rafmacalaba/datause-encoder-data huggingface dataset:
stock adjustment), we discuss the robustness of our results to the omitted-variables and speci fi cation errors below.

## 4.2 Identi fi cation Among "Movers"

For the sample of movers, an additional source of bias is introduced through selective matching. Thus, if cov(∆m<sup>q</sup> t<sup>, μ</sup> jkt<sup>)=0,sothatthechangeinunobservedteacherqualityiscorrelatedwith</sup> teacher-level shocks, then α1 captures both the effect of shocks and changes in teacher quality for the movers. If children who changed teachers (movers) did so from very bad to bad teachers (so that ∆m<sup>q</sup> t<sup>ispositive),butthese(unobservably)badteachersaremoreabsentthantheaveragein</sup> the sample, the covariance condition implies that our estimated impact for the movers is biased towards zero. Note that there is a difference in interpretation between the selective matching and the precautionary spending channel developed in the theory and appendix 2. For the latter, even when cov(∆m<sup>q</sup> t<sup>, μ</sup> jkt<sup>) = 0sothatthereisnobiasinthecoefficient,theimpactofteachershocksis</sup> still closer to zero for the sample of movers–this is due to heterogeneity in the treatment impact and accordingly has different estimation implications.

Estimating the impact of teacher shocks requires data both on the extent of these shocks and a means of distinguishing the uncertainty faced by households in terms of teacher inputs. The primary measure of shocks to teacher inputs used in this paper is teacher absenteeism. As will be shown below, this absenteeism arises from a number of factors, largely unpredictable for the households, such as illness and attendance at funerals. We fi rst estimate the impact of teacher shocks for the non-movers and discuss the robustness of our results to the concerns raised above. We then show that the results do not extend to the sample of movers. We are unable, however, to distinguish between the selective matching (which results in a bias in the estimates) and precautionary spending (which results in heterogeneity in the impact of teacher shocks) channels.

# 5 Data

The data are from Zambia, a landlocked country with a population of 10 million in Sub-Saharan Africa. The educational environment is discussed in some detail in Das and others (2004a) and Das and others (2004b). For our purposes, an important factor is the overall decline in GDP per capita in the country from the mid-1970s due to a decline in worldwide copper prices, the country’s main export (per-capita income declined almost 5 percent annually between 1974 and 1990). The decline in per capita income has had an impact on educational attainment. For instance, net primary school enrolment currently at 72 percent is historically low, following a moderate decline over the 11
**Markets for Cotton By‐Products: Global Trends and Implications for African Cotton Producers**

**John Baffes** THE WORLD BANK <u>jbaffes@worldbank.org</u>

The views and findings of this paper reflect those of the author and not the World Bank. I would like to thank Rafiq Chaudhry, Armelle Gruère, Valerie Kelly, Patrick Labaste, Stephen Mink, Ale‐ jandro Plastina and Marc Sadler for comments and suggestions, as well as seminar participants at the World Bank (December 2009) and the International Cotton Advisory Committee (May 2010). Rachel Weaving provided excellent editing. The results of the country studies are based on find‐ ings from field visits by Kathryn Vasilaky and Laoura Maratou (Uganda, May 2009), Kathryn Vasi‐ laky (Tanzania, June 2009), and Noureni Zanfongnon (Benin, June 2009 and Burkina Faso, July 2009). The author is grateful to the owners and managers of the cottonseed processing facilities as well as the heads of the various cotton organizations for their time and information provided dur‐ ing the interviews. Financial support for the study was provided by the Development Prospects Group and the Africa Region of the World Bank, as well as the EU‐funded Trust Fund, TF092488.
- Reddy, K.S. 2019. “Pot the Ball? Sovereign Wealth Funds’ Outward FDI in Times of Global Financial Market Turbulence: A Yield Institutions-Based View.” _Central Bank Review_ , August 30. https://www.sciencedirect.com/science/article/pii/S1303070119300782.

- Vermeulen, R., and J. de Haan. 2014. “Net Foreign Asset (Com) Position: Does Financial Development Matter?” _Journal of International Money and Finance_ 43: 88–106.

