rafmacalaba/datause-encoder-data huggingface dataset:
Sudan and Sudan, while the opposite is true for Somalia, where people living in male-headed households have a higher MPI. In Nigeria, there is no statistically significant difference between the poverty levels of the two groups. These differences also hold for incidence of multidimensional poverty, where female-headed households are 39 percentage points poorer in Ethiopia, and 17.6 and 10.3 percentage points poorer in South Sudan and Sudan, respectively.

_Figure 3. MPI for male- and female-headed households by country_ <!-- Start of picture text --> 0.450<br>0.400 58.5%<br>0.350 61.7%<br>0.300<br>41.5%<br>0.250<br>42.1%<br>0.200 35.7%<br>65.9%<br>0.150<br>38.2% 64.3%<br>34.1% 58.0%<br>0.100<br>0.050<br>0.000<br>Ethiopia Nigeria Somalia South Sudan Sudan<br>MPI Male-headed household Female-headed household<br><!-- End of picture text --> Source: Authors’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Note: Population shares of headship included above each bar.

With the above results, it is important to consider the gender differences by displacement status as the gender of the household head may be correlated with disruption in family structures that occurred after displacement. Looking at the sample composition by headship at the country level, 45% of the population in Ethiopia, 34.3% in Nigeria, 49.8% in Somalia, 44.7% in South Sudan, and 41.6% in Sudan live in female-headed households. Disaggregation by displacement status shows that there is a higher proportion of the population living in female-headed households in displaced communities in Ethiopia (51.4 % of refugees vs 32.2% of hosts), South Sudan (53.3% of IDPs vs 43.6% of non-IDPs), and Sudan (47% IDPs vs 30% non-IDPS).

Table 5 shows the MPI, incidence (H) and intensity of poverty (A) by the gender of the household head and displacement status for the five countries. Disaggregating the household gender differences by displacement, we find interesting results across four countries. In Ethiopia, the observed gender difference that hurts female-headed households is driven by the difference in MPI among refugees. The difference in MPI between displaced households is largest in Ethiopia, with an MPI of 0.355 for those in female-headed refugee households and an MPI of 0.111 for those in male-headed refugee households. Further, there is a 40 percentage point difference in the incidence of poverty among refugees, with 57.9% of those in femaleheaded households and only 19.1% of those in male-headed households being multidimensionally poor. Interestingly, there is no statistically significant difference between the poverty levels of the two groups among host community households.

In South Sudan, individuals living in female-headed households are more likely to be poor compared to their male-headed counterparts both in IDP and non-IDP households. Female17
<!-- Start of picture text --> Figure 6<br><!-- End of picture text --> <!-- Start of picture text --> Export Income and GDP per capita in 2004, Selected Countries<br>20,000 5<br>16,000 4<br>12,000 3<br>8,000 2<br>4,000 1<br>0 0<br>ExpY GDP pc EXPY/GDPpc<br><!-- End of picture text -->

# **VI. Recent Changes in the Productivity of Argentine Exports**

In this section we examine trends in Argentine export income during the last few years and analyze in more detail the productivity of the top Argentine exports. Figure 7 below plots Argentine export income, GDP per capita and their ratio for the period 1994-2004. During the 1990s the ratio was below one (i.e. export income was lower than GDP per capita) but for the last five years of the sample it was above one. Nevertheless, the main fact revealed by the graph is that Argentine export income has been very close to GDP per capita in the last 11 years. That may be undermining prospects for economic growth<sup>3</sup> .

**Figure 7**

<!-- Start of picture text --> Export Income and GDP per capita for Argentina, 1994-2004<br>13,000<br>1.10<br>12,500<br>1.05<br>12,000<br>11,500 1.00<br>11,000<br>0.95<br>10,500<br>10,000 0.90<br>1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>Export Income GDP per capita Ratio<br>Ratio<br>PPP 2000 $<br><!-- End of picture text --> 3 As mentioned in the data section, the results presented here differ to some extent from those presented in Hausmann and Klinger (2006) due to the different samples used. However, observe that the differences are only in terms of levels and not in terms of the evolution. Moreover, the difference in levels does not affect the relative comparisons among countries since the differences are not country specific.

