rafmacalaba/datause-encoder-data huggingface dataset:
7

# Host Countries and Regions

FDI in the developing world has consistently been concentrated in a small number of individual countries. This was true in an earlier era, when investmentd in the primary sector predominated, and it has also been true in recent years, when manufacturing investments have been more common. Thus, a few economically large or upper-middle-income countries in Latin America and Asia have been the principal recipients, and the regional distributions generally reflect these country concentration patterns.

Table 2-4 lists the escimated regional distributions of FDI stocks. In 1960 orn-half of the FDI stock in developing countries was in Latin America, with slightly less than one-fourth in Asia. During the 1960s the share in Latin America irncreased and the share in Asia decreased, while other regions' shares did not change significantly. Then, during the 1970s, the Asian share increased, while the Latin American and Africarn shares decreased--trends that continued in the _1sOs._ Table 2-4. FDI Stocks by Region of Host DevelopLng Countries, Selected Years (percent of all developing countries)

|Rep-ion|1960|1971|1980|
|---|---|---|---|
|Latin America|48.3|57.6|53.1|
|Africa|17.0|17.1|10.6|
|Asia|23.3|15.2|25.8|
|Middle East|8.5|6.8|3.5|
|Europe|2.8|3.3|7.1|

Source: Stopford and Dunning (1983), as reported in OECD (1987), table 3-2, p.<sup>187</sup> .
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Do African Countries Pay More for Imports?

## Yes

# by

## Alexander J. Yeats

## Table of Contents

|1.|Introduction|1|
|---|---|---|
|II.|The Methodological Approach|4|
|III.|TheEmpirical Findings|9|
|IV.|Correlation Analysis of Unit Value<br>Differences|14|
|V.|Additional Evidence on the Extent of<br>Discriminatory<br>Pricing|19|
|VI.|Summary and Policy Implications|20|
|Ref|erences|24|
|App|endices|26|
**Table 3: Upward and Downward Mobility in Education and Occupation, relative to fathers**

||**Education**|**Occupation**|
|---|---|---|
||(% ofpopulation > 30yrs.)|(% ofpopulation)|
|**Up**|61.9|43.6|
|**Same**|29.8|46.8|
|**Down**|8.3|9.6|

_Source_ : World Bank Staff estimates based on data from Perceptions of Poverty, Prosperity and Economic Mobility in Nepal, 2014.

Prospects for upward mobility have been increasing Nepal for both education and occupation. In Figure 1 we show the likelihood of doing better than one’s father by individuals grouped with 10-year age cohorts. The sample sizes for some age groups are small and thus the estimates are fairly noisy but point estimates suggest that while the likelihood of being better educated than one’s father was below 50 percent for those born before 1965, the number is closer to 70 percent for the cohort born between 1985 and 1995. The pattern is similar for occupation as well, except there is a discontinuous jump (roughly 10 percentage points) in predicted probability of upward mobility for the 1975-1985 birth-cohort in comparison to the earlier cohorts. One possible explanation for this is the fact that in contrast to the earlier cohorts, this group of children would have been in school around the time Nepal’s economy started liberalizing (1990-1994) and thus may have been in a favorable position to seize the opportunities created by the consequences of this liberalization. In any case, younger cohorts have been more upwardly mobile than the cohorts born before 1975 who appear to have a low steady state transition probability of around 33-34 percent of doing better than their father.

**Figure 1: Upward Mobility by Age Cohorts**

(a) Better educated than father <!-- Start of picture text --> 1985-1995 1975-1985 1965-1975 Before 1965<br>.7<br>.65<br>.6<br>.55<br>Predicted probability<br>.5<br>.45<br><!-- End of picture text --> ( _Continues in next page_ ) 3
-21-

