rafmacalaba/datause-encoder-data huggingface dataset:
controlling for household characteristics in the probit model as provided in Columns 2 and 4 of the same table.

The results using the linear probability model and the _probit_ model with marginal effects both give similar conclusions that (i) access to the Green Card when interacted with the macro shock through the crisis (in ) is associated with a reduction in the probability of reducing health care utilization (both for preventive and curative care); (ii) the household level income shock is associated with a significant increase in the probability of reducing curative health care services but not preventative health care services ( ); and (iii) the interaction between the Green Card and the income shock at the household level, on its own, does not take on a significant coefficient ( ) but that the 3 and 6 coefficients are jointly significant in both models.

# **6.3.  Propensity Score Matching**

Lastly, we employ propensity score matching techniques for assessing the protective impact of the Green Card program on health care utilization through the Crisis. We first estimate each household‘s propensity to receive the binary treatment (of having the Green Card), using a _probit_ or _logit_ model, as a function of observable characteristics; and then match the treated and untreated households on the basis of this propensity score, to compare their differences in the outcome variable (Rosenbaum and Rubin, 1983). We construct cells based on the propensity score, p(x), and compute the expected value of the outcome variable for the treated and untreated observations with the same (or similar) propensity scores (calculating in each cell) and then aggregate over all propensity score cells.<sup>16</sup> The observations in the treatment and derived control group are comparable to each other since they are equally likely to be treated. Maintaining the selection on observables, the difference between the two observations gives the effect of the treatment for that particular value of the propensity score. Repeating this exercise over many values of the propensity score and then aggregating over the whole sample gives the average treatment effect (ATE). We determine the propensity score using _logit_ and _probit_ regressions separately. The specification for the regressions determining the propensity score is provided in Table 6. Household characteristics (household size, composition, housing conditions and assets) as

> 16   The propensity score matching results are obtained using the psmatch2 package in STATA. Leuven and Sianesi (2003).

18
**Table 7: Differences in Predictions for Headcount Poverty Rate and Gini Coefficient across Prediction Models & Sample Comparisons**

||**across Prediction Model**<br>**Differe**|**s & Sample Comparisons**<br>**nces in Headcount Poverty Rate Predic**|**tions**<br>_Prediction from Standard Interviews_|
|---|---|---|---|
||_Prediction from Standard Interviews -_<br>_Prediction from Experiment Interviews_|_Same Households:_<br>_Prediction from Standard Interviews -_<br>_Prediction from Experiment Interviews_|<br>_of Non-Experiment Households -_<br>_Prediction from Standard Interviews_<br>_of Experiment Households_|
|Model|(1)|(2)|(3)|
|1. Experiment Only|**0.05**|**0.03**|0.01|
|2. Experiment & Non-Experiment|**0.07**|**0.06**|0.00|
|3. WMS Model|**0.03**|**0.04**|0.00|
|4. Non-Experiment Only|0.01|0.00|0.00|
||**Di**<br>_Prediction from Standard Interviews -_<br>_Prediction from Experiment Interviews_|**fferences inGiniCoefficient Predictions**<br>_Same Households:_<br>_Prediction from Standard Interviews -_<br>_Prediction from Experiment Interviews_|_Prediction from Standard Interviews_<br>_of Non-Experiment Households -_<br>_Prediction from Standard Interviews_<br>_of Experiment Households_|
|_Model_|(1)|(2)|(3)|
|1. Experiment Only|**-0.03**|**-0.02**|-0.01|
|2. Experiment & Non-Experiment|**-0.04**|**-0.03**|-0.01|
|3. WMS Model|**-0.03**|**-0.02**|-0.01|
|4. Non-Experiment Only<br>**Note:**Bold indicates scenarios in wh|-0.01<br>ich the experiment sample based predictio|0.00<br>n is outside of the 95 percent confidence|-0.01<br>interval for the prediction based on the|

**Note:** Bold indicates scenarios in which the experiment sample based prediction is outside of the 95 percent confidence interval for the prediction based on the comparator sample (standard interviews for columns 1 and 2, standard interviews of non-experiment households in column 3).

