rafmacalaba/datause-encoder-data huggingface dataset:
# existing roads.  Below we discuss other evidence that is supportive of this conclusion. **_6.3 Additional support_**

We expect to see a lower degree of fungibility in communes with initially low levels of spending on rehabilitation relative to their allocation. Thus, our impact estimates for road rehabilitation should be higher in such communes. Tables 4a and 4b present the same impacts as given in Table 3 re-estimated for the sample divided into the 53 project communes that rehabilitated less pre-project than the amount subsequently allocated by the project, and the 45 that rehabilitated more. As can be seen, the mean RTP1 allocation was larger for the group that rehabilitated less pre-project.<sup>17</sup> The tables suggest a lower degree of fungibility for prior low spenders than for high pre-project rehabilitation spenders. The impact estimates for rehabilitated km are significant in Table 4a, but not significantly different from zero in Table 4b. The latter result is the same as for the sample as a whole. And as in Table 3, we cannot statistically reject the null hypothesis that the rehabilitated kilometers equal the allocated kilometers for either sample. Furthermore, the impact estimates of built kilometers attributable to the project are significantly different from zero for both groups, as are total road works.

Hence, the stipulation that the resources be used for rehabilitation was more binding in places that were implementing less rehabilitation prior to the project. Yet, although the degree of fungibility was affected commensurately, the project resources stuck to the sector regardless. The test that the total road works equal the amount allocated can not be rejected for the preferred PS-based estimates. For both commune samples, the evidence points to a flypaper effect.

Additional corroborating evidence comes from available SIRRV data on other construction carried out during the project period. If the resources allocated for road rehabilitation had been displaced to the non-road sector, we would expect this to be reflected in commune spending on other basic infrastructure that is typically funded by the provincial authorities who disbursed the road funding. The data on school, health center, commune center and office construction do not suggest that resources were diverted to these other uses. The DD estimates in Table 5 generally do not indicate differential

> 17 Note also that the first group rehabilitated 16 km of roads on average during the project period while the second group rehabilitated 13 ─ both considerably more than the average RTP1 allocation.

18
Figure 4.3 Per capita education spending, Yunnan versus national average (2015 in CNY) <!-- Start of picture text --> 3000<br>2628<br>2500<br>2398<br>2232 2205<br>2116<br>2000 1922 1952<br>1772<br>1500 1459 1413<br>1237<br>1092 1152<br>1000 958<br>749<br>607<br>500<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015<br>National Education Spending Per Capita<br>Yunnan Education Spending Per Capita<br><!-- End of picture text --> _Source_ . National Bureau of Statistics of China 2016b. <u>http://www.stats.gov.cn.</u> In the meantime, Yunnan’s per student spending at the upper secondary level is still among the lowest in the country (Figure 4.4). In 2015, for example, spending per student at upper secondary vocational school is below CNY 11,000, compared with the national average of over CNY 14,000. Across all school types at the upper secondary level, Yunnan’s per student spending is only a small fraction of that in regions with the most advanced municipalities, such as Shanghai. As financing upper secondary education is largely public, and predominantly sourced through fiscal spending of local government, the large resource gaps to a large extent reflect the large regional gaps in fiscal capacity. Noticeably, Yunnan’s per student spending gap with the national average is larger for vocational track than for academic track, suggesting a persisting supply constraint for vocational education.

Figure 4.4 Per student spending: national, Yunnan, and Shanghai (in CNY) <!-- Start of picture text --> 100,000<br>80,742<br> 80,000<br>58,795<br> 60,000 49,597<br> 40,000 30,500<br> 20,000 14,019 12,391 11,919 8,920 14,084 10,730 13,482 10,306<br> -<br>普 专 普普 职普 普普<br>Specialized school Technical school Vocational secondary Regular secondary<br>school school<br>普普 普普 普普<br><!-- End of picture text --> _Source_ . National Bureau of Statistics of China 2015a.

48
productivity—while in both cases the coefficients are positive, they are not statistically significant. However, column (5) shows that the component of management practices that deals with the communication of targets to personnel is significantly associated with higher labor productivity. This suggests that it is not the number, nor the difficulty of targets that motivates workers to achieve better outcomes, but the communication of the targets throughout an establishment’s workforce and not just to the upper echelons of management. Finally, column (6) shows that there is no significant link between the basis of managers’ performance bonuses and labor productivity. The absence of a link between bonuses and performance is surprising, as previous analyses based on Chinese data by Kato and Long (2006), Lin, Shen, and Su (2011), and Feng, and Johansson (2017) all found a positive and significant relationship between executive pay and firm performance.

