rafmacalaba/datause-encoder-data huggingface dataset:
|**`Eigenvalues of long-run matrix:`**|
|---|
|**`real imag modulus`**|
|**`-1.000 0.0000 1.000`**|
|**`-1.000 0.0000 1.000`**|
|**`-0.4205 0.0000 0.4205`**|
|**`-0.006870 0.0000 0.006870`**|

|**`I(2) matrix Gamma:`**||
|---|---|
|**`Drm2`**|**`Drgdp DDISC CIa`**|
|**`Drm2 1.0000`**|**`0.00000 0.00000 0.00000`**|
|**`Drgdp 0.44943`**|**`1.0000 0.00000 0.00000`**|
|**`DDISC 0.00000`**|**`0.00000 1.0000 0.00000`**|
|**`CIa -0.58382`**|**`0.00000 0.00000 1.0000`**|

72
suffering from psychological distress (Goldberg and Williams, 1988). We supplement these two indicators with five secondary outcomes: (i) The PHQ-8 score (range: 0-24); (ii) the GHQ-12 score (0-12); (iii) the score on the Rosenberg self-esteem scale (0-30) (Rosenberg, 1965); (iv) the score on the Child and Youth Resilience Measure-Revised (0-34) (Jefferies et al., 2019); and (v) the locus of control score (1-10). The discrete PHQ-8 and GHQ-12 scores allow the assessment of impact on the severity of distress in the sample, while the remaining outcomes capture several distinct dimensions of mental health (Shah et al., 2024).

The full set of human capital outcomes is shown in Table 2. The outcomes measured differ slightly by round. At the rapid resurvey, primary and secondary outcomes focused on intentions and expectations as useful predictors of subsequent investments in human capital, as it was too early to see broader human capital changes. These outcomes include life expectancy; desired fertility; educational aspirations (aspires to tertiary education), desired age of first marriage, desired age of first pregnancy, expectations of paid work at age 25; timepreferences; and the score on a set of competencies (skills), using a module developed by the investigators (Baird et al., 2019). The competencies involved making correct change during a set of hypothetical market transactions that aimed to test the respondent’s numeracy, reading and listening comprehension, and ability to follow instructions.

At midline and endline, we added a number of primary and secondary outcomes that are further down the causal pathway, i.e., that provide more concrete evidence of whether or not there are knock-on effects of improved mental health on human capital accumulation. These outcomes include an indicator for being enrolled in school; the incidences of marriage and childbearing since baseline; risky sexual behavior (condom use during last sexual encounter); and self-efficacy (Schwarzer and Jerusalem, 1995).

# **2.3.3 Estimation**

There are two main parameters of interest in this study, which we estimate separately. The first is the Intent-To-Treat (ITT) estimate of IPT-G on mental health and human capital accumulation. We obtain the parameter estimate _τ_ ˆ by estimating the following linear regression model on the sample restricted to the control and IPT-G groups: _Yi_ denotes outcomes _Y_ for individual _i_ residing in cluster _c_ . Assignment to IPT-G ( _Therapy_ ) at the cluster level is denoted by the indicator _Tc_<sup>_Th._</sup> . _ε_ denotes an idiosyncratic error term. We estimate this specification separately in each survey round. To improve power, we pool the two treatment groups (IPT-G and IPT-G+) at the _rapid resurvey_ round as both groups 11
J <!-- Start of picture text --> ite)<br>i<br>2<br>a2)<br>=<br>w<br>re)<br>fe)<br><!-- End of picture text --> <!-- Start of picture text --> ; - alls I lls<br>RN & & & © © A © & © » Ft 2 .b © 6<br>FESM QD FFEOY QD SESSQe QD SESSOH DM KR SSLRY ON SSoN oS<br>[-_] Quasi experiments ME RCTs<br><!-- End of picture text -->
_R_<sup>2In addi-</sup> _ad j_<sup>, they could all be near the mean, with a sharp decrease in data variability.</sup> tion, logarithmic transformations are not immune to a re-transformation bias. There are various ways to solve this issue, but the most frequently used ones are based on scale correction of the predicted values. Both the relative Gini index and the relative Theil index are scale invariant,<sup>2</sup> which can render those corrections ineffective at reducing bias.

