rafmacalaba/data-use-mentions-extended huggingface dataset:
,552|26,947|241,548|75,501|
|Revenue|74.0|‐8.0|‐45.5|‐4.0|‐0.4|‐44.5|‐0.3|

_Source:_ World Bank staff calculations based on the data posted at <u>http://npa.gov.gh.</u> _Notes_ : Cross‐subsidies are in cedis per kiloliter except cedis/tonne for LPG. Consumption is taken from the statistics for Jan–Sep 2014 and is in kiloliters except for LPG, which is in tonnes. Revenue is in millions of cedis. Premix is a fuel for fishing. LM = local marine.

Illegal diversion of premix, a heavily subsidized fuel for fishing, has been problematic for decades. Premix fuel is gasoline with low octane and is exempt from a special petroleum tax of 17.5 percent applied to all other fuels except fuel oil, as well as the excise duty, the refinery debt recovery levy, and several other charges. Its ex‐refinery price, before any taxes and charges, was half of that of gasoline before the price of gasoline was deregulated. For many years, the price of gasoline has been double— and between 2011 and 2013 even triple or quadruple—the price of premix fuel, creating enormous scope for commercial malpractice and illegal gains. The government, in announcing the establishment of the National Premix Committee, summarized the situation at the end of 2008 as follows (MFA undated):

- Points of sale for premix proliferated, increasing from 128 in 2001 to about 900 in 2008.

- Corruption related to the administration of premix fuel became “uncontrollable,” with rampant diversion to illegal markets. Adulteration of automotive gasoline with premix was financially attractive but damaging to vehicles.

- Eligible purchasers of premix faced serious shortages.

As of 2015, the price difference between gasoline and premix was a factor of two. Commercial malpractice has not only
- 4 -

on the measure, and countries affected by it. 4/ The latter is particularly useful for analyzing "discriminatory" measures like bilateral quotas, "voluntary" export restraints, the Multifibre Arrangement, or prohibitions applied against specific countries. In addition, the date(s) that the restriction was first imposed, modified or removed are recorded (if the latter actions occurred) along with the value of imports (by country) in the tariff line item. For some countries like the United States the records are quite detailed and extend back to the mid-1960s. However, full country coverage has only been established in the Data Base for the 1981-86 period.

Several technical points should be noted concerning the Data Base and its applications. First, the information it contains does not provide any indication of change in the intensity of application of a measure. If, for example, the administration of import licensing requirements, or technical standards, becomes less rigorous it is not possible to incorporate this fact

- 4/ The entries in the Data Base have been compiled from government publications and other official sources like customs schedules and documents, GATT reports, official notifications to GATT as well as documents of other international agencies. Procedures for the periodic verification of entries with member states of UNCTAD have been established to ensure the accuracy of the Data Base. The reliance on official sources for compilation of the Data Base may cause the importance of some NTBs to be understated, especially in cases when there is a lack of "transparency," or where measures like "voluntary" export restraints NVERs) are not reported in official publications. See UNCTAD (1983) (1988) or Laird and Yeats (1988) for technical details involving construction of the Data Base. It should also be noted that problems have been encountered in tabulating Japanese NTBs due to the fact that the barriers sometimes originate in private organizations. For example, in one recent case reported in the Japanese press (see Yomiuri Shimbun, June 24, 1988, p.
In all eight countries, children living in resource‐poor households are more likely to be stunted than children living in resource‐rich households. A household is classified as resource rich if it is in the top 60% of an asset index and as resource poor the bottom 40% of the index value. Annex 1 describes the construction of the asset index. The difference in stunting rates by access to resources is especially marked in Peru where only 6% of the children in resource‐rich households are stunted, but 20% of children in resource‐poor households are stunted—a quadrupling of the prevalence (Figure 4). Similarly, the other LAC country of the study—Bolivia—showed large differences in stunting rates by wealth. Children in resource‐poor Bolivian households were more than twice as likely to be stunted as children in resource‐rich households. The other countries with a large prevalence gap by resource access were Bangladesh based on the Helen Keller data and Nepal. In Cambodia, Ethiopia, Indonesia, and Zimbabwe, the stunting rates for resource‐poor and resource‐rich children were within 4 percentage points.

