rafmacalaba/datause-encoder-data huggingface dataset:
Figure C5: Distribution of poor population across urban versus rural areas <!-- Start of picture text --> (A) $2.15 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>(B) $3.65 poverty line<br>Official definition DOU DB<br>100 100 100<br>80 80 80<br>60 60 60<br>40 40 40<br>20 20 20<br>Urban Rural Urban center Rural Core Town<br>Urban cluster Suburb Other rural<br>Share of poor population (%)<br>Share of poor population (%)<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: WorldPop 250m is used for the DOU and DB methods. Each boxplot shows the distribution of the share of the poor population over different geographic areas in 20 countries.

Figure C6: Comparison of urban shares of poor populations between official and DOU/DB urban definitions <!-- Start of picture text --> (A) DOU vs. official definition (B) DB vs. official definition (C) DB vs. DOU<br>100 BGD 100 100<br>EGY EGY EGY<br>80 80 80<br>60 60 60<br>TCDETH TZA GAB TCD ETHGABTZA<br>UGA NERUGA NER UGA<br>40 TZA AGOGABCOL 40 MWI LSO AGO COL 40 LSOMWI AGOCOL<br>ETH CIV BFA SENMRC TI V BFAMRTSENCIV<br>20 BFANERVNMGINMWITCDGNBSENLSOMRTGHA 20 GINGNBBGDGHA 20 GINGNBGHA BGD<br>VNM VNM<br>20 40 60 80 100 20 40 60 80 100 20 40 60 80 100<br>Share of poor in urban area (%) Share of poor in urban area (%) Share of poor in urban area (%)<br>Official definition Official definition DOU<br>DOU DB DB<br>Share of poor in urban area (%) Share of poor in urban area (%) Share of poor in urban area (%)<br><!-- End of picture text --> Source: International Urban Poverty Database.

Note: WorldPop 250m is used for the DOU and DB methods. Dashed lines are 45-degree lines. Urban areas include the categories “Urban center” and “Urban cluster” for the DOU method and the categories “Core” and “Suburb” for the DB method. Dashed lines are 45-degree lines. Poverty is measured using the $2.15 poverty line.

73
# **Table 2:  Distribution of Data Points by Type of Data in 2008 WGI**

||**Commercial**<br>**Business**<br>**Information**<br>**Providers**|**Surveys of**<br>**Firms or**<br>**Households**|**Non-**<br>**Governmental**<br>**Organizations**|**Public Sector**<br>**Organizations**|**_Total_**|
|---|---|---|---|---|---|
|**_Number of Data Points_**<br>||||||
|Voice and Accountability|677|385|794|367|2223|
|Political Stability/Absence of Violence|1087|189|0|327|1603|
|Government Effectiveness|738|398|321|398|1855|
|Regulatory Quality|801|219|304|427|1751|
|Rule of Law|920|420|474|743|2557|
|Control of Corruption|919|513|295|398|2125|
|**_Total_**|5142|2124|2188|2660|12114|
|**_Shares of Total for Each Indicator_**||||||
|Voice and Accountability|0.30|0.17|0.36|0.17|1.00|
|Political Stability/Absence of Violence|0.68|0.12|0.00|0.20|1.00|
|Government Effectiveness|0.40|0.21|0.17|0.21|1.00|
|Regulatory Quality|0.46|0.13|0.17|0.24|1.00|
|Rule of Law|0.36|0.16|0.19|0.29|1.00|
|Control of Corruption|0.43|0.24|0.14|0.19|1.00|
|**_Total_**|0.42|0.18|0.18|0.22|1.00|
|**_Weighted Shares of Total for Each Ind_**|**_icator_**|||||
|Voice and Accountability|0.31|0.02|0.56|0.11|1.00|
|Political Stability/Absence of Violence|0.78|0.04|0.00|0.17|1.00|
|Government Effectiveness|0.59|0.12|0.07|0.22|1.00|
|Regulatory Quality|0.47|0.06|0.17|0.29|1.00|
|Rule of Law|0.53|0.10|0.14|0.23|1.00|
|Control of Corruption|0.50|0.16|0.13|0.22|1.00|
|**_Total_**|0.53|0.08|0.18|0.21|1.00|