To summarize the discussion thus far, the regression to obtain a predictor is the right choice only if the _R_<sup>2</sup> _ad j_<sup>ishigh,alltheassumptionsarerespected,andthebiascaused</sup> by the log-transformation is negligible. In addition, it is important to bear in mind other potential sources of bias (Newhouse et al., 2014) arising from the differences between the survey design and the questionnaires.

# **4 Estimates of Biases Based on Moroccan Data**

In this section, using data from Morocco, we will show that the most common regression techniques can fail to accurately estimate three parameters: the Gini index, Theil index, and Head Count Ratio (HCR) with an endogenous poverty line equal to 60% of the median value of the regional expenditure per capita. We will compare estimates obtained with a direct estimator from the HBS at the rural/urban level in four different regressions. In all cases, the reported results will have a high negative skewed bias. The following regression techniques are used:

- Log-linear regression over two (split) data sets. We split the data set using a dummy (0= urban, 1= rural) variable, ran the regressions, and then computed the indices;

- Log-linear regression over the whole data set. The fitted values obtained are then split using the dummy (0= urban, 1= rural) variable and then the indices are estimated, both with ordinary least squares and feasible generalized least squares;

- Random forest regression over the split data set. With a log-transformation of the dependent variable;

- Quantile regression over the split data set with a log-transformation.

The fundamental problem with these regression techniques, even the robust ones, is that all fitted values will be near the mean value; therefore, variability will be reduced compared to the real regression. To attenuate this problem, we use two methods previously used in the literature (inter alia, Douidich et al., 2016 and Newhouse et al., 2014),

> 2The property of scale invariance states that inequality remains unchanged when all incomes increase by the same proportion. See Clementi et al. (2022)) for a discussion of differences between (relative) scale invariant and non-scale invariant (absolute) measures of inequality.

6
Figure 12: **Domestic and reference prices for plantation white sugar in 1981 rupees, 1965-94** <!-- Start of picture text -->
-B  REF-IMP<br>- REF-EXFP<br>t.~~~NiNf~A  YY.,S..J.<br><!-- End of picture text -->

Sources and notes: Weighted average prices for UP, Maharashtra and TN. See Annex 7 for details.

Table 6: **Mill sugar: descriptive statistics for constant 1981 price series**

|Mean|Standard<br>Deviation|Coefficient<br>of<br>Variation|Z Statistics|LS Growt|
|---|---|---|---|---|
|World free market<br>345.8<br>price, USD / MT<br>Domestic prices, Rs /quintal|261.2|0.76|0.61|-0.50|
|Average mill price<br>363.3|70.1|<br>0.19|0.19|<br>-0.57_**_|
|Import reference<br>343.4|190.8|<br>0.56|0.44|0.53|
|Export reference<br>285.0|188.7|<br>0.66|0.53|0.66|
|Source and notes: See Annex 7 for details|||||

** Indicates 5% level of significance

* Indicates 15% level of significance

Table 7 shows the average NPCs for mill sugar in each of the three states during the four sub-periods we have distinguished in this study ( see methodology notes). It also shows the weighted average NPCs of the three states, which we have taken to be an approximation of the average all India NPC. The estimates indicate significant protection to mill sugar for most of the period. Except for some years of high international prices, the exportable hypothesis NPCs also indicate that, at the prevailing Rupee/dollar exchange rates, it has generally not been profitable to export sugar.

