rafmacalaba/datause-encoder-data huggingface dataset:
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Bloom, N., and J. Van Reenen. 2010. “Why do management practices differ across firms and countries?” _Journal of Economic Perspectives_ 24(1), 203-24.

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Bouchard Jr., G., J.C. Loehlin. 2001. “Genes, evolution, and personality.” _Behavior Genetics_ 31(3), 243-273.

Bruch, C. 1992. “Research on women business owners: Past trends, a new perspective and future directions.” _Entrepreneurship Theory and Practice_ 16(4): 5-30.

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- Cai, H., H. Fang, and L.C. Xu. 2011. “Eat, Drink, Firms, Government: An Investigation of Corruption from Entertainment and Travel Costs of Chinese Firms.” _Journal of Law and Economics_ , 54, 55-78.

Cassar, A., F. Wordofa, Y. J. Zhang. 2016. “Competing for the benefit of offspring eliminates the gender gap in competitiveness.” PNAS 113(19), 5201-05.

Cattan, S. 2014. “Psychological traits and the gender wage gap.” Unpublished.

Chattopadhyay, R., E. Duflo. 2004. “Women as policy makers: Evidence from a randomized policy experiment in India.” _Econometrica_ 72(5), 1409-43.

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25
news media. On the civil society side, a particularly important component is the Ghana Integrity Initiative, the local chapter of Transparency International. It brings together a variety of organizations, religious groups, media representatives, legal practitioners and individuals committed to reform.

The question of how much, and how best, to coordinate activities with the government is a complex one in Ghana. The new Government has indicated they do not intend to nominate anyone to GACC but will negotiate a framework for liaison with the Coalition, either through the Attorney General or the Office of Accountability. Still, the Coalition has asked the Chairman of the Public Accounts Committee to become a partner, and has made similar approaches to the Auditor General. Former President Jerry Rawlings did, however, approve the coalition's request for a World Bank diagnostic survey on corruption. This survey was conducted by the CDD from March to July, 2000, with additional support from the United Kingdom's Department for International Development, the Canadian International Development Association, and the United Nations Development Program. These agencies have also agreed to assist GACC with post-survey analyses, dissemination of findings, and the formulation of specific policy proposals.

The GACC agenda offers an ambitious set of purposive incentives, including: • Creating a forum for interaction among the three arms of government, public and private sector institutions, and civil society groups to work on anti-corruption strategies

- Encouraging the exchange of information and joint sponsorship of programs, where appropriate, to achieve effective utilization of resources

• Enhancing transparency and reducing opportunities for corruption in government, public and private institutions, and civil society organizations

- Sponsoring initiatives leading to appropriate legal and institutional reforms

- Strengthening the capacity of its constituent organizations and other anti-corruption bodies where necessary

- Serving as the primary organization for the implementation of Ghana’s anti-corruption plan

Projects contributing to those goals have included, first and foremost, the diagnostic survey mentioned above; in addition, GACC has prepared an action plan that will serve as the basis for national debate and consensus building. Coalition members are pushing for freedom of information and whistle-blower protection legislation, and efforts continue to strengthen working relationships among public agencies, the private sector and civil society.