23
**<u>Exhibit 2.20: Growth rates of several financing sources</u>**

|_Medians_|**2004**|**2005**|**2006**|**2007**|**Average**|
|---|---|---|---|---|---|
|Real yearly long term debt growth, %||||||
|Peer group (WS)|-11.2|-11.0|-11.8||-11.3|
|Egypt - Second tier (Coface)|-21.3|-16.4|-16.1|-13.8|-16.9|
|Egypt - Top tier (WS)|-15.6|-19.2|-35.7||-23.5|
|LMI countries, excl. Egypt (WS)|-15.0|-20.6|-20.0||-18.5|
|Real yearly short term debt growth, %||||||
|Peer group (WS)|-2.0|-5.9|-2.0||-3.3|
|Egypt - Second tier (Coface)|-5.1|-6.5|-1.1|-7.4|-5.0|
|Egypt - Top tier (WS)|-5.9|-4.2|3.4||-2.2|
|LMI countries, excl. Egypt (WS)|0.5|-6.6|-9.2||-5.1|
|Real yearly paid-in capital growth, %||||||
|Peer group (WS)|-1.0|-5.6|-5.9||-4.1|
|Egypt - Second tier (Coface)|-9.8|-4.7|-7.2|-8.6|-7.6|
|Egypt - Top tier (WS)|-9.0|-3.0|-4.3||-5.4|
|LMI countries, excl. Egypt (WS)|-5.6|-8.7|-10.5||-8.3|
|Real yearly retained earnings growth, %||||||
|Peer group (WS)|5.5|2.5|3.4||3.8|
|Egypt - Second tier (Coface)|1.9|9.8|6.4|7.9|6.5|
|Egypt - Top tier (WS)|-2.6|37.6|10.6||15.2|
|LMI countries, excl. Egypt (WS)|2.6|-3.6|-2.2||-1.1|
|Real yearly other creditors growth, %||||||
|Peer group (WS)|7.8|3.4|2.8||4.6|
|Egypt - Second tier (Coface)|0.7|-2.7|-2.2|-2.2|-1.6|
|Egypt - Top tier (WS)|4.3|13.3|11.4||9.6|
|LMI countries, excl. Egypt (WS)|3.4|-0.2|-2.6||0.2|

_Note: Peer group consists of companies in Colombia, Chile, Hungary, Indonesia, Israel, Jordan, Mexico, Morocco, Pakistan, Philippines, and Turkey. LMI countries are Colombia, Indonesia, Jordan, Morocco, and Philippines._

# **2.3 Performance of Privatized SOEs vs. Their Peers**

Since June 1994, 52 SOEs have been privatized via public offerings in Egypt. Of these, 30 former SOE are in the real sector. We compared these companies with non-privatized companies to understand whether former SOEs have significantly different characteristics in terms of profitability and leverage. We find that former SOEs have somewhat poorer performance, but higher leverage. These findings could suggest a lack of competitiveness of former SOEs and potentially preferential access to external finance. Exhibit 2.21 shows that most of the former SOEs can be found in the consumer staples industry. The largest privatization wave occurred in 1996.

18
Table 4: MDB Mobilization Effects: Controlling for Anticipation Effects

||Number<br>of loans|Size (% GDP)|Banks|Maturity|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|MDBcs,t|0.4378***|0.0628***|0.4477**|0.6169***|
||(0.144)|(0.024)|(0.217)|(0.136)|
|MDBcs,t-1|0.3278**|0.0112|0.5824***|0.0868|
||(0.135)|(0.024)|(0.183)|(0.132)|
|MDBcs,t-2|0.1437|0.0515***|0.4831**|0.2512***|
||(0.107)|(0.018)|(0.236)|(0.092)|
|MDBcs,t+1|0.1878|0.0277|0.2271|0.0986|
||(0.258)|(0.029)|(0.202)|(0.109)|
|MDBcs,t+2|0.1553|0.0450|0.2789|0.1153|
||(0.135)|(0.048)|(0.257)|(0.106)|
|Number of Loanscs,t-1|0.7307***<br>(0.077)||||
|Size (%GDP)cs,t-1||0.2036***<br>(0.072)|||
|Banks per Loancs,t-1|||0.2151***<br>(0.032)||
|Maturitycs,t-1||||0.0706***<br>(0.018)|
|2<br>∑<br>_k_=0<br>_MDB_cs,t-k|0.909***|0.126***|1.513***|0.955***|
|_Wald test_ : _p value_<br>|0.000|0.008|0.003|0.000|
|2<br>∑<br>_k_=1<br>_MDB_cs,t+k|0.343|0.072*|0.506|0.214|
|_Wald test_ : _p value_|0.356|0.083|0.178|0.168|
|Observations|22,113|22,113|22,113|21,388|
|R-squared|0.829|0.404|0.680|0.531|
|Sector-country FE|Yes|Yes|Yes|Yes|
|<br>Country-year FE|Yes|Yes|Yes|Yes|
|Sector-year FE|Yes|Yes|Yes|Yes|
|<br>Average MDB|0.049|0.049|0.049|0.039|
|Average Dep. Var.|0.730|0.105|1.545|0.736|

