rafmacalaba/datause-encoder-data huggingface dataset:
WPS3784 In contrast, significant spending spillover effects are likely to arise from the higher incomes for coffee farmers. On average, some 40 percent of coffee farmers’ income is spent on food products. The share of income spent on food is actually higher is some regions, mirroring the geographical disparities in coffee production in Rwanda. Food expenditures have the highest share in consumption in Cyangugu and Kibuye, accounting for about 54 percent of total consumption and the lowest share in Umutara and Kibungo, with 38-40 percent (table 8). The marginal spending of extra incomes on local goods and services can have strong multiplier effects in the rural economy, especially in Cyangugu and Kibuye<sup>9</sup> . Given the constraint on land however, provinces where a growing number of farmers engage in producing exportable crops will increasing rely on other regions and the international market for their food and other goods imports. Once again lower transport costs are important, as is an open trade regime<sup>10</sup> , to reap the benefits of specialization.

# **4. The Poverty Impacts of Higher Quality Coffee**

Enhancing the quality of agricultural products produced in Rwanda’s rural economy is another vehicle for poverty reduction. A key element of the government’s strategy for the coffee sector is to increase the proportion of washed or quality coffee in output and exports. Fully washed coffee has been commanding prices that are more than double that of standard coffee. Here we investigate the potential impacts of this strategy on poverty. It is worth noting that improvements in rural transport logistics are crucial to the success of the fully washed coffee strategy since the coffee cherries must be washed within 6 hours of being harvested or the quality starts to depreciate. Clearly farmers that are currently more than 6 hours distant from a washing station will be unable to benefit from this strategy.

> 9 The magnitude of multiplier effects depends on supply elasticities of local goods, as demonstrated by Delgado et al. (1998), after Haggblade et al. (1991). If capital constraints or high transaction costs result in inelastic supply of non-tradable goods, then costs of production will quickly rise with expanded demand, and the growth in production of local goods and services will be dampened.

> 10 For example, tariffs on imports of rice are currently high at more than 25 percent.

10
ADMARC is also marginal. Survey investigations for the year 2008 indicate that on average, only 8 percent of the maize sold by farmers was purchased by ADMARC (Jayne et al. 2008). Private traders were the main buyers of maize from small holders, accounting for about 75 percent of all maize sold. Outside of Lilongwe and Blantyre, ADMARC purchased marginal quantities in the rural areas surveyed.

# _<<B>>Domestic trade and transport in Malawi_

The transport of goods is undertaken by ship, by train, and by truck. Transport by ship between various ports of Lake Malawi is growing but accounts for a marginal share of all maize traded (less than 1 percent). The Malawi rail network is a rail line with a total length within Malawi of 797 km that runs from Zambia in the west through Lilongwe to the south, where it splits into a line further south to Blantyre and Beira in Mozambique and a line to the east, to Nacala in Mozambique. This rail network has not been fully operational in recent decades because of civil war in Mozambique, among other factors<sup>4</sup> . The bulk of domestically traded goods are transported by road. Transport by trucks is reported to account for 70 percent of the domestic freight of all goods (Lall et al. 2009).