11
2 RENT CONTROL IN KUMASI, GHANA As will be discussed below, other problems restricting the supply of rental (and owner occupied) housing must also be confronted, such as problems in land markets, provision of infrastructure, finance, and other regulations, such as planning and building regulations.

Shelter is the largest single form of fixed capital investment in most economies. In developing countries, the shelter sector usually ranges between 10 and 30 percent of household expenditure or 6 to 20 percent of Gross National Product. Housing ranges from 10 to over 50 percent of gross fixed capital formation and from 3 to 8 percent of Gross Domestic Product. The share of housing investment in GDP r_.ses as economies develop (see, for example, Mayo, Malpezzi and Gross, 1986).

In general, these patterns have not been evident in Ghana over the last 15 years. Figure 1.3 shows that since 1970 the published numbers indicate that the share of housing investment in GNP has been falling from about 5 percent in the early 1970s to only about 3 percant in the years since 1980. However, such data are only indicative. Ghosh and Mohan (1983) reports that Ghanaian housing investment data are derived from data on cement imports and estimates of population growth, so, for example, changes in housing investment ziven a partictular change in populacion and level of cement imports will not be reflected in the data. On the other hand, the trend is still clear and is consistent with observation and anecdotal evidence (and survey data presented later in this paper).

Figure 1.3: Housing Investment and Capital Formati.,n While these data have to be interpreted with more than the usual 14% caution, taken at tace value, they indicate **_I_S9OP IGCGOP_** that while both investment and housing % \ investment fell during the 70s, at the same *o. - V - ... .. time housing's shar. of fixed capital formation rose, because other fixed capital 8s . . . did even worse. Gross Fixed Capital Formation (GFCF) has declined from roughly 10 percent of GDP at the beginning of the **4x ....... ......... ...** period to 5 percent in the early 1980s. The picture is just as bleak when annual _2%¶_ **1 74** ;s _18 8, 82 84 88_ levels as well as ratios are studied. Roughly, real housing investment per capita, and real GFCF per capita, were halved during the period. It is understandable that when times are hard, people defer housing and other investments in the struggle to buy food and other current essentials.

The overall investment picture looks better in the post 1983 period. However, the recent upturn in investment is largely due to increased public investment. Private investment is still lagging. While official housing investment data for the past several years are not available, data presented in Chapter 6 suggest that housing investment has picked up somewhat.

In addition to unfavorable long term trends, Ghana started out from low levels of investment. Table 1.1 presents some indicators of access to housing infrastructure for Ghana and several other African countries. Over a quarter
**<mark>Table 4. Food-price inflation and experiential food insecurity, controlling for individual-level fixed effects</mark>**

||Worried ab<br>last 7 d|out food,<br>ays(se)|Food insec<br>last 12 m|ure in the<br>onths(se)|Months fo<br>last 12 m|od insecure,<br>onths(se)|
|---|---|---|---|---|---|---|
||OLS|Logit|OLS|Logit|OLS|Poisson|
|Maize price 12 months ago (month 1)|0.004***|0.005***|0.004***|0.005***|0.019***|0.014***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.003)|(0.005)|
|Change in inflation rate of maize (past 12 months)|0.038***|0.250**|0.074***|0.092***|0.339***|0.220***|
||(0.011)|(0.025)|(0.011)|(0.021)|(0.046)|(0.054)|
|Dietary diversity (Food Consumption Score)|-0.003***|-0.005***|-0.002***|-0.003**|-0.017***|-0.013***|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.003)|(0.003)|
|Daily per capita calorie consumption (thousands)|-0.013|-0.010|-0.022*|-0.016|0.004|0.050|
||(0.011)|(0.018)|(0.011)|(0.021)|(0.043)|(0.041)|
|Annual per capita expenditure (Kwacha)|-0.000|-0.000|-0.000|-0.000|-0.000|-0.000*|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Number of observations|22,820|8,596|22,820|8,800|22,820|16,402|
|Adjusted R<sup>2</sup><br>*** p<0.01, ** p<0.05, * p<0.1.|0.045|0.082|0.072|0.139|0.079|---|