_Notes_ : The table presents the estimates of equation 2. The dependent variables are: (i) the number of loans (column 1), (ii) the size of syndicated loans (% of GDP, column 2), (iii) the average number of banks per loan (column 3), and (iv) the average maturity of syndicated loans (in years) (column 4). MDBcs,t is a dummy equal to 1 if there is at least one MDB providing a syndicated loan in country-sector _cs_ at time _t_ . The table also reports, in the bottom rows, the sample averages of the MDBcs,t and outcome variables. Standard errors clustered at country level are in parentheses: * p _<_ 0.1, ** p _<_ 0.05, *** p _<_ 0.01.

28
improvements in productivity or the business environment that would have laid the foundation for sustained long-term growth. In fact, the sudden abundance of financial flows in non-core countries resulting from the adoption of the euro exacerbated weak competitiveness that had already adversely affected these countries before the adoption of the common currency.<sup>9</sup> 22. One of the factors contributing to this loss of competitiveness was the sharp rise in wages in non-core countries. Between 2001 and 2011 per unit labor costs in Greece rose by 33 percent, 31 percent in Italy, 27 percent in Spain and 20 percent in Ireland, while they grew by only 11 percent in the United States and by only 0.9 percent in Germany (Table 5).<sup>10</sup> This increase in wages was primarily driven by increases in public sector wages—aimed at swaying the electorate towards the ruling parties—which then also triggered increases in private sector wages. Expenditure increases were considered to be manageable, though, owing to the substantially lower borrowing costs for sovereigns in the aftermath of the adoption of the euro and rapidly rising revenue reflecting the increase in economic activity and the real estate boom (Table 6 and Figures 3, 5 and 6).

**Table 5: Percent increase in unit labor costs, Q1 2001–Q1 2011**

|**Greece**|33.2%|
|---|---|
|**Italy**|30.7%|
|**Spain**|26.6%|
|**Ireland**|20.0%|
|**United States**|11.5%|
|**Germany**|0.9%|
|Source: OECD.Stat.||

**Table 6: General government deficit,** **<u>percentage of GDP</u>**

||**2000**|**2001**|**2002**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|**2009**|**2010**|**2011**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Greece**|-3.7|-4.5|-4.8|-5.6|-7.5|-5.2|-5.7|-6.5|-9.8|-15.6|-10.3|-9.1|
|**Ireland**|4.7|0.9|-0.4|0.4|1.4|1.7|2.9|0.1|-7.3|-14|-31.2|-13.1|
|**Italy**|-0.8|-3.1|-3.1|-3.6|-3.5|-4.4|-3.4|-1.6|-2.7|-5.4|-4.6|-3.9|
|**Portugal**|-3.3|-4.8|-3.4|-3.7|-4.0|-6.5|-4.6|-3.1|-3.6|-10.2|-9.8|-4.2|
|**Spain**|-0.9|-0.5|-0.2|-0.3|-0.1|1.3|2.4|1.9|-4.5|-11.2|-9.3|-8.5|
|**France**|-1.5|-1.5|-3.1|-4.1|-3.6|-2.9|-2.3|-2.7|-3.3|-7.5|-7.1|-5.2|
|**Germany**|1.1|-3.1|-3.8|-4.2|-3.8|-3.3|-1.6|0.2|-0.1|-3.2|-4.3|-1.0|
|Source: Euro|stat||||||||||||

> 9 Gwartney _et al_ (2010).

> 10 Dadush (2010) p.32.

11
estimates for male relative deprivation are twice as high as that for females if we consider the whole society. If we focus on the youth, the reversal of gender positions also applies and the gender gap increases. Now young males appear to be much more deprived than young females.