Table 6: Determinants of labor productivity

|**Y = Log (salesper worker)**|||||||
|---|---|---|---|---|---|---|
||(1)<br>Average<br>score|(2)<br>Response to<br>problems|(3)<br>No. of metrics<br>monitored|(4)<br>Difficulty of<br>targets|(5)<br>Knowledge<br>of targets|(6)<br>Basis of<br>bonuses|
|**Management score**|0.537***|0.266*|0.0744|0.143|0.287***|‐0.0940|
||(0.205)|(0.147)|(0.116)|(0.107)|(0.0939)|(0.0970)|
|**Log(size)**|0.0911***|0.101***|0.106***|0.111***|0.104***|0.116***|
||(0.0352)|(0.0338)|(0.0340)|(0.0338)|(0.0341)|(0.0342)|
|**Age **|‐0.000704|‐0.000728|‐0.000919|‐0.000843|‐0.00110|‐0.00103|
||(0.00192)|(0.00191)|(0.00191)|(0.00193)|(0.00191)|(0.00191)|
|**Foreign owned**|0.00646***|0.00652***|0.00643***|0.00646***|0.00639***|0.00642***|
||(0.00169)|(0.00169)|(0.00170)|(0.00167)|(0.00163)|(0.00166)|
|**Multi‐establishment**|‐0.104|‐0.0984|‐0.101|‐0.0981|‐0.105|‐0.0994|
||(0.101)|(0.101)|(0.101)|(0.101)|(0.0997)|(0.101)|
|**Exporter**|0.00584***|0.00616***|0.00603***|0.00605***|0.00586***|0.00615***|
||(0.00198)|(0.00197)|(0.00200)|(0.00193)|(0.00186)|(0.00194)|
|**Top manager's experience**|0.00309|0.00326|0.00343|0.00332|0.00318|0.00352|
||(0.00251)|(0.00250)|(0.00252)|(0.00254)|(0.00250)|(0.00252)|
|**Female top manager**|‐0.000211|‐0.000233|‐0.000239|‐0.000330|‐0.000379|‐0.000327|
||(0.00101)|(0.00102)|(0.000987)|(0.00101)|(0.000995)|(0.00101)|
|**Legal status(ref cat = solep**|**roprietorship): **||||||
|**Publicly listed company**|0.00638***|0.00639***|0.00651***|0.00647***|0.00664***|0.00661***|
||(0.00215)|(0.00213)|(0.00215)|(0.00218)|(0.00212)|(0.00215)|
|**Limited Liability Company**|0.00730***|0.00721***|0.00748***|0.00741***|0.00722***|0.00744***|
||(0.00133)|(0.00135)|(0.00133)|(0.00133)|(0.00132)|(0.00134)|
|**Partnership**|0.0113***|0.0109***|0.0110***|0.0109***|0.0111***|0.0108***|
||(0.00262)|(0.00265)|(0.00257)|(0.00268)|(0.00260)|(0.00262)|
|**Limited Partnership**|0.00749***|0.00747***|0.00774***|0.00768***|0.00737***|0.00771***|
||(0.00149)|(0.00152)|(0.00146)|(0.00147)|(0.00144)|(0.00147)|
|**Other**|0.00517|0.00510*|0.00526*|0.00512|0.00437|0.00507*|
||(0.00316)|(0.00295)|(0.00309)|(0.00317)|(0.00320)|(0.00308)|