Notes: All models control for fixed effects and observations are weighted to be representative of the population. For OLS, estimates are average marginal effects, and for the Logit and Poisson estimators the estimates are marginal effects averaged over the values of the observed controls but at a fixed point (zero) for the unobserved controls (i.e. individual-level fixed effects). We refer to the Logit and Poisson effects as conditional average marginal effects. The models in columns (ii) and (vi) are estimated in Stata using xtlogit and the model in column (iv) is estimated using xtpoisson; models in columns (i), (iii), and (v), are estimated using xtreg. Unlike the OLS fixed effects estimator, which uses all observations, the Logit and Poisson estimators only use observations whose outcome variable changes across time and whose outcome variables are not ‘0’ in both rounds, respectively. Therefore, number of observations presented in columns (ii) and (iv) reflect the number of observations tied to individuals whose status changes between waves. Panel sampling weights were rescaled to sum to the number of observations in the data and are treated as importance weights in Stata. Standard errors, in parentheses, from columns (ii), (iv), and (vi) are calculated using the jackknife method, clustered at the EA (enumeration area) level.

17
panel B and panel C with those in panel A shows the pattern of the effects remain same for changes in the employment share of agriculture, manufacturing, and trade and services. However, the numerical magnitudes vary by the pre-war market access; the effects of the CIV trade shock on the structural change on the integrated segment of the non-Abidjan route are stronger for a location with better pre-war market access. The decline in the share of agricultural employment in the integrated non-Abidjan areas was on average 5.7 percentage points when the treatment locations consist of locations with pre-war market access in the upper 40 percentile of the distribution (i.e., for the treatment dummy _DT_<sup>0</sup><sup>_._40</sup> ) , and the effect increases to 6.3 percentage points for the areas in the upper 30 percentiles of the pre-war market access distribution (i.e., for _DT_<sup>0</sup><sup>_._30</sup> ). This implies a 21 percent higher impact for _DT_<sup>0</sup><sup>_._40</sup> , and a 34 percent higher impact for _DT_<sup>0</sup><sup>_._30</sup> compared to the average impact over the whole integrated segment reported in panel A of Table 2. The estimates for the employment shares of manufacturing, and trade and services also show a monotonically increasing effect of the CIV trade shock with better pre-war market access within the integrated zone, but the numerical magnitudes are much smaller.

# **(7.3) Long-term Effects of Temporary Trade Shock**

The evidence discussed above in section (7.2) suggests that the temporary trade shock caused by the CIV civil war led to significant changes in the employment pattern in the non-Abidjan route when compared to the directly affected Abidjan route. In this section, we analyze whether there are reasons to expect that the changes observed in the employment pattern may be longer-lasting, and may not revert back to the pre-war pattern of resource allocation and sectoral specialization. If the empirical estimates are capturing only the reallocation of mobile factors without any sunk investments, then one would expect the share of Abidjan route in total trade to revert back close to its original level after the end of the CIV-crisis. Empirical evidence in this regard is quite the contrary. For Burkina Faso, we have trade flow data till 2016 which is plotted in Figure 2a. The analysis of trade flow confirms that the share of maritime trade flow through Abidjan route never recovered to its pre-war level even 10 years after the end of the CIV crisis. This alerts us to the 28
<!-- Start of picture text --> Category 3: Peak 2000-2010<br>Micronesia (Federated States o f ) Mongolia Morocco<br>1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030<br>Myanmar Republic of Moldova Saint Lucia<br>1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030<br>Saint Vincent and the Grenadin e s Sao Tome and Principe South Africa<br>1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030<br>Sri Lanka Swaziland Tonga<br>1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030<br>Trinidad and Tobago Tunisia Turkey<br>1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030<br>Turkmenistan Uzbekistan Viet Nam<br>1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030<br>Zimbabwe<br>1950 1970 1990 2010 2030<br>Year<br>United Nations Medium Variant Projections<br>4 4 4<br>3 3 3<br>2 2 2<br>1 1 1<br>3 2 2<br>2.5<br>2 1.5 1.5<br>1.5 1<br>1 1<br>5 4<br>2<br>4 3<br>1.5 32 2<br>1 1 1<br>2.5 5 2.5<br>2 4 2<br>1.5 32 1.5<br>1 1 1<br>2.5 3 3<br>2 2.5 2.5<br>1.5 2 2<br>1.5 1.5<br>1 1 1<br>Population aged 12-24 (1950=1.0)<br>4 5 4<br>3 4 3<br>3<br>2 2 2<br>1 1 1<br>6<br>4<br>2<br>0<br><!-- End of picture text --> 56
Table C.3: Baseline Parameters (continued)