The maize trade in Malawi is undertaken by large farmers, small, medium, and large traders, wholesalers, maize processing firms, and ADMARC. Most district-to-district trade of maize is from farmers to small and medium traders and occasionally to larger traders and wholesalers. Approximately 75 percent of all traders buy directly from farmers and sell as retailers (Fafchamps et al. 2005). Survey evidence indicates that less than 1 percent of all traders are involved in wholesaling as a stand-alone business. The dispersion of the size distribution of trader businesses and the prevalence of many small-scale businesses suggest constant returns to scale in trade (Fafchamps et al. 2005). Evidence further suggests that the number of small traders operating in rural areas is increasing (Jayne et al. 2008). Trading channels vary by location, but without exception, only marginal quantities are sold to ADMARC. The bulk of the maize trade is 8
|Durable Expenditures<br>(7)<br>(8)<br><br>(-0.10∗∗∗)<br>(-0.14∗∗∗)|-(0.08)∗∗∗<br>-(0.07)∗∗∗|(-1.64∗∗∗)<br>(-1.50∗∗∗)|-(0.02)∗∗∗<br>-(0.02)∗∗∗|-Yes∗∗∗<br>-Yes∗∗∗|-Yes∗∗∗<br>-No∗∗∗|-No∗∗∗<br>-Yes∗∗∗|-9123∗∗∗<br>-9123∗∗∗|t variables are annual expenditure and<br>education, household size, number of|
|---|---|---|---|---|---|---|---|---|
|_less_Non-<br>(6)<br>(-0.15∗∗∗)|-(0.07)∗∗∗|(-1.50∗∗∗)|-(0.02)∗∗∗|-Yes∗∗∗|-No∗∗∗|-No∗∗∗|-9123∗∗∗|o dependen<br>rital status,|
|Income <br>(5)<br>(-0.12∗∗∗)|-(0.07)∗∗∗|(-1.37∗∗∗)|-(0.01)∗∗∗|-No∗∗∗|-No∗∗∗|-No∗∗∗|-9123∗∗∗|theses. The tw<br>ender, age, ma<br>|
|(4)<br>(-0.14∗∗∗)|-(0.04)∗∗∗|(-0.40∗∗∗)|-(0.01)∗∗∗|-Yes∗∗∗|-No∗∗∗|-Yes∗∗∗|-13156∗∗∗|are in paren<br>ols include g<br>.05, ∗p<0.1.|
|res<br>)||)|||||∗|ors<br>ontr<br>p<0|
|Expenditu<br>(3)<br>(--0.04∗∗∗|-(0.04)∗∗∗|(-0.33∗∗∗|-(0.01)∗∗∗|-Yes∗∗∗|-Yes∗∗∗|-No∗∗∗|-13156∗∗|standard err<br>old-head c<br>p<0.01, ∗∗|
|Non-Durable<br>(2)<br><br>(-0.14∗∗∗)|-(0.04)∗∗∗|(-0.40∗∗∗)|-(0.01)∗∗∗|-Yes∗∗∗|-No∗∗∗|-No∗∗∗|-13156∗∗∗|ion (2). Robust<br>usehold/Househ<br>cance levels: ∗∗∗|
|(1)<br>(-0.13∗∗∗)|-(0.05)∗∗∗|(-0.51∗∗∗)|-(0.01)∗∗∗|-No∗∗∗|-No∗∗∗|-No∗∗∗|-13156∗∗∗|ents of equat<br>le goods. Ho<br>ling. Signif|
|Variables<br>Variance of Income (log)||Income (log)||Household Controls|Province FE|Confict|Observations|The table shows the estimated coefci<br>Income_less_expenditure on non-durab<br>males per household, and type of dwel|

30
**Figure 7: Employment transition matrices for women by employment status, formality and sectors between 2012 and 2018, by age groups**