19
**ANNEX 2: Summary of the regulatory review**

|||||**FF Defini**|**tion in law**||**Regulations**||**Standardized**|
|---|---|---|---|---|---|---|---|---|---|
|**LPI 2014**<br>**rank**|<br>**Country**|**Regulator (Ministy or**<br>**Self‐regulator)**|**LPI 2014**<br>**score**|**Agent**|**Principal**|**Market Access**|**Operations**<br>**(Statutory)**|**Operations**<br>**(Voluntary)***|**STC or**<br>**GT&Cs**|
|1|Germany|Transport & Infrastructure|4.12|○|○|○|○|○|○|
|2|Netherlands|Infrastructure(Transport)|4.05||○|○|○|○|○|
|5|Singapore|Self‐regulator|4||○|○(Self‐regulator)||○|○|
|9|USA|Transport|3.92||○|○|○||○|
|10|Japan|Transport|3.91|○|○|○|○|○|○|
|28|China|Trade|3.53|○|○|○|○||○|
|34|South Africa|Customs/Transport|3.43|||○(Customs)|○||○|
|35|Thailand|Transport|3.43|X|○|○|○(liabilityonly)|○|○|
|57|Philippines|Trade|3|○|○|○|○|○|○|
|65|Brazil|Transport|2.94|X|○|○|○||○|
|72|Pakistan|Self‐regulator|2.83|||○(Self‐regulator)||○ ****|○|
|74|Kenya|Customs /Transport|2.81|||○(Customs)|○(Transport)|○**|○**|
|105|Nepal|Trade|2.59|X|○|○|○||○|
|153|Sudan|Transport|2.16|X*||○||X|X|

* Apart from STC/GT&Cs

|○|exist|
|---|---|
|X|not exist|
|blank|not confirmed|

** In Sudan, no definition of Freight Forwarder, but the role of Freight forwarder on the ground is Agent *** In K <mark>enya, FEAFFA Code of Consut is adopted, COMESA regulations also applies for Transit regime.</mark>

<mark>**** In Pakistan, since a regulatory bill has not passed yet, no rigorous operational rule has not been in place</mark> 29
Table_3: Distribution of Loan Purposes by Type of Lender (Percent)

|Purpose|Sample<br>Size|Production|<br>Farm<br>Machinery|Constructio<br>|n Consumptio|n<br>Social<br>(Wedding,<br>Funeral,<br>etc)|Other|
|---|---|---|---|---|---|---|---|
|County and<br>Source|(loans)|||||||
|Gmzning||||||||
|Institutional|212|93.0|4.0|2.0|0|1.0|0|
|NMn-Institutional|47|11.0|4.0|23.0|15.0|26.0|21.0|
|Tai||||||||
|Institutional|57|89.0|2.0|4.0|0|0|3.0|
|Nm-institutioral|25|36.0|0|32.0|0|20.0|12.0|
|Jurong||||||||
|Institutional|29|48.0|10.0|10.0|3.0|10.0|19.0|
|Nm-Institutional|31|6.0|19.0|29.0|13.0|23.0|10.0|
|Xiajiang||||||||
|Institutional|158|67.9|3.8|5.8|1.3|8.4|14.8|
|Nb-Institutional|85|33.8|1.5|28.0|9.2|20.0|9.5|
|Power Project(13)|
|---|
|Power Project(13)|
|Jakarta Sewerage and Sanitation Project(JSSP)|
|Jakarta Sewerage and Sanitation Project(JSSP)|
|Jakarta Sewerage and Sanitation Project(JSSP)|
|Jakarta Sewerage and Sanitation Project(JSSP)|
|Jakarta Sewerage and Sanitation Project(JSSP)|
|Jakarta Sewerage and Sanitation Project(JSSP)|
|Jakarta Sewerage and Sanitation Project(JSSP)|
|East Java Water SupplyProject|
|East Java Water SupplyProject|
|East Java Water SupplyProject|
|East Java Water SupplyProject|
|East Java Water SupplyProject|
|East Java Water SupplyProject|
|East Java Water SupplyProject|
|SwampReclamation Project(02)|
|SwampReclamation Project(02)|
|SwampReclamation Project(02)|
|SwampReclamation Project(02)|
|SwampReclamation Project(02)|
|SwampReclamation Project(02)|
|SwampReclamation Project(02)|
|SwampReclamation Project(02)|
|SwampReclamation Project(02)|
|SwampReclamation Project(02)|
|Provincial Irrigation Development Project(02)|
|Provincial Irrigation Development Project(02)|
|Provincial Irrigation Development Project(02)|
|Provincial Irrigation Development Project(02)|
|Provincial Irrigation Development Project(02)|
|Nucleus Estates and Smallholders Sugar Project|
|Nucleus Estates and Smallholders Sugar Project|
|Nucleus Estates and Smallholders Sugar Project|
|Nucleus Estates and Smallholders Sugar Project|
|Nucleus Estates and Smallholders Sugar Project|
|Nucleus Estates and Smallholders Sugar Project|
|Nucleus Estates and Smallholders Sugar Project|
|Nucleus Estates and Smallholders Sugar Project|
|Small Enterprise Development Project(03)- SEDP Project(03)|
|Small Enterprise Development Project(03)- SEDP Project(03)|
|Small Enterprise Development Project(03)- SEDP Project(03)|
|Small Enterprise Development Project(03)- SEDP Project(03)|
|Non-Formal Education Project(02)|
|Non-Formal Education Project(02)|
|Non-Formal Education Project(02)|
|Non-Formal Education Project(02)|
- 24 -

