rafmacalaba/datause-encoder-data huggingface dataset:
14 (1990), the impact of financing is incorporated into the estimation equations through two additional variables: the external public debt,"<sup>6</sup> expressed as the (absolute) ratio of total output, and the (natural log) ratio of public consumption expenditures to total output. Table 5 provides the sample average and Mexico-specific values of some key variables.

**Table 5:**

|**Average values for**|**46 devel**|**oping co**|**untries an**|**d Mexico**||
|---|---|---|---|---|---|
||**(s0**_I_<br>**_)/_**|_(sPl)_|(CG/|DEBT/Y|X||
|Sample average|1.08|1.35|0.18|0.30|0.076|
|Mexico|1.07|1.73|0.08|0.21|0.075|

Note: these averages pertain to the period 1970-1990.

# _Estimation Results_

The first set of four equations in Table 6 presents the basic estimates of the growth equation, using only the various capital stocks as explanatory variables. The main result here is that the direct impact of a change in the public capital stock is almost identical to that of a change in the private capital stock; and both are significant. Another noteworthy result, of interest more from a technical perspective, is that the assumption of constant returns to scale is not rejected by the data. (Under constant returns to scale the coefficient on the effective depreciation rate should be 0). This last result carries through in all subsequent equation specifications.

The second set of equations, (5) - (8), shows the effect of incorporating all the other variables as well into the growth equation. In all cases, the efficiency index has a positive and highly significant impact. Both financing variables are also statistically significant (most of the time) and have the expected negative sign. That is, higher public debt levels are associated with lower rates of growth, as are higher levels of government consumption. Another consequence of introducing both variables is that the growth elasticity of public capital regains it statistical significance, albeit the size of the impact is somewhat smaller than before.

A number of tentative conclusions can be drawn with respect to the relationship between public capital and economic growth on the basis of equation (7) in Table 6, which yields the best fit for the entire cross-country sample.17 [In the comparative statics exercises described next, it is convenient to remember -- from equation 3 earlier -- that the investment ratios in Table 6 may be interpreted as the natural log of the steady-state capital-output ratios; i.e., (sz/X) = exp(kz) = (KZ /Y)*.]

> **1lt** would have been preferable to use total (i.e., internal and external) public debt as a measure of the burden of financing public capital, but comparable data on internal debt is not available for many countries.

> 17As usual, in drawing such conclusions from cross-country data it is implicitly assumed that all countries share the same structural parameters (i.e., the production function elasticities), and only vary with regard to the behavioral parameters.
**Figure 4.1 Allocation of national pension system assets by infrastructure sector**

Year-end 2006 or most recent available; US$ millions /1 <!-- Start of picture text --> 25<br>20<br>Transport construction<br>Telecoms & EADB (Corp.<br>15 bonds) /1<br>Electricity utility (Corp. bonds)<br>10 Telecoms (Equity)<br>5<br>0<br>Cape Verde Tanzania /1 Uganda<br><!-- End of picture text --> Sources: Pension system asset data sourced from national pension funds and financial authorities; Axco country reports. /1 As of July 2007, for Cape Verde. EADB provides development finance within the EAC to projects in sectors including agriculture, transport and communication, construction and manufacturing, electricity, water supply, and housing. For Tanzania: Cost of financing Feasibility Study of Kigamboni Bridge.

In Uganda, 1.8 percent of total national pension system assets as of June 2007 were invested in local infrastructure (via total holdings of $8.6 million in corporate bonds issued by Uganda Telecom and the East African Development Bank, EADB). Even here, the data are incomplete and provide only a very rough estimate, as they only cover the holdings of the National Social Security Fund (NSSF), the public pension fund.<sup>29</sup> In Tanzania, an estimated 0.07 percent ($0.41 million) of national pension system assets was invested in local infrastructure to fund a feasibility study for construction of the Kigamboni Bridge.

