rafmacalaba/datause-encoder-data huggingface dataset:
Table 2: Inspection rates and lagged shipment frequency

||2016|2016|2016|2016|2013-16|2013-16|
|---|---|---|---|---|---|---|
|Bin_n_=1|-2.195***<br>(0.089)|-2.192***<br>(0.089)|-3.645***<br>(0.123)|-0.335***<br>(0.070)|-3.659***<br>(0.113)|-0.241***<br>(0.038)|
|Bin_n_=2|-2.466***|-2.459***|-3.936***|-0.339***|-3.953***|-0.222***|
||(0.107)|(0.107)|(0.139)|(0.077)|(0.121)|(0.042)|
|Bin_n∈_[3_−_5]|-3.060***|-3.047***|-4.521***|-0.316***|-4.399***|-0.208***|
||(0.108)|(0.108)|(0.146)|(0.068)|(0.128)|(0.036)|
|Bin_n∈_[6_−_10]|-3.381***<br>(0.122)|-3.356***<br>(0.121)|-4.928***<br>(0.162)|-0.405***<br>(0.070)|-4.871***<br>(0.149)|-0.268***<br>(0.037)|
|Bin_n∈_[11_−_100]|-3.468***|-3.377***|-5.320***|-0.255***|-5.256***|-0.205***|
||(0.125)|(0.125)|(0.177)|(0.066)|(0.170)|(0.035)|
|Bin_n>_100|-4.543***<br>(0.228)|-3.859***<br>(0.265)|-6.212***<br>(0.380)|-0.259*<br>(0.144)|-5.787***<br>(0.326)|-0.266***<br>(0.082)|
|Ship_t−_1||-0.003***<br>(0.001)|-0.006***<br>(0.002)|-0.001<br>(0.001)|-0.006***<br>(0.001)|-0.000<br>(0.000)|
|Insp_t−_1|||0.196***<br>(0.010)|0.050***<br>(0.003)|0.180***<br>(0.009)|0.032***<br>(0.002)|
|ln UV_t_|||-1.484***|-0.085***|-1.490***|-0.052***|
||||(0.064)|(0.018)|(0.061)|(0.010)|
|AV tarif_t_|||0.022|0.010|0.001|-0.000|
||||(0.015)|(0.008)|(0.003)|(0.001)|
|Multi-goods|||0.082***|0.010***|0.082***|0.005***|
||||(0.004)|(0.003)|(0.004)|(0.001)|
|Product x Origin|Yes|Yes|Yes|Yes|No|No|
|Product x Origin x Year|No|No|No|No|Yes|Yes|
|Firm|No|No|No|Yes|No|No|
|Firm x Year|No|No|No|No|No|Yes|
|R<sup>2</sup>|0.430|0.430|0.448|0.667|0.455|0.680|
|Adjusted-within-R<sup>2</sup>|0.005|0.005|0.036|0.001|0.034|0.001|
|N|627927|627927|627903|621830|2263449|2239590|

Table notes: Regressions for 2016, and for pooled regressions over the entire 2013-16 period. Data are annual totals or rates calculated for triplets defined by shipments associated with a given combination of HS10 product, origin country and importing firm. The dependent variable is the inspection rate. The independent variables of interest are dummy variables indicating lagged shipment frequency bins and the continuous lagged shipment frequency measure, _Ship_<sup>_ofk_</sup> _t−_ 1<sup>.Frequencybinsaredefinedfor</sup><sup>_n_=0,1,2,3-5,</sup> 6-10, 11-100 and _>_ 100 shipments in the year prior to the inspection decision. The dummy variable indicating observations with 0 lagged shipments is omitted from the regression. The lagged inspection rate, unit value, _ad valorem_ tariff and multiple goods variables control for variation in expected risks/harms. Standard errors are clustered by Product _×_ Origin Country and reported in brackets. The symbols ***,**,* represent significance at the 1%, 5%, and 10% level.

16
Policy Research Working Paper 5988

# **Abstract**

The mobile revolution has transformed the lives of Kenyans, providing not just communications but also basic financial access in the form of phone-based money transfer and storage, led by the M-PESA system introduced in 2007. Currently, 93 percent of Kenyans are mobile phone users and 73 percent are mobile money customers. Additionally, 23 percent use mobile money at least once a day. New potential for mobile money has come with the rise of interest-earning bank-integrated mobile savings systems, beginning with the launch of the M-KESHO system in March 2010. The authors examine the mobile savings phenomenon, using data collected in a special survey in late 2010. They show that the usage of bank-integrated mobile savings systems like M-KESHO remains limited and largely restricted to better-off Kenyans. However, what the authors term “basic mobile savings”—the use of simple mobile money systems as a repository for funds—is widespread, including among those who are otherwise unlikely to have any savings. Holding other characteristics constant, those who are registered for M-PESA are 32 percent more likely to report having some savings.

