rafmacalaba/data-use-mentions-extended huggingface dataset:
48

# **Table 2**

## **Dependent Variable: Growth Rate of Real GDP Innovation Pillar Indicators**

|**Years : 1960 - 2000**|**Reg 2.1**|**Reg 2.2**|**Reg 2.3**|**Reg 2.4**|**Reg 2.5**|**Reg 2.6**|**Reg 2.7**|
|---|---|---|---|---|---|---|---|
|**Growth of Capital**|**0.2128*****<br>**(0.0442)**|**0.2157*****<br>**(0.0451)**|**0.2252*****<br>**(0.0394)**|**0.2128*****<br>**(0.0513)**|**0.2033*****<br>**(0.0494)**|**0.2127*****<br>**(0.0504)**|**0.2141*****<br>**(0.0504)**|
|**Growth of Labor**|**0.5694*****<br>**(0.1335)**|**0.5775*****<br>**(0.1392)**|**0.5948*****<br>**(0.1259)**|**0.
# **2. LSMS+: Land Rights and Decision-Making Measures in Defining Ownership**

# **2.1 Multidimensionality of Ownership**

This paper uses three nationally representative, multi-topic household surveys supported by the LSMS+ program, namely the Tanzania National Panel Survey (NPS4 2018/2019), Ethiopia Socioeconomic Survey (ESS4 2018/2019), and the Malawi Integrated Household Panel Survey (IHPS 2016). The LSMS+ modules on asset ownership and rights are comparable across countries and were administered directly to all household members 18 and older on different asset classes, including ownership and rights to residential and non-residential land (Hasanbasri et al., 2021).<sup>10</sup> A specific emphasis of the survey data collection was on self-reporting and interviewing respondents privately — and hence conducting interviews within the household simultaneously, when possible.<sup>11</sup> This paper focuses on characterizing landowners’ rights and decision-making over non-residential land parcels. A respondent is defined as the reported landowner if they answer upfront that he/she owns the parcel. A _parcel_ is defined as a continuous piece of land which can have more than one parcel. Parcels were first identified and rostered through the household questionnaire and then carried forward to individual interviews. Non-residential land in these contexts is mainly used for agriculture. In Ethiopia, for example, 87 percent of non-residential parcels have been used in agriculture in the last 12 months.<sup>12</sup> Rights and decision-making questions were asked directly to individuals who reported themselves as owners or as individuals with use rights on the land. Since the paper aims to analyze rights associated with ownership, we restrict our sample to those who report themselves as landowners. Later in the paper, however, we address sensitivity of our findings to the inclusion of non-owners with use rights (which is only possible to do in Ethiopia and Tanzania). Most individuals who provided answers on rights and decision making were owners instead of users of land.

For
, as we only observe employment and not its features such as expropriation, withholding salaries, and harassment, this evidence is incomplete and social cohesion may be weakened in those dimensions.

Table 4 shows the estimation results using two approaches when the dependent variable is the dummy of replacing the sample household with another household next year. Columns (1) and (2) show the results of estimating (1) for all individuals in LFS, and column (3) is for the matched individuals. In all estimations, we find that Afghan individuals have a significantly higher replacement rate. The marginal effect in column (3) implies that being an Afghan refugee raises the probability of replacement by 5.6% compared to the matched sample of Iranians. The coefficients of shock variable are significant in estimations with all 9
occurred, as our methodology does (and as the CMMHH permits). Finally, the high-frequency phone surveys do not contain the needed level of detail on work arrangements —employer types and contractual arrangement— to distinguish pre-COVID PFPS workers in the core sectors from other private sector employees. Notwithstanding these limitations, we attempt to learn what we can by comparing outcomes for private sector wage workers to our projections.