**Figure 4: Stunting resource‐poor vs resource‐rich households**

<!-- Start of picture text --> Stunting in children 0 to 23 or 35 months<br>47<br>44<br>42<br>37<br>35 35<br>32 32<br>30<br>28 28<br>26<br>25 25<br>22<br>20<br>16<br>6<br>BGD, HK BOL* IDN NPL* ZWE<br>BGD, IFPRI ETH* KHM PER*<br>Resource poor Resource rich<br>Source: Author estimates. Data for Bangladesh from 2010 (HK) and 2011 (IFPRI); Bolivia 2008; Cambodia 2010; Ethiopia 2011;<br>Indonesia 2010 (RKD); Nepal 2011
the survey’s measure of family labor, i.e. if males, females, or children household members participated in certain types of activities is extremely imprecise, it made a positive contribution to output.<sup>23</sup> Each added sick member over the last three months is estimated to have reduced output by 4 points. Participation in exchange work, presumably to break seasonal bottlenecks, is estimated to have increased production by 8 points and the coefficient on extension is positive but insignificant.

One advantage of (2) is that it allows us to combine direct and indirect impacts to assess if and to what extent inheritance regimes affects productivity.<sup>24</sup> To obtain such estimates, we add (in col. 3 and 6), an indicator variable for whether or not a household practiced crop rotation, the one investment that, based on our earlier results is significantly affected by inheritance regimes (see table 7). Results provide no basis for the conjecture of matrilineal systems being associated with lower levels of productivity, irrespective of the inclusion of a crop rotation dummy. For patrilineal regimes, a significant negative effect emerges for specifications that do not control for crop rotation but disappears if gender or the respective indicator are included, suggesting that even in patrilineal systems, failure to practice crop rotation rather than the inheritance regime per se, is associated with lower levels of productivity.

# **5. Conclusion and policy implications**

Data from Malawi allow us to assess the extent to which large farms (estates) established sporadically and in isolation from the customary sector in the late 1980s helped to establish a dynamic commercial farm sector. We find that in most cases, estates’ productive performance is inferior to that of smallholders while in the smallholder sector, fear of land loss is high and associated with significant productivity losses for female cultivators, and, contrary to earlier literature, the inheritance regime is not a major impediment to smallholder investment or productivity. The estimated present value of
<sup>3</sup> Similarly, we need to aggregate occupations. We follow the Census categories to do this, with one small exception. The occupations we use are:

1. _White collar_ (the Census category of “Managerial and Professional Specialty Occupations,” occupation codes 3-199). Managers and accountants are examples.

> 3“Agriculture and Construction” is composed of Census industries 1-3, respectively “Agriculture,” “Mining” and “Construction.” “Manufacturing” is composed of Census industries 4-5, respectively durables and non-durables manufacturing. “Non-tradable services” is composed of Census industries 8, 10, 14, and 16, respectively “Utilities,” “Retail Trade,” “Entertainment and Recreational Services,” and “Public Administration.” “Tradable services” is composed of Census industries 6, 7, 9, 11, 12, 13, and 15, respectively “Transportation, Communication, and Other Public Utilities,” “Communications,” “Wholesale Trade,” “Finance, Insurance, and Real Estate,” “Business and Repair Services,” and “Personal and Related Services,” and “Professional Services.”

10
, at least initially. As column (2) in Panel B of Table 3 shows, the impact on the decision-making index is large at 0.42 SD in wave 1 of the household survey and significant at the 10% level. However, the effect is insignificant if I pool both waves (column 1) and negative but insignificant in wave 2 (column 3).<sup>46</sup> This drop in effect size after wave 1 seems to be partly driven by attrition, since the attriters between wave 1 and 2 in the comparison group have lower involvement in decision-making at factory baseline and in wave 1 than the comparison group respondents that remain in the panel.<sup>47</sup> Note that this pattern of attrition works against finding a positive effect on women’s bargaining power in wave 2 and the pooled estimate.

The large effect on decision-making in wave 1 is mainly driven by women reporting more decision-making power about purchases of clothing for themselves, followed by decisions about

> 45This means that column (1) of Panel A in Table 3 shows the first stage.