34
# **Table A.2: Summary statistics, RLMS 1994-2017 (237 395 obs)**

||**Mean**|**_SD_**|
|---|---|---|
|Subjective welfare|3.83|_1.5_|
|Log of household income|9.93|_0.9_|
|Household size|3.14|_1.3_|
|Number of adults<br>|2.59|_1.0_|
|Number of children|0.56|_0.8_|
|age16_20|0.07|_0.3_|
|age21_30|0.19|_0.4_|
|age31_40|0.19|_0.4_|
|age41_50|0.17|_0.4_|
|age51_60<br>|0.16<br>|_0.4_<br>|
|age61_70|0.12|_0.3_|
|age71_80|0.08|_0.3_|
|age80plus|0.03|_0.2_|
|Female|0.58|_0.5_|
|Russian nationality|0.87|_0.3_|
|Incomplete secondary|0.21|_0.4_|
|Complete secondary|0.32|_0.5_|
|Secondary + vocational|0.25|_0.4_|
|University and higher|0.22|_0.4_|
|Single|0.16|_0.4_|
|Married|0.63|_0.5_|
|Divorced/widowed/separated|0.21|_0.4_|
|Have poor health|0.40|_0.5_|
|Other household members in poor health|0.53|_0.5_|
|Employed|0.61|_0.5_|
|Unemployed/out of labour force|0.39|_0.5_|
|Log ofpercapita living space (sqm)|3.10|_3.1 _|

_Note_ : data are unweighted 37
importance of the size as factor of productivity and efficiency. Actually, small domestic firms appear to gain more from concentration than big and foreign ones. This is the case in _Textile_ and _Metal & Machinery Products,_ in addition to _Agro-Processing_ and _Chemicals & Pharmaceutical Products._ This constitutes an interesting result that would justify a policy of concentration of small enterprises as a powerful means of efficiency and competitiveness of the industrial sector, thus contributing to industrial development and economic growth Actually, like other developing countries, MENA is increasingly concerned about improving competitiveness and productivity as the region face the intensifying pressure of globalization. This is particularly true in MENA _Textile_ , _Garment_ and _Leather_ industries, in which export specialization can be high in some countries. Among the region, the World Bank firm-surveys provide a standard instrument for identifying key obstacles to productivity, and prioritize policy reforms. This instrument can be used to boost competitiveness and diversify MENA economies. This factor should be taken into consideration if MENA wants to face the increasing international competition of countries such as _China_ and _India,_ which have successfully diversify their economy and benefit, in addition, from low labor costs. **Targeting reforms on small and medium enterprises, as well as on those industries and investment climate variables which are the most inadequate and which favor the most productivity would constitute an important element of MENA strategy of growth and employment for the future.** 26
44

# TABLE 7

# PARAMETRS OF THAI TRANSLOG<sup>PRIMARY COST FUNCTION</sup>

# (Primary Capital Fixed)

<!-- Start of picture text --> Dependent Variable Coefficients<br>[CIpjm Bahl 1-ratios<br>Intercept -11.538<br>(-17.318)<br>in (PM/PI) -.0414<br>(-0.415)<br>in (MCM/PI) 0.3237<br>(-3.709)<br>In Q 0.8311<br>(16.299)<br>inK j 0.1616<br>(2.964)<br>| 'hn(pm/pL)]1 4 0.0129<br>(2.728)<br>I A (h InCN/P131 2 -0.0266<br>(3.160)<br><!-- End of picture text -->
# **Measuring Poverty in Tanzania: Comparison of Diary and Recall Approaches to Food Consumption Data Collection**

Akuffo Amankwah<sup>1</sup> , Darcey Jeanne Genou Johnson<sup>1</sup> , Josephine Ofori Adofo<sup>2</sup> , Maryam Gul<sup>1</sup> , Amparo Palacios-Lopez<sup>1</sup> 1 Living Standards Measurement Study, World Bank Group, Washington DC, USA.