|||Women|||
|---|---|---|---|---|
||Private informal|**27**<br>2<br>5<br>4<br>6|2|6% (10%)|
||Private formal|16<br>**33**<br>12<br>3|36|2% (2%)|
|20-29|2<br>Public formal|1<br>**81**|4<br>12|6% (7%)|
||Unemployed|7<br>2<br>5<br>**18**<br>68||11% (7%)|
||Out of labor force|9<br>1<br>2<br>7<br>**81**||75% (74%)|
||Private infor|mal<br>Private formal<br>Public formal|Un-employed|OLF|
||Private informal|**32**<br>5<br>3<br>3|57|8% (12%)|
||Private formal|20<br>**22**<br>25|33|1% (4%)|
|30-39|Public formal|4<br>4<br>**81**|11|11% (11%)|
||Unemployed|12<br>3<br>3<br>**11**<br>71||8% (5%)|
||Out of labor force|10<br>4<br>1<br>5<br>**80**||71% (68%)|
||Private info<br>|rmal<br>Private formal<br>Public formal|Un-employed|OLF<br>|
||Private informal|**37**<br>2<br>1<br>6|0|11% (11%)|
||Private formal|7<br>**29**<br>9<br>3|52|2% (1%)|
|40-49|Public formal|3<br>**86**|11|17% (15%)|
||Unemployed|3<br>2<br>3<br>**8**<br>84||2% (1%)|
||Out of labor force|9<br>1<br>1<br>**89**||68% (71%)|
||Private info<br>|rmal<br>Private formal<br>Public formal|Un-employed|OLF|
||Private informal|**22**<br>78||11% (8%)|
||Private formal|14<br>**57**|13<br>16|1% (1%)|
|50-59|Public formal|4<br>1<br>**86**|9|18% (16%)|
||Unemployed|NA||0% (0%)|
||Out of labor force|6<br>1<br>**93**||70% (76%)|
||Private info|rmal<br>Private formal<br>Public formal|Un-employed|OLF|

_Notes_ . This figure relies on panel data from the Egypt Labor Market Panel Survey in 2012 and 2018. We restrict our analysis to women who were interviewed in both rounds. We focus on those aged at least 20 years old in 2012 and at most 59 years old in 2018. We use transition matrices to examine individuals’ transition between their work status/sector in 2012 and their work status/sector in 2018, by age groups. We rely on the market definition of work status, search required (reference 1 week). The percentages reported at the end of each row show the shares of individuals in each employment status/sector in 2012, with the shares in 2018 reported in brackets Panel weights are used.

15
19:00-20:00 time slot, which is the evening time with the greatest weight of shopping-purpose mobility in 2019, and only if looking at areas that have a shopping center, was there some evidence of travel behavior consistent with a substitution effect. Lower income travelers reduced mobility to shopping areas more than higher income groups, which is contrary to what would be expected if there were a relevant trip substitution effect as a consequence of e-commerce (Figure 5 (a)). In Buenos Aires, trips to shopping areas decreased more than to other areas of the city in 2020, and the decline was greater in the morning slot that previously concentrated shopping trips (9:00-12:00), suggesting a certain substitution effect. However, this trend reversed in 2021, suggesting that the steep drop in 2020 was largely a result of the enforcement of mobility restrictions during part of the pandemic period. Moreover, similarly to Bogota, in both 2020 and 2021, trips to shopping areas declined _less_ from areas inhabited by people belonging to higher socioeconomic strata that are higher adopters of e-commerce (Figure 5 (b)).

**_Figure 5:_** _Differences in change in travel to areas with shopping centers in 2020 and 2021 versus 2019, compared to other areas (percentage points), by traveler’s socioeconomic strata/income_

<!-- Start of picture text --> a. Bogota b. Buenos Aires<br>Workdays Saturday Sunday Workdays Saturday Sunday<br>10 10<br>5 5<br>0 0<br>-5 -5<br>-10 -10<br>-15 -15<br>-20 -20<br>Strata 1-2 Strata 3-4 Strata 5-6 Strata 1-2 Strata 3-4 Strata 5-6 Low Medium High Low Medium High<br>2020 2021 2020 2021<br><!-- End of picture text --> _Note: Negative values indicate that trips to shopping center zones fell more or grew less than to other areas;_ Source: Team’s estimate based on CDR data The results indicate that either there was no shopping trip demand substitution effect from increased e- commerce adoption or this effect was accompanied by other trends that affected mobility to shopping areas to a greater extent:

- A drop in employment in commercial areas, which would have caused the drop in mobility to these areas to be greater among the “compulsory” mobility segment than among the segments that include trips for shopping purposes.

- A reduction in consumption among the lower strata, which would have caused the drop in mobility to shopping areas to be greater among people of lower socioeconomic status, contrary to what would be expected as a result of the adoption of _e-commerce_ .

