rafmacalaba/datause-encoder-data huggingface dataset:
underlying assumptions. From a substantive point of viiewv, **it** swould imply that policy factors are more important than traditionally assLumed.

# **4.3 Impact of civil strife on asset accumulation**

One of the advantages of using evidence on civil strife in the context of a household survey is that, in addition to exploring determinants of politically motivated conflict at the cormmunity level, the data als,o allow to examine the impact of civil stnfe on economnic outcomes, at both at the commanity and the individual level. As the incidence of civ/il strife at the community level is not a choice variable for individual households, and since migration as is a response that is riot only associated with very high costs but also not open to all households in a conmrmunity, doing so is less affected by problems [f endogeneity than use of aggregate or cross-countr) data.

Table 4 reports the results of a probit equation Using a 0,1 durmmy indicating that a given househcld increased its asset stock (something that was the case for 81 ]o of households, as indicated in the bottom of the table), as well as a tobit equation for the net increase in household,' assets at constant prices, between the two periods 1992 and 1999.2° In line with our hypothesis, presence of civil strife at the comnmunty level is estimated to have had a highly significant impact or mNvestment, both for the probit and the to Dit equation. The probit equation suggests that an increase of **1.0%** in the proportion of households affected by civil strife is equivalent to about one year of schooling. Moreover, neither theft nor violent attacks (constructed as explained above) have any impact o0i household levcl investment (columni 3 of table 4), again reinforcing the differenace betweern these phenomena.

In addition to highlighting the investment-reducing impact ol civil conflict, the regressLons provide a number of other insights. First, the negative coefficient on the head's age supports the life-cycle dimension of investrnent. Younger households are estimated to be both more likely to invest and to make larger investments, in line with accumLilation of assets over the life-cycle, with investment reaching a

> **2 The** independent variable for civil stnfe (as well as theft and physical attack) **is** the share of households within any given :ommuuotty repoiting to have been affected by civil strife.

24
# **Table A6: Variables most commonly selected by Boosting**

|||Total Emp|loyment|Sale|s|Pro|fits|
|---|---|---|---|---|---|---|---|
|Predictors|Count|NoWin|Win|NoWin|Win|NoWin|Win|
|# Workers with tertiary education|23|24.79|22.16|2.34|2.03|2.26|5.60|
|Registration Time|21|2.50|7.84|2.86|5.03|3.42|3.88|
|# Workers with secondary education|21|5.86|0.38|2.35|6.71|2.25|10.23|
|Age|21|1.78|34.90|5.45|2.69|5.41|3.01|
|Taxes paid|18|0|3.05|1.58|3.45|2.00|3.37|
|Projected annual turnover|18|6.93|4.92|3.46|10.67|1.92|7.15|
|Initial application score|18|4.54|0.38|0.87|2.82|0.83|4.33|
|Length first answer application|17|2.51|1.30|2.50|3.42|2.33|4.27|
|# Brothers|17|0.93|1.73|1.59|3.32|1.31|2.13|
|Business plan scores|17|2.03|0.79|1.48|1.03|2.21|2.80|
|# Sisters|14|2.55|0.49|1.59|0.55|1.63|2.89|
|# Workers with primary education|14|7.70|4.37|1.46|1.16|2.87|1.38|
|Amount needed to expand|14|0|3.27|2.75|2.52|1.56|3.43|
|Length all answers application|13|0|0|1.63|0.71|2.45|2.30|
|# Children|12|0|2.69|0|3.55|0|6.32|
|YouWin: Total award paid|12|0|1.23|1.38|3.01|1.93|4.04|
|Business plan scores: component understanding|11|0|0|2.09|1.24|2.25|2.15|
|Business plan scores: component articulation|11|4.44|0|0.51|2.22|0.49|7.87|
|# Years living abroad|11|0|0|0.92|2.93|0.32|4.29|
|# Freezers|11|0|0|1.45|3.66|0.97|3.10|
|Gender|11|0|0|0.75|3.02|1.17|4.52|
|Raven test - Q11A1 (correct)|11|0|0|1.85|0.55|2.21|1.87|
|Projected employment|10|0|0|0.47|5.82|1.32|2.62|
|Marketing training|10|0|2.66|1.22|1.90|1.44|2.58|
|Raven test – Q1A8 (correct)|10|0|0|1.90|0|3.32|0|