30
A panel data set would allow us to account for both idiosyncratic effects assuming that they were fixed over time.<sup>3</sup> The Post Harvest Survey, however, is a repeated cross section of farmers. We thus need additional modeling to deal with the unobserved effects. We propose to model agricultural productivity in maize to control for η (and the year effects It, as discussed above) and to model the share of land devoted to cotton to control for φ. In what follows, we discuss these two methodological features of our empirical model.

# Productivity in Maize Farming

Our empirical analysis relies on a modified difference-in-differences approach. The simple difference in average cotton productivity after controlling for farm level variables in any two of the reform phases (i.e., the introductory, the failure, and the success phases) is not a consistent estimate of the impacts. It does not take into account the general trend or time-varying aggregate effects in agriculture, It. In addition, there are unobserved idiosyncratic farm effects that can affect inputs choices, ηt. To account for these household and agricultural effects, we perform a difference-in-differences analysis using a model of maize productivity.

In principle, the second differencing works because maize is a major food crop that is produced by virtually all cotton farmers.<sup>4</sup> Table 5, which reports the percentage of households that grow maize, provides evidence supporting this. We find that in the cotton provinces, maize is grown by virtually all households. Participation in maize production is always above 90 percent in the relevant regions. In the Eastern and Lusaka provinces, the percentage of maize producers is nearly 100 percent. Table 6 reports additional evidence that further supports our differencing strategy. We report the percentage of farmers that grow maize, conditional on being cotton growers. These shares are nearly 100 percent in the three main cotton-growing provinces.

> 3The unobservables η and φ are indexed by ht because, given the cross-sectional nature of the data, the unit of observation is a household-time period combination. However, if the data were a panel, η and φ would be indexed by h only.

> 4A key characteristic of cotton farming in Zambia is its scale: cotton is grown by smallholders, family farms endowed with small farms, usually smaller than four hectares and with an average size of around 2 hectares.

12
As in several previous studies (Yang 2007), we use cross-country (panel) fixed effects regression. The fixed effects control for unobserved country specific heterogeneity. Our analysis differs from the previous works in that we have used a large subsample of developing countries (129 countries) for which the data is available. Also this is one of the first studies on the determinants of the remittance flows to explicitly introduce emigrant stocks as a share of the home country population.

# **_3.3 Results_**

The cross-country results show that remittances increase in response to disasters, especially for countries that have larger stocks of migrants abroad. For every $1 disaster cost, remittances would increase by $0.5 (-2.0 + 24.6*0.10) for a country where the emigrant stock is about 10 percent of the origin country population (see table 2). In the subsequent year, the increase would be an additional $1 (-1.97 +29.7*.10). Over a period of two years, remittances for such a country would increase by $1.5.

# **Table 2: Remittances increase in response** **<u>to disasters</u>**

||Disast|er variable|
|---|---|---|
|Dependent variable:|Disaster|People affected/|
|Remittances as share of GDP|cost/GDP|population|
|Disaster variable|-2.00|-0.01*|
|Disaster variable lagged|-1.97|-0.01**|
|Disaster variable x Emigrant<br>stock/origin country population|24.6|0.06***|
|Disaster variable (t-1)x Emigrant<br>stock/origin country population|29.7*|0.06|
|Lagged Remittances/GDP|0.81***|0.80***|
|Observations|3,682|3,682|
|R-squared|0.87|0.88|

* significant at 10%; ** significant at 5%; *** significant at 1% Source: Authors’ estimations based on International Emergency Disasters Database (EM-DAT) and World Development Indicators (WDI), World Bank.