Institutional investors in some of the other focus countries may have been investing a portion of their assets in infrastructure development, through their holdings of government securities and other financial instruments, but existing data compiled by the funds and their regulators makes it impossible to quantify this. For six countries—Chad, the Democratic Republic of the Congo, Ghana, Malawi, Nigeria and Zambia (all but two of which, Nigeria and Zambia, are largely defined benefit systems)—national authorities or local market analysts confirmed that none of the pension system’s assets were invested in infrastructure development.

Only two focus countries, Cape Verde and Mozambique, were able to specifically report investment by their insurance sectors in infrastructure assets as of year-end 2006—in the telecoms sector in both cases (table 4.2). Cape Verde’s insurance sector held 0.5 percent ($0.09 million) of the sector’s total 29 Moreover, it is unclear as to the actual extent that the EADB bond issues finance infrastructure in Uganda as the declared intent of its issues is to provide development finance within the East African Community to projects in sectors including agriculture, transport and communication, construction and manufacturing, electricity, water supply, and housing.

30
# **4.2 Approach 2**

## _Model_

Approach 2 uses a single predictor to impute NSS-type consumption into the CPHS, namely observed CPHS consumption, a variable that is arguably highly predictive of NSS-type consumption, but which is entirely ignored in approach 1. In other words, in this approach we will convert the observed CPHS consumption into NSS-type consumption. Let CPHS-consumption expenditure for household _i_ be denoted by _xi_ . Section 3.2 establishes the following stylized facts: (a) The variance of NSS log consumption is higher than the variance of CPHS log consumption. The re-calibration of the survey weights has reduced this gap in the second moment, but some gap still remains, and (b) CPHS log-consumption is near normally distributed, while NSS log consumption shows a more marked deviation from normality. Specifically, the third moment of NSS log consumption is approximately twice the third moment of CPHS log consumption. (A similar ordering applies to the fourth moment.) To accommodates the above-mentioned stylized facts, consider a model where CPHS log consumption is described as a linear combination of NSS log consumption and a normally distributed error term: where _εi_ is an independent identically distributed error term with mean zero and unit variance. In practice we do not observe _yi_ and _xi_ for the same household _i_ given that the two measures of consumption come from different cross-sectional surveys with their own samples of households that cannot be linked. Accordingly, the model that describes the relationship between the two cannot be estimated using standard regression analysis (which is the reason why observed CPHS consumption was excluded as a predictor in approach 1). Instead, the parameters _a_ , _b_ , and _σ_ will be estimated using method of moments.

A minimum of three moment conditions will be required. The first three moments of the log consumption distribution are natural candidates. The mean and variance of both sides of eq. (2) solve: where _μq_ and _σq_<sup>2evaluate the mean and variance of the variable</sup><sup>_q_,respectively.At this</sup> point we have two moment conditions and three unknown parameters, meaning that a 33
|Table Al-I continued: Variables|||||
|---|---|---|---|---|
|Description|Name<br>|Number<br>of obser-<br>|Mean|Std.<br>Dev.|
||________|vations|||
|Incidence of illness|||||
|Female relative to male percent with ARI in the past 2 weeks (6<br>observations refer to 4 weeks): children under 5 (27 observations<br>refer to under 4, 8 to under 3)|arid|<br>80|0.8958|0.145|
|Female relative to male :percent with fever in the past 2 weeks (6<br>observations refer to 4 weeks): (27 observations refer to under 4, 6<br>to under 3)|fevd|<br>71|0.9485|0.075|
|Female relative to male : percent with diarrhea in the past 2 weeks (1<br>observation refers to 1 week, 1 to 4 weeks) : children under 5 (27|||||
|observations refer to under 4, 8 to under 3)|diad|<br>89|0.9266|0.132|
|Consultation and no treatment of illness|||||
|Female relative to male : percent with ARI who were taken for<br>consultation (usually includes hospital, health center, clinic, doctor,<br>or other health professional)|aritd|<br>69|0.9528|0.10|
|Female relative to male percent with fever who were taken for<br>consultation|fevtd|<br>52|0.9436|0.076|
|Female relative to male percent with diarrhea who were taken for<br>consultation|diatd|<br>77|0.9702|0.17|
|Female relative to male percent with ARI who received no<br>treatment|arind|<br>55|1.1588|<br>0.467|
|Female relative to male percent with fever who received no<br>treatment|fevnd|<br>53|1.149|0.372|
|Female relative to male percent with fever or ARI who received no<br>treatment (this is a constructed variable, see text)|f and|<br>60|1.1547|<br>0.316|
|Female relative to male: percent with diarrhea who received no|||||
|treatment<br>I|<br>diand|<br>70|1.1548|<br>0.381|
|Anthropometrics|||||
|Female relative to male percent whose weight-for-age is below 3<br>standard deviations of the reference population|wfa3sdd|<br>75|0.9824|0.208|
|Female relative to male: percent whose weight-for-age is below 2<br>standard deviations of the reference population|wfa2sdd|<br>79|0.9765|0.113|
|Female relative to male: percent whose height-for-age is below 3<br>standard deviations of the reference population|hfa3sdd|<br>70|1.0289|<br>0.448|
|Female relative to male : percent whose height-for-age is below 2<br>standard deviations of the reference population|hfa2sdd|<br>74|0.948|0.073|
|Female relative to male : percent whose weight-for-height is below 3<br>standard deviations of the reference population|wfh3sdd|<br>68|0.7911|0.352|
|Female relative to male: percent whose weight-for-height is below 2|||||
|standard deviations of the reference population|wfh2sdd|<br>74|0.8377|0.206|

