia et al., 2010).

Households who migrate have incurred substantial losses in physical assets left behind in their municipalities of origin and have suffered human capital depreciation due to household disintegration or post-traumatic stress disorders (see Ib ́a ̃nez and Moya, 2010 for a quantification of these welfare losses). Most households, in addition, move to urban areas, where there is little demand for their agricultural experience. Many of them, consequently, face extreme hardship upon arrival 9
**<u>Table 3: Details on land takings in sample villages (village level information)</u>**

||**All**||**By r**|**egion**||
|---|---|---|---|---|---|
||**China**|**N & NE**|**Coastal **|**Central **|**SW**|
|**Incidence of land taking**||||||
|Share of villages with land taking (%)|29.47|19.10|40.56|19.00|34.18|
|Total area taken in an average village(mu)|103.10|176.23|106.75|91.82|66.93|
|... of which irrigable (%)<br>|83.21|62.20|100.00|88.79|79.50|
|Average number of households in village affected by<br>takings|88.14|94.51|116.87|52.05|65.24|
|Share of villages with takings before RLCL in force<br>(2000/1-2003/3) (%)|16.59|10.38|23.08|12.39|17.60|
|Share of villages with takings after RLCL in force<br>(2003/4-2004/12) (%)|22.52|13.68|31.62|16.81|23.97|
|**Purpose and impacts of taking (%)**||||||
|Infrastructure as main reason for taking|51.39|52.94|35.94|48.48|69.37|
|.. of which initiated above village|81.33|96.30|84.78|75.00|75.32|
|Commercial as main reason for taking|48.61|47.06|64.06|51.52|30.63|
|.. of which for private sector|47.77|66.67|36.59|35.29|67.65|
|.. of which initiated by higher level government|44.59|33.33|53.66|64.71|20.59|
|No. of project has been completed|65.84|72.55|59.84|81.82|64.86|
|No. of project has started|88.54|94.12|79.69|96.97|93.69|
|No. of takings generated jobs|28.01|30.71|31.43|26.32|24.33|
|No of jobs generated within village|13.49|14.24|13.82|16.70|12.16|
|No of jobs generated total|64.10|39.21|25.14|20.74|116.62|
|**Compensation for land taken**||||||
|Share of takings followed by reallocation (%)|27.95|35.29|18.75|48.48|29.09|
|Amount of compensation paid (Y/mu)|15054.8|6729.9|24798.8|6478.1|9956.5|
|Amount of compensation before reform (Y/Mu)|13177.6|6056.7|21805.8|4881.8|8988.5|
|Amount of compensation after reform (Y/Mu)|17557.8|7627.4|28463.6|7648.7|11643.6|
|Share of takings involved monetary compensation||||||
|<br>(%)|82.37|72.44|82.08|89.47|85.88|
|Any compensation retained by village|39.01|31.37|59.38|42.42|18.02|
|> 50% of compensation retained|14.86|9.80|24.22|24.24|3.60|
|Payment to those who lost land only (%)|56.35|60.78|43.75|66.67|65.77|

_Source:_ Own computation based on 722 villages from the 2005 NBS/WB survey.

25
18

**Figure 9: Unconditional Probability of the Regime S or D**

<!-- Start of picture text --> 1<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5 Probability of supply regime<br>Probability of demand regime<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> Source: Constructed by the authors.

The disequilibrium econometrics approach based on the short-side rule is consistent with the theory of equilibrium credit rationing derived from asymmetric information models. Because of moral hazard and adverse selection problems, an excess demand in the loan market may exist, since a rise in the loan interest rate can reduce the banks’ expected return.

If each bank attracts only the least profitable customers, each may have an excess supply of loanable funds, but none will accept to reduce its interest rate. Consequently, there may be an excess credit supply in the banking sector, without a fall in the interest rate to clear the loan market. This excess of supply can be observed in Figure 10, illustrating the estimated demand and supply for the private sector’s real claims.
create this variable. Analogously, the Non-investment grade dummy variable refers to firms that had an issuer rating below BBB-/Baa3 on March 17, 2020. In our sample, 9.9% of firms had an investment grade rating, while 4.6% of had a non-investment grade rating. The remaining firms had no issuer rating.