|Parameter|Description|Units|Value|
|---|---|---|---|
|_✓_<sup>_e_</sup><br>0|Energy Technology Index in 2004||1.195|
|__<sup>_e_</sup>|Energy Technology Index Growth Rate||0.0225|
|_⇢e_|CES Parameter for Petroleum and Biofuels||0.5|
|_↵_<sup>_e_</sup>|Share of Biofuels in CES Function||0.09|
|_✓_<sup>_yw_</sup><br>0|Timber Processing Technology Index in 2004||1.52|
|__<sup>_yw_</sup>|Timber Processing Technology Growth Rate||0.0225|
|_c_<sup>_o,yw_</sup>|Timber Processing Cost|share of _X_<sup>_O_</sup><br>_t_|0.0224|
|_✓_<sup>_r_</sup><br>|Ecosystem Services Technology Index||0.71|
|_↵_<sup>_A,r_</sup><br>|Share of Agricultural Land in CES Function||0.02|
|_↵_<sup>_P,r_</sup><br>|Share of Pasture Land in CES Function||0.14|
|_↵_<sup>_C,r_</sup>|Share of Managed Forest Lands in CES<br>Function||0.26|
|_⇢r_|CES Parameter for Ecosystem Services||0.123|
|_✓_<sup>_R_</sup>|E↵ectiveness Index of Protected Lands||10|
|_c_<sup>_o,r_</sup>|Cost of Recreation Services||0.0296|
|_✓_<sup>_o_</sup><br>0|Total factor Productivity Index in 2004||1.854|
|__<sup>_o_</sup>|Total Factor Index Growth Rate||0.0225|
|_Preferences _|_and Welfare_|||
|_↵f_|AIDADS Marginal Budget Share at Subsistence||0.189|
||Income for Services from Processed Food|||
|_↵l_|AIDADS Marginal Budget Share at Subsistence<br>Income for Services from Processed Livestock||0.035|
|_↵e_|AIDADS Marginal Budget Share at Subsistence<br>Income for Energy Services||0.112|
|_↵w_|AIDADS Marginal Budget Share at Subsistence<br>Income for Services from Processed Timber||0.036|
|_↵r_|AIDADS Marginal Budget Share at Subsistence<br>Income for Ecosystem Services||0.049|
|_↵o_|AIDADS Marginal Budget Share at Subsistence||0.579|
||Income for Other Goods and Services|||