22
Table 9: Eect of a direct SLM training intervention on contact farmers' labor allocation

||Pooled Sam|ple|||Endline||||
|---|---|---|---|---|---|---|---|---|
||Control<br>mean [SD]|ITT|N|R<sup>2</sup>|Control<br>mean [SD]|ITT|N|R<sup>2</sup>|
|Hours spent on|6.095|-2.822|346|0.032|6.429|-1.272|168|0.073|
|preparation of land|[14.550]|[3.356]|||[15.353]|[2.998]|||
|Hours spent on seeding|8.214|-3.558**|346|0.021|10.357|-6.567*|168|0.065|
||[15.996]|[1.069]|||[17.862]|[2.943]|||
|Hours spent on|2.607|-1.408|346|0.062|1.738|-0.917|168|0.087|
|transplantation|[7.973]|[0.957]|||[6.666]|[0.736]|||
|Hours spent on irrigation|0.000|-0.038|346|0.028|||||
||[0.000]|[0.044]|||||||
|Hours spent on sacha|10.583|0.454|346|0.198|5.833|-0.690|168|0.072|
||[15.576]|[1.098]|||[14.252]|[1.514]|||
|Hours spent on protection|0.000|0.969|346|0.042|0.000|0.513|168|0.122|
||[0.000]|[0.922]|||[0.000]|[0.499]|||
|Hours spent on harvesting|11.012|-2.288|346|0.122|15.810|-2.234|168|0.114|
||[18.260]|[1.417]|||[19.573]|[1.164]|||
|Total weeks spent on|28.262|-2.986|346|0.029|30.381|-7.084**|168|0.069|
|farming in last year|[18.386]|[2.210]|||[19.196]|[2.508]|||

Sources: Contact farmer survey, 2010; Household survey, 2012, 2013. Notes: All models include the same explanatory variables as models in Table 4. ***, **, and * indicate signicance at the 1, 5, and 10 percent critical level. ITT=intent-to-treat eect; SLM=sustainable land management.

40
effect of price on the budget share is extracted from the ratio by using the theory linking quality and quantity elasticities.

Household characteristics consist of the logarithm of household size, a series of agegender ratios,<sup>7</sup> a dummy variable for skilled workers, and a rural indicator. Since time and regional differences in tastes may result in demand not being the same across time and across region, the covariates are augmented with regional and time dummies. In the first stage, cluster means are subtracted from the variables in equations (1) and (2) and then those equations are estimated using OLS to obtain estimated parameters β<sup>ˆ</sup> _i_ . These estimates measure the influence of income and household characteristics on demand and quality.

The size of the quality effect can be easily estimated using the methodology of Prais and Houthakker (1955). The underlining assumption is that, ceteris paribus, the richer the household, the higher the quality of the products that the household consumes. Because unit values not only vary with the choice of quality, but also with actual market prices, prices theoretically should be included in the estimation. The fact that prices are not observed in household surveys makes it impossible to obtain directly parameter estimates for the price variable. Nevertheless, it is possible to estimate consistently the non-price parameters if we assume that all households in the same cluster face the same vector of prices. Therefore the estimation of equation (2) is conducted by adding dummies for each cluster.<sup>8</sup> A further problem with the estimation of equations (1) and (2) arises in the case when households do not purchase a particular category of goods. Because the logarithmic specification can be used only to describe the behavior of those households who purchase positive amounts, the logarithmic form can exclude a large part of the households for narrowly defined product groups. To avoid this problem, Deaton and Muellbauer (1980)

> 7 The gender and age composition of the household is likely to be reflected in the consumption basket of the household. The gender-age ratios are constructed as the number of a determinate category of individuals over total household members. In the estimation, individuals are divided by gender and by three age groups (children, adults, elderly), resulting in six ratio variables.

> 8 This is equivalent to estimating the regression of deviations from cluster means

6
both 1984-88 and 1989-93, while for the middle group the estimated coefficient is around 1.0 in both periods and also significantly different from zero.

Second, for the countries in the high group each $1 of additional grants has been accompanied by $0.36 more imports (this result is significantly different from one in absolute terms).<sup>23</sup> Although a smaller coefficient for this group could have been expected ex-ante based on the fact that debt forgiveness does not necessarily free foreign exchange and therefore may not lead to an improvement in a country's import capacity, the results from the less restrictive model suggest that this weak relationship existed even before the surge in debt forgiveness. Indeed, the relationship appears to be unstable over time: during 1984-88 the point estimate is 0.5 but not significantly different from zero, while during 1989-93 the estimated coefficient becomes 0.4 and significantly different from both zero and one.

