rafmacalaba/datause-encoder-data huggingface dataset:
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figures--and ask key questions about their agricultural operations and the problems they face. Indirect questions, about which family members are overextended during various agricultural operations, are helpful in gaining an understandiig of where women need assistance (McKee 1986). Onetime surveys and T&V Monthly Technical Review Meetings also provide rapid low-cost data on African women who farm. A more elaborate survey design using a larger sample and random selection would allow the results to be generalized.

-

ceemi: **CIERO:** IEMXSIVE SINGLE E IIuI .-

------ ------ - -r- ------ -- ----Farimrs dfaptéM.d-~ ion. ifvesi abitityto dotalIs-of. theii farmin operations..recatthe **A** singa: quetfomaIre,' administerëd to each married Ïdît.t "n' raa sampte of. forty rurat householtd, 5waùsod té estimateannusl.production and tabor.tinme. Resutts.from.this survey were witibn 7X for ments amul ccoa' production hours and 11X for wmmntsanual food production hours, of results -obtoined using daily interviews T the Wortd Bank reports favorably on the accuracy throughoutithe catendar year (Hemn, 1988. p. 318). of farmer eitimates of yietd compared to more expensive àndtime-consmuinigmethods traditIonatty used by research teast. (Vern, et at, 1988)

2.14 Another important and readily available indicator of the magnitude of women's agricultural responsibilities is the off-farm employment rates of men. When more men work off-farm, women are more likely to be active farmers and key decision-makers in farming activities. Extension can then be adjusted to target women more directly.

## Box 5: NIGERIA: DIAGNOSING OF GEIDER ROLES IM T&V EXTENiSION PROJECT

The Imo State Agricultural Development Project In Nigeria uses an excellent yet Inexpensive technique for cotlecting and using data to guide extension delivery on a continuing basis. At the T8V Monthly Technical Review Meeting, staff analyze who in the family la responsible and who provides tabor for each new recoammendation. Information collected through diagnostic surveys provides usefut information for this analysis. This enables extension staff to uncderstand faamily labor allocation and target women to receive the new information where appropriate. The technique shoutd probably be expanded to include whether and how the proposed recofnuendations wiltl change tabor patterns, incentives, or resource attocation within the famiy. <Okoro 1989>

# 3. Monitoring and Evaluating Extension Programs for Women Farmers

2.15 The monitorine function provides managers with feedback on how and how much extension has progressed comrared with what was planned. Evaluation seeks to explain and if possible meas.ure the level of efficiency of implementation relative to costs and accrued benefits (Murphy and Marchant 1988). A key indicator of the effectiveness of T&V extension is the adoption rate, which provides immediate feedback about program performance.' Adoption should be incrementally measured for women farmers, who more likely than men farmers to be only partial adopters.

For more information about monitoring and evaluation of agencies using the T&V extension system, see Murphy and Marchant 1988.
20 percent of working age men becoming part of the work-ready pool, with over 800 workers so far having the opportunity to work in New Zealand. The majority of RSE applicants were not working in paid employment prior to the program taking place, so the main opportunity cost of their employment in New Zealand will be the time they would have spent on agricultural production in Tonga. Our follow-up surveys will measure changes in agricultural production in both households participating and those not participating in the RSE, allowing measurement of this effect along with other impacts of the RSE on individuals and households in Tonga.

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**Table 10.  Trends in Rural Public Finance**

|(percent)|1986|1988|1990|1992|1998|2000|2002|2005|
|---|---|---|---|---|---|---|---|---|
|**Rural share of total**<br>**expenditures**|**24.7**|**28.3**|**29.7**|**30.9**|**28.2**|**26.2**|**28.6**|**32.6**|
|County|19.2|21|21.3|21.6|19.9|18.9|21.9|26.7|
|Township|5.5|7.3|8.4|9.3|8.3|7.3|6.8|5.9|
|Townshipshare of rural|22.3|25.8|28.3|30.1|29.3|27.8|23.7|18.1|
|**Rural share of total**<br>**revenues**|**21.7**|**28.6**|**28.4**|**30.1**|**20.3**|**19.7**|**17.1**|**18.2**|
|County|12.5|17.1|16.1|16.6|11.5|12|11|13.3|
|Township|9.2|11.5|12.3|13.5|8.8|7.7|6.1|4.9|
|Townshipshare of rural|42.4|40.2|43.3|44.9|43.5|38.9|35.6|26.9|
|**Fiscalgap for rural**|**-3**|**0.3**|**-1.3**|**-0.8**|**-7.9**|**-6.6**|**-11.6**|**-14**|
|County|-6.7|-3.9|-5.2|-5|-8.5|-6.9|-10.9|-13.4|
|Township|3.7|4.2|3.9|4.2|0.6|0.4|-0.7|-1|
|**As % GDP**|**-0.7**|**0.1**|**-0.3**|**-0.1**|**-1**|**-1.1**|**-2.1**|**-2.9**|