**Table 7: Relative Labor Deprivation**

||**RLD (All)**|**RLD (Youth)**|
|---|---|---|
|**When the reference group is universal**|||
|Society|0.279|0.269|
|Decomposed by gender|||
|Female|0.202|0.179|
|Male|0.077|0.090|
|**When the reference group is gender specific**|||
|Society|0.240|0.221|
|Male|0.292|0.291|
|Female|0.137|0.118|

Source: MHYS 2009-2010.

Qualitative insights from a recent World Bank (2011) study can explain and validate these findings. As mentioned earlier, this study found that an overwhelming majority of both female and male interviewees believed men to be relatively more disadvantaged than women in the domestic labor market and this was motivated by gender specific norms, precisely as postulated in the Akerlof and Kranton (2000) study on identity.

**Figure 1 – Lorenz curves of individual relative labor deprivation**

<!-- Start of picture text --> RLD for the society RLD for males and females<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Percentiles (p) Percentiles (p)<br>45° line Society 45° line Male<br>Society with gender as reference of RD Female<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br>1<br>.8<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> Source: MHYS 2009-2010.

18
hm = ∑ (1) cc=12 Where hm is the y-o-y inflation rate for household cc=1 hccm hccm , in month and year . Similarly, _c_ =1....12 ππ SS ∗ππ represents the 12 COICOP categories. is the share of each COICOP category for household h in ππ mm tt particular months and years. These shares are obtained from the unit-level records collected for each household through the HIES (2018-19). SS is y-o-y inflation for the same month and year . Multiplying the share of each COICOP category at the household level with y-o-y inflation ccm ππ mm provides the inflation of each COICOP category at the household level. Aggregating the tt household-level inflation rates of all COICOP categories estimates the inflation rate at the household level.

Here _d_ (=1...10) represents deciles, and N is the total number of households in each decile. This shows that decile-wise inflation is the sum of all households’ inflation divided by the total number of households in that specific decile. Using these two equations, the decile-wise monthly inflation series is obtained from November 2018 to May 2022.<sup>3</sup> The decile-specific inflation rate for each COICOP category is also estimated by employing the above formula. However, this is done separately for each COICOP category. For instance, to estimate the inflation for, say, food and non-alcoholic beverages in the first decile, the sum of household level inflation of food and non-alcoholic beverages in the first decile is divided by the total number of households in this decile. This is done for all the COICOP categories and for all deciles. Subsequently, this provides the components of inflation for each decile (as shown in Figure 5).

# **2.1. Key Assumptions**

The calculation and interpretation of decile-disaggregated inflation is based on certain underlying assumptions.

Firstly, the inequality of inflation impact exhibits through three channels: its effect on consumption through prices, the change in the asset position of a household, and the differential passthrough to labor income. The methodology of this paper restricts addressing the first channel only. While data limitations prevent us from an examination of the latter two, we believe that the price channel is the most important in the context of lower-income groups in Pakistan due to low levels of asset ownership and a high degree of labor informality, while the labor channel

> 3 With slight modification, these same equations are used to estimate quantile-wise inflation as well.

6
||(0.061)|(0.063)|(0.103)|(0.098)|(0.108)|
|---|---|---|---|---|---|
|**Common Currency**|-0.594***|0.316|1.053*|1.350*|1.070***|
||(0.109)|(0.445)|(0.588)|(0.72)|(0.337)|
|**Importer (i)/Exporter (j) dummies**|YES|YES|YES|YES|YES|
|**Observations**|3,795|3,456|3,382|3,215|2,870|
|**R**<sup>**2**</sup>|0.809|0.788|0.664|0.683|0.699|

The dependent variable is the log of bilateral exports. All regressions include importer and exporter fixed effects. Superscripts ***, **, * denote statistical significance at 1, 5 and 10% respectively. Cluster robust standard errors are provided in parentheses.