24
# **Table 3: Firm Performance and Government Support during COVID-19**

||E<br>Round|l Salvador||Round|Georgia||
|---|---|---|---|---|---|---|
||1|Round 2|N|1|Round 2|N|
|Percentage change of permanent full-time<br>employees since December 2019|-0.161|-0.088*|785|-0.142|-0.103|853|
|Firms that the total number of temporary workers<br>decreased|0.332|0.328|776|0.283|0.447***|845|
|Firms that total hours of operation per week|||||||
|decreased|0.905|0.509***|800|0.633|0.384***|952|
|Firms that anticipate falling in arrears on<br>outstanding liabilities within next 6 months|0.515|0.198***|783|0.285|0.349*|794|
|Received national or local government support|0.013|0.421***|797|0.315|0.480***|960|
|Cash transfers|0.001|0.113***|791|0.027|0.059**|904|
|Deferral of payments|0.003|0.100**|792|0.160|0.210***|936|
|Access to new credit|0.002|0.140***|791|0.005|0.025|899|
|Fiscal relief|0.007|0.080***|791|0.072|0.273***|907|
|Wage subsidies|0.000|0.360***|789|0.120|0.194***|916|
|Other support|0.005|0.021|706|0.006|0.027*|910|
|Average change monthly sales compared to one<br>year ago|-0.588|-0.249***|792|-0.484|-0.297***|885|
|Overdue on obligations to financial institutions<br>Adjusting or converting their production or<br>services|0.270<br>0.224|0.376***<br>0.768***|736<br>802|0.107<br>0.269|0.139*<br>0.404***|910<br>957|
|Decreased demand for products or services<br>compar to one year ago|0.751|0.668|802|0.689|0.554***|951|
|Decreased liqdt or cash flow avail. since|||||||
|COVID-19 began|0.835|0.832|803|0.706|0.829***|958|
|Started or increased online business activity|0.326|0.565***|803|0.251|0.347***|964|
|<br>Started or increased delivery of goods, services<br>or carryout|0.366|0.567***|803|0.234|0.329***|965|
|Started or increased remote work|0.472|0.538|801|0.433|0.509***|988|
|Exporting directly (at least 10% of sales)|0.052|0.107|734|0.093|0.073|878|

Note: This table shows descriptive statistics from El Salvador and Georgia COV-ES data. Round 1 and Round 2 columns show the respective statistics of firms in the respective round. N denotes the number of observations. Means are weighted using the sampling weights from the ES. Asterisks reported in the columns for Round-2 are for unconditional t-tests comparing means across the two rounds. For these t-tests, standard errors are clustered at survey strata, and *, **, *** denotes the significance level at 10%, 5%, and 1%, respectively. See Section 4 for more details.

36
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10

# **3. The “mesoeconomic” approach**

Beyond those three stylized facts about country‐level correlations, however, the macroeconomic approach to the poverty‐growth ‐inequality triangle has yielded relatively little. During the period of study, inequality was uncorrelated with growth, so the cross‐country evidence is limited to pointing out that growth leads to poverty reduction, and that it does so more effectively if initial inequality is lower. But it tells us very little about which country characteristics or policy choices might contribute to faster poverty reduction, through reductions in inequality and a correspondingly higher (total) growth elasticity of poverty reduction. If there were no other way to investigate the relationship between growth and distributional dynamics, the conclusion would presumably have to be: ‘since we do not know much about the determinants of distributional change, and since on average growth lowers poverty, let’s focus on whatever policies (we believe) yield the highest average growth rates’.<sup>11</sup> Fortunately, there are other data sets that permit an investigation of this relationship. The closest in nature to the cross‐country approach is to look at poverty dynamics within large countries that combine three data characteristics: (i) a sufficiently long time‐series of repeated cross‐sectional household surveys; (ii) national accounts information on economic growth that is disaggregated both by sectors (e.g. agriculture, industry, and services) and sub‐nationally (e.g. by province or state); and (iii) sufficient information on other time‐varying poverty determinants (such as inflation rates, patterns of public spending, and the like). With such data, one could adapt equation (5) to a panel of sub‐national units, and write: Here, a subscript _i_ denotes a sub‐national unit (call it a “state”), while _t_ denotes a time period. _J_ denotes � a broad sector of economic activity, such as primary (P), secondary (S) or tertiary (T); and ��� denotes the share of the sector _J_ in the total output of state _i_ at time _t_ . Naturally, equation (6) is estimated using discrete data on state‐level poverty and sector output growth, so it is more commonly written in the form: This general specification is designed to shed light on a number of possible “economic determinants” of � the distribution component in equation (3). Differences in �� across sectors _J_ can be informative of whether the pattern of growth matters for poverty reduction. In almost every case where it was tried, researchers have comfortably rejected the null hypothesis that the elasticities are the same across

> 11  This is reminiscent of Dollar and Kraay’s (2002) conclusion: “In short, existing cross‐country evidence – including our own – provides disappointingly little guidance as to what mix of growth‐oriented policies might especially benefit the poorest in society. But our evidence does strongly suggest that economic growth and the policies and institutions that support it _on average_ benefit the poorest in society as much as anyone else.” (p.219, emphasis added).