Firms that rely more heavily on bond finance are potentially more strongly affected by the PEPP, as the PEPP is restricted to purchasing marketable debt securities. To represent a firm’s bond issuance, Bonds/assets is calculated as the ratio of the sum of the principal amounts of all outstanding bonds as of the most recent reporting date in 2019 available in S&P’s Capital IQ (most commonly, end of Q2 or Q4 in 2019) divided by total assets. Bonds are defined to include commercial paper and notes. Assets for the same reporting date as bonds are obtained from Compustat. We matched data from Capital IQ and Compustat with data from Eikon based on ISIN security numbers. In our sample, firms finance on average 5.75% of assets using bonds.

The implications of monetary policy for a firm depend, among other things, on whether it serves to alleviate credit constraints and debt overhang. Some firms no doubt already were subject to credit constraints and debt overhang before the COVID-19 crisis, while the occurrence of this crisis can only have aggravated these problems for many firms, even if to different extents. To proxy for potential pre-existing credit constraints and debt overhang, we consider a range of firm-level variables similarly to Ehrmann and Fratzscher (2004) and Maio (2014), measured as of the end of 2019 and thus predating the COVID-19 crisis.

Among these, Leverage is the ratio of liabilities to assets. Highly leveraged firms potentially experience greater credit constraints and debt overhang that could be alleviated by the PEPP. The sample mean of Leverage in our sample is 53.1%. Log assets is the natural logarithm of total assets, and it proxies for firm size. Larger firms may be less credit constraint. ROA is net 13
**Table 7: Household expenditure and poverty by microenterprise adoption: 2000-2010**

|**Indicators**|**2000**|**2005**|**2010**|
|---|---|---|---|
||**HHs with**<br>**enterprise as**<br>**an additional**<br>**activity**<br>**(N=1,290)**<br>**HHs without**<br>**enterprise as**<br>**an additional**<br>**activity**<br>**(N=3,740)**|**HHs with**<br>**enterprise as**<br>**an**<br>**additional**<br>**activity**<br>**(N=1,317)**<br>**HHs without**<br>**enterprise as**<br>**an additional**<br>**activity**<br>**(N=4,714)**|**HHs with**<br>**enterprise as**<br>**an additional**<br>**activity**<br>**(N=1,701)**<br>**HHs without**<br>**enterprise as**<br>**an additional**<br>**activity**<br>**(N=6,139)**|
|Per capita income (Tk./month)|1,168.7<br>778.9<br>(6.31)|1,210.4<br>851.4<br>(10.63)|1,612.6<br>1,184.9<br>(5.26)|
|Per capita expenditure<br>(Tk./month)|788.7<br>701.5<br>(6.04)|1,050.6<br>878.9<br>(6.81)|1,318.2<br>1,179.9<br>(5.74)|
|Moderate poverty headcount (%)|49.6<br>58.5<br>(-5.54)|33.5<br>42.8<br>(-6.08)|26.5<br>35.1<br>(-6.70)|
|Extreme poverty headcount (%)|33.8<br>44.5<br>(-6.78)|20.5<br>28.2<br>(-5.62)|15.0<br>20.9<br>(-5.41)|
|Percentage of households with<br>microenterprise|25.6%|21.8%|21.6%|

Note: Households do not depend solely on microenterprise income. Almost all of the microenterprise owners draw income from farm and/or other nonfarm activities. For example, in 2000, 69.5 percent of the enterprise owners draw income from farm activities. The corresponding figures for 2005 and 2010 are 81.5 percent and 82.0 percent, respectively. Income figures are CPI-adjusted (2000=100). Figures in parentheses are t-statistics of the differences between microenterprise owners and non-owners. Source: HIES 2000, 2005 and 2010 39
The sample contains 765 youth (between 10 and 24 years of age) and 725 adults (above 24 years of age). The age in the sample is self-reported. The gender distribution of the sample is 53 percent males and 47 percent females. The composition of the race turned out to be: 40 percent white, 45 percent _pardo_ , 10 percent Afro-Brazilian, 1 percent Asian, and 3 percent Indian (selfreported).

Youth in poor urban neighborhoods in Fortaleza are at considerable risk of growing-up without parents and, in particular, growing-up without their father in the household. Only 7 percent of the youth are growing up with their biological father and 57 percent with their biological mother (see Figure 1).