> 46Note that I do not find a similar drop-off in effects for the other outcomes in Tables C.9 and C.10 in the Online Appendix, which show results separately for each household survey wave.

> 47The attriters between wave 1 and 2 in the comparison group have a mean decision-making index of -0.06 SD at factory baseline and -0.68 SD in wave 1. This compares to -0.01 SD at factory baseline and -0.44 SD in wave 1 for the comparison group respondents in the panel.

20
work also analyzes how to get structurally accurate (‘true’) estimates for returns to education, taking endogeneity into account.<sup>3</sup> These estimations provide important inputs to policy debates and decisions, especially in low and middle income countries where education attracts tremendous attention as a

> 3 Alternative ways to estimate returns to education include instrumental variable techniques (Angrist and Kruger (1991), Card (2001), Cruz and Moreira (2005), among others), twin studies (Behrman et al. (1994, 1996), Miller et al. (1995), Bound and Solon (1999), among others) or randomized control trials.  Our deliberate focus on cross section survey data arises from this being still the most common approach, especially in international comparisons, developing country settings, and education policy debates, as cross‐country and time varying estimates remain costly to obtain.

2
# II. Description of Sudan’s Rural Population

## Rural Population and Poverty

The majority of Sudan’s population is rural. There has been little change in the rural/urban composition of Sudan since 2009 (Figure 8a). Unlike most other developing countries in the development stage of their economic transition, the share of the rural population has increased, though only slightly, from 64 percent to 65 percent. While the poverty rates in urban areas have increased, the poverty rate in rural Sudan has decreased dramatically from 58 percent to 35 percent, and is now comparable to poverty rates in urban areas (Figure 8b). As the majority of Sudan’s population is still rural, almost twice as many poor people reside in rural areas compared to urban areas (Figure 8c). By focusing on the welfare of these households, Sudan can achieve further substantial reductions in national poverty.

Figure 8: Rural/Urban Composition of Sudan’s Population and Poverty, 2009 and 2014/15 <!-- Start of picture text --> (c) Proportion of Total Poor,<br>(a) Proportion, Rural/Urban (b) Poverty Rate, Rural/Urban<br>Rural/Urban<br>Urban Rural 2009 2014/14 Urban Rural<br>64% 65% 64%<br>80%<br>36% 35% 36%<br>20%<br>2009 2014/15 URBA N RURA L 2009 2014/15<br>58%<br>37%<br>35%<br>27%<br><!-- End of picture text --> _Source:_ Authors’ calculations using NHBPS 2014/15 and NBHS 2009.

### **The increase in urban poverty and decrease in rural poverty might be explained by the relatively high**

**prices of staples (Figure 9a).** At the time NHBPS 2014/15 was conducted
Against the backdrop of sex-disaggregated indicators on individual land ownership and rights that have been endorsed as part of the monitoring of the Sustainable Development Goals (SDGs),<sup>3</sup> it is common for the required microdata to be provided by the most knowledgeable household member(s) interviewed by household and farm surveys - often a single respondent in each household (Doss et al., 2019; Doss et al., 2008; Deere et al., 2012; Ruel and Hauser, 2013). Under these circumstances, the surveys may not ask the most knowledgeable household member(s) to identify the individual owners and rights holders of each asset. As a result, (confounded) conclusions regarding the gender asset gap would be (and have been) anchored in the comparison of male- versus female-headed households.<sup>4</sup> And in the event that surveys do ask the most knowledgeable household member(s) to uniquely identify the reported, documented and/or economic owners for each asset of interest,<sup>5</sup> this information is seldom paired with the identification of individuals holding various rights to these assets. This in turn limits our understanding of the inter-relationships among ownership and rights indicators, and whether these relationships vary across individuals.