Table 4 The Relation between Firm Speculative Asset Holdings and Subsidized Loans with Exogenous Loan Supply: 1984 through 1988 (Dependent variable: ratio of investment in speculative assets to the beginining of year book value of total assets)

||1984|1985|1986|1987|1988|
|---|---|---|---|---|---|
|Subsidized loans (Pi)|0.254<br>(4.26)|0.175<br>(5.63)|0.0612<br>(4.74)|0.2S9<br>(9.10)|0.193<br>(3.02)|
|Constant: regime I (Po)|0.0271<br>(2.59)|0.0219<br>(2.82)|0.0243<br>(7.01)|0.0178<br>(3.58)|0.0444<br>(4-54)|
|Constant: regime II (a)|0.0501<br>(11.98)|0.0219<br>(10.42)|0.0141<br>(6.25)|0.0278<br>(8.12)|0.0373<br>(6.61)|
|Proportion of frms in||||||
|regime I (X)|0.527<br>(30.23)|0.488<br>(28.59)|0.441<br>(27.44)|0.423<br>(25.93)|0.363<br>(22.32)|
|Number of finns|241|240|238|238|241|

Absolute values of t- statistics are in parentheses.
raise the following question: how different are the selected firms compared with all firms in the population? For example, are the selected firms more or less productive than others? We compare the productivity of the selected firms with that of all the firms in the sample (Table 2). The statistics show, in general, the difference in productivity between selected firms and all firms in the sample is significant. As expected, this difference is negative in a significant manner for several sub-groups, implying that the selected firms are likely those with higher productivity. The following sections focus on the firms that survived for at least five consecutive years.<sup>5</sup>

**Table 2 – Average output per worker**

||||2001|||||2005|||
|---|---|---|---|---|---|---|---|---|---|---|
||All|Sltd||||All|Sltd||||
||<br>firms|eece<br>firms||Differ|ence|<br>firms|eece<br>firms|D|iffere|nce|
|Total|218.4|227.3|-8.9|***|(-2.93)|346.5|367.1|-20.5|***|(-4.86)|
|Firm size|||||||||||
|Less than 50|357.9|402.9|-44.9|***|(-3.05)|517.9|708.8|-190.9|***|(-10.42)|
|50-100|206.8|281.8|-75.0|***|<br>(-15.33)|301.6|391.2|-89.6|***|<br>(-14.85)|
|100-500|153.4|191.2|-37.7|***|(-12.94)|247.1|300.2|-53.1|***|(-12.08)|
|More than 500|153.1|178.9|-25.8|***|(-5.59)|282.7|321.4|-38.7|***|(-4.41)|
|Firm’s age|||||||||||
|<br>Less than 5 years|263.9|287.7|-23.9|***|(-2.86)|371.4|389.8|-18.4|**|(-2.19)|
|6-10 years|243.7|305.9|-62.2|***|(-10.26)|347.5|418.5|-71.0|***|(-6.97)|
|11-20 years|185.0|213.5|-28.5|***|(-5.27)|298.6|362.1|-63.6|***|(-9.86)|
|<br>More than 20 years|100.8|112.6|-11.8|***|(-5.11)|194.0|227.0|-32.9|***|(-5.39)|
|Main part of paid-in capital|||||||||||
|State capital|128.5|120.0|8.5||(1.40)|269.3|320.1|-50.8|**|(-2.18)|
|<br>Collective capital|226.6|223.7|2.9||<br>(0.39)|360.5|370.1|-9.6||<br>(-0.62)|
|Corporate capital|251.2|249.4|1.7||(0.17)|376.9|401.0|-24.1|***|(-2.58)|
|Personal capital|228.9|222.1|6.8||(1.11)|329.7|332.2|-2.5||(-0.56)|
|Hong Kong SAR, China;|||||||||||
|<br>Macao SAR, China; and|||||||||||
|Taiwan, China, capital|266.3|288.1|-21.8||(-1.35)|312.3|349.0|-36.7|***|(-2.59)|
|<br>Foreign capital|375.7|467.4|-91.6|***|<br>(-3.71)|469.2|602.6|-133.4|***|<br>(-4.96)|
|Region|||||||||||
|<br>Inland province|147.9|142.9|5.0||(1.41)|303.6|286.9|16.6|***|(2.87)|
|<br>Coastal province|254.0|261.6|-7.6|*|(-1.84)|362.8|391.1|-28.3|***|(-5.34)|
|Number of observations|117457|49375||||192734|78330||||

Note: t-statistics in brackets. *** significant at 1%; ** significant at 5%; * significant at 10%. The selected firms are those that survived at least 5 years.