2 Africa Chief Economist Office, World Bank Group, Washington DC, USA.

**Key Words** : Food consumption expenditure; Recall design; Diary design; Poverty; Inequality; Tanzania.

**WB Thematic Areas:** Poverty; Inequality and Growth.

**JEL Codes** : C83; D12
**Table 7: Determinants** **<u>of land rental markets participation</u>**

||**M**|**WI**|**N**|**ER**|**N**|**GA**|**TZ**|**A**|**U**|**GA**|**ETH**|
|---|---|---|---|---|---|---|---|---|---|---|---|
||**Rent in**|**Rent in**|**Rent in**|**Rent in**|**Rent in**|**Rent in**|**Rent in**|**Rent in**|**Rent in**|**Rent in**|**Rent in**|
|Land owned|-0.156***|-0.155***|-0.009***|-0.009***|-0.109***|-0.110***|-0.018***|-0.018***|-0.122***|-0.125***|-0.008|
||(0.006)|(0.006)|(0.002)|(0.002)|(0.012)|(0.012)|(0.003)|(0.003)|(0.011)|(0.011)|(0.007)|
|Number of adults|0.015***|0.015***|0.010***|0.010***|0.004|0.004*|0.007***|0.006***|0.014***|0.016***|0.006|
||(0.002)|(0.002)|(0.003)|(0.003)|(0.003)|(0.003)|(0.002)|(0.002)|(0.005)|(0.005)|(0.006)|
|Age of head/10|-0.004***|-0.004***|-0.007*|-0.007*|-0.008**|-0.008**|-0.008***|-0.009***|-0.013**|-0.014**|-0.029***|
||(0.001)|(0.001)|(0.004)|(0.004)|(0.003)|(0.003)|(0.003)|(0.003)|(0.006)|(0.006)|(0.006)|
|Head is illiterate|-0.004|-0.002|0.014|0.016|-0.006|-0.009|0.005|0.011|-0.055**|-0.058***|-0.025|
||(0.007)|(0.007)|(0.023)|(0.022)|(0.012)|(0.012)|(0.015)|(0.015)|(0.023)|(0.022)|(0.035)|
|Head has primary|-0.008|-0.007|0.012|0.013|-0.010|-0.010|0.013|0.016|-0.014|-0.013|-0.020|
||(0.005)|(0.005)|(0.023)|(0.022)|(0.011)|(0.011)|(0.012)|(0.012)|(0.019)|(0.019)|(0.034)|
|Female head|-0.022***|-0.023***|-0.035**|-0.035**|-0.008|-0.007|-0.002|-0.002|0.017|0.019|-0.126***|
||(0.004)|(0.004)|(0.015)|(0.014)|(0.014)|(0.014)|(0.009)|(0.009)|(0.019)|(0.019)|(0.017)|
|Value of durable|0.011***|0.011***|0.002|0.002|0.002|0.002|||0.000|0.000||
|goods/1000|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|||(0.000)|(0.000)||
|Inefficiency from|-0.005***|-0.005***|-0.012**|-0.011**|0.002|0.003|-0.010***|-0.011***|-0.034***|-0.030***||
|frontier with manager|(0.002)|(0.002)|(0.005)|(0.005)|(0.005)|(0.005)|(0.004)|(0.004)|(0.008)|(0.007)||
|Share of non-ag wage|0.004||0.048||0.015||-0.044||0.143**|||
|income (EA level)|(0.014)||(0.055)||(0.033)||(0.033)||(0.061)|||
|Distance to the nearest||-0.479**||-0.952***||0.263||-0.334**||-0.498|0.074|
|major road/1000||(0.196)||(0.310)||(0.239)||(0.150)||(1.050)|(0.299)|
|Observations|9,889|9,889|2,171|2,171|2,721|2,721|2,354|2,354|2,004|1,962|2,812|
|Pseudo R-squared|0.209|0.210|0.056|0.062|0.115|0.116|0.078|0.080|0.147|0.144|0.032|

_Note:_ We report rent out for Ethiopia because it is the only country where the share of lessors exceeds 5%. However, we do not include owned area in the regression because 62% of plots rented out have missing values for the area and therefore we do not know exact land endowment for 64% of lessors. Robust standard errors are in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%.