The results of cross-section regressions, based on equation 8, are presented for the years 1960-2000 for sample 1 (see table 2). Across all years, the regressions explain at least approximately 70% of the total variation in bilateral exports, which is typical. The coefficient on the distance variable is around 1, which is what theory predicts. Sharing a common border or a common language have little effect upon trade, a result explained by the positive correlations of these variables with both migration variables, the inclusion of other covariates which account for the variation in these variables and the implementation of importer and exporter fixed effects. There is a very strong impact of sharing a colonial heritage, although this effect decreases over time, as historical network effects deteriorate and institutions diverge from one another.<sup>20</sup> Sharing common legislative origins are also found to significantly bolster trade in three of the five periods. The coefficients on the common currency and the RTA variables are very unstable however, being both significantly positive and negative across the years. This is due to endogeneity bias as argued by Glick and Rose (2002) and Baier and Bergstrand (2007) respectively.

Turning to the key variables of interest, the immigrant and emigrant variables are significant in every decade and the coefficients are remarkably stable over time. In 2000, a 10% increase in immigrants and emigrants is associated with bilateral trade increasing by 0.5% or 0.7% respectively. In other words, an increase in the global stock of 8,890,000 immigrants/emigrants is associated with an increase in world trade of $29bn and $42bn respectively, or $3,280 or $4,760 per immigrant/emigrant. In terms of the hypotheses which have featured so strongly in the literature, the coefficient on immigrants might be interpreted as a measure of one side of the transaction cost channel - since bilateral migration is regressed upon unidirectional trade. Nevertheless since the coefficient on the immigrant stock variable is not statistically larger than the coefficient on the stock of emigrants in any decade no firm conclusions can be drawn with regards separating the two mechanisms.

> 20 The erosion of colonial links is well documented in Head, Mayer and Ries (2010)

16
# **DO CAPITAL INFLOWS BOOST GROWTH IN DEVELOPING COUNTRIES?**

# **EVIDENCE FROM SUB-SAHARAN AFRICA**

**César Calderón and Ha Nguyen**<sup>**1**</sup>

JEL: F35, O40 Key words: Economic Growth, Capital inflows, Aid, FDI, Rainfall

> 1 This paper is a part of the World Bank Africa Region’s project on Sub-Saharan Africa’s Macroeconomic Vulnerabilities. We thank the support of the region’s chief economist office. We are grateful to <mark>Markus Bruckner, Francisco Ferreira, Aart Kraay, Norman Loayza and Luis Serven for detailed comments and feedback.</mark> We thank <mark>Derrek Grunfelder-McCrank for excellent research assistance. All errors are our own. Contacts:</mark> César Calderón at <u>ccalderon@worldbank.org</u> and <mark>Ha Nguyen at hanguyen@worldbank.org .</mark>
LAYS above. A different approach could be to choose a basket of top-performing countries and adjust learning relative to the mean performance in that basket, as follows: The intuition of the _LAYS_ adjustment is the same as before. The main difference is that for some of the countries in the basket (those whose value of _L_ is above the mean for the top performers), by construction the _LAYS_ estimate is now **greater than** their average years of schooling (since for them R��� �1).

Figure 4.5 compares _LAYS_ values using these two different numeraires—the mean of the top 5 performers (in terms of learning per year) versus the score of the top performer (Singapore)—based on both the TIMSS (left panel) and PISA (right panel) mathematics scores. Figure 4.6 shows how average years of schooling compares to _LAYS_ in the two cases, again for TIMSS (top panel) and PISA (bottom panel). Since learning per year averaged across the top 5 performers will be less than that among the top performer, using the former of course yields _LAYS_ estimates that are larger. Nevertheless, as Figures 4.5 and 4.6 show, the impact of changing the numeraire in this way is very small.

**Figure 4.5:** **_LAYS_ using top 5 performers versus top 1 performer (Singapore) as the numeraire**

<!-- Start of picture text --> TIMSS PISA<br>0 5 10 15 0 5 10 15<br>LAYS (Numeraire = Singapore) LAYS (Numeraire = Singapore)<br>Source: Authors’ analysis of TIMSS 2015, PISA 2015, and Barro‐Lee data.<br>Notes: Illustration for case where learning starts at Grade 0 for TIMSS and age 6 for PISA.<br>15 15<br>10 10<br>5 5<br>LAYS (Numeraire = Top 5) LAYS (Numeraire = Top 5)<br>0 0<br><!-- End of picture text --> 23
[OLICY RESEARCH WORKING PA'FER 1597

# Summary findings

The Uruguay Round will gelnerallv hlave a limited impact on Egyptian policies affecting goods, invesmnienr, and services. It will have a more significant impact on intellectual property, although this will take up to a decade to materialize fully.