Second, for a country with 10 percent emigrant stock as a share of population, for each 1 percent of population affected by a disaster, remittances would increase by 0.5 percent of GDP contemporaneously and by another 0.5 percent in the next year. Over a period of two years, remittances to that country would increase by 1 percent of GDP.

9
**Figure 14. Fertility and Pensions**

**Figure 15. Productivity and Pensions**

<!-- Start of picture text --> n g<br>μ1,2 μ1,2<br>tax=0.1 tax=0.12 tax=0.15<br>tax=0.1 tax=0.12 tax=0.15<br>Source: Author Source: Author<br><!-- End of picture text --> The key dynamics to take into account is the presence of altruistic transfers and endogenous fertility. When the economy reaches the point in the demographic transition where fertility has declined so much that the only motive for having children is the genetic motive, parents realize that they can no longer save child-rearing resources by switching to capital savings and away from having children as an old age savings mechanism. After this point, an increase in public pensions will reduce the steady state capital stock in line with conventional wisdom. However, while altruistic transfers are still operative, a rise in public pensions increases productivity growth and reduces fertility—and therefore speeds up the process by which a country will go through the demographic transition.

We have argued in this paper that a shift from a fixed contributions PAYG system towards a fixed benefits system would even further reduce fertility and increase productivity growth. This will happen as parents realize that having more children will not provide them with more pension benefits, but only draw down on their resources. While this is an issue for our future research, we expect that the fertility paths would shift down (up) even further than in Figure 14 (15).

The model incorporates perfect capital markets, which is a limitation that should be addressed in future work on this topic. We would expect that if capital markets were imperfect, the return on capital savings would fall and children be rendered more attractive as a savings mechanism.<sup>18</sup> Over the course of economic development capital markets would develop, and the relative return on capital savings would increase—depressing further the savings motive for having children, as illustrated in this paper.

In our model we chose to abstract from education. The effects of education in an intertemporal setting are well understood: the quality/quantity trade-off informs us that an increase in child survival will lower fertility and thus increase the resources (for education) devoted to each child. In this context, an issue for our future research is to incorporate human capital formation alongside a twosided altruism formulation of utility (modifying (6) such that _Ut_ = _π1ut-1_ + _ut_ + _π2ut+1_ ). This would allow us

> 18 In most developing countries an enormous problem is access to credit (very high interest rate), savings, insurance and other capital market instruments—especially in rural areas, where microfinance is often the only option for small-scale credits.

17
# **The Effect of Product Standards on Agricultural Exports from Developing Countries**<sup>‡</sup>

**Esteban Ferro**<sup>*****</sup> **, John S. Wilson**<sup>**†**</sup> **, and Tsunehiro Otsuki**<sup>**§**</sup>

**Keywords:** standards, agriculture, international trade, emerging markets **JEL Classification Codes:** F14, Q17, O13

> ~~*~~ Consultant, <u>eferro@worldbank.org,</u> ~~†~~ Lead Economist, <u>jswilson@worldbank.org,. at DECRG - Trade and</u> International Integration, The World Bank Group. 1818 H St. NW, Mailstop: MC3-303, Washington DC, 20433. §Professor, at Osaka School of International Public Policy, Osaka Universityotsuki@osipp.osaka-u.ac.jp. ‡ 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. This research was funded by World Bank-Netherlands Partnership Program at the World Bank. Without implicating them, we would like to thank Aaditya Mattoo, Caglar Ozden, Maggie Chen, Mariem Malouche, Jose Daniel Reyes, Alberto Portugal-Perez for helpful ideas and comments throughout the preparation of this paper and to participants at the IATRC Conference. Special thanks to Fritz Shuster at Agrobase Logigram for providing the data as well as his support with questions regarding the Homologa database.
Policy Research Working Paper 10280