21
# **_Household Characteristics_**

Total agriculture income and income generated from other than agriculture sector will attract more migration workers to move into the community. People are more likely to move into the communities where household incomes are higher or more economic activities exist.

# **_Community RIC_**

A better business environment provides more economic activities or an increase in population. Concerning the impacts of RIC components on migration, better access to drinkable water, better health-care environment, and available business management services would attract more migrant workers to move in. A higher price of rice may be a result of more people moving into the community to push the price of rice up. Negative external shocks or environment such as natural disasters would have negative effect on migration. Safe environment (no theft) may result due to less economic activities. However, if the activities increase, safety and security may be strengthened for an anticipated increase in crime. A suburban area has more migration of moveins than that of a rural area due to the size of nearby markets.

**Table 4.4 Migration Model for Yemen**

|CommunityMore Mo|ve-in = 1<br>Independent Variables|(1)Probit|(2)Probit|
|---|---|---|---|
|**Household Characte**|**ristics**<br>ln (# of household members)<br>|-0.154<br>[0.144]|-0.177<br>[0.159]|
||ln (total agricultural income)<br>ln (income other than agriculture)|**0.029*****<br>**[0.009]**<br>**0.032*****|**0.027****<br>**[0.011]**<br>**0.031****|
|||**[0.011]**|**[0.012]**|
|**Community RIC**|ln (hours to markets)||**2.865*****<br>**[0.341]**|
||ln (% electrified)||-0.006<br>[0.040]|
||ln (% drinkable water)||**0.426*****|
||||**[0.049]**|
||ln (% high school educated)||-0.066<br>[0.111]|
||Physician available||**1.057*****|
||||**[0.168]**|
||Management service available||**0.383****<br>**[0.178]**|
||Formal financial institutionavailable||0.234|
||||[0.157]|
||No theft||**-0.636*****<br>**[0.145]**|
||Disaster happened||**-0.944*****<br>**[0.213]**|
||ln (rice price)||**2.046*****<br>**[0.338]**|
|Other Dummy|Rural|**-0.565*****|**-0.373*****|
|||**[0.134]**|**[0.123]**|
||Observations|665|665|

_Source_ : 2010 Yemen RIC Survey; _Notes:_ Sampling weights are applied; standard errors in parentheses; *,**, and *** denote coefficients significant levels at the 10, 5, and 1% levels respectively 17
Further, we see that women’s lower use of fertilizers, pesticides and tools in farming increase the agricultural gender gap. These variables are the largest driver of the gender gap in the value of crop sales. Indeed, 47% of male-managed plots use fertilizer versus 26% of female-managed plots, 54% of malemanaged plots use pesticides versus 31% of female-managed plots, and 59% of male-managed plots use agricultural tools/machinery versus 33% of female-managed plots (Table 1 Panel B). Reaching parity in the use of fertilizers, pesticides and agricultural equipment between men and women farmers could help to reduce the gap in output value by 0.19 points, or about 18 percent of the raw gender gap in harvest value. It would also reduce the gender gap in crop sales by about 0.3 points, or about 30 percent of the raw gender gap. Moreover, we also find that female plot managers and less likely to control (i.e., be able to make decisions over) crop revenue and face lower returns from this revenue, which further increases the gender gap.

We next look at the decomposition by age group. Appendix Table 8 shows results for harvest value. Women’s lower likelihood of farming an export crop is similarly important for younger and older adults. The lower number of days that household and non-household men work on female-managed plots and the lower ownership of agricultural equipment are more important in explaining the gender gap in crop value for younger adults, while the lower use of fertilizer, pesticides and control over income are more important among older adults. These results are similar for gender gaps in crop sales (shown in Appendix Table 9), though the result on lower labor provision by household men is flipped, mattering only for older adults.