Note: * The fiscal gap is measured as revenue share minus expenditure share. Source: Compendium of Township Financial Statistics, 1986-1992; World Bank (2002); and MOF provincial dataset 2003; and Compendium of Local Fiscal Statistics, 2005.

More importantly for the rural areas, the TSS also produced sharply divergent trends for townships and counties. Up through 1992, township revenues had grown rapidly, due to the rapid growth of township and village enterprises, and they financed growing expenditures. These trends were reversed after the TSS. Between 1992 and 1998, the township share of revenues fell steeply, as the product taxes from TVEs were converted to VAT, whose revenues were mostly captured by higher-level governments – as discussed earlier. Township revenues continued to fall from 1998 to 2005, with the RFR and the reassignment of the EIT cutting into the rural tax base. The abolition of the agricultural taxes also added to this decline. In 2005 townships accounted for less than 5 percent of total revenues – compared to 13.5 percent in 1992.

The township’s share of expenditures also fell from 1992 onward, albeit more slowly than revenue. This decline is mainly caused by the reassignment of some functions to the county level – for example, education, which had absorbed up to 40 percent of township expenditures, was reassigned to counties starting in 2002. In the 1990s it may have also reflected a squeeze on services provided by townships (and villages) as their revenues declined.

In contrast, the county’s share of expenditures has increased since the late 1990s even 21
|Mean|Std. Dev|.<br>p(10)|p(25)|p(50)|p(75)|p(90)|
|---|---|---|---|---|---|---|
|Te<br><br>|chnology <br>|Intensive<br>|||||
|High Technology<br>0.554<br>LowTechnology<br>0.531|0.142<br>0.152|0.389<br>0.326|0.446<br>0.417|0.565<br>0.535|0.670<br>0.649|0.718<br>0.718|
|<br><br>Kn<br><br>|owledge <br>|Intensive<br>|||||
|Knowledge Intensive<br>0.546|0.141|0.364|0.462|0.569|0.651|0.713|
|Not-Knowledge Intensive | 0.507|0.154|0.269|0.424|0.515|0.621|0.694|
|Notes: ‘Management Score’ is calculated follow|ing MOPS|(See Bloom|et al.,|2018). ‘H|igh Techn|ology’ and ‘Low|
|Technology’ sectors are as defined by EUROST<br>*No-KnowledgeIntensive’servicesareas|AT indicat<br>definedby|ors based on <br>EUROSTAT|NACE<br>indica|Rev. 2. <br>torsbase|‘Knowled<br>don<br>NA|ge Intensive’ and<br>CERey.<br>2.|

||Mean|Std. Dev.|p(10)|p(25)|p(50)|p(75)|p(90)|
|---|---|---|---|---|---|---|---|
|||Manufac<br>|turing<br>|||||
|Family-Owned|0.531|0.153|0.319|O417|0.532|0.655|0.718|
|NonFamily-Owned|0.562|0.134|0.415|0.485|0.590|0.652|0.718|
|||Servi|ces|||||
|Family-Owned|0.529|0.151|0.326|0.439|0.536|0.647|0.704|
|NonFamily-Owned|0.521|0.142|0.344|0.450|0.538|0.631|0.695|
|||Manufac|turing|||||
|Family Run|0.530|0.151|0.326|0.422|0.530|0.646|0.717|
|Professionally Run ||0.550|0.147|0.354|0.456|0.576|0.660|0.721|
|||Servi<br>|ces<br>|||||
|Family Run|0.533|0.150|0.326|0.448|0.550|0.649|0.708|
|ProfessionallyRun||0.518|0.147|0.308|0.438|0.532|0.633|0.696|