# **Abstract**

This paper explores the use of digital technologies, their association with performance outcomes, and the main constraints to greater use among microenterprises. The study uses a sample of more than 3,300 firms across seven Sub-Saharan African countries, of which over 70 percent are informal and over half are self-employed enterprises with no full-time workers. The analysis finds that productive use of digital technologies is low: less than 7 percent of firms use a smartphone, less than 6 percent use a computer, and roughly 20 percent still do not use a mobile phone. Even fewer firms use digital tools enabled by these access technologies: among firms with smartphones, less than half use the internet to find suppliers, and only half with a computer use accounting software or inventory control/ point-of-sale software. Women are less likely to use all digital technologies than men. A greater range of uses based on internet-enabled computers or smartphones relative to uses based on 2G phones are conditionally associated with higher job levels. However, there may be a tension between higher productivity and more jobs: the highest productivity firms are not generators of the highest jobs, and vice versa. That formal high-sales and high-jobs firms are more strongly associated with the use of internet-enabled tools than high-productivity firms suggests that relaxing constraints preventing the latter from using more such digital tools and expanding sales and jobs could be important. Among these constraints, more than seven in ten non-users indicate that lack of attractiveness (“no need”) is the main impediment to productive use of digital technologies. The most important conditional correlates of smartphone and computer adoption are related to having a loan, having electricity, having business linkages with large firms as customers, and managers having vocational training.

This paper is a product of the Africa Chief Economist Unit and the IFC Economic Policy Research Unit. It is part of a larger effort by the World Bank 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 mdutz@ifc.org.

_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
association, different levels of education, etc. on the likelihood of being poor for rural areas and Haiti as a whole. The status of the household—poor or nonpoor—is regressed on relevant individual and household characteristics using the probit regression technique. Standard errors are adjusted for the clustering process inherent in the sampling procedure of the HLCS survey. Given the way the regression model is specified, findings reveal when impacts for rural areas in Haiti are different from impacts for Haiti as a whole.

The analysis of poverty correlates reveals a conditional correlation between poverty and characteristics of household heads and indicates groups which are particularly vulnerable. The probability of a household being poor is analyzed based on relevant individual and household characteristics. The main conclusion emerging from the analysis is that disparities in assets such as education are indeed strongly correlated with poverty.

Other poverty studies for other countries such as Brazil, for example Ferreira, Lanjouw, and Neri (1998), show that in 1996 education was the central personal attribute determining the likelihood that a household would experience poverty. Other factors such as age, family size, race, and rural living are also important in determining the likelihood of poverty. The findings on Haiti in this section are very much in line with those of Ferreira, Lanjouw, and Neri. A discussion of some of the variables explaining income poverty follows below.

It is important to note the limitations of this analysis at the outset. First and foremost, the analysis does not capture the dynamic impact of certain causes of poverty over time. Most notably, the impact of changes in economic growth – most certainly a key determinant of poverty – cannot be assessed using this static, cross-section model. Second, the analysis is limited by the variables available at the household level from the 2001 HLCS. Other factors – physical conditions such as variations in climate or access to markets – could not be included due to lack of data at this level. Finally, though theory holds that many of the variables included in the analysis do indeed contribute to (cause) poverty (or poverty reduction), the statistical relationships should be interpreted as correlates and not as determinants since causality can run both ways for some variables.

Rural living is in many ways very different from urban and metropolitan living in Haiti (Table 8.1). The largest statistical differences in poverty reduction between rural and other areas are found in the effect of education, region, gender, and social capital.

Living in rural areas in Haiti does not by itself affect the probability of being poor. Hence, individual and household characteristics are more important than geographical location. This is good news for policy-makers as there are no nonmeasurable rural variables kicking-in and affecting the likelihood of a household in rural areas falling below the extreme poverty line.

45
We use the information in the standard errors of the rule of law index described in the previous section to anchor the range of estimates for measurement error in governance. In particular, we begin with a value of au = 0.27, which is the average across countries of the standard errors obtained from the unobserved components model used to construct the aggregate governance indicator. This captures the extent to which the governance measure is a noisy indicator of the true rule of law within a country. As we discussed in the previous section, these estimated standard errors are already large relative to the units in which rule of law is measured.