Notes: NoWin and Win denote Non-winners and Winners respectively. Count denotes the number of times (out of 30) that the variable was chosen by boosting in predicting employment, sales, and profit outcomes. The remaining columns then show the average influence the variable has on the prediction.
by the International Monetary Fund (IMF), the World Bank and the African Development Bank.

The crisis and the initial response to it led to a severe recession and increased poverty (World Bank 1995). It is reported that by 1990, real GDP stood 20 percent below its 1985 level. Furthermore, per capita income fell by about 50 percent between 1986 and 1993. The loss of competitiveness also led to the loss of export markets for agricultural products and made it hard for domestic food crops and industrial products to compete with imports. This squeeze implied a decrease of demand for labor both for tradable and non-tradable goods with adverse effects on living standards for both rural and urban areas. Also, reduced economic activity combined with a slackening of tax collection crippled the ability of the state to provide services, thus worsening the impoverishment.

In 1994, the Central African Economic and Monetary Community<sup>4</sup> of which Cameroon is a member devalued the CFA franc by about 50 percent in nominal terms (30 percent real), and implemented additional trade and fiscal reforms. This presented Cameroon with an opportunity to reverse the socioeconomic downturn. The country did experience some positive growth after the devaluation, but it was only in mid 1996, after some failed stabilization and adjustment efforts, that the government showed strong commitment to meaningful policy reforms. The successful implementation of these reforms led to macroeconomic stability and an average growth rate of real GDP in the neighborhood of 5 percent between 1997 and 2000. On the basis of the 1996 and 2001 household surveys, it is estimated that the incidence of poverty fell by 13 percentage points from about 53 percent to about 40 percent. However, income inequality remained high with the Gini index of inequality decreasing only by 3 percentage points, from 44 to 41 percent. Furthermore, other social indicators have not shown such an improvement.

A shift in borrowing strategy around 1986 combined with the severity of the socioeconomic crisis left the country saddled with an unsustainable debt burden. The stock of external debt increased from less than 33 percent to more than 75 percent of GDP between 1985 and 1993 (Government of Cameroon 2003). In October 2000,

> 4 Mostly known under its French acronym CEMAC for _Communauté Economique et Monétaire d’Afrique Centrale._

3
International Capital Mobility and the Costs of Import Restraints by Jaime de Melo and David Roland-Holst

# Table of Contents

|1.|<br>Introduction|1|
|---|---|---|
|2.|<br>Qualitative Analysis of Import Restraints|2|
|3.|<br>An Ppplication to the Welfare Effects of U.S.<br>Import Restrictions|10|
|4.|<br>Conclusions|16|
|Foo|tnotes|17|
|Ref|erences|18|
|App|endix|19|
||Al.<br>Model Outline|19|
||A2.<br>Benchmarking to 1984|20|
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organizations, including municipalities, local business associations, unions, and NGOs were used to obtain lists of informal enterprises. These were then used to conduct the informal interviews. In some cases, after a successful interview with an informal business, the owner was asked to identify other unregistered businesses that fit the sample criteria, in different locations across the city. Firms from manufacturing, retail, construction, and other services sectors from ten different cities were included in the sample. Manufacturing and retail sectors each represent a quarter of the sample while construction and other services constitute 11% and 38% respectively. In terms of firm size, there were no fixed quotas for company size, however preference was given to the larger companies from the AISA lists that were used to source formal companies. Data from 647 formal and 419 informal firms were eventually included in the sample used in the paper. Majority of the informal firms are small covering 80% of the sample. Medium and large firms are 8% and 2% of the sample respectively. Kabul has the highest number of firms representing a quarter of the sample. The rest of the firms are roughly equally distributed across the remaining four regions.