Notes: ‘Management Score’ is calculated following MOPS (See Bloom et al., 2018). ‘Family-Owned’ is a firm, where the majority of the shares are held by the founder and/or family member of the founder. ‘Non Famlily-Owned’ is a firm where it is owned by anybody else (where the majority of the shares are held by private individuals, managers, dispersed shareholders, private capital or risk capital, etc. and run by managers). ‘Family Run’ is a firm, where it is run by the founder or a family member of the founder. ‘Professionally Run’ is a firm where it is run by other than the founder and/or family member of the founder.
The Global Findex data is particularly well-suited to measure the gender gap in access to finance as the definition of account penetration includes only individual or joint accounts and not the use of someone else’s account. The use of savings and credit refers to individual behavior as well. This is in contrast to other demand-side data on financial behavior that measure account penetration, savings behavior and credit use at the household level.<sup>8</sup> In contrast, individual-level indicators allow us to directly measure women’s control over their assets, an important component of economic empowerment.

# **_2. 2 Descriptive Statistics_**

We start our analysis by documenting ownership and use of financial services around the world by gender. In particular, we provide descriptive statistics on three key dimensions of financial services: account ownership (and barriers to account ownership), savings and credit.

All reported country, regional, and income-group statistics are weighted using individual weights provided by Gallup<sup>9</sup> and by country-level adult population.

# <u>2.2.1 Accounts</u>

According to the Global Findex data approximately half of adults worldwide report having an account at a bank, credit union, cooperative, post office or microfinance institution (MFI). Not surprisingly, there is enormous variation in the use of financial services between high income and developing economies: account penetration is close to universal (90 percent) in high-

> 8 For instance, the European Bank for Reconstruction and Development (EBRD) _Life in Transition Survey (LITS)_ includes the question: “Do you or anyone in your household own: A Bank Account.”

> _9_ All country-level averages use individual weights that are provided by Gallup to ensure a nationally representative sample. First, base sampling weights are constructed to account for oversamples and household size. Second, poststratification population statistics are used to weight the data by gender, age, and, where reliable data are available, education or socioeconomic status.

9
# **Table 3. Fixed Effect Regression Results**

||**Column 1**|**Column 2**|**Column 3**|**Column 4**|
|---|---|---|---|---|
||||**Printing**|**Household**|
||**Aggregate**<br>**Paper**|<br>**Newsprint**|<br>**and**<br>**Writing**|**and**<br>**Sanitary**|
|Internet Pentration<br>(per 100 people)|-0.0040**|-0.0063***|-0.0065**|-0.0045|
||(0.0017)|(0.0022)|(0.0027)|(0.0048)|
|GDP per capita (in<br>logarithm)|1.6430***|0.3270|1.8510***|3.3440***|
||(0.2170)|(0.2880)|(0.3260)|(0.5820)|
|Percentage Post High|||||
|School Students<br>(over whole<br>population)|-0.9610|12.4383**|11.3895*|-15.2800|
||(3.9590)|(5.1470)|(6.0860)|(11.0400)|
|Workers in Primary<br>Sector (percentage)|-1.1640|-2.8450***|2.6310**|4.1030**|
||(0.7470)|(0.9870)|(1.2600)|(2.0050)|
|Workers in|||||
|Manufacturing|0.6450|-6.3890***|1.8520|5.2570*|
|Sector (percentage)|||||
||(1.2300)|(1.5610)|(1.8490)|(3.0540)|
|Workers in the|||||
|Services|2.012***|1.0320*|-0.2760|0.8360|
|(percentage)|||||
||(0.4540)|(0.5790)|(0.6780)|(1.0660)|
|Observations|460|455|424|353|
|R-squared|0.97|0.96|0.95|0.91|
||Standard e|rrors in parenth|esis||
||*** p<0.01|, ** p<0.05, *|p<0.1||

# **7. Quantifying the Decrease in Paper Consumption and its Effects on the Environment**

We can use our estimates in the previous section to predict the extent to which the increase in Internet adoption has decreased paper consumption. We showed that a 1 percent increase in Internet penetration decreases aggregate paper consumption 20
In the following section, the complex functional and economic relationships that sustain these two-fold mechanisms for the population of Colombia are described in detail. Figure 7 aims to capture that complexity.