This paper is a product of the Poverty Reduction and Economic Management Unit, Africa Region. 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://econ.worldbank.org. The author may be contacted at gdemombynes@worldbank.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
# **Table 5   The error-correction model with asymmetric price transmission**

|||Brazil||Brazil|Ethiopia|Kenya|Tanzania|Colombia|Mexico|
|---|---|---|---|---|---|---|---|---|---|
|Type of coffee||Brazilian||Robusta|Brazilian|Colombian|Colombian|Colombian|Other|
|||natural|||natural|mild|mild|mild|milds|
|Reform year||1990||1990|1992|1993|1994|1995|1993|
|price decreases<br>Short-term<br>|Before reforms (_δ1_)<br>|0.52|***|0.84 ***|0.13|0.05|0.14||0.14|
|transmission|After reforms (_δ 3_)|0.78|***|0.69 ***|0.46 ***|1.06 ***|0.12<br>*|0.30 ***|0.28<br>**|
||Change significant|Yes|*|No|Yes **|Yes ***|No||No|
|price increases|Before reforms (_δ 2_)|0.50|***|0.81 **|0.17 **|0.01|-0.02||0.40 ***|
||After reforms (_δ 4_)|0.77|***|0.72 ***|0.44 ***|0.40 ***|0.17 ***|0.30 ***|0.44 ***|
||Change significant|Yes|***|No|Yes **|Yes<br>*|Yes<br>*||No|
|Asymmetric price transmission<br>|Before reforms|No||No|No|No|No||No|
|significant|After reforms|No||No|No|Yes<sup>- </sup>***|No|No|No|
|Net change in the short-term tran|smission<sup>1)</sup>|||||||||
|||-|0.01|-0.06|0.06|0.62<br>*|-0.21||0.10|
|Speed of adjustment|Before reforms (_θ1_)|-0.09|***|-0.10|0.00|0.00|-0.04||-0.18 ***|
||After reforms (_θ2_)|-0.13|*|-0.10 ***|-0.17 ***|-0.22 ***|-0.25 ***|-0.31 **|-0.14 ***|
||Change significant|No||No|Yes ***|Yes<br>*|Yes ***||No|
|Target share of world price|Before reforms (_γ1_)|0.50|***|0.47 ***|0.40 ***|0.72 ***|0.56 ***||0.54 ***|
||After reforms (_γ2_)|0.81|***|0.79 ***|0.73 ***|0.93 ***|0.53 ***|0.69 ***|0.82 ***|
||Change significant|Yes|***|Yes ***|Yes ***|Yes ***|Yes<br>*||Yes ***|
|Adjusted R-squared||0.59||0.52|0.22|0.27|0.15|0.45|0.20|

* significant at 10%

- ** significant at 5%

- ***  significant at 1%

Yes- means that short-run transmission is significantly higher for price increases than for price decreases 1) Change in the transmission of price decreases net of change in the transmission of price increases, _(δ3 - δ1) - (δ4 - δ 2)_ 25
# Policy Research Working Paper Series