# **3.1 Stylized Facts of Afghan Enterprises**

Before presenting the multivariate analysis that studies the relationship between risk and uncertainty and firm’s characteristics, this section examines some salient features of firms in Afghanistan both in the formal and informal sectors.

Table 2 provides a summary of features of Afghan firms in the sample. The first panel looks at some of the more general firm characteristics. Starting with informal firms in 2008, the table shows that most of them are 10 years of age or younger (75% of the sample). A similar picture can be seen for formal sector firms, where 80% of the firms are reported to be less than 10 years of age. The sample for 2014, on the other hand, has a relatively lower age of firms that are less than 10 years of age with 40% of firms greater than or equal to 10 years. It is possible that the selectivity of larger firms from the AISA lists may be partly responsible for the high share of old firms in 2014. Regarding manager’s years of experience, firms (both formal and informal) in 2008 and 2014 have on average 13 years of experience. Female ownership of businesses is very low in Afghanistan, with less than 1.5% informal firms being womenowned. In the formal sector, this ratio was around 4.5% and 2.7% in 2008 and 2014 respectively. Similarly, the share of firms with female managers is also very small, with no more than 2% in 2008 in both formal and informal sectors. The share is higher in 2014 at 4.2% but remains very low. In terms of investment undertaken by firms, the levels were higher in 2008 when 28% informal and 40% formal firms reported having undertaken investment. In 2014, only 28% formal firms indicated making investments.

The next panel highlights the level of access to finance by firms and shows that a little less than a quarter of informal firms have an account compared to 43% of formal firms that claim to have a bank 10
# Short-Term Impact of Brexit on the United Kingdom’s Export of Goods<sup>�</sup>

Hiau Looi Kee<sup>y</sup> Alessandro Nicita<sup>z</sup> World Bank UNCTAD Key Words: Overall Trade Restrictiveness Index, Brexit JEL Classi...cation Numbers: F10, F14

> �The preliminary ...nding of this paper was ...rst presented at the World Bank’s Policy Research Talk on Sep 27, 2016, titled “Trade, FDI and Global Value Chains.” We thank Chad Bown, Aaditya Mattoo and participants in World Bank’s Policy Research Talk for feedback and comments. The results and opinions present in this paper are our own, and do not necessarily represent the views of the World Bank and UNCTAD, their Executive Directors, or the countries they represent.

> yDevelopment Research Group, The World Bank, Washington, DC 20433, USA; Tel.: (202) 473-4155; Fax: (202)522-1159; e-mail: hlkee@worldbank.org.

> zUNCTAD. Email: Alessandro.Nicita@unctad.org.
# **Early Development Economics Debates Revisited**

Michele Alacevich<sup>∗</sup>

> ∗ I would like to thank Pier Francesco Asso, Jean-Jacques Dethier, Valentina Kalk, Paolo Ramazzotti, Paul Streeten, and Paola Villa for their useful comments and insights on a previous version of this paper, and the staff of the World Bank Group Archives and of the Rare Book, Manuscript, and Special Collections Library of Duke University, Durham, NC, for their invaluable assistance. The usual disclaimer applies. This paper is part of a larger research on "The Evolution of Development Economics and the Role of the World Bank: Archival Inquiries 1948-1968", supported by the Research Committee and the Archives of the World Bank.
Note that this welfare measure does not take into account changes in prices (Cline 1996). Because of trade, price changes are more likely to depend on global production than local production. Unless temperatures warm well above 4<sup>◦</sup> C, climate change is not expected to change global production and therefore global agricultural prices noticeably (Reilly et al. 1996). The omission of prices is therefore likely to be of second order importance. However, if local prices were to change because of local conditions, the welfare estimate from the Ricardian model will overestimate the size of the revenue change. For example, if production falls, prices will rise, and so the true revenue will fall less than what the Ricardian model predicts.