**Figure 7: Main Relationships within the Health Insurance System of Colombia**

<!-- Start of picture text --> Taxes National General Grant<br>Budget (PGN)<br>Territorial Entities:<br>Contributions pool Promotion & Prevention<br>Departments, Districts,<br>FOSYGA<br>Municipals<br>Solidaridy<br>Purchase of services<br>EPS – c EPS – s<br>Purchase of services Purchase of services<br>IPS – c IPS – s ESE<br>Pro&Pre Pro&Pre Collective actions<br>personal personal (Pro&Pre – P Health)<br>ONGs – Org. Int.<br>POS – c POS – s<br>Citizens<br>Users<br>Subsidy<br>Compensation<br>Subsidy to the offer<br>Contributions<br><!-- End of picture text -->

**Source:** Técnicas de Salud, 2011.

# **_(a) Insurance Component_**

There are three ways through which the citizens of Colombia may enter the health insurance system (Sistema de Seguridad Social en Salud, SGSSS) to receive health care services: (i) the contributory regime (see Figure 7, suffix –c); (ii) the subsidized regime (see Figure 3, suffix –s); and (iii) the associated regime. The latter regime is temporary—it is expected to disappear when coverage to the SGSSS is extended to the entire population.

- The contributory regime applies to all national and foreign citizens who are residents in Colombia with legal work contracts (salaried), or with fixed income

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Table 1: The Cyclicality of Wages and Employment: Differences between Booms and Recessions

|Percentiles:|16th|50th|84th|
|---|---|---|---|
|Brazil||||
|DC<sup>w,y</sup>|0.0177|0.1277|0.2293|
|DC<sup>l,y</sup>|0.1666|0.1913|0.2187|
|Chile||||
|DC<sup>w,y</sup>|-0.1597|-0.0612|0.0403|
|DC<sup>l,y</sup>|-0.0062|0.1117|0.2108|
|Colombia||||
|DC<sup>w,y</sup>|0.2118|0.2747|0.3349|
|DC<sup>l,y</sup>|0.2723|0.3291|0.3794|
|Mexico||||
|DC<sup>w,y</sup>|0.0414|0.0881|0.1375|
|DC<sup>l,y</sup>|0.2613|0.2819|0.2992|

# 4 Extensions: Compositional Effects and Differences across Sectors

Workers are heterogeneous, and those who exit and enter the labor market along the business cycle are not a representative sample of the labor force. In particular, lowearning workers and those with less attachment to the labor market tend to enter the labor force during expansions and exit during recessions. Solon, Barsky and Parker (1994) show that such composition bias is important, and that failing to control for the time-varying characteristics of the labor force in the employed population introduces a downward bias in estimates of the cyclicality of wages in the United States. Similar conclusions are reached for other developed countries (e.g., Carneiro, Guimaraes and Portugal, 2012).

The literature has largely ignored the potential importance of compositional effects in wage cyclicality estimates in developing countries. This may be a concern, although there are reasons to think that the aforementioned compositional bias in the estimated elasticities may be more limited in developing countries. Large informal sectors in developing countries typically act as a buffer, expanding in recessions and contracting in expansions, thus avoiding rapid increases in unemployment and inactivity (Bosch and 16
# **1. Introduction**

An enormous literature attempts to measure the size of implicit international trade costs and to explain them.<sup>1</sup> Despite all this activity, there is to date very little hard evidence on the effectiveness of policy changes that might be expected to mitigate these costs.<sup>2</sup> But the dearth of hard evidence has not restrained policy makers, who signed an ambitious trade facilitation agreement in the 2013 World Trade Organization (WTO) ministerial while promising hundreds of millions of US dollars in initiatives to support trade facilitation in developing countries.<sup>3</sup> One of the most prominent of the trade facilitation measures contained in the WTO agreement, and one of the most challenging for developing countries to implement, is the adoption of risk-based methods of selecting shipments for inspection. These procedures combine information technology and statistical targeting procedures to sharply reduce the frequency with which goods are physically inspected.

This paper investigates the impacts, on time and on import activity, of a substantial decline in physical inspection rates that occurred as the Albanian customs authority substantially upgraded its risk-based inspection capabilities. During the period for which we have comprehensive data, 2007-2012, the rate of physical inspections fell from 43 percent of import consignments to just 12 percent. This dramatic reduction in inspections - together with an identification strategy that exploits the properties of risk-based selection strategies for inspecting shipments - allow credible estimates of the impact of reduced inspections on the time goods spent under the control of the customs authority, and of the causal impact of time spent in customs on import flows.