**Figure 1:    Youth and Adults that Have Been Brought Up With Their Parents** **<u>Present in the Household (percent)</u>**

<!-- Start of picture text --> 100<br>90 Youth Adults<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Live with Father Live with Mother Live with None<br>Source: Data collected by authors in poor urban neighborhoods in the Northeast Brazil.<br><!-- End of picture text --> 13
Alternatively, labour coefficients from an input-output matrix may give an indication of the employment changes resulting from exogenous changes in output.

If employment changes can be calculated or assumed, then it is still necessary to allocate (or take away) the jobs to individuals. Here it is possible to draw upon the household survey data to estimate the probability of an individual being employed in a variety of different activities based upon their characteristics and those of their household. In this way one may account for the fact that the growth resulting from a reform may not directly benefit the poor if there is no corresponding rise in the unskilled wage.

Similarly, in circumstances where remittance income is important it may be desirable to have a somewhat deeper analysis of the impact of reform from this source. If estimates are available for the sectoral growth rates likely to result from the reform and the sector source of remittance income is known then it is possible to scale up or down household remittances pro-rata. Alternatively, one may estimate the probability of receiving remittances based upon household characteristics and increase or decrease employment (and therefore remittances) on this basis. Better still, if suitable data are available, it may be possible to model the decision to migrate based upon the expected remittance returns and then simulate the change in income resulting from a shift in the probability of employment in particular sectors.

> One may also attempt to integrate the above extensions of the basic methodology in the

> form of a simple household model. Such a model can allow a household to allocate their resources between a number of different activities (including wage labour) in order to maximize their utility when faced with an exogenous set of prices. One can then simulate

> the manner in which households change their resource allocations when prices change as a result of reform.

> Alternatively, if reforms are likely to give rise to supply or demand responses with significant knock-on effects for domestic prices, then it is possible to endogenise the

> process of price formation through the use of a multi-market model. However, such models can require substantial additional data and resources and are often inappropriate for analyzing circumstances where non-agricultural income is important.

> Other potential methodological developments include greater attention to the dynamics of poverty and the ways in which reforms may cause entry into or exit from poverty. Furthermore, with suitable data it may be possible to simulate the impact of structural reforms on price volatility and the consequential impact upon household vulnerability.

Efforts may also be made to analyse the quantitative aspects of non-income dimensions of poverty. However a proper analysis of the linkages between reform and other dimensions of poverty will require an exploration of the qualitative poverty material in each country.

> All of the approaches described above have focused on ex ante prediction of welfare

> effects resulting from structural reforms. This is, of course, what policymakers are most

30
3 of these measures are sensitive to the choice of consumption rather than income as the measure of a family’s means. Second, we review the findings of multi-country and global studies of out-of-pocket health expenditures. These studies are relatively few and have typically used just one or two of the abovementioned measures. Third, in part to help guide future research but also to set the stage for our new empirical results presented below, we highlight the challenges faced by researchers in this area. These include identifying and accessing relevant household surveys, choosing between different adaptations of these surveys, computing out-of-pocket expenditures, and computing consumption and income. Finally, we present new estimates of out-of-pocket health expenditures for 146 countries. Our estimates cover more countries than previous global studies (133 countries), and in contrast to previous multi-country and global studies, where typically just one or two measures are used, we report estimates for all measures used in previous studies. We also explore the implications for 48 countries of choosing consumption rather than income as the measure of a family’s means, and present multiple regression results showing the macroeconomic and health system (partial) correlates of the various measures.

# **Previous studies of out-of-pocket health expenditures**

A large literature exists on out-of-pocket health expenditures. This section summarizes this literature, beginning with an overview of the indicators used, and then moving on to the results of the literature to date, focusing on multi-country and global studies.<sup>4</sup>