# 3. Description of Data

The FAO has developed a typology of AEZs as a mechanism to classify the growing potential of land (FAO 1978). The AEZs are defined using the length of the growing season. The growing season, in turn, is defined as the period where precipitation and stored soil moisture is greater than half of the evapotranspiration. The longer the growing season, the more crops can be planted (or in multiple seasons) and the higher are the yields (Fischer and van Velthuizen 1996, Vortman et al. 1999). FAO has classified land throughout Africa using this AEZ concept. Our study will use these FAO defined AEZ classifications.

The economic data for this study were collected by national teams (Dinar et al 2008). The data were collected for each plot within a household and household level data was constructed from the plot level data. In each country, districts were chosen to get a wide representation of farms across climate conditions in that country. The districts were not representative of the distribution of farms in each country as there are more farms in more productive locations. In each chosen district, a survey was conducted of randomly selected farms. The sampling was clustered in villages to reduce sampling cost. All economic data were collected in national currency and converted to USD using official exchange rates.

A total of 9597 surveys were administered across the 11 countries in the study. The number of surveys varied from country to country. Not all the surveys could be used. Some surveys contained incorrect information about the size of the farm, cropping area or some of the farm operating costs. Implausible values were treated as missing values. It is not clear what the sources of these errors were but field and measurement errors are most likely. They may reflect a 5
qualifications, and the survey report notes that estimates of teacher academic credentials also reflect the impact of policies implemented more than a decade ago (UNICEF 2022).

This difference in teachers’ qualifications is confirmed by data supplied by Ministries of Education to UNESCO Institute of Statistics (UIS). In these data, countries report on the percentage of their teachers who have met their country’s “minimum required qualifications”.<sup>4</sup> The data shows that in East Asian countries, almost all primary education teachers have met the minimum qualifications required to enter the profession, with a few exceptions. However, many Pacific Island countries have a significant shortage of qualified primary teachers (again, as defined by countries’ own definition of what are these qualifications). For instance, in the Solomon Islands, around 25% of primary teachers do not have the minimum qualifications, while in Micronesia, two-thirds of teachers lack the necessary requirements.

Figure 1. Share of teachers with minimum required qualifications in primary education, 2015–2019, median, % <!-- Start of picture text --> 99.5 99.7 100.0 100.0 100.0<br>97.0<br>100.0 95.3<br>92.3 92.9 93.3<br>90.0<br>78.3<br>80.0 74.2 75.4 75.8<br>70.0<br>60.0<br>50.0<br>38.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>%<br><!-- End of picture text --> Source: Authors’ calculation using UNESCO Institute of Statistics database. Note: Blue: East Asian countries; Yellow: Pacific Island countries.

Taken together, many countries face the dual challenge of (i) upgrading the qualifications of existing teachers (to at least meet minimum requirements); and (ii) improving the quality of the preservice teacher training provided (to a growing number of teachers) to ensure that all future teachers meet minimum

> 4 Specifically, the indicator shows: “Percentage of teachers by level of education taught (pre-primary, primary, lower secondary and upper secondary education) who have received at least the minimum organized pedagogical teacher training pre-service and in-service required for teaching at the relevant level in a given country, in a given academic year.”

3
- 14 -

1.15 Positive Features of Urbanization. On the positive side, the progressive concentration of population in utrban areas, especially in the larger cities, has contributed to the development of national human resources. It has afforded economies of scale from large sunk invescments (at least in the early stages). It has expanded domestic markets, with vast pools of demand stimulating the growth of new agricultural as well as non-agricultural activities. It has promoted specialization of labor and induced technological innovation. In fact, strong correlations can be drawn between urbanization and per capita income (.70). As can be seen in Table 1.15 the more urbanized countries in LAC tend to have higher per capita GNP. Moreover, per capita value added tends to be higher in the larger cities.

1.16 Resource Mobilization Potential. On the revenue side, it is also increasingly recognized that urban governments could make a greater contribution to domestic resource mobilization. True enough, the increase in overall taxable capacity associated with rising urbanization is not easily tapped by the central government, especially in Latin America, as the city dwellers tend to resist payment of new taxes for which they cannot readily identify local benefits. It must be recognized also that comparisons with the much higher returns from urban property and service taxes which are being reaped in some of the developed countries, could never be attained in LAC countries. This is due not only to their comparatively lower fiscal effort (which could be improved) but also to an objectively lower taxable capacity. These points will be developed in Section 4 of the report.