Recently, the international momentum behind improving the availability and quality of individualdisaggregated survey data on asset ownership and control has accelerated, in part thanks to the United Nations Evidence and Data for Gender Equality (EDGE) initiative. Over the period of 2014-16, the UN EDGE initiative supported the implementation and analysis of the Methodological Experiment on Measuring Asset Ownership from a Gender Perspective (MEXA) in Uganda (see Box 1; Kilic and Moylan, 2016), which in turn informed the design of the EDGEsupported country pilots that were implemented by the national statistical offices across Georgia, Maldives, Mexico, Mongolia, the Philippines and South Africa. These activities ultimately

> 3 <mark>These include the SDG 1.4
worse off, and a simple comparison between children exposed and not exposed across communities will not provide unbiased estimates of program effects.

In contrast, in urban areas there is no evidence of selection in program placement at the locality level. The estimated coefficients of the older cohort at time zero are both close to zero and statistically insignificant. Once again, this is consistent with the geographic targeting policy described previously, as well as with the descriptive evidence performed with census data. Therefore, to control for program placement, we use differences across i terms for each locality ii group . Because these terms are estimated relative to average z-score in control communities, αα these differences automatically eliminate any general time trend that makes our cohorts different vv from each other for reasons other than Oportunidades exposure. Finally, we control for the dynamics in height by comparing the middle cohort measured in 2005 with the older cohort measured in 2002 (5 to 7 years old at the time of measurement), or the younger cohort measured in 2005 with the middle cohort measured in 2002 (1 to 3 years old at the time of measurement).

**_Measuring the impact for communities included between 1997 & 1998 (Group 1)_** Panel A in Table 4 shows that 1 1 ) gives an estimate of full relative to partial exposure. mm05 oo02 Because both coefficients are measured relative to children in baseline communities, the difference (αα −αα between them accounts for any locality-specific effect that can be expressed as an additive component common to both cohorts. Furthermore, we are comparing children 5 to 7 years old in both cases to account for any height-specific life-cycle effect. However, we are comparing different cohorts. This means that the estimate will also capture any time effect that might have

existed between the time period the older cohort was in its critical growth years and the time period 17
Barro, R. and J. Lee (1994), “Sources of Economic Growth,” Carnegie-Rochester Series on Public Policy, 40(1): 1-46.

Barwell, Ian (1996), “Transport and the Village: Findings from African Village-level Travel and Transport Surveys and Related Studies,” World Bank Discussion Paper 344. Africa Region Series. Washington, D.C.: World Bank.

Becker, Gary S. (1957) _The Economics of Discrimination_ . Chicago: University of Chicago Press.

Beneria, L. (2003), “Global/Local Connections: Employment Patterns, Gender, and Informalization,” Chapter 4 in Gender, Development and Globalization: Economics as if All People Mattered. New York: Routledge.

Berik, Günseli (2000), “Mature Export-Led Growth and Gender Wage Inequality in Taiwan,” _Feminist Economics_ , 6(3): 1-26.

Bhagwati, J. (2004), _In Defense of Globalization_ . New York: Oxford University Press.

Black, S., and E. Brainerd (2004), “Importing inequality? The Impact of Globalization on Gender Discrimination,” _Industrial Labor Relations Review_ , 57(4): 540–559.

Bloom, David E., David Canning, Günther Fink and Jocelyn E. Finlay (2009), “Fertility, Female Labor Force Participation and the Demographic Dividend,” _Journal of Economic Growth_ , 14:79101.

Boal, William M, and Michael R Ransom. (1997). “Monopsony in the Labor Market.” Journal of Economic Literature, 35(1): 86–112 Bøler, E. A., Javorcik, B. and K. H. Ulltveit‐Moe (2018). “Working across time zones: Exporters and the gender wage gap,” Journal of International Economics, 111, 122‐133.

Brush, C., N. Carter, E.J. Gatewood, P. Greene and M. Hart, (2006), _Growth
Four, of specific interest to us is how these numbers would differ when splitting the sample of studies according to the level of development of countries. Again here, we find important heterogeneity. While effects appear larger in developed countries for digital and energy studies, cross-sectoral and transport studies based on developing countries produce larger elasticities. Notably, larger estimates are found when looking at specific outcomes in developing countries, such as the impact of digital investments on micro-level output, at 0.16, or of transport investments on labor market outcomes, at 0.18.