The exercise relies on detailed administrative data that report inspection type, time spent in customs, and the value and quantities of imports at the transaction level. A comprehensive

> 1 Anderson and van Wincoop (2004), a survey article on the topic, had been cited more than 2500 times in Google Scholar as of November 2014. A topic of particular concern in this literature is the impact of national borders for trade, as in McCallum (1995), which had over 2200 citations at that point in time.

> 2 Typically, implicit trade costs are estimated in cross-sectional studies, and so are unable to estimate the impact of specific reforms.  In some cases, the literature offers rough evidence on high-level policy changes such as the introduction of a preferential trade agreement, but even these studies offer little guidance as to the effects of particular policies that might or might not be included in such agreements.

> 3 World Trade Organization (2013) indicates that 1.2 billion US dollars in aid had been disbursed in support of trade facilitation reforms in developing countries since 2006.  The document also pledges ongoing support by 27 national and international agencies.

2
The data for the exercise is a combination of the 73 household surveys described earlier in section 4.2 and more aggregate data on income groups (usually vintiles) for 25 high income and 22 developing countries. The final sample covers more than 90 percent of the world’s population (see Table 9 in Annex II for country coverage).

**Figure 6: GIDD methodological framework**

<!-- Start of picture text --> Population Projection by Education Projection<br>Age Groups ( Semi- Exogenous )<br>( Exogenous )<br>Household Survey<br>( new sampling weights<br>by age and education )<br>CGE<br>(Growth, New Wages, New<br>Prices, Sectoral Reallocation )<br>Simulated Distribution<br><!-- End of picture text -->

# 5.2 Simulation Results

In the baseline scenario, prices of agricultural products continue to rise modestly from their 2004 levels, with the total increase reaching nearly 5 percent above the OECD industrial exports price index (MUV) by 2010. This gradual rise in prices is driven partially by lower crop yields due to climate change, partially by a re-orientation of the food consumption basket in developing countries to meats and more processed foods, which raise the demand for feed grains and are thus less ‘efficient’ in meeting caloric intake requirements, and partially by the lack of investment in agriculture due to years of declining prices. However, this rise in agricultural prices is fully offset by a decline in the price of processed food—where large productivity gains are realized in fast-growing developing countries—such that the price of the agriculture and food bundle (at the global level) remains nearly constant throughout the model horizon.

17
# **5. Regressions Results**

In this section, we give the estimation results of the equations presented in section 3. Table 10 (in Appendix) first provides estimates for the emergency assistance and food consumption score equations without other covariates than pre-earthquake asset index, assetlosses, post-disaster housing damage, location and participation in associations. Second, Table 11 (in Appendix) presents estimates when adding other covariates such as gender, age and education of the head of the household, the number of infants, children, youths, adults and elderly people in the household, the fact of living in a private housing or not, the fact of having agricultural practice or not, the main income source, and other covariates which correspond to typical household coping strategies: aids and transfers from other Haitians in Haiti or abroad, type of transfers, debts, savings and banking accounts. We also add among the covariates a _reduced coping strategy score_ computed according to how frequently household members had to resort to survival strategies related to food consumption during the week before the survey.

# _(a) Emergency assistance equation_

It is shown in Tables 10 and 11 that asset losses have no significant effects on receiving either general or food assistance. However, adding more covariates in Table 11, the preearthquake assets index appears to have a statistically significant effect on food assistance and less so on general assistance. Destroyed housing has a significant and positive impact on getting general assistance, while it does not on getting food assistance. The place where households sleep appears to have a significant effect on assistance allocation. Sleeping in a camp does not appear to have a significant effect on getting food assistance (as it was the case according to the February survey). Furthermore, of the various association memberships, participation in agricultural organizations and participation in school committees have significant positive effects on receiving assistance, whereas participation in religious organizations has a small but significant negative effect. Only agricultural organization membership has a large and significant positive effect on food assistance.

Interestingly enough, assistance seemed not to be better targeted in June than it was in February: indeed, although households headed by higher educated people had lower access to post-earthquake assistance in June, female headed households had less access to general assistance and families with disabled people had less access to food assistance. Finally, the fact that associations may favour assistance allocation is an interesting result that should be considered further.

20