Table 1.15: PER CAPITA INCOME AND URBANIZATION TREND

-GNP in Current US Dollars-

||1976|1977|1978|1979|1980|1981|1982|1983|1984|
|---|---|---|---|---|---|---|---|---|---|
|LAC SAMPLE||||||||||
|GNP Per Capita a/|1307|1406|1596|1862|2136|2373|2058|1699|1673|
|X Urban/Total Population|58.9|59.5|60.0|60.5|61.0|61.6|62.2|62.7|61.6|
|MARKET ECONOMIES||||||||||
|GNP Per Capita_a/_|7229|7940|8811|10046|11350|12638|13153|13812|14962|
|X Urban/Total Population|76.9|77.0|77.1|77.2|77.3|77.4|77.6|77.7|77.8|

a/ Weighted average.

Sources: United Nations National Accounts. The World Bank, Bank of Economic and Social Data.
# Crossing Boundaries: Gender, Caste and Schooling in Rural Pakistan

Hanan G. Jacoby Ghazala Mansuri<sup>∗</sup> JEL codes: I21, J16, O15, Z13 Keywords: Social Fragmentation, Stigma, Demand for Education, Inequality of Opportunity

> ∗The World Bank, 1818 H St. NW, Washington DC 20433. e-mail: hjacoby@worldbank, gmansuri@worldbank.org. The data used in this study were collected by the Pakistan Institute of Development Economics, funded by a grant (no. 84150) from the World Bank Research Support Budget, both of which we gratefully acknowledge. Thanks also to Salma Khalid for excellent research assistance, Naghma Imdad for help with the caste questions in PRHS-II, and to Jishnu Das, Chico Ferreira, and Sylvie Lambert for comments on an earlier draft. The views expressed in this paper are those of the authors and should not necessarily be attributed to the World Bank, its executive directors, or the countries they represent.
# _Discussion_

The median prevalence of any functional difficulty among adults age 15 and older stands at 10% in the countries under consideration. This result is overall consistent with earlier estimates of prevalence among adults for LMICs (WHO-World Bank 2011; Mitra and Sambamoorthi 2014).

The prevalence of at least a lot of difficulty ranges from under 1% in Vanuatu to 6% in Papua New Guinea. The median household level prevalence is at 23%, which is consistent with earlier estimates (e.g. Mitra 2018). Overall, these results show that functional difficulties affect sizeable shares of individuals and households in LMICs.

They tend to affect older individuals and people in rural areas more, as well as women more often than men. More research on gender is needed given the higher prevalence found among women in this study and in other studies (WHO-World Bank 2011; Mitra and Sambamoorthi 2014). Functional difficulties are significantly associated with aging in the countries with increasing prevalence for older age groups. More research is also needed on older adults in LMICs, for whom knowledge about their situation is scarce.

The prevalence estimates in this paper likely offer a lower bound estimate of prevalence given that only four to six functional difficulties are measured. More data collection efforts are needed for instance using the WG Enhanced short set or the Extended set of questions of the WG to capture psychosocial and mental health-related functional difficulties. Further, the use of the WGSS or similar questions can address issues of prevalence and, through disaggregation, inequality. They do not, however, address questions regarding why such differences might exist. Surveys that can collect detailed information on the environment such as the Model Disability Survey (Cieza et al 2018) would provide information to help understand the determinants of functional difficulties.

Because functional difficulties affect sizeable shares of individuals and households in LMICs, a study of the correlation of such difficulties with inequalities is thus warranted and is presented in the rest of this paper.

# **Educational attainment**

Next, we present several educational attainment indicators. We start with the share of adults who have ever attended school in Figure 4.1. Across the three disability categorizations, results consistently point at significantly lower rates of having ever attended school for adults with functional difficulties. For instance, in Indonesia, 57% of persons with any difficulty have ever attended school compared to 92% of persons with no difficulty.

16