In sum, the results above confirm the previous findings that the increase in ODA debt forgiveness since 1989 has not curtailed the high group's ability to import. Furthermore, it could be argued that total grants have helped meet the financial needs of recipient countries, particularly those in the low and middle groups and increasingly so for those in the high group (for the high group grants have become more effectively used in recent years in terms of meeting financial needs).

# _Additional hypothesis: Presence of compensatoryflows_

_Is there a crowding-out effect between total grants and other sources offinance?_ The previous results for the high group suggest that a crowding-out effect occurs between total grants and other sources of funds, as mentioned earlier. This possibility is studied next by looking at the correlation between total grants and different types of net transfers, classified by source. Again, correlations were calculated for both a restricted and unrestricted model, but we present and discuss the results for the unrestricted model only when they differ significantly from the restricted model. All the relevant results are presented in Table 7.

One major conclusion emerges from the analysis of Table 7: Over the past decade and on an incremental basis, bilateral lenders have partially substituted total grants for new lending for countries that have not received significant amounts of ODA debt forgiveness. However, this substitution has been offset by larger net debt flows from multilateral sources (partially for the countries in the middle group and more than fully for the countries in the low group).

This conclusion is supported by several findings. First, there is a statistically significant positive correlation between multilateral net transfers (new lending) and total

> 23 Note that this result is consistent with the one reported earlier; that is that the high group **has been the least** constrained in terms of scarcity of foreign exchange. Nevertheless, these results complement each other; that is, the one in the previous section reters to annuial averages over 1984-93, while the one discussed here refers to increments over time.

15
# **EXECUTIVE SUMMARY**

- (i) Without a competitive transport industry the Maghreb countries will not benefit fully from reforms aimed at increasing market share of international trade. This rationale prompted a study to identify the transport barriers obstructing the Maghreb countries' trade with the rest of the world, particularly with countries of the European Union (EU), to measure the benefits to be gained from their removal, and to outline possible corrective policies.

- (ii) Based on specific local surveys, more than thirty import and export barriers were identified. Those barriers that are directly transport related derive from traditional distortions (price barriers, discriminatory market access); administrative, regulatory, tax-related restrictions (nontariff barriers); traffic agreements (protection of national flags); or lack of infrastructure and equipment. Those barriers indirectly connected with transport derive from trade harmonization (simplification of customs procedures and tariff structures, elimination of quotas, and reduction of customs tariffs on transport equipment) or technology lags (such as telecommunications and handling). The topology has been broken down into barriers to imports, exports, infrastructure and equipment, and intra-Maghreb trade.

- (iii) The economic impact of these barriers can only be measured if a common denominator is used that measures the different distortions in like terms. In this report barriers are quantified in terms of "tariff equivalents," expressed as a nominal rate of protection based on the f.o.b. value of the merchandise. However, the nominal rate of protection only measures the direct costs of distortions whereas the effective rate of protection would measure both the direct and indirect effects. Research in industrial countries indicates that effective rates are generally twice as high as nominal rates.

- (iv) Two macroeconomic approaches are used to measure the effect of restriction practices. A preliminary nominal and ad valorem measurement of the cost of international transport and insurance can be obtained by comparing the c.i.f. and f.o.b. values of trade. This measurement, which is approximate, points out possible distortions, where the freight and insurance costs are higher for the Maghreb countries than for any other group of countries considered. We find that these costs are five times higher than those in the European Union and twice as high as those in developing countries.

- (v) A second macroeconomic approach, based on the balance of payments, estimates the total freight and insurance costs for a given country and expresses this cost as a percentage of the total f.o.b. value of an item. This percentage is the equivalent of a "nominal shipment rate" for the item concerned. For the three countries considered, this rate varies (during the reference year 1990) from 3.5% for Algeria and Tunisia to 7% for Morocco.
7 referencing of all households further enables merging of the data with the areas’ agro-ecological and geographic characteristics (including distance to markets and population centers and food market prices). The effect of environmental and market factors on PHLs can thus also be studied.