17
<!-- Start of picture text --> 130,000160,000 Colombia. 180,000160,000 Narifio. and Putumayo<br>140,000 140,000<br>120,000 120,000<br>= 100,000 - Z 100,000 -<br>} 80,000 Z 30,000<br><!-- End of picture text --> <!-- Start of picture text --> 40,000 40,000 _<br>20,000 20,000 i]<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>= Coca crops ~ Ha Sprayed —— Ha man. eradicated<br>PANEL A PANEL B<br><!-- End of picture text -->
|**Appendix Table 6.2: Robustness of Firm Gr**|**owth Results to**<br>I.H.S.<br>Monthly<br>Sales|**Using PDS**<br>I.H.S.<br>Yearly<br>Sales|**Lasso**<br>I.H.S.<br>Monthly<br>Profits|I.H.S.<br>Yearly<br>Profits|Sales &<br>Profits<br>Index|Total<br>Employment|I.H.S.<br>Employment|
|---|---|---|---|---|---|---|---|
|**Impacts in Second Follow-up Survey**||||||||
|Assigned to Insourcing|0.798|0.697|0.652|0.471|0.124|0.177|0.118|
||(0.571)|(0.575)|(0.616)|(0.598)|(0.108)|(0.721)|(0.117)|
|Assigned to Outsourcing|1.182**|1.221**|0.775|1.393**|0.228**|0.478|0.215*|
||(0.571)|(0.574)|(0.616)|(0.598)|(0.108)|(0.723)|(0.118)|
|Assigned to Training|0.469|0.681|0.090|0.656|0.087|0.352|0.093|
||(0.573)|(0.576)|(0.617)|(0.599)|(0.108)|(0.721)|(0.117)|
|Assigned to Consulting|1.079*|0.998*|0.760|1.194**|0.182*|0.918|0.248**|
||(0.571)|(0.573)|(0.613)|(0.596)|(0.108)|(0.721)|(0.117)|
|Mean of Control Group|11.620|14.088|9.925|12.274|-0.123|6.723|2.105|
|Sample Size|678|678|678|678|678|693|693|
|P-value: all treatments zero|0.224|0.274|0.551|0.132|0.262|0.759|0.221|
|P-value: all treatments equal|0.593|0.743|0.641|0.356|0.575|0.762|0.487|
|Notes:<br>Regressions control for stratification batch<br>Robust standard errors in parentheses. *, *<br>I.H.S. denotes the inverse hyperbolic sine tr<br>**Monthly sales**is real (January 2018) sales i<br>and**Yearly profits**are real profits in the pas<br>standardized z-scores of these first four sal<br>PDS Lasso selects from baseline variables in<br>measures (selects full-time employment) h|fixed effects and<br>*, *** denote si<br>ansformation. F<br>n the past month<br>t month and pa<br>es and profits m<br>Table 1. Only y<br>ave any controls|baseline v<br>gnificance<br>irms that a<br>;**Yearly Sa**<br>st year resp<br>easures.**To**<br>early sales (<br>selected.|alue of outc<br>at the 10, 5,<br>re closed ar<br>**les**are real<br>ectively.**Sa**<br>**tal Employ**<br>selects own|ome.<br>and 1 perce<br>e coded as h<br>sales in the<br>**les and Pro**<br>**ment**is tota<br>er's age), a|nt levels re<br>aving zero<br>past year; <br>**fits Index**is<br>l number of<br>nd the two|spectively.<br>profits and sales<br>**Monthly profits**<br>the average of<br>workers.<br>employment|.|

74
# OViii INTRODUCMON

Table 7 Population Aged 0-14, 15-64, and 65 and Over for the World, Less Developed and More Developed Countries, and Continents, 1985, 2000, and 2025 (millions)

|Year and||Less|More||||Europe,||
|---|---|---|---|---|---|---|---|---|
|age group|Wortd<br>d|<br>eveloped de|veloped|Africa|Amertca|Asia|USSR|Oceania|
|1985|||||||||
|0-14|1,634|1,374|260|253|208|994|171|7|
|15-64|2,924|<br>2,139|784|<br>289|<br>409|1,699|511|16|
|65.|287|<br>153|134|<br>17|<br>49|131|<br>88|<br>2|
|2000|||||||||
|0-14|1,937|<br>1,685|252|<br>382|<br>230|1,154|<br>164|<br>8|
|15-64|3,860|<br>3,015|845|<br>462|<br>534|2,305|<br>540|<br>20|
|65+|407|239|168|<br>26|<br>62|206|<br>110|<br>3|
|2025|||||||||
|0-14|2,163|<br>1.915|248|<br>564|<br>229|1,202|<br>160|<br>9|
|15-64|5,498|<br>4,649|848|<br>964|<br>688|3,279|<br>541|<br>25|
|65.|754|514|240|<br>60|<br>122|420|<br>147|<br>5|