- A complementarity effect of increase in telework, leading to an increase in shopping trips among the teleworking population and offsetting the possible substitution effect of _e-commerce_ . Most studies on the effects of teleworking on travel demand have found this complementarity effect (e.g., Hook et al. 2020).

In Bogota and especially in Buenos Aires, carrier trips on weekdays originating in areas with e-commerce distribution centers fell less than those originating in other areas, consistent with the overall steep increase in e-commerce (Figure 6). In Bogota, this effect was accentuated in 2021 versus 2020, as the difference in the 11
# **Understanding Effective Teaching Practices in Chinese Classrooms: Evidence from a Pilot Study of Primary and Junior Secondary Schools in Guangdong, China**

Andrew Coflan Andrew Ragatz Amer Hasan Yilin Pan

**JEL codes:** I21, I28

**Keywords** : Teachers, classroom observation, teacher training

# **Acknowledgments**

The authors gratefully acknowledge support from the Results in Education for All Children (REACH) Trust Fund. We would like to thank the Guangdong Department of Education as well as representatives from the counties of Dianbai, Lianjiang, and Wuhua for their assistance in collecting the data. Teachstone provided training to classroom observers and certified their proficiency. They also provided valuable follow-up advice during data analysis. Constructive inputs and feedback were received by Lorena Sernett, Sarah Hadden and Daniel LaCava for the final report. Any errors are our own. 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.
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34
17 measured in cubic meters.<sup>14</sup> Estimation is made on the sub-sample of 206 piped households who combine water from the piped network with water from other sources. In order to control for possible selection bias, Mill’s ratio derived from the estimation of the discrete choice model describing the relationship between households’ characteristics and connection status (see section 4.1.) is included in the two equations as an additional regressor.

Shin’s test reveals that households perceive marginal price rather than average price. We use the (log of) instrumented marginal price. The price for non-piped water is measured by the (log of) time cost to collect water (measured in minutes). Estimation results are presented in Table 5.

Estimated own price elasticity for piped water consumption is -0.69 (significant at the 1 percent level), which is in the range of price elasticity estimated for piped households relying on piped water only. Price elasticity of piped residential water demand is thus found to be the same across the population of piped households, whether they collect water from other sources does not matter here. Interestingly, the expected marginal price is found to have a significant and negative (but lower in magnitude) impact on non-piped water consumption as well. An increase in the price of piped water induces a lower consumption of both piped and non-piped water, showing that households do not substitute non-piped water to piped water when the price of piped water increases. Time cost is significant in both models, and the signs illustrate the substitution between piped and non-piped water. The shorter the time needed to walk to the source and to wait at the source, the higher the consumption of non-piped water, and the lower the consumption of piped water. In other words, the more convenient access to non-piped water for piped households, the more they substitute piped water with non-piped water.

Income elasticity is significant (at the 15 percent level) in the first equation only, estimated at 0.11. Households having a private connection in the yard on average consume less piped water.<sup>15</sup> Again, the longer the time piped water is available, the higher the consumption from the piped network, and the lower the consumption of non-piped water. An extra hour of piped

> 14 Households were questioned about the total amount of water collected from each source (in litres per day). We assume that they go to the source every day and we compute accordingly the equivalent non-piped consumption in cubic meters per month.