|**Title**|Author|Date|Contact<br>**for**paper|
|---|---|---|---|
|WPS1264 A Rock and a Hard Place: The Two<br>Faces of U.S. Trade Policy Toward Ko|J. Michael Finger<br>rea|March 1994|M. Patefha<br>37947|
|WPS1265 Parallel Exchange Rates in<br>Developing Countries: Lessons from<br>Eight Case Studies|Miguel A. Kiguel<br>Stephen A. O'Connell|March 1994|R. Luz<br>34303|
|WPS1266 An Efficient Frontier for International<br>Portfolios with Commodity Assets|Sudhakar Satyanarayan<br>Panos Varangis|March 1994|D. Gustafson<br>33732|
|WPS1267 The Tax Base in Transition: The Case<br>of Bulgaria|Zeljko Bogetic<br>Arye L. Hillman|March 1994|F. Smith<br>36072|
|WPS1268 The Reform of Mechanisms for<br>Foreign Exchange Allocation: Theory<br>and Lessons from Sub-Saharan<br>Africa|Eliana La Ferrara<br>Gabriel Castillo<br>John Nash|March 1994|N. Artis<br>38010|
|WPS1269 Union-Nonunion<br>Wage Differentials<br>in the Developing World: A Case<br>Study of Mexico|Alexis Panagides<br>Harry Anthony Patrinos|March 1994|I. Conachy<br>33669|
|WPS1270 How Land-Based Targeting Affects<br>Rural Poverty|Martin Ravallion<br>Binayak Sen|March 1994|P. Cook<br>33902|
|WPS1271 Measuring the Effect of Extemal<br>Shocks and the Policy Response to<br>Them: Empirical Methodology Applied<br>to the Philippines|F. Desmond McCarthy<br>J. Peter Neary<br>Giovanni Zanalda|March 1994|M. Divino<br>33739|
|WPS1272 The Value of Superfund Cleanups:<br>Evidence from U.S. Environmental<br>Protection Agency Decisions|Shreekant Gupta<br>George Van Houtven<br>Maureen L. Cropper|March 1994|A. Maranon<br>39074|
|WPS1273 Desired Fertility and the Impact of<br>Population Policies|Lant H. Pritchett|March 1994|P. Cook<br>33902|
|WPS1274 The New Trade Theory and Its<br>Relevance for Developing Countries|Asad Alam<br>|March 1994|A. Alam<br>87380|
|WPS1275 Female-Headed<br>Households,<br>Poverty, and the Welfare of Children<br>in Urban Brazil|Ricardo Barros<br>Louise Fox|March 1994|K. Binkley<br>81143|
|WPS1276 Is There Persistence in the Growth<br>of Manufactured Exports? Evidence<br>from Newly Industrializing Countries|Ashoka Mody<br>Kamil Yllmaz|March 1994|M. Pateha<br>37947|
|WPS1277 Private Trader Response to Market<br>Lberalizaton In Tanzania's Cashew<br>Nut Industry|Steven Jaffee|March 1994|C. Spooner<br>32116|
> - 15 °

returns to human capital in host-country labor markets may be minimal (Taylor 1987).

Empirical work is therefore needed to pursue further an analysis of explanations for internal and international migration motivated by absolute versus relative income considerations. By including both absolute income and relative deprivation variables in a single household model of internal and international migration decisions, it is possible to isolate empirically the differential influence of relative deprivation on these two types of migration, provided that not all migration decision units are drawn from the same reference group. Estimated absolute household income if a household member does not migrate can be used to control for the effects of both the motivations to migrate and the capital constraints associated with absolute income in the migration equation. Controlling for the initial absolute income of households and for their human capital, the integrated model would predict that the initial relative deprivation of households will have a positive 'influence on the propensity to send migrants to destinations where the potential returns to migration are large enough to alter significantly the relative income positions in the village, and where the risk of reference group substitution is small.

# II. EVIDENCE **FROM** MEXICO

# A. DATA

Data from a survey of rural Mexican households were used to test for the effects of absolute income and relative deprivation on migration both to Mexican destinations and to the United States. The sample consists of 61
time and reductions in school-days lost due to bad weather at about 6 and 10 days per child, respectively. Suppose the daily wage rate for juveniles in this poor region is Rs 30 and that all the days lost due to bad weather are likewise valued. Then, at this opportunity cost, the total annual savings for the whole village amount to Rs 0.34 million. In this less healthy environment, an all-weather road should have a greater impact on premature adult mortality: as argued in Section 4.3, let this rate decline by 10 percent instead of the 5 pe cent in Himachal Pradesh. The aggregate ‘measurable’ annual benefits at this stage are, using the two-to-one ratio, Rs 4.31 (= 1 _._ 5 _×_ (2 _._ 70 + 0 _._ 17)) million. The present value thereof at _ρ_ = 0 _._ 10 over the 30-year cycle is Rs 40.34 million, which greatly exceeds the upper end of the estimated range of _PV_<sup>_∗_</sup> ( _I_ ; _ρ_ = 0 _._ 10), namely, Rs 30.8 million. No help is needed from any other quarter.

One can argue, of course, that the implicit assumption of a zero opportunity cost of labor is too strong, so that the estimated commercial benefits are too high. Suppose, in a drastic step, we were to halve them, so that the present value of the ‘measurable’ annual benefit stream of Rs 2.28 million at _ρ_ = 0 _._ 10 would be Rs 21.36 million, which is somewhat above the low end of the estimated range of _PV_<sup>_∗_</sup> ( _I_ ; _ρ_ = 0 _._ 10), namely, Rs 20.5 million. Some help is now arguably needed from other quarters. Taking the middle of the range for _PV_<sup>_∗_</sup> ( _I_ ; _ρ_ = 0 _._ 10), the value of _B_<sup>+</sup> ( _ρ_ = 0 _._ 10) is Rs 2.71 million, so that Rs 0.43 million must be found from elsewhere. If none can be, the project will just pass at _ρ_ = 7 _._ 4 percent.