Two PHL measures are derived from the crop sections of the agricultural modules of the main<sup>10</sup> cropping season: 1) whether the household incurred PHL for maize (conditional on having a strictly positive amount of maize harvested) and 2) the proportion of harvested maize lost as a share of total production (expressed as a ratio between 0 and 1. In particular, following questions about the amount harvested for each crop, households were asked for each crop with a strictly positive harvest: “Did you incur any PHL due to rodents, pests, insects, flooding, rotting, theft, and other reasons?” and if yes, “What proportion was lost?”<sup>11</sup> The measures thus mainly concern on-farm PHL during handling and storage after the harvest, essentially excluding losses incurred by traders and retailers or during consumption. That they also include some of the farmers’ field drying and marketing losses can however not be totally excluded, and the measures should be seen as upper bounds of on-farm-PHL during storage and handling.

In each survey, households were only surveyed once, though not during the same months. Specifically, the surveys were each time conducted throughout the year, with the households to be interviewed during a particular period, each time sampled from across the country so that they would enable a nationally representative picture of the situation in the country during that period. A household could thus have been surveyed shortly after the harvest or several months thereafter, though for each period they constitute together a representative sample of the country. This feature of the survey is exploited to annualize each household’s self-reported PHL. Annual adjustment is

> 10 Only in Uganda and Tanzania are there households with a second cropping season, but they are few, and limited to specific AEZs. The study has abstracted from them in calculating the PHL statistics (Tables 1-3), but controls for their potential effect indirectly through the inclusion of AEZ indicator variables in the multivariate analysis of PHL drivers (Table 4).

> 11 The first question was the same across the four surveys; the second question differed only slightly. In Tanzania, it was asked: “Out of 10 units, how many were lost?” In Uganda, farmers reported PHL as a percentage of total quantities produced instead. In Malawi, farmers were given the option of either reporting a total amount of losses in local units (subsequently converted in kilograms and divided by produced quantities to obtain the PHL measure), or directly reported the losses in percentages, as in Uganda. Very few outliers (PHL measure beyond one) were detected for Malawi, and in such cases, PHL values were replaced by one.
Table 5 Effect of Female Corporate Leadership on Firm Productivity

|Dep. Variables|TFP|log LP|LP<br>Growth|TFP|log LP|LP<br>Growth|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|FemHead|-0.087**|-0.159***|-0.016**|-0.093***|-0.151***|-0.014**|
||(0.034)|(0.038)|(0.008)|(0.031)|(0.034)|(0.007)|
|Foreign|0.387***|0.493***|0.051***|0.342***|0.480***|0.044***|
||(0.049)|(0.049)|(0.012)|(0.050)|(0.047)|(0.010)|
|OwnLargest|-0.113|-0.352***|-0.036***|-0.098|-0.332***|-0.028***|
||(0.070)|(0.088)|(0.010)|(0.077)|(0.080)|(0.007)|
|Middle|-0.048*|0.176***|0.032***|-0.062**|0.158***|0.032***|
||(0.027)|(0.028)|(0.005)|(0.028)|(0.027)|(0.005)|
|Large|-0.158***|0.284***|0.045***|-0.190***|0.248***|0.041***|
||(0.050)|(0.051)|(0.007)|(0.053)|(0.048)|(0.007)|
|Age6_10|0.053|0.089***|-0.009|0.045|0.082***|-0.010|
||(0.047)|(0.022)|(0.007)|(0.046)|(0.026)|(0.007)|
|Age10plus|0.014|0.088***|-0.015**|-0.015|0.084***|-0.015*|
||(0.041)|(0.028)|(0.008)|(0.037)|(0.032)|(0.008)|
|Exporter|<br>0.198***|<br>0.227***|<br>0.029***|<br>0.202***|<br>0.234***|<br>0.024***|
|Initial LP|(0.038)|(0.045)|(0.006)<br>-0.104***|(0.035)|(0.034)|(0.005)<br>-0.091***|
||||(0.017)|||(0.012)|
|Trade Creditc||||0.040|0.469***|0.060***|
|||||(0.083)|(0.167)|(0.013)|
|Overdraftc||||0.124|0.262|0.025|
|||||(0.104)|(0.159)|(0.016)|
|Webc||||0.408***|0.456***|0.042**|
|||||(0.080)|(0.100)|(0.016)|
|Inf. Competititonc||||0.027|-0.023|-0.004|
|||||(0.121)|(0.088)|(0.011)|
|Corruptionc||||<br>0.107|<br>0.110|<br>0.028***|
|||||(0.076)|(0.072)|(0.010)|
|Outagec||||-0.091*|-0.059|-0.012|
|||||(0.051)|(0.087)|(0.010)|
|capAggc||||0.421*|0.120|-0.008|
|||||(0.216)|(0.218)|(0.027)|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|20,157|51,079|45,711|16,638|41,248|37,365|
|Adjusted R<sup>2</sup>|0.283|0.388|0.372|0.265|0.360|0.284|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country level in columns.