long discussed that the road freight efficiency has been deteriorated with more empty trucks observed.

44. The paper recast light on the question of how road freight costs are determined using very large, unique shipping data from Eastern Europe and Central Asia. The data covers over 10,000 shipping operators during the period from 2010 to 2020. Freight costs and time, and other basic characteristics were recorded for over 83,000 shipping routes observed.

45. It was found that economies of scale in both volume and distance matter to freight transport costs. The elasticity with respect to freight volume (tonnage) is high at about 0.3 to 1.0. Thus, the load factor is an important element to determine freight costs. The elasticity of distance is relatively small but significant. It varies from 0.04 to 0.16, depending on specification. Longer distance shipping tends to be cheaper. The elasticities also differ across types of commodities.

46. The impact of speed or the quality of roads looks complex and heterogeneous among different types of commodities. According to the OLS results, transport efficiency matters to capital goods, such as equipment, minerals, metals and materials. On the other hand, consumer goods, agricultural commodities and food products are less sensitive to driving speed. However, it seems that there exist important unobservables that affect both driving speed and freight costs. When the carrier-specific fixed-effects are incorporated, it is found that the transport costs decrease with driving speed for particular types of cargos, such as agricultural commodities, food products, and other consumer goods. The elasticities are estimated at 0.03 to 0.11. If observed driving speed is considered as a proxy for road conditions, it can be concluded that the significant road corridor investment in the region contributed to increasing shipping efficiency and lowering average freight costs.

47. There are other important determinants of freight costs, including terrain conditions, such as elevation. In mountainous areas, freight costs tend to be higher. In the region, there is considerable variation in freight costs. Some areas have better accessibility than others. In general, the observed changes in freight costs look relatively modest. Local economies are expected to benefit more from the corridor infrastructure improvement over the long term.
trade of those two countries.'<sup>**6**</sup> Thus, the exchange rates, both of the major currencies and

> the currencies under study, are calculated as the number of units of the currency necessary to purchase a weighted basket of strong currencies.<sup>7</sup>

# **II.b** Empirical Strategy

We use daily data in our empirical experiments.1<sup>8</sup> To assess how verifiable different exchange rate regimes are, we use explicit statistical tests that attempt to replicate those implicitly carried out **by** financial market participants to learn about the actions of the monetary authorities. For countries that have announced a basket band **-** such as Chile and Israel during the sample periods we use (examined in section III below), we seek to establish the amount of information (days) needed to reach a judgment on whether the data support the hypothesis that the exchange rate is following the announced regime. In the case of currencies that have declared their regime as a pure float **-** like post-crisis Mexico and Thailand (section IV below) **-** the purpose of the exercise is to offer a standard of comparison for the first set of currencies.

**A** test that fails to reject the announced regime for the currencies following basket bands, has low power if the same test also fails to reject an analogous hypothesis applied to floating currencies. We wish to see whether the public can distinguish the two sorts of policies statistically, rather than having to rely on the assumption that it can instantaneously intuit the true policy of their central bank.

> **16 A** second advantage of using **GDP** weights is that one does not need to obtain the full set of bilateral trade data and recompute a new set of weights for each country. But using bilateral trade weights is a possible extension for future research.