# **4 Sources of change in productivity volatility**

> 5 Given the difference between this subsample of firms and the full sample, the results should be interpreted with caution and caveat.

11
| POLICY RESEARCH WORKING PAPER 2752

# Summary findings

Deininger and Chamorro use data from Nicaragua to examine the impact of the award of registered and nonregistered title on land values and on investments attached to land. They find that receipt of registered title increases land values by 30 percent and greatly increases the propensity to invest, bringing investment closer to the optimum.

Consistent with descriptive statistics indicating great demand for regularization of land rights, especially from the poor, this finding suggests that titling can have a positive distributional effect. Of overriding importance, however, are the legal validity and official recognition of the titles issued.

This paper-a product of Rural Development, Development Research Group-is part of a larger effort in the group to explore the impact of land policies on household welfare and productivity. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Maria Fernandez, room MC3-542, telephone 202-473-3766, fax 202-522-1151, email address mfernandez2@worldbank.org. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at kdeininger@worldbank.org or jscja,prrp@hotmail.com. January 2002. (29 pages) _The Policy Researcb Working Paper Seoies disseminates iork the findings of in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polisbed. The papers carry the names of the autbors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the vieuJ of the World Bank, its Executive Directors, or the countries they represent._ Produced by the Policy Research Dissemination Center
Policy Research Working Paper 10633

# **Abstract**

This paper investigates the relationship between disruptive technologies and access to finance for digital tech firms in Africa. Through textual analysis of data from Crunchbase and Pitchbook, the study explores how firms across different age cohorts incorporate disruptive technologies into their offerings in e-commerce, fintech, and information technology services. The findings reveal three key insights for African digital tech startups. First, African startups are less likely to incorporate disruptive technologies into their offerings compared to other regions, except for mobile payments. Second, incorporating these technologies is associated with more funding, but this link is weaker in Africa than in other regions. These results hold when excluding mobile payments and addressing potential endogeneity using instrumental variables. Third, firms that do incorporate disruptive technologies tend to secure funding earlier, with lower initial amounts, but are more likely to succeed in terms of exit or valuation growth than their peers.

This paper is a product of the International Finance Corporation. It is part of a larger effort by the World Bank Group to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at marciocruz@ifc.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank. org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
that this is the only significant difference found between the treatment arms, it could be a spurious effect.

Seventy-one percent of all women (those with recent pregnancies and ‘baseline women’) reported meeting with a CHW in the preceding three months either in their communities or at home. Forty-five percent reported having such meetings in their own home. As seen in Panel C of Table 7, there are small and statistically insignificant differences in these rates between the different treatment arms.<sup>9</sup> The follow-up survey included a module in which the CHWs reported their satisfaction regarding different features of their role. Overall, high levels of satisfaction were reported as can be seen in Panel A of Table 8. Above 98 reported being satisfied or very satisfied with their role overall, with their relationship with the health center staff and with the respect they receive from their communities. High but slightly lower rates of satisfaction were recorded with respect to their ability to meet the needs of the communities and aspects related to the cooperative. About 80 percent were satisfied with the work ethics and knowledge of the other cooperative members while about three-quarters of CHWs were satisfied with the way decisions are being made by the cooperative and their ability to influence those decisions. About half of the CHWs were satisfied with the support (financial or other) they receive from the community. Only 21 percent reported being satisfied with their compensation. As seen in Table 8, there is no apparent relationship between satisfaction overall and the CHW cooperative performance payments. For only one of the 10 categories is there a statistically significant difference at the 10 percent level.

To assess the motivation of CHWs, the survey respondents were also asked to state whether they agree with a list of statements related to their work as community health workers. The outcome measure used in the regressions presented in Panel B of Table 8 is whether the respondents agreed or strongly agreed with each of the statements. There is very little variation in responses, with almost all CHWs agreeing with statements such that the role as CHW makes them feel good about themselves, that they are proud to serve their communities and that they are feeling motivated to work as hard as they can. There are negligible differences in responses of CHWs in the different treatment arms.