# 5.3 Aggregate Projection Results

# **5.3.1 Jordan**

As shown in Table 5-1, using our methodology, for wave 1 (summer of 2020) we project the aggregate net rate of PFPS job losses in the core WBES sectors to have been 15.8 percent relative to the December 2019 baseline, which includes

|_Table 5-1: Jordan: Job Loss Projections_<br>_Levels, CFUWBES Waves 1 and 2 and_|_PFPS Jobs as_<br>_CMMHH, 2021_<br>As of Wave 1|_a Percent of P_<br> <br>Cumulative<br>Wave 2|_re-COVID_<br>CMMHH 2021|
|---|---|---|---|
|Modeled Job Losses: Percent of PFPS<br>jobs lost (relative to Dec 2019) as<br>projected|15.8|25.2|N/A|
|_Of which_: Due to Permanent closures|7.1|9.4|N/A|
|Survey-measured job losses: Percent of<br>PFPS Jobs Lost(relative to Dec 2019)|16.8|25.5|23.3|
|Source: Author estimates.||||

losses due to permanent firm closure of approximately 7.1 percent.

_Table 5-2 Jordan: Selected Potential Jobs at Risk Influence Variables, Means from CFUWBES_

|Variable|Wave 1|Wave 2|
|---|---|---|
|Temporarily closed due to COVID (weeks of closure )*
**Figure 23: Concentration of Economic Activity, Russian Federation (Herfindahl index)**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>2002<br>2011<br>10<br>5<br>0<br>Real GDP Population Mineral exports Non‐mineral FDI<br>exports<br><!-- End of picture text --> _Source:_ Rosstat.

0
and non-poor farmers. Using the three-category poverty classification, the overall gain in yield for targeting consumption non-poor and asset non-poor farmers instead of their ultra-poor counter parts is 4.170kg and 6.404kg respectively. Comparing poor to ultra-poor farmers, the estimates further show that the overall net gain in yield for targeting poor farmers instead of ultra-poor farmers is also positive and significant. This implies that although a significantly higher crowding out would be incurred when FISP is targeted at non-poor farmers, the productivity gain in targeting such farmers outweighs the additional crowding out effect. Thus, overall, the food security goal of FISP would be better served if the program were targeted at non-poor farmers.

# **Conclusions and Policy Recommendations**

Targeted farm input subsidy programs have become major development policies in many SubSaharan African (SSA) countries. Like other targeted development programs, the success of TFISPs depends integrally on the effectiveness of the targeting process used in identifying and reaching beneficiaries. Targeting plays a crucial role in that it determines the beneficiaries of the program, the amount of inputs they receive, and hence how the inputs are used. The eventual impacts of the program are therefore closely linked to the quality of the targeting process. Notwithstanding that, the weight of the empirical evidence suggests that targeting of most TFISPs in SSA has not been effective. The goal of this study has been to provide guidance for determining the category of farmers that should be targeted in order to maximize the benefits of TFISPs using a two-year panel data from Malawi. Specifically, the study estimated the overall gain in productivity for targeting non-poor farmers instead of poor and ultra-poor farmers after accounting for differences in input use efficiency and crowding out across poverty groups. The study also investigated how nitrogen use efficiency and crowding out of commercial fertilizer by subsidized 28
**Table 5.3. Impact of PqTR reduction on repetition rates**

||**2017-18**|**2018-19**|
|---|---|---|
|Control|0.266***|0.253***|
||(0.010)|(0.006)|
|Time|-0.014|-0.001|
||(0.014)|(0.009)|
|Treatment|0.007|-0.016|
||(0.011)|(0.012)|
|DiD (Treatment and Time)|-0.028*|-0.021|
||(0.016)|(0.015)|
|Control (N)|118|292|
|Treatment(N)|379|134|

_Data Source: EMIS 2016-2019 Note: Robust standard errors in parentheses. * p<0.10, ** p<0.05, *** p<0.01 2017/18 compares Repetition rates at end 2016/17 and end 2017/18. Treated schools are those where PqTR brought below 90 in 2017._ _2018/19 compares Repetition rates at end 2017/18 and end 2018/19. Treated schools are those where PqTR brought below 90 in 2018._

**Table 5.4** focuses on repetition rates in lower primary (Grades 1-4). Again, we find significant impacts from the 2017 allocation of teachers, with lower primary repetition rates reduced by 3.5 percentage points, but we do not observe similar impacts from the 2018 deployment.