35
developing countries, such as India, are moving from providing only low-end back-office services (data entry, etc.) to more integrated and higher-end services bundles in fields such as customer care, human resource management, and product development. This move – reflected in a rising index of revealed comparative advantage (RCA) in business services (Figure 3) – is creating space for other developing countries, from China to Senegal, to step into the more standardized segments of the market.

**Figure 2. Average Growth Rate of Business Service Exports for Selected Countries during 1995-2005**

<!-- Start of picture text --> Ireland 31.6<br>India 25.4<br>China 16.6<br>Brazil 15.1<br>Romania 14.7<br>Argentina 14.4<br>Israel 13.6<br>Malta 12.3<br>Finland 12.2<br>Spain 12.2<br>United Kingdom 11.9<br>Jamaica 11.5<br>Nigeria 10.9<br>New Zealand 10.9<br>Morocco 10.6<br>Malaysia 10.6<br>Mauritius 9.8<br>Sweden 9.7<br>Norway 9.4<br>United States 9.3<br>(%) 0 5 10 15 20 25 30 35<br><!-- End of picture text -->

**Figure 3: Shifting Comparative Advantage -- India's RCA for Services Exports**

<!-- Start of picture text --> Transport services Travel services Business services<br>1.8<br>1.6<br>1.4<br>RCA<br>index 1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text --> 10
was that when a country relies on export or import specialization, larger damages occur in response to disasters. The authors conclude that diversification of the economy can help mitigate the effects of natural disasters.

Using a panel vector auto-regression model, Raddatz (2007) examined the dynamic impacts of external shocks, including natural disasters, on the volatility of output. Focusing on low-income countries, he uses a sample of 40 countries over the period from 1965 to 1997. For the disaster measurement, the author employs the annual data on the number of disastrous events, compiled from the EM-DAT database. The analysis indicated that the effects of external shocks in general on per capita GDP are modest and contribute to only a small portion of its volatility, leading the author to conclude that output volatility is largely determined by internal causes rather than external shocks. However, shocks derived from some natural disasters did appear to have and important effect. In particular, it was observed that climatic disasters lead to a decrease of 2% in real per capita GDP one year after the disaster, while humanitarian disasters reduce it by 4%. On the other hand, geological disasters were found to be insignificant in terms of contribution to the variance of output.

A recent study by Noy (2009) investigated the short-run macroeconomic response to natural disasters using a panel dataset over the period 1970-2003. Taken from the EMDAT database, three measures of disaster damages were employed: the number of people killed; the number of people affected; and the amount of direct damage. In light of potential factors that can influence the disaster impacts, the author took into account differences in population size, size of economy, and timing of incidences. The regression of annual GDP growth rate on the disaster measure and other control variables revealed that the impact of natural disaster is statistically significant when it is measured as the amount of property damage incurred. As other studies suggest, it was also found that the macroeconomic costs were much higher in developing countries than in developed countries. Noy further analyzed the determinants of these negative macroeconomic effects following disasters. He concluded that higher level of literacy, better institutional 7