33
Any cropland or livestock that coincides with the projected urban expansion area is considered part of the estimated overlap. These steps are replicated for each SSP scenario to arrive at estimated crop and livestock future overlap. However, it is important to note that this projection exercise assumes that the relevant food sectors will remain stagnant in terms of their technologies and productivity. While this allows us to estimate the overlap of urban expansion on crop and livestock production, it is important to recognize that actual overlap may differ if there are technological advances or changes in the economic or sociopolitical landscape affecting food production.

To determine current crop and livestock production, which are used as baseline values, we sum all crop and livestock production that that are _not_ located in urban areas in 2015. Note that the most recent crop and livestock global data are from 2010, instead of 2015. Moreover, since the crop and livestock data come from two independent sources with two different methodologies, it is possible for a certain area to have both cropland and livestock. After generating the baseline values, we compare them with the absolute values of projected overlap to calculate the percentages for potential food losses. This method is then repeated for each of the seven regions to derive the regional results, and for select cities.

Due to the above limitations and model uncertainty of climate projections on land use, we caution against over-interpreting precise quantitative results, but rather one should focus on broad magnitudes and directions.

# **2.5 Summary statistics**

## _In_

Table 2 we present grid-level summary statistics at baseline of urban extent (in 2015) and of crop and livestock production (in 2010) for the eight crop groups and the eight types of livestock, respectively. In Table 3 we present baseline total values for food crops and livestock globally and for each of the seven regions. For simplicity of the regional breakdowns, we aggregate the seven crop groups into four (fruits and vegetables, cereals and roots, sugars and oils, and pulses) and the eight livestock types into three (poultry, pigs, and ruminants).

_Table 2. Baseline summary statistics_

|**Variable**|**Obs**|**Mean **|**SD **|**Min**|**Max **|
|---|---|---|---|---|---|
|**Grid-Level Variables**||||||
|Urban Extent, 2015 (%)|2,033,283|0.004|0.035|0|1|
|Food crops, 2010, mt|832,827|9243.536|<br>23981.23|0|1507168|
|_Cereals_|832,827|1237.446|<br>4514.959|0|248531.1|
|_Fruits_|832,827|370.511|3325.975|0|424014.9|
|_Oils_|832,827|348.771|2564.369|0|180197.9|
|_Pulses_|832,827|33.505|216.89|0|25092.7|
|_Roots_|832,827|376.579|2397.17|0|355336.9|
|_Sugar_|832,827|966.807|<br>12084|<br>0|1507168|
|_Vegetables_|832,827|1104.765|<br>5004.414|0|293567.5|
|Non-food crops, 2010, mt|832,827|66.487|472.811|0|34934|