In fact, the Orissa Survey provides strong support for the contention that the benefits in the domains of education and health are even more substantial relative to the ‘commercial’ ones. The respondents in those villages that had received an all-weather road – by December 2009 were asked to rank the three domains commercial, education and – health according to the size of the corresponding benefits they had enjoyed from the new road thus far. The commercial and health domains were roughly equally ranked (with 31 and 29 first, and 23 and 28 second ranks, respectively), whereas education lagged far behind (Bell and van Dillen, 2012). It is possible that the latter reflects the fact that 28 of the 30 villages have their own primary school, but only two have a secondary school, whereby these poor families may place little value on secondary education. The respondents were also asked to rank the benefits stemming from the commercial domain against those from education and health combined: 24 ranked the former first, 18 put them on a par, 28 ranked the latter first, and one expressed no opinion. These rankings suggest that in the respondents’ eyes, the benefits in the domains of education and health are roughly as large as those in the commercial domain, which are much more readily measurable to outsiders. These self-reported assessments 21
8 a conti of regimes; B can be high, low, somewhat high or rel'tively low. Even though for analytical purposes we can (and should) define threshold values for B -- B - one being the more natural threshold -- this approach is not based on a dichotomized view of trade regimes.

In order to evaluate the effect of trade policies the Krueger-Bhagwati study combined the concept of bin with the definition of five phases in the evolution of trade regimes. Phase I is characterized by across-the-board imposition of quantitative controls, usually associated to a balance of payments crisis. During Phase II the control system bec more complex and discriminatory, increasing the anti-export bias of the regime. Phase 3 is the beginning of the liberalization process and is characterized by the implementation of a (nominal) devaluation and relaxation of some QRs. During Phase 4 further steps towards replacing quotas by tariffs are implemented. In Phase 5 the economy has become fully liberalized: current account tr'&nsactions are fully convertible and QRs are not used any longer. In a much needed synthetic paper Jagdish Bhagwati (1986) makes an effort to further clarify the definition of trade regimes, using the framework developed by him and Krueger in the NBER study. According to Bhagwati it is useful to distinguish between three basic trade strategies:

1. Import Substitution (IS) Strateg: Bias < 1

2. Export Promotion (EP) Strategv: Bias 8 1 3. Ultra Export Promotion Strategy: Bias > 1.

Three points are worth noting. First, within the tradition of the NBER study there still is room for a continuum of regimes. We can face a mild IS strategy -- where B is slightly below 1 -- or we can have a regime heavily oriented towards IS -- with a bias index well below one. Second, Bhagwati has chosen to label the neutral regime (B - 1) "export promotion".
financial education video, and 0 if the parent watched the health education video. As mentioned before, these assignments were unknown to the parents prior to arriving at school, and both videos were shown simultaneously and in identical settings.

# **4.5. Study Timeline**

The sample selection and randomization for the student intervention occurred in April-May 2010. A baseline survey was conducted in early August 2010 among students and parents in both treatment and control schools. The financial education program was rolled out immediately after. By mid-August 2010, teachers began using the financial education materials in the classroom. The program continued until November 2011 for a total of three school semesters.

Over the course of this study, two rounds of follow-up surveys were conducted. The first followup survey was implemented in early December 2010, four months after the program started. The results of this survey measure the short-term effects of the program. A second follow-up survey was implemented in December 2011 to assess the longer-term impacts.

The parent intervention was introduced in May 2011, and parent outcomes recorded through an exit survey and the December 2011 follow-up survey.

# **5. Program Take-up and Implementation**

# **5.1. Student Program**

Following school assignment to treatment and control, the program distributed textbooks to treatment schools and organized training sessions for the teachers. The vast majority of treatment schools received the financial education textbooks and distributed them to students. In both rounds of the follow-up survey, we asked school principals as well as teachers and students a series of questions regarding implementation and usage of the financial education program. Follow-up 1 spans the first semester of the program, while follow-up 2 covers the second and third semesters.