Finally, there is an important issue regarding how to interpret these elasticities for policy recommendations. Deciding on which policies or sectors to prioritize, and possibly on potential financing strategies, would require that we translate elasticities into specific rates of returns (see Gardner and Henry (2023)). This in turn implies the need for infrastructure capital stock figures. Using a basic Cobb-Douglas production function approach for illustration, the marginal rate of return for a specific type of infrastructure can be approximated by the following formula: _mrr_ = γ _In f_ . _In f_<sup>_<u>Y</u>_, where γ</sup><sup>_In f_is the elasticity,</sup><sup>_Y_is GDP,</sup> and _In f_ is the stock of infrastructure.

There are however several difficulties involved in finding suitable proxies for _Inf_ . Regarding public capital, different methods combine national account data, public budget data, and information from the Private Participation in Infrastructure (PPI) database to estimate infrastructure investments, which can then be used through inventory methods to compute estimates of stocks. As discussed in Fay et al. (2019), each method generates both exclusion and inclusion errors. In addition, public budget data is the only source allowing for sectoral breakdown. This practically means that the only recent source available, namely IMF data on Gross Fixed Capital Formation (GFCF) would be of little use here, not even
# **_Data_**

Since the country-pair fixed effects and both importer and exporter time trends absorb the variation of the “usual” gravity variables (distance, gross domestic product, and other country-pair characteristics like common language, common border, and so on), our data consist of bilateral trade flows and trade agreement indicators from the CEPII database (Abreha and Robertson 2023). The dataset includes over 1 million observations that cover 232 exporters, 179 importers, 262 Regional Trade Agreements, and the 1990-2016 period.

For this specific paper, the results reported are for only the seven RTAs in which Morocco is a partner and the data described are available.

# **_Results_**

Figure 3.1 below contains the baseline gravity model results. Each row represents a single trade agreement. The β<sup>k</sup> coefficients represent the effect on trade flows of each listed agreement. The β<sup>l</sup> coefficients represent the average effect of all other RTAS in total trade flows excluding the agreement named in the given row.

**Figure 3.1 PPML HDFE Gravity estimates of Morocco’s Trade Agreements and Total Trade**

<!-- Start of picture text --> Agadir Agreement<br>EFTA-Morocco<br>EU-Morocco<br>PAFTA<br>Turkey-Morocco<br>United States-Morocco<br>GSTP<br>-.5 0 .5 1 1.5<br><!-- End of picture text --> 12 | P a g e
sup>|0.542|0.542|0.543|0.543|

_Notes_ : The regressions are OLS estimations of equation (2) for the period 1999-2005. The dependent variable is the logarithm of the number of products exported to country _j_ in year _t_ by firm _f_ . Fixed effects by firm, country and year and a constant are included in all specifications. The destinations are China and other Asian countries that already integrate WTO such as India, Indonesia, Korea, Japan, Pakistan, Philippines, Singapore and Thailand. Tariff _fjt_ is the tariff measure faced by firm _f_ when exporting to country _j_ in year _t_ . Tariff _fjt ×_ China _j_ is an interaction term between the firm level tariff measure and a dummy variable equal to one when the country of destination of exports is China and zero otherwise, China _j_ . GDP _jt_ is the natural log of the GDP of country _j_ from the Penn World Tables. RER _jt_ is the bilateral real exchange rate between France and China and countries in the control group using producer prices of France and importer countries from the International Financial Statistics (IFS). Heteroskedasticity-robust standard errors clustered by firm-country pairs are reported in parentheses. _∗∗∗, ∗∗,_ and _∗_ indicate significance at the 1, 5 and 10 percent levels respectively.

Table 2 reports similar results for the intensive margin of exports. After controlling for observable country level characteristics in columns (2) and (3), the average reduction of Asian tariffs leads to almost 3 percent increase in French firms’ export sales. The coefficient of the interaction term between the tariff measure with the China dummy variable in column (4) shows a larger effect due to Chinese liberalization relative to the average Asian tariff cuts. This allow us to conclude that China’s tariff reductions increase by a larger amount export sales of French firms towards this destination relative to the others: Chinese tariff cuts boost by almost 19 percent (7 times _β_ ) French export sales.