> 15 In this model also the dummy variable equal to 1 if the household has two connections (in the house and in the yard) did not come out significantly.
9

|Table 2<br>Studies of the R|eturn to Education Calculated|from Macroeconomic Data|
|---|---|---|
|McMahon (1987)|Regression on Growth in<br>GDP per person employed|Increase in GDP per person<br>employed explained by inputs of<br>labor and capital and expenditure<br>on primary and secondary education<br>(taken together)<br>and<br>higher<br>education.|
|Baumol, Blackman and<br>Wolf. (1989)|Regression on Growth in<br>GDP_per capita_|Growth in output of 103 countries<br>1960- 85_per capita_explained by<br>initial<br>GDP<br>_per_<br>_capita_<br>and<br>enrolment rates<br>in<br>primary,<br>secondary and tertiary education.<br>GDP evaluated at international<br>prices.|
|Barro (1991).|Regression on Growth in<br>GDP_per capita_|Growth in output of 98 countries<br>1960-_85 per capita_explained by initial<br>_GDPpercapita,_enrolment rates in<br>primary and secondary school, the<br>share of government consumption<br>in GDP, the investment ratio,<br>measures of political and social<br>instability, price distortion and<br>fertility. Fertility rates are also<br>explained|
|_Wodd Bank Development_<br>_Report, 1991_|Regression on Growth in<br>GDP|Growth in GDP of 68 economies<br>the period 1960-87 explained by<br>means of increases in capital, labor<br>and agricultural land, and level and<br>change in educational attainment.|
|Mankiw, Romer and<br>Weil<br>(1992)|Regression on Growth in<br>GDP per working-age person|Growth in output of 75 developing<br>countries 1960-85 explained by<br>average investment ratio and<br>average fraction of working-age<br>population at secondary school.|
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17
# _Transfers_

The direct transfers as a proportion of MIPP are high for the Bolsa Familia, BPC, and rural pension transfers (Figure 9). These programs are equivalent to 39.2%, 47.3% and 129% of the market income plus pensions in the first quintile, respectively. A large proportion of these direct transfers recipients are people living in poverty and not involved in economic activities, thus explaining these programs’ high incidence. 35F<sup>36</sup> For households in the second MIPP quintile, rural pensions, BPC and Bolsa Familia represent a nonnegligible share of MIPP (9.8, 5.6, and 2.9 percent). Meanwhile, unemployment insurance is equivalent to about 3.1% of MIPP among households in the poorest quintile, whereas the Abono Salarial is equivalent to 1.5% and 1.3% of the MIPP among households in the second and third quintiles.

The incidence of primary education is higher than 100% of MIPP of households in the first quintile, decreasing substantially in the second quintile (21.1%) and reaching 0.3% in the richest group. Higher education is also more important for the poorest quintile than for the richest, though it is equivalent to about 31.4% of MIPP in the first quintile, dropping to 7% in the second quintile, and less than 1% in the fifth quintile. Preschool education incidence is about 11% in the first quintile, decreasing to 2.2% in the second quintile and less than 1 percent in the rest of the distribution.

**Figure 9. Incidence of transfers by quintile of per capita market income plus pensions (% of market income plus pensions)**

<!-- Start of picture text --> a.Direct transfers b. In‐kind transfers<br>300 300<br>250 250<br>200 39.2 200<br>150 47.3 150 31.4<br>100 100<br>115.9<br>129.0<br>50 50<br>21.1<br>0 0<br>1 2 3 4 5 1 2 3 4 5<br>Quintiles of per capita MIPP Quintiles of per capita MIPP<br>Rural Pension BPC Education Primary Education Pre School<br>Other Salario Familiar<br>Abono Salarial Bolsa Familia Education Young Adult Education Upper Secondary<br>Unemployment Benefits Health Benefits Education Tertiary<br>Share of market income plus pensions (% ) Share of market income plus pensions (% )<br><!-- End of picture text --> Source: authors’ estimations based on surveys PNAD-C, POF, and PNS, and administrative data from the Ministry of Finance, Ministry of Health, and Government Open Data Portal.

Looking at how the benefits of transfers are distributed across the population, we find that Bolsa Familia, rural pensions, and BPC are highly favorable to the first quintile (Figure 10 panel a). More than half of Bolsa Família transfers and rural pensions are concentrated in the first decile, while close to 46% of the 36 While high, the Brazilian incidence ratio of direct transfers among first quintile households is still below that of South Africa (500%).

26