We then consider several higher values up to a maximum of a, = 0.54, or twice the initial level. One way to rationalize substantially higher measurement error such as this is to note that good rule of law is just one of many dimensions of good governance or institutional quality. Thus the extent to which rule of law is itself an imperfect indicator of broader notions of good governance that matter for economic growth in the very long run can also be thought of as measurement error. A rough way to calibrate the importance of this type of measurement error is to consider the simple correlations, **r,** of the rule of law indicator with the other five dimensions of governance discussed in the previous section, which range from r = 0.76 (voice and accountability) to **_r_** = 0.93 (government effectiveness), and average **r** = 0.85 across all governance components. Under the assumption that each of these indicators is a noisy measure of the broadest possible concept of good governance, a straightforward calculation shows that ca,, ranges from 0.27 to 0.48 under this interpretation.<sup>27</sup> For the benchmark level of measurement error of c,, = 0.27 (based on the actual rule of law standard errors discussed above), we find that income has a surprisingly large negative effect on governance, with a coefficient of -1.28. This implies that an exogenous doubling of per capita income in the very long run leads to an estimated decline in governance of -1.28 x 0.7 = -0.90, or nearly one standard deviation of the governance index. Moving across the columns in table 5 shows that the estimated feedback from per capita income to governance is positive only if the measurement error in governance is very high, for values of c,, > 0.50. In addition, as measurement error in governance becomes larger, the effects of history on current institutions, o, becomes smaller in absolute value and the 27. Suppose thatg, _=g+u,, i=l,..,N_ are _N_ noisy indicators of g, with _E[u,=0, E[u']=a,,_ and 2 **2** _E[u,_ * _u,] =0_ for all i and j. Then the correlation between any two indicators is _r,_ = _g_ **_8 .0g_** +0 **_0 +0_** One minus the minimum and maximum correlation across all pairs i and j provide upper and lower bounds, respectively, on the ratio of the variance of measurement error relative to the variance of the observed variable, **a2**

**2 22** **_0-g +_** 0;

2 1
# **1 Introduction**

Aid allocations of many multi- and bilateral donors became more policy-selective starting in the 1990s – that is, donors provided more aid to recipient countries with better policies. This fact, which we discuss in more detail below, raises two important questions that this paper seeks to answer: First, does increased policy-selectivity of donors provide recipients with an incentive to improve policies? Second, has the increased policy-selectivity of donors translated into improved policy levels in aid recipient countries? Clearly, the second question is relevant only when we answer the first one affirmatively.

We address these two questions theoretically and empirically. We develop a game-theoretic model that shows that policy-selective aid by a donor leads to a contest for aid among recipients in terms of policies, which increases policy levels in recipient countries in equilibrium. Comparative static results of the model show, however, that incentives to participate in that contest depend on the level of aid and other resource windfalls a recipient receives unconditionally of its policy choice. This result holds if the donor’s aid allocation is both policy- and poverty-selective, as a poverty-selective donor, conditional on policies, balances out resources across recipients. A recipient receiving a sufficiently large amount of unconditional aid has no incentive to participate in the contest for aid. Because not all aid is given policy-selectively, including large financial flows by China and other emerging donors more recently, it is then an empirical question whether for a recipient government improving policy levels tends to lead to more aid as our model predicts.

We provide evidence for an economically and statistically strong and robust relationship between lagged policy and current aid conditional on lagged aid using recipient-year panel data. Thus, our estimates suggest that a positive change in policy is associated with an increase in aid money. We estimate that an increase in the World Bank’s CPIA policy index from 1.5 to 2 for a recipient is associated with about a 13% increase in aid.

The second question – of whether policy-selective aid produces better policies – poses the challenge of empirically disentangling selection effects – i.e. donors are policy-selective – from incentive effects – i.e. selectivity leads to better policies. If we find a positive correlation between policy levels and policy-selective aid a recipient receives, that relationship would be consistent with both selection and incentive effects. We overcome this challenge by recognizing an important insight generated by our 2