> **17** See Appendix 2 for a detailed description of the construction of the numeraire.

> **'** Data on major currencies and some of the emerging countries was extracted from Bloomberg and Datastream. Data for the case studies of Chile and Israel was downloaded from the respective Central Banks Web pages.

**19**
2 and more on investment items -- like education and housing. Households receiving remittances also have a higher likelihood of investing in entrepreneurial activities.

This paper proceeds in five sections. Section 1 defines official international remittances and presents the new data set of 115 developing countries. Section 2 uses this macro-level data set to estimate an econometric model to identity the determinants of remittances within the developing world. Moving to the level of the household, Section 3 examines how the receipt of international remittances affects the welfare and poverty status of households in various developing countries. Section 4 then analyzes how households consume and invest their remittance earnings, and the impact of these remittance expenditures on entrepreneurial activities and economic development. Section 5 summarizes the findings and highlights areas for future work.

# 1.  Official International Remittances; Data Set of 115 Developing Countries

It should be stressed at the outset that a variety of data problems make it difficult to accurately examine the impact of international remittances on the developing world. These data problems relate to the way in which information on international remittances is collected and compiled. While the International Monetary Fund (IMF) publishes annual records of the amount of “worker remittances” received by developing countries in its _Balance of Payments Statistics Yearbook_ , these IMF data rely entirely on the reporting conventions of the central banks of some 185 developing countries. In other words, while the central banks of many developing countries may receive remittances from their citizens working abroad, and correctly record these income flows as “worker remittances,” the central banks in other countries may report such remittance flows under another line item in the balance of payments. In short, there is no reliable and consistent rule for enforcing the way that developing countries report “worker remittances” in their balance of payments. This means that some countries which produce large numbers of international migrants – like the Philippines and the Russian Federation – report receiving little or no “worker remittances” because they (probably) record these remittance flows under another category in their balance of payments.<sup>4</sup> This “mis-categorization” of remittances results in an underestimate of the actual flow of remittance monies returning to certain developing countries.
Figure 6 examines spending on economic competencies in greater detail. The single largest component of this type of intangible investment is expenditure on brand equity, specifically on advertizing. Next, firm expenses on labor training and own-account spending on organizational improvement are also major components.

<!-- Start of picture text --> Figure 6: Expenditures on Economic Competencies (% of GDP)<br>0.8%<br>0.6%<br>0.4%<br>0.2%<br>0.0%<br>2000 2002 2004 2006 2008<br>Advertising Market research<br>Direct firms expenses Costs of employee time<br>Purchased organizational structure Own-account organizational structure<br><!-- End of picture text --> Note that compared to CHS, the labor training estimate we show is an underestimate since it does not include training in services firms. In general, as discussed in Section 2, spending on economic competencies is more difficult to measure than other types of intangible investment, and there is also some controversy about whether advertizing should be included at all. However, with the exception of labor training in services, all our estimates are methodologically comparable to CHS estimates. And unless trends in services differ from those in manufacturing, we are still estimating overall trends without any obvious bias, if not levels. Hence, the key message of Figure 6 is sobering: there is little sign of a positive trend in Brazilian spending on economic competencies.

22
7<!-- Start of picture text --> Figure i<br><!-- End of picture text --> <!-- Start of picture text --> PRIMARY SOURCE OF DRINKING AND COOKING<br>WATER IN THE RAINY SEASON BY VILLAGE<br>100-<br>60<br>p<br>60<br>UC<br>N 40<br>T<br>20<br>EDiM Sri.BEUUN<br>-SPURNq wAki-LER TRIUCK RAIN<br><!-- End of picture text --> Figure 2 I'RIMARY SOURCE OF DRINKING AND COOKING WATER IN THE DRY SEASON BY VILLAGE <!-- Start of picture text --> 100<br>s0<br>p<br>E 60<br>U<br>C<br>N 40<br>T<br>0-<br>BDEY tBWEA S URUNC R<br>m IPRIHO SYRIAN 7tWKIR 11001 BRAN<br><!-- End of picture text -->
Morduch 2005). Women are viewed as more reliable customers, and they do, in fact, tend to repay their loans more frequently than men do. By the same token, they tend to be less prone to moral hazard (Karlan and Zinman 2005). We examine our primary set of results for different demographic groups in order to identify systematic differences in the responses to different mechanisms.