Insofar as this reflects a continuing defensiveness against liberalization, it does not bode well for a country that will be facing growing competitive pressures as the world economy becomes more inregrated. But Egypt's Uruguay Round commitments do, to a large extent, lock in the policy changes pursued by dhe government since the late 1980s. In this respect, they are qu!te significant. Maximum tariffs have been established for ahlmost all tariff lines, and the gap between these "bindings" and the level of applied tariffs is in most cases relatively small. This constitutes a level of commitment that substantially exceeds the developing country average.

The government is negotiating a more far-reaching agreement with the European Union to liberalize trade on a reciprocal basis. Commitments by Egypt in the Uruguay Round can be seen as facilitating implementation of a Euro-Mediterranean Agreement. Such an agreement could facilitate further nondiscriminatory liberalization of Egypt's trade **regime.** Of particular importance in this connection is opening service markets to greater competition, where much remains to be done.

This paper-a product of the f'rivate Sector and Finance Team, Europe and Central Asia, and Middle Eastand NorthAfrica - Technical Department was presenited at a University of Cairo conference, The Impact of the Uruguay Round on Egypt, held in Cairo on January 13-15, 1996. Copies of this paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Faten Hatab, room H8-087, telephone 202-473-5835, fax 202-477-8772, Internet address fhatab@c?worldbank.org. Mlay 1996. (3 1 pages) _The_ **_Policy f.esearcb_** _Working Paper Scries dissbmiates the_ **_cyork findings_** _of_ m **_progress to encourage the excrange_** _of_ **_ideas about development issues. An_** _objective o/ the series is to get the_ **_findings out a,uickly, even if tbe presentations are less than fully polisbed. The_** _papers_ **_carry_** _the_ **_names_** _of the authors and should be used and cited accordingly. The_ **_findings, interpretations, and conclusions are the authors' oumn and_** _should_ **_not_** _17e atirih7uleu lo The W,orld Bank,_ **_its_** _Executive Board of_ **_Directors, or any_** _of_ **_its member countries._** P'rod(luced _by_ **thc** l'ol icy **Researcli** Dissemination Center
**Annex 1: Correlates of Upward Mobility (by age, ethnicity and urban-rural status), OLS**

||(1)|(2)|
|---|---|---|
|VARIABLES|Better educated than father|Better occupation than father|
|_Reference: Born After 1995 _|||
|Born 1985-1995|-0.02|0.18***|
||(0.01)|(0.01)|
|Born 1975-1985|-0.08***|0.21***|
||(0.02)|(0.01)|
|Born 1965-1975|-0.16***|0.15***|
||(0.02)|(0.01)|
|Born before 1965|-0.23***|0.15***|
||(0.02)|(0.02)|
|Constant|0.72***|0.09***|
||(0.01)|(0.01)|
|VARIABLES|||
|_Reference: Hill Brahmins_|||
|Hill Chhettri|-0.02|-0.02|
||(0.02)|(0.02)|
|Terai Brahmin & Middle Caste|-0.16***|-0.07***|
||(0.03)|(0.03)|
|Dalit|-0.09***|-0.10***|
||(0.03)|(0.02)|
|Newar|0.07**|0.01|
||(0.03)|(0.03)|
|Hill Janajati|-0.09***|-0.03|
||(0.03)|(0.02)|
|Terai Janajati|-0.05*|-0.05*|
||(0.03)|(0.03)|
|Muslim and Others|-0.17**|-0.13**|
||(0.07)|(0.06)|
|Constant|0.70***|0.25***|
||(0.02)|(0.02)|
|VARIABLES|||
|_Reference: Born rural, lives rural_|||
|Born rural, lives urban|0.08***|0.14***|
||(0.01)|(0.01)|
|Born urban, lives urban|0.05***|0.05***|
||(0.02)|(0.02)|
|Constant|0.63***|0.19***|
||(0.01)|(0.01)|

Notes: Standard errors in parentheses. * Significant at ten per cent; ** significant at five per cent; *** significant at one per cent. _Source_ : World Bank Staff estimates based on data from Perceptions of Poverty, Prosperity and Economic Mobility in Nepal, 2014.

22