**Table 5.4. Impact of PqTR reduction on repetition rates in lower primary**

||**2017-18**|**2018-19**|
|---|---|---|
|Control|0
The equations for national highways are also estimated after controlling for state budgetary spending on roads, despite the official rule that state budgets do not contribute to national highways. As expected, the coefficient on state road spending is insignificant.

State Roads: Columns (3) through (5) of Table VII present the estimates for the electoral cycle in state roads. The electoral cycle in state roads (state highways and district and village roads) is not as significant as in national highways. Election year road construction in state roads is significantly greater than four years and two years before elections, but no different from road construction one and three years before elections. The size of the effect is rather large. New state roads increase by 925 kilometers (or 575 miles) compared to the average of other years, which is 56 per cent of the average annual growth in state roads. Perhaps the statistical insignificance is due to the fact that the dependent variable lumps together different types of roads with different strategic values. Elections may only be affecting state highways, so the effect is confounded by the "noise" added through district and village roads.

Data on spending on roads and bridges in state government budgets is available since 1972, hence it is possible to control for state spending on roads in the equation for the construction of new state roads. But, since the data on state roads is only available upto 1987, including roads expenditure reduces the sample size by 40 per cent. The regression results are reported in column (5) of Table VII. Expenditure on roads has a positive coefficient but is not statistically significant. Road construction in the election year is higher than construction in the next year only at the 10 per cent level of significance. The lack of significance could be as much due to the loss of observations as due to the inclusion of the spending variable.

since road development plans are very long-term plans, sudden injections of extra funds to build significantly more roads in an election year did not seem feasible.

25
financial burden resulting from higher benefit amounts and a more significant number of beneficiaries.

# Limitations

## **Despite its usefulness, this study suffers from some modeling limitations.**

**First, estimates of coverage may be interpreted as an upper bound.** Due to limited information in the household survey data, the study did not estimate the factor for some groups: Identifying orphans, children accommodated with another family, or pregnant women 45 days before the term was not possible. Additionally, it is important to note that some crucial parameters of the DMI were not included in the simulation, and this omission may have implications for the overall results. The DMI has different scores if children attend school, and the survey lacks this data for ages between 11 and 13. So, we assumed all children from that age group went to school. Furthermore, additional asset and employability criteria were not included in the model due to insufficient data available from the household survey.<sup>19</sup> . While we have data on the unemployed, specifics regarding their registration with the employment agency are unavailable. The exclusion of these parameters may affect the accuracy and comprehensiveness of the analysis. Moreover, the survey does not comprehensively account for income from capital. Given these additional eligibility constraints, our coverage estimates may be an upper bound.

## **Second, the model does not consider behavioral responses, which may include a higher program take-**

**up or a more substantial disincentive effect** . An increase in the take-up rate of benefits is possible, though the extent of this increase remains uncertain. Encouragingly, higher take-up rates could mean that more eligible households access the assistance they are entitled to, potentially leading to a broader positive impact on poverty reduction. Additionally, the model does not estimate the potential disincentive effects of the MSA reform. The evidence on the disincentive effects of Monthly Social Allowance programs across Europe is mixed. Terracol (2009) reveals that the French guaranteed
The results show that rents tend to be lower in areas exposed to flood risk in three of the four cities. The relationship between job density and rents is less clear, which is most likely a result of the nature of city shape and the role of informal job markets. However, job density seems to play a role in the household decision to settle in high risk areas.

Overall, the study indicates that the drivers of rents in cities in SSA are still poorly understood and that lack of data is the main barrier to a better understanding. This analysis shows that household surveys that include rent or housing costs, housing characteristics, access to public services, tenure arrangements, and household experiences with flooding or other natural hazards are an important source of information in developing countries to value the willingness to pay to avoid hazard risk, and thus, the benefits associated to risk reduction investments. In housing markets with no or limited access to information about flooding risks, surveys with information on the experience of households with flooding and their expectation of future floods capture better the internalization of flooding risks by households than modeled flood maps.

The remainder of the paper is organized as follows. Section 2 provides a general overview of the flood and vulnerability contexts in the cities studied. Section 3 introduces the data and the empirical estimation strategy. Section 4 presents the empirical results. Section 5 concludes the paper.