> 9 Because of the unbalanced attrition of the baseline women, we also ran the regressions using only the sample of recently pregnant women. Also for that group there are no statistically significant differences between the groups.

14
# 22. Please rate your overall perception of:

<!-- Start of picture text --> 22. Please rate your overall perception of: .'<br>45<br>very very 40<br>g-Ood poor "<br>a. The efficiency p3<br>of customs 1 2 3 4 5 6 25 il3 ---I a.<br>b. The general condition 20<br>of roads you use 1 2 3 4 5 6 _5<br>c. The efficiency of mail<br>10<br>delivery 1 2 3 4 5 6<br>d. The quality of public<br>health care provision 1 2 3 4 5 6 Customs Condition Mail Quality<br>are of roads delivery of public<br>efficient are bi health is<br>sufficiot officion sufficient<br><!-- End of picture text -->

**Percentage of firms who ticked (1), (2), or** (3) **for question** 22

The overall picture shows that in none of the four polled services did governments satisfy even 50 percent of the surveyed businessmen. The highest "satisfaction ratio" rested in mail delivery that was considered efficient by 48 percent of respondents worldwide (country average based estimate) and by 44 percent of respondents in developing countries. The lowest satisfaction rate with any government service was with public health. This service was evaluated as efficient by less than 30 percent of businessmen worldwide and by 21 percent of businessmen in developing countries.

# a. The efficiency of customs

In developed countries almost 70 percent of businessmen found customs to be efficient. In all other regions this indicator was at least 25 percentage points lower (41 percent in SSEA). Only 19 percent of surveyed businessmen in CIS evaluated their customs as efficient.

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>3t<br>20<br>0+<br>World LDC DC SSEA MNA CEE LAC SSA CIS<br><!-- End of picture text -->

**Percentage of firms who ticked (1), (2), or (3) for question 22.a**

39
# **2. Background**

# **2.1. HIES rounds sampling design prior to the HIES 2016/17**

The HIES rounds have been central for monitoring poverty and welfare indicators in Bangladesh. Prior to the HIES 2016/17, the HIES rounds were designed to produce annual poverty estimates at the division level for urban and rural areas and Statistical Metropolitan Areas (SMAs) (see Munoz 1998 and 2004). The sample size of the HIES gradually increased over time and, as of the latest round completed in 2010, the sample size was 12,240 households. To avoid the effects of seasonality, the sample was proposed to be randomly split across quarters – a full round of the HIES data collection takes 12 months – but there was never an explicit attempt by BBS to produce quarterly poverty estimates before the HIES 2016/17.

All of the previous HIES rounds follow a stratified clustered sampling design – each of urban, rural, and SMA areas in a division forms a stratum. Primary Sampling Units (PSU) are constructed based on Census of Population and Housing Enumeration Areas and represent a cluster. PSUs are selected with probability proportional to size (PPS) and 20 households from each PSU are selected using systematic sampling.

It is worth noting that PSUs for all of the previous rounds of the HIES were selected from the Integrated Multiple-Purpose Sample (IMPS). The IMPS is a master sample updated after each Census of Population and Housing, and which has been used in Bangladesh as the sampling frame for the selection of Primary Sampling Units (PSUs) in all previous rounds of the HIES and other surveys such as the Labor Force Surveys and Demographic and Health Surveys.

The HIES 2016/17 deviates from the sampling design used in the previous rounds of the HIES in several ways. First, the objectives of the HIES 2016/17 have changed significantly from the one used to design the sample in previous rounds. Second, the HIES 2016/17 did not use the IMPS for the selection of PSUs. Third, the formulas used for the estimation of the optimal sample size and the allocation of the sample across strata were derived to explicitly take into account the clustered design of the sample.