10
Figure 1: Average shares by deciles of per capita consumption <!-- Start of picture text --> 27.0%<br>22.0%<br>17.0%<br>12.0%<br>7.0%<br>2.0%<br>1 2 3 4 5 6 7 8 9 10<br>Lower bound - imputed Upper bound - imputed<br>Lower bound 2022 Upper bound 2022<br><!-- End of picture text -->

# Source: Own estimations based on 2016 and 2022 HIES

The analysis reveals that the imputation method performs well in preserving the distribution of imputed parameters for both food and non-food components. Therefore, the 2016 simulated consumption aggregate, which incorporates the imputed components, is expected to exhibit an approximate increase of 15 percent compared to the official consumption aggregate for the bottom 40 of the distribution.

- b. How should the poverty incidence be estimated in 2016 to reconstruct trends?

The 2016 consumption distribution has shifted to the right due to the inclusion of new components in the welfare aggregate (Figure 2). Comparing it with previous 2016 official poverty thresholds will underestimate the incidence of poverty by construction. The 2016 poverty lines were drawn from the 2005 consumption aggregate based on 2005 HIES and updated by inflation. These reflect old consumption patterns and missing components of the welfare aggregate measured in 2022.<sup>8</sup> Thus, the re-estimated 2022 poverty lines should be contrasted to the imputed 2016 aggregate for estimating comparable poverty and extreme poverty headcount levels and trends.

> 8 The 2016 official poverty lines were approximately 17 years old, constructed using HIES 2005, and later updated mainly by inflation. The table below provides how the poverty lines for the last five HIES rounds were estimated over the years.

|Year|2000|2005|2010|2016/17|2022|
|---|---|---|---|---|---|
|Food PL|Updated from 1991/92|Re-estimated (CBN)*|Updated from 2005|Updated from 2010|Re-estimated (CBN)*|
|Non-food PL|Updated from 1991/92|Re-estimated (CBN)|Re-estimated (CBN)|Updated from 2010|Re-estimated (CBN)|

*Re-estimation involves pricing the same food basket (11 food categories) for 2005 and 2022, respectively.

10
We then discuss different ways that migration and the prospect of migration can change the family. Individuals who plan on migrating may invest less in building relationships at home, or may accelerate marriages as they think longer term. Some migration occurs specifically for the purpose of getting married. Migration can affect the stability of existing marriages, and lead to migrants meeting a new pool of potential marriage partners, changing who they might marry.

Using the American Community Survey, we provide descriptive evidence about the extent to which migrants from developing countries change their marital status after moving to the United States. We first find that changing marital status after migration is the norm for most adult immigrants: only one-third of ever-married adult migrants are currently married to a spouse they wed before migration. The model of a fixed family sending off a migrant who remains part of that fixed family is thus empirically not the reality for the majority of migrants. The likelihood of such changes importantly depends on the initial marital status before migration: for initially unmarried migrants, getting married after migration is substantially more likely than the separation/divorce from one spouse for migrants initially married. When migrants get married after migration, they are significantly less likely to marry someone who is also born in the origin country, although the incidence of withingroup marriage is substantial with 66 percent marrying someone from their country of origin (in many cases still only someone they have met after migrating). By tracking cohorts, we show that migrants who were married at the time of migrating are more likely to return migrate than those who were unmarried, but that accounting for this does not affect our main findings on family structure changing for the majority of individuals after migration. The data also reveal that instances of split migration for pre-existing married couples do not last long after migration, so what the literature calls “transnational households” (see, for instance, Ashraf et al., 2015; Clemens and Tiongson, 2017; Abarcar et al., 2020) appear to be fragile and temporary living arrangements, with the two spouses likely to resume co-residing again reasonably quickly (at origin or at destination), or otherwise to dissolve their marital union.