Over 95 percent of treatment school principals report that they received the textbooks for the first semester, and 93 percent report receiving them for semesters 2 and/or 3. The large majority of teachers also say that students received the textbooks (i.e. the books were actually distributed to students), 94 percent and 92 percent in follow-up 1 and 2, respectively. On training, 78 percent of teachers report that they received training on how to use the financial education material in the first semester, and 65 percent report receiving it in the second/third semesters.

In terms of usage, 87 percent of students report that teachers actively used the financial education textbooks in classrooms in the first semester, though a drop-off occurs in semesters 2 and 3, with 74 percent of students reporting usage. However, the percentage of principals reporting that 13
19

# **A.   A Regional Decomposition of the Long-run Trade Elasticity**

We begin the analysis by focusing on the regional dimensions of the diminished responsiveness of trade to income in the 2000s. The technical appendix shows the details of how to decompose the long-run global trade elasticity into a weighted average of individual regions’ (or, equivalently, countries’) trade elasticity to their own income, where weights depend on a region’s relative import and growth shares. The upshot of the decomposition is that a decline of the world trade elasticity can be explained by a combination of three factors: an increase in the import share of a region with lower trade elasticity; an increase in the relative growth of a region with lower trade elasticity; and a decrease in the elasticity of a region’s imports to its own income.

We estimate equation (1) separately for advanced and emerging/low income economies, using both yearly and (when available) quarterly data. Then we repeat the exercise for selected countries. For brevity, Table 5 reports only the estimates for the long-run trade elasticity, including for the world for comparison, along with the trade and GDP growth weights. A few results stand out.

First, the responsiveness of trade to income in the 2000s is lower relative to the 1990s for both advanced economies and developing countries. Furthermore, formal tests show that there is a structural break in the trade-income relationship for both groups of countries. However, a breakdown by country uncovers interesting differences. The change in the world long-run trade elasticity is driven by a few countries that have a large share in world trade and/or are growing faster relative to the rest of the world. China and the United States turn out to be particularly important as they account for 13 and 20 percent, respectively, of the change in the world trade elasticity in the long 1990s, and for 32 and 8 percent, respectively, in the 2000s.<sup>11</sup> In both cases, the elasticity of imports to their own GDP is significantly lower in the 2000s compared to the long 1990s. Using yearly data, the trade elasticity for the United States declined from 3.7 to

> 11 These numbers are the product of their share in world imports and their growth relative to world growth (see Appendix 1).
# **1. Introduction**

The sharp increase in food prices in 2008 and the impact of the 2009 global economic crisis highlights the vulnerability of the poor to external shocks. Global poverty rose anywhere from 100 million (Ivanic and Martin 2008) to close to 200 million people (Dessus et al, 2008) as a result of the food price shock which saw the World Bank food price index increasing from 180 to 334 between June 2007 and June 2008. Wheat prices doubled during the year leading up to April 2008 and rice prices increased from around $370 a ton to over $1000 a ton between January and April of 2008. The reason for the sudden increase in these prices was a confluence of factors such an export ban on rice and wheat from a few key exporters, diversion of foodgrain towards the production of biofuels, higher energy prices and the depreciation of the dollar.

While the pressure on prices has eased since its peak in mid 2008, domestic prices remain high in many countries (Wodon and Zaman, 2009). In Benin, the price of maize (which contributes 20% of caloric intake on average) has risen by 31% in the first quarter of 2009, compared with October-December 2008. During the same period, cassava prices rose by 10% in Uganda (where it represents 13% of daily calorie intake), and wheat prices rose by 13% in Sri Lanka. Hence, many of the households who fell into poverty due to the food price spike of 2008 are likely to have remained in poverty.

The global economic slowdown of late 2008 and 2009 that came at the heels of this food price shock has dealt an additional blow to countries around the world. Global GDP is expected to contract by 2.2% in 2009 and while the slowdown is greatest for OECD countries, the developing world is facing its share of pain with estimated growth falling from 5.8% to 1.2% in 2009 (World Bank 2010). At the household level, employment, real wages, earnings and remittances have all fallen, though with significant variations across regions and countries (Paci et al 2009). An additional 64 million people could be under the $1.25 a day poverty line by the end of 2010 (World Bank 2010).

Past experience on the impacts of crises on health and nutrition is sobering. The economic crisis in Cameroon in the early-1990s is estimated to have increased the prevalence of malnutrition from 16 percent to 23 percent between 1991 and 1998 (Pongou et al 2005). The 1988-1992 Peruvian economic crisis is estimated to have led to 17,000 additional infant deaths (Paxson and Schady 2005), while the 1997-98 financial crisis in Indonesia increased infant 2