## <u>Indicators and caveats</u>

Table 1 lists the different indicators used in the studies to date. The first simply asks how much households spend per capita (in international dollars) on out-ofpocket health expenses. This number is sometimes reported in single-country studies (see e.g. Banthin et al. 2008), usually based on data from international databases like the OECD’s Systems of Health Accounts (SHA) and the World Health Organization’s (WHO) Global Health Expenditure Database (GHED). The second set of indicators focus on dispersion: the coefficient of variation (the ratio of standard deviation to the mean) and Q90/Q50 (the ratio of health expenditure of the 90<sup>th</sup> percentile to the median). Gruber and Levy (2009), who first used them to analyze out-of-pocket health expenditures, interpret both as proxies for health care expenditure risk, but acknowledge they are only proxies and do not properly capture ex ante risk. None of the above-mentioned indicators relate out-of-pocket expenditures to a household’s consumption or income. The third indicator – the outof-pocket health expenditure budget share – does this. This does not tell us, however, whether the budget share varies with a household’s income or consumption. The fourth measure – progressivity, measured most commonly using Kakwani’s (1977) index – gets at this by telling us whether the budget share is larger for poorer households (out-of-pocket expenditures are regressive) or smaller
Policy Research Working Paper 7315

# **Abstract**

The paper estimates the effect of product standards on firms’ export decisions using two novel datasets. The first covers all exporting firms in 42 developing countries. The second covers pesticide standards for 243 agricultural and food products in 63 importing countries over 2006–12. The analysis shows that product standards significantly affect foreign market access. More restrictive standards in the importing country, relative to the exporting country, lower firms’ probability of exporting as well as their export values and quantities. The relative restrictiveness of standards also deters exporting firms from entering new markets and leads to higher exit rates from those markets. Moreover, firm characteristics mediate the effect of product standards on firms’ export decisions. Smaller exporters are more negatively affected in their market entry and exit decisions by the relative stringency of standards than larger exporters. Positive network effects of exporters from the same country may help reduce the burden of importing countries’ standards on firms’ decisions to enter new markets.

This paper is a product of the Trade and International Integration Team, Development Research 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://econ.worldbank.org. The authors may be contacted at afernandes@worldbank.org and eferro@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
uncertainty about the simulations generated by the ex‐ante evaluations is bound to remain. However, a preliminary validation of the model using historical data suggests that the model performs well in predicting poverty and distributional changes (i.e. projected changes are very close to actual data).

# **4. Presentation of simulation results**

Before describing the impact on poverty, it is useful to trace the way the macro impacts are transmitted to the households. In the Philippines, given the nature of the macroeconomic impacts, the main transmission channel is the labor market.

What did the Filipino labor market look like in the baseline year before the crisis? In 2006 – the last year for which household poverty data are available and therefore the baseline data for our projections – 69% of the working‐age population (15‐64) was active in the labor force, of which about 91% was employed. Among those employed, 51% were employed in services, 34% in agriculture, 10% in manufacturing, and 6% in other industries. The employed population includes those working for wages/salaries, the self‐employed and those contributing labor to household‐based enterprises or activities that produce income for the household. On average, 67% of household income comes from labor earnings and 15% comes from international remittances, which form about 22% of non‐labor income (see Annex 2, Table B for detailed figures).

The _population growth rate_ between 2006 and 2010 is projected to be 8.2%, with a higher growth rate (10.8%) projected for the working age population.

_Employment growth_ projections for 2009 and 2010 are computed using past trends in output and employment and GDP projections shown in Table 1 above.<sup>11</sup> Lower GDP growth rates in 2009 and 2010 due to the crisis would translate into employment impacts. Historically, the **Table 2: Employment projections (individuals aged 15‐64 yrs)** output elasticity of labor force participation has been slightly positive in the Philippines, **% difference in levels between benchmark and crisis** which is reflected in our results. Labor force **2009 2010** participation is slightly higher in the crisis _Inactive_ ‐1.2 ‐1.8 scenario than in the benchmark scenario, _Active_ both in 2009 and 2010. Employment rates Unemployed 20.1 35.6 among the active population, however, Employed ‐1.0 ‐1.8 show a different pattern. The overall employment rate in the crisis scenario is 1.0 _Sectoral employment_ and 1.8 percent lower than in the Agriculture ‐1.0 ‐1.4 benchmark scenario in 2009 and 2010 Manufacturing 0.5 0.6 respectively (see Table 2). Interestingly, the Other Industries ‐0.9 ‐1.3 impact of the crisis on poverty is larger in Services ‐1.2 ‐2.5 2010 than in 2009 despite a recovery in _Source_ : Own calculations using GDP data and HIES (2000, 2005) economic growth will recover in 2010. This is due to the fact that the 2010 figure reflects the cumulative

> 11 See Annex 2, Tables A.2 and A.3 for detailed projections.

12