# **2 Urbanization and vulnerability to floods in four Sub-Saharan African cities**

Urbanization is associated with development, higher incomes, better access to employment, better public services, and better access to information. However, if the urbanization growth rate exceeds the rate of development, or is accompanied by poor quality institutions, urbanization can increase population inequality. This results in poorer people being more exposed to poor infrastructure, low quality housing, and natural hazards. In particular, some urban conditions such as high population and asset densities, unplanned settlements, low quality drainage systems, unimproved solid waste management, poor housing conditions, and certain
% 80% 0<br>70% 70%<br>60% 60%<br>50% FT 50% FT<br>40% PT 40% PT<br>30% UE 30% UE<br>20% NP 20% NP<br>10% 10%<br>0% 0%<br><!-- End of picture text --> _Source:_ Author’s calculations based on 2017 UNDP-WB-EC Roma Regional Survey.

_Notes:_ Abbreviations: FT= employed full-time; PT= employed part-time; U= unemployed; NP= non-participant. Each bar represents the proportion of husbands and wives on each labor market state.

37Even though we are simulating the policy experiments as equating women’s parameters to men’s, policies aiming to promote cultural changes can also change men’s preferences and should target men. Hara and Rodriguez-Planas (2020) evaluated the effects of a Japanese reform in the 1990s that ended gender segregation in junior-high school. Their evaluation is on married men’s and women’ time spent in home production. They found that men and women of the cohorts that took classes under the reform share more equal home production: men augmented home production time while women diminished it.

30
fiscal balance is the main indicator distinguishing both countries. Is it possible that this variable captures the essence of the difference between the two cases?

**Table 1 Summary of Public Debt Indicators in Argentina and Brazil 2000-2001**

||**Arge**<br>|**ntina**<br>|**Br**<br>|**azil**<br>|
|---|---|---|---|---|
||2000|2001|2000|2001|
|PublicDebt (% ofGDP)|50.0|62.0|49.6|53.3|
|Interest payments (% ofGDP)|4.1|5.4|7.2|7.3|
|Interest/ tax revenue (%)|22.7|30.9|30.9|29.8|
|Interest/currentrevenue (%)|16.6|22.9|18.0|16.5|
|Overall fiscalbalance (% ofGDP)|-3.6|-6.8|-3.6|-3.6|
|Primaryfiscalbalance (% ofGDP)|0.4|-1.4|3.6|3.8|

Source: Source: World Bank staff calculations based on official data for Brazil, Bacen and for Argentina, Minsterio de Economia.

14. To answer this question, it is necessary to examine in retrospect the relationship between primary balances and debt levels in both countries. While primary balances were similar until 1998, they diverged from 1999 onwards as Figure 1 shows. When debt ratios increased in 1999, the primary balance registered a significant surplus signaling Brazil’s commitment to adjustment. Statistical evidence supports the hypothesis that primary balances acted as signaling tools in Brazil (Herrera, 2002) and in other European countries that successfully overcame credibility problems (Drudi and Prati,
has the potential to significantly expand its marketable research. As President, Vladimir Putin reportedly considered the balance of these issues to favor Russia and encouraged more vigorous enforcement of intellectual property laws in Russia.

# **4.5. Sensitivity Analysis**

The results depend on the choice of parameters in the model, alternate ways of reconciling data and method of solution of the model. In this section, we begin, with an assessment of the bias from the sequential approach to micro-simulation analysis. We then assess the impact on the results if we adjust the household data to be consistent with the input output data. We then turn to ―piecemeal sensitivity‖ analysis on the parameters. Finally, we discuss the results of our ―systematic sensitivity analysis.‖

# **Decomposition of the Gains and Biases from the Sequential Approach to Micro-Simulation Analysis**

We have aggregated the households in our model into deciles, from poorest to richest. Then we have decomposed the equivalent variation gains (as a percent of consumption) of the ten aggregated households into changes in factor prices, goods prices and taxes and transfers. The results are in table 4.2 labeled ―integrated model.‖ For all the deciles of aggregated households the impact of goods prices is relatively unimportant compared with the impact of factor prices. In part because households cannot easily change their factor endowments between unskilled labor, skilled labor and capital, but they can substitute among commodities consumed, impacts on factor incomes through changes in factor prices tend to dominate the household welfare impacts in these kinds of models.<sup>52</sup> We have also executed a single representative consumer model based on input output data that is not reconciled with the household data. We feed the factor and good price changes into a household model for the determination of household equivalent variation of the 55,098 households. Aggregation

> 52 See, for example, Harrison, Rutherford and Tarr (2003).