Overall, we find that the reason why women plot managers have lower harvest value and crop sales compared to their male counterparts is their concentration in food crops and lower use of both labor and non-labor inputs. Increasing fertilizer, pesticides and farming equipment use among women could help to substantially increase their revenues. Equipping women with the means to grow higher-value crops, like those that are exported (such as cotton), could further help to increase farm revenues.

# **4. Discussion and Recommendations**

The analysis above showed that <mark>the most important drivers of gender gaps across sectors are women’s lack of capital and male workers, lack of control over income and lower economic benefits from marriage. We also found sector-specific drivers, highlighting the importance of conducting this kind of analysis for policy prioritization.</mark> <mark>Rounding out our findings from the decomposition analysis, additional survey data provide further context and information on challenges that hamper gender equality. While we have discussed above that women in</mark> 15
# **Appendix Table 13: Balance of Controls among Girls in Cappelen et al. (2020)**

||Mean<br>among<br>Control|Preschool<br>vs Control<br>Difference|Robust<br>Standard<br>Errors|
|---|---|---|---|
|Age|7.51|0.05|0.11|
|Black|0.22|0.09|0.09|
|Hispanic|0.73|-0.11|0.09|
|White|0.05|0.02|0.05|
|Time of Experiment|10.25|-0.57|0.37|

_Notes_ : The table investigates whether control girls and girls assigned to the preschool arm in Cappelen et al. (2020) are comparable in terms of predetermined characteristics. The first column shows means among the control girls. The second column reports the difference in means between the control and preschool girls. The third column shows robust standard errors. “Time of Experiment” is the time of day when the child took part in the experiment using a 24-hour clock. The table is comparable to Cappelen et al.’s (2020) Table 1. _N_ = 148.

48
# **A Data and spatial methods**

## **A.1 City points**

City locations (latitude and longitude) and census populations were collected from the City Population website and spot-checked with official sources where available.<sup>49</sup> Using city-specific growth rates based on multiple censuses where available, or national urban growth rates from United Nations (2008) otherwise, I generated a population for all years for each city.

For 14 of the 15 countries in the ultimate sample, City Population claims to list all cities above a given (country-specific) population, typically 5,000, 10,000, or 20,000.<sup>50</sup> However, it does not explicitly cite the year for which this claim is made. Of the 738 cities with location and population information in these 15 countries, 9 city points fall below this cutoff for all years 1990 to 2008. These are included in the sample until explicit population cuts are made.

## **A.2 Lights**

The lights data are described in Henderson, Storeygard and Weil (2012). For the present study the 30 satellite-years of lights data were first combined into one binary grid encoding whether a pixel was lit in at least one satellite-year. The resulting contiguous ever-lit areas were converted into polygons, and split by national borders. Only lights within 3 kilometers of one of the city points described above with a known census population were kept in the sample. The 3-km buffer is used because of georeferencing error in both the points and the lights (Balk et al., 2004; Elvidge et al., 2004). While some of the other lights are likely to be small settlements, some are noise from the sensor or from fires lasting for too long to be excluded by the data cleaning algorithm, mines or other facilities. In general, they are smaller and weaker lights as well.

The resulting sample is 485 lights in 15 countries. Populations for each point were summed across all points assigned to each light. In 49 lights, more than one city was present; in 26 of these, exactly two were present. In 13 cases, a point fell within 3 km of multiple lights. In such cases, the point’s population was only retained by the light to which it was closest. The light with the largest 1992 population within each country was designated the primate. In most countries, this corresponded to the

> 49http://www.citypopulation.de/. In 9 cases where coordinates or population were unavailable from City Population, coordinates from Google Earth or World Gazetteer (http://www.worldgazetteer.com) were used.

> 50For Angola, there is no explicit cutoff.

47