5
**Table 8**

**Cross-national Growth Regressions 1994-2004 - Restricted Sample: Non-high-income Economies**

||(1)<br>**OLS**|(2)<br>**OLS**|(3)<br>**OLS**|(4)<br>**OLS**|(5)<br>**OLS**|(6)<br>**IV**|(7)<br>**IV**|(8)<br>**IV**|(9)<br>**IV**|(10)<br>**IV**|
|---|---|---|---|---|---|---|---|---|---|---|
|Log of GDP pc 1994|-0.010|-0.020***|-0.023***|-0.022***|-0.022***|-0.015*|-0.021**|-0.025***|-0.025***|-0.025***|
||(0.008)|(0.007)|(0.007)|(0.007)|(0.007)|(0.009)|(0.008)|(0.008)|(0.008)|(0.008)|
|Log of EXPY 1994|0.044**|0.036**|0.032**|0.030**|0.035**|0.058***|0.041**|0.040*|0.040*|0.049**|
||(0.017)|(0.014)|(0.015)|(0.014)|(0.016)|(0.021)|(0.019)|(0.020)|(0.020)|(0.022)|
|Log of % secund. school att. 1985||0.011**|0.008|0.008|0.009||0.011**|0.007|0.007|0.008|
|||(0.004)|(0.005)|(0.005)|(0.005)||(0.005)|(0.005)|(0.005)|(0.005)|
|Rule of Law Index 1996|||0.012**|0.012**|0.012*|||0.012**|0.012**|0.012*|
||||(0.005)|(0.006)|(0.006)|||(0.006)|(0.006)|(0.006)|
|Domestic credit provided by banking<br>sector, % of GDP||||0.000|0.000||||0.000|0.000|
|||||(0.000)|(0.000)||||(0.000)|(0.000)|
|Diversification (1 - Herfindahl Index)|||||-0.021|||||-0.031|
||||||(0.035)|||||(0.036)|
|Constant|-0.295***|-0.169**|-0.104|-0.090|-0.117|-0.380***|-0.203*|-0.154|-0.153|-0.203|
||(0.103)|(0.082)|(0.101)|(0.095)|(0.105)|(0.130)|(0.110)|(0.128)|(0.128)|(0.143)|
|Observations|45|33|33|33|33|45|33|33|33|33|
|R-squared|0.26|0.35|0.46|0.46|0.47|0.24|0.35|0.45|0.45|0.46|
|First stage F-statistic of excluded<br>instrument p-value (H0:<br>underidentified/weakly identified)||||||0.000|0.000|0.000|0.000|0.000|
|Hansen J statistic p-value (H0:<br>overidentified)||||||0.037|0.011|0.007|0.007|0.003|
|Stock-Wright S-statistic p-value (H0: all|||||||||||
|endogenous regressors==0 and overid.<br>rest valid, weak-instrument-robust<br>inference)||||||0.027|0.011|0.011|0.008|0.008|
|Robust standard errors in parentheses<br>* significant at 10%; ** significant at 5%; *** sig<br>Instruments for IV regressions: log of populatio|nificant at 1%<br>n 1994 and log of la|nd area|||||||||

33
# **Appendix A: Monthly temperature and precipitation anomalies**

# **Figure A1: Monthly temperature anomalies for 18 meteorological stations in Bolivia, 1948-2008**

<!-- Start of picture text --> CAMIRI<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius)<br><!-- End of picture text --> <!-- Start of picture text --> CHARAÑA<br>7.0<br>5.0<br>3.0<br>1.0<br>-1.0<br>-3.0<br>-5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius)<br><!-- End of picture text --> <!-- Start of picture text --> COBIJA<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Temperature Anomalies compared to<br>Average for 1960-1990 (degrees Celsius)<br><!-- End of picture text -->

## **CONCEPCION**

<!-- Start of picture text --> 7.0<br>5.0<br>3.0<br>1.0<br>-1.0<br>-3.0<br>-5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius)<br><!-- End of picture text --> <!-- Start of picture text --> COCHABAMBA<br><!-- End of picture text --> <!-- Start of picture text --> 4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>LA PAZ/EL ALTO<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>M ar-48 Aug-53 Feb-59 Aug-64 Feb-70 Jul-75 Jan-81 Jul-86 Dec-91 Jun-97 Dec-02 Jun-08<br>Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius)<br>Temperature Anomalies compared to Average for 1960-1990 (degrees Celsius)<br><!-- End of picture text --> 20