73
# **1. Introduction**

Achieving gender equality and the empowerment of women and girls by 2030 has been enshrined as a global goal by development actors including the United Nations.<sup>1</sup> To work toward this objective, policymakers will need to increase the participation of women in the economy and society. For genderrelated policies and programs to be designed and implemented effectively, it is necessary to equip policy makers and project teams with appropriate knowledge and instruments. Moreover, it is important to identify the relative size and drivers of gender gaps across the economy in order to determine how best to boost economic growth through efforts to achieve gender parity.

This approach is valuable for a country like Burkina Faso, where over 40 percent of the population lives below the poverty line (World Development Indicators, 2022). Burkina Faso ranks 144th out of 157 countries in terms of its human capital, and 184th out of 191 countries in terms of its Gender Development Index<sup>2</sup> . Previous research from Burkina Faso has found evidence of discrimination in favor of boys within the household (Haddad and Reardon, 1993), widespread male control of household assets (van den Bold et al., 2015) and limited decision-making power for women (Kieran et al., 2015). In this paper, we provide a systematic analysis of gender gaps across Burkina Faso’s main economic sectors; examine how they vary over time, between age groups and across regions; identify the primary drivers of these gaps; and provide evidence-based policy recommendations.

Using nationally representative household data from 2019, we find that women’s participation in paid work is 20 percentage points lower than that of men, and 10 percentage points lower when looking at both paid and unpaid work. Women in Burkina Faso earn 82 percent less in monthly wage earnings compared to men, while their business revenues are on average 61 percent lower. Within agriculture, women’s harvest values and crop sales are both approximately 60
Table 2. Datt-Ravallion Decompositions

|||Growth|Redistribution|
|---|---|---|---|
||Bangladesh,2000–10|85%|15%|
|US$1.25-a-day|Ghana,1998–2005|103%|-3%|
||Nepal,1996–2003|132%|-32%|
||Argentina,2000–10|43%|57%|
||Brazil,2001–09|54%|46%|
||Chile,2000–09|57%|43%|
||Colombia,2002–10|84%|16%|
||Costa Rica,2000–08|113%|-13%|
|US$4-a-day|Ecuador,2003–10|64%|36%|
||Honduras,1999–2009|105%|-5%|
||Panama,2001–09|63%|37%|
||Paraguay,1999–2010|43%|57%|
||Peru,2004–10|84%|16%|
||Thailand,2000–09|105%|-5%|
|US$5-a-day|Moldova,2001–10|98%|2%|
||Romania,2001–09|98%|2%|

_<mark>Source:</mark>_ <mark>Authors’ calculations with data from SEDLAC (CEDLAS and the World Bank), RIGA for Ghana and Nepal, and from household surveys for Bangladesh, Moldova, Romania, Peru, and Thailand.</mark> <mark>Consumption-based measures of poverty are used in the case of Bangladesh, Ghana, Nepal, Peru, Thailand, Moldova, and Romania. Income-based measures of poverty are used in the case of Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, Honduras, Panama, and Paraguay.<
br>**Commu**|<br> <br>**, 1992.**<br>**&**<br>Sub-total<br>excluded<br>**nity**<br>**from the**|<br>**TOTAL**|
|**comp. 1358**|**and other**|**the analysis**|**compo**|**nente**|**School**|**s**<br>**analysis**||
|Experimental<br>585|624|<br>1,209||299|1,|237<br>1,53|6<br>2,745|
|Control<br>0||0<br>413||0||388<br>38|8<br>801|
|**TOTAL**<br>585<br>Of which: Native<br>Rural|624|<br>1,622<br>769<br>853||299|1,|625<br>1,92|4<br>3,546|

Source: PARE's database.

6