Table 6 breaks down the analysis of Table 5 by demographic categories. Despite some variation in significance across the specifications, we find that the sign patterns are broadly consistent across the demographic subsets we consider. Strikingly, we find no gender differences, and only minor differences between the old and the young. The patterns are also similar among better educated individuals, more trusting individuals (as measured by the social survey, i.e., GSS, questions), and individuals who have a savings account in a commercial bank.<sup>9</sup> These results are reinforced by findings below that individual play within the game is not driven predominantly by personal characteristics.

# **6 Joint play**

In line with most of the theoretical literature on microfinance mechanisms, the focus thus far has been largely on levels of risk-taking by individuals, but the bank is less concerned with individual risk-taking than with overall repayment. Joint liability games offer the possibility of implicit insurance such that risk-taking can rise while repayment rates remain steady. But joint liability does not eliminate the risk of collusion against the bank. In this section we consider determinants of joint play and coordination.

## **6.1 Asymmetric predictions**

We begin with a set of asymmetric predictions. To see these, consider the payouts in Table 1. Under joint liability, a player whose partner always plays safe faces a decision problem identical to the individual liability case, since a safe partner never defaults. However, the safe project becomes less attractive as the probability that the partner

> 9 One surprising result in the games with “trusting” participants (GSS≥2) is their strong increase in risktaking in the dynamic joint liability games once monitoring is introduced.  The regressions, though, pertain just to 24 individuals, and might not be robust to expanding the data set.

18
Table 4: Analysis of classroom quality, teacher, and structural characteristics on children’s physical health & <u>well-being</u>

||DV<br>no|:Physical hea|lthand well-|being (Raw s<br>w/ teacher|core)|
|---|---|---|---|---|---|
||<br>controls|w/ controls|w/ teacher|<br> <br>& structure|IV|
||(1)|(2)|(3)|(4)|(5)|
|**Observed classroom quality**||||||
|ECERS-R|0.107**|0.073|0.079|0.083*|0.107*|
||(0.046)|(0.049)|(0.049)|(0.050)|(0.059)|
|**Teacher characteristics**||||||
|% of teachers w/ sr. secondary edu.|||0.275|0.297|0.283|
||||(0.378)|(0.381)|(0.386)|
|% of teachers w/ post secondary edu.|||0.272|0.285|0.278|
||||(0.386)|(0.390)|(0.395)|
|Mean years of teaching|||<br>-0.013*|<br>-0.014*|<br>-0.015**|
||||(0.008)|(0.008)|(0.008)|
|% of teachers w/ no ECED experience|||-0.048<br>(0.099)|-0.046<br>(0.099)|-0.057<br>(0.099)|
|% of teachers w/ no training|||0.007|0.018|0.017|
||||(0.133)|(0.134)|(0.134)|
|**Structural characteristics**||||||
|Student-to-staff ratio||||0.013*<br>(0.007)|0.013*<br>(0.007)|
|Hours of operation per week||||<br>0.008<br>(0.012)|<br>0.008<br>(0.012)|
|Constant|7.774***<br>(0.142)|4.320***<br>(0.325)|4.075***<br>(0.497)|3.906***<br>(0.512)|3.860***<br>(0.522)|
|Observations|7,984|7,984|7,984|7,984|7,984|
|R-squared|0.005|0.153|0.155|0.157|0.155|
|No. ofclusters (centers)|565|565|565|565|565|
|First-stage F-statistic of excluded<br>instrument|||||647.760|
|**Control included:**||||||
|Child characteristics|N|Y|Y|Y|Y|
|Kindergarten dummy|N|Y|Y|Y|Y|
|District dummy|N|Y|Y|Y|Y|

Note: Robust standard errors clustered at the center level in parentheses. Child characteristics include age, agesquared, gender, whether child has special needs (reported by teacher), and mother's highest education level. Kindergarten dummy is 1 for kindergartens or Islamic kindergartens and 0 for non-project playgroups or project playgroups. District dummies are included for the 8 districts where centers and children are located. Models 1-4 are OLS regressions. Model 5 is a two-stage least squares model to correct for measurement error in the ECERS-R. The F-statistic of the first stage is reported.

* p<0.1, ** p<0.05, *** p<0.01

21