We then note that the anticipation of these potential changes in the migrants’ family can influence selection into whether migration occurs at all, and we provide arguments that suggest that non-migrant individuals might be more inclined to finance the migration of a married individual, even though the pre-existing marital union can clearly increase the 3
those amounts to poor and non-poor households. Therefore, the Takaful program was relatively less “effective” and less efficient (given the Takaful program’s budget) at protecting poor and vulnerable households than the Karama program (given the Karama program’s budget). The food credit program, energy subsidy expenditures, and public education expenditures reach a very small proportion of their potential to reduce inequality; this is due primarily to the large amounts spent on those programs combined with their near-universal coverage.

**Figure  18. Fiscal Gains to the Poor Effectiveness Indicator**

<!-- Start of picture text --> 0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Takaful Karama Food credit Subsidies Education Health<br><!-- End of picture text --> _Source:_ Based on HIECS 2015 and budget data.

# **CONCLUSIONS**

This study implements the CEQ methodology for the case of Egypt. Having unprecedented access to the administrative data of the GoE, this study is able to map several of the country’s main policies and assess their impact on poverty and inequality. From the expenditure side, the exercise accounts for social spending (including pension fund contributions, the food subsidy program, the Baladi bread program, and the recently implemented Takaful and Karama programs); we also account for education and health spending, as well as expenditures on fuel and electricity subsidies. For the revenue side, the exercise includes the direct personal income tax, the alcohol and tobacco excise, the fuels excise, and the direct and indirect burdens created by the GST.

Fiscal policy in Egypt reduces inequality and poverty through a series of fiscal policy elements. The flagship, nearly universal program of the food smartcard and Baladi bread allowances of the Tamween program has a relatively low contribution to the equity efforts of the GoE. Instead, poorer households tend to receive larger benefits from education spending in basic education and were well targeted by the recently introduced Takaful and Karama programs. Direct taxes are also found to be slightly progressive, with richer households liable for larger shares of the tax than their estimated market income share. Subsidy spending is not equality enhancing, as a large share of the benefits end up among the richest households—probably linked to the consumption patterns and ownership of assets, such as cars. Overall, Egyptian fiscal policy in 2015 achieves inequality reduction around the median of its comparator group 36
Here the bars denote fixed reference values, while _D_<sup>_i_</sup> and _R_<sup>_i_</sup> are the demand rates and rationing rates for _i_ =poor, non-poor, respectively. _TD_ can thus be interpreted as the ―self-targeting effect‖ (greater demand for work amongst the poor) net of the rationing effect (the extent to which the poor might be rationed more). (Since the decomposition is not exact—given the nonlinearity in equation (1)—there is also a residual.) Applying this decomposition, and using the all-India values for the reference we find that 85.6% of the national TD is attributable to the difference in demand between the poor and nonpoor while 13.7% is due to the difference in rationing rates. (The residual is negligible.) There are differences across states, though the demand effect dominates in 17 of the 20 states. So, despite the rationing, the bulk of the pro-poor targeting is coming through the self-targeting mechanism.

Targeting performance is better in states with higher overall participation rates. Figure 5 plots the two participation rates from Table 4 against the overall participation rate (Table 1). We see that the TD—the gap between the two lines—rises with the overall participation rate, and the two are strongly correlated (r=0.748).

Targeting performance also tends to be worse in the states with higher levels of rationing (the correlation between TD and rationing rate is -0.71). However, this arises because overall participation rates are low in states with higher degrees of rationing. Indeed, once one controls for the participation rate, there is no significant partial correlation between the TD and the rationing rate (the t-statistic is -0.611).<sup>17</sup> So we find that higher overall participation rates tend to come with better targeting performance and lower rationing rates. The fact that targeting performance improves as the program expands makes this an example of what Lanjouw and Ravallion (1999) call ―early capture‖ by the non-poor, which they showed to be a common feature of access to safety-nets

> 17 The TD is also positively correlated with the demand rate (r=0.671), but this too vanishes when one controls for the participation rate (the t-statistic for the partial correlation coefficient is 0.151).

12