Note: "More developed" comprises Europe, U.S.S.R., Northern America (United States and Canada), Australia, New ZeaLand, and Japan. "Less developed" conprises the rest of the Fertility and mortality. Underlying these projections are fertility and mortality rates summarized in Table 8 for the world, less developed countries, and more developed countries. (Rates for continents are in Table 12.) The expected reduction in the world population growth rate of .2 percentage points in the next decade is based on simultaneous reductions in the crude birth rate of 3 points per thousand and in the crude death rate of 1 point per thousand, the bulk of these reductions taking place in less developed countries. The crude birth rate in less developed countries, 31 per thousand (double the rate in more developed countries), will decline 4 points per thousand in each of the next two decades. In more developed countries this rate will decline at a considerably slower pace. The crude death rate, on the other hand, is actually lower in less developed countries, with their younger populations, than in more developed countries, and will decline in the former while it rises in the latter.

Unlike the crude birth rate, the total fertility rate is not affected by differences in age structure. This rate for the world as a whole, estimated at 3.4 for 1985-90, will fal' 1 percent a year until 2000 and .8 percent a year between 2000 and 2025. The decline will be almost entirely in less developed countries, where total fertility will go from 3.9 in 1985-90 to 3.1 in 2000-05 to 2.5 in 2020-25. In effect, the average woman in less developed c3untries will have one-and-a-half fewer children in 2025 than in 1985. These declines may appear relat.vely moderate. Howevcr, given that fertility is unlikely to decline much further in China and a few other large countries, the reductions required in other countries will still be fairly substantial. For instance, for Africa a substantial reduction in total fertility from 6.1 to 5.1 is expected within 15 years, and an even larger reduction to 3.3 within the following 20 years. Such reductions are possible, on the basis of past experience with fertility transition, but in the recent past have not happened automatically but required
# **Table 1. Background Characteristics**

|**Panel A. Background Information**|||
|---|---|---|
|Age|28.43||
||(6.98)||
|Educational Attainment|9.20||
||(2.69)||
|Literacy|_Read Kiswahili_|_Write Kiswahili_|
||0.95|0.88|
|Respondent is Head Of Household|0.84||
|Number of Biological Children|2.06||
||(1.83)||
|Total # of Dependents|2.96||
||(2.36)||
|Age Began Seeing Clients|18.67||
||(5.14)||
|Number of Regular Clients (at time of|2.24||
|background survey)|(1.07)||
|Marital Status|||
|_Widowed_|0.23||
|_Divorced / Separated_|0.20||
|_Cohabitating_|0.13||
|_Never Married / Not Cohabitating_|0.44||
|Has working as a CSW changed likelihood of marrying?|||
|_Yes, made it more likely_|0.41||
|_Yes, made it less likely_|0.03||
|_No change_|0.57||
|Tribe|||
|_Luhya_|0.39||
|_Luo_|0.51||
|_Other_|0.10||
|Respondent is in a peer group|0.44||
|Respondent has outside job|0.84||
|**Panel B. HIV Knowledge**|||
|Tested for HIV|0.60||
|HIV Knowledge Test Score (0-1 scale)|0.94||
||(0.06)||
|**Panel C. Access to Savings**|||
|Has Savings Account|0.37||
|Savings Withdrawn in Past Month|72.82||
|(for women with savings accounts)|(430.27)||
|<br>Participates in ROSCA|0.64||
|Observations|192||
|Notes: Monetary values in Kenyan shillings. Exchange rate wa|s roughly 70||
|<br>Kenyan shillings / $1 US during study period. Standard deviati|<br>ons in parentheses.||
Economic benefit is another factor associated with adoption of ICS and modern fuels choice. For instance, Garica-Frapolli et al. (2010) find that the ICS intervention in rural Mexico contributed substantial quantity of fuelwood savings, which constituted 53% of overall benefit. In Kenya, Sudan and Nepal, Malla et al. (2011) find that significant economic benefits from cooking system interventions, mainly due to fuel and cooking time savings. Similar findings are reported by Habermehl (2007) in Kampala, Unganda and Habermehl (2008) in Malawi. They find that the economic benefit of the ICS program from fuel savings and reduced cooking time were quite significant. In Maharashtra and Karnataka, India, Thurber et al. (2014) find that the highest rate of adoption of "Oorja" ICS, using pelletized biomass, came from LPG using households mainly because of reduced fuel costs. However, their study also finds that only 9% of households that purchased Oorja ICS were using the stove due to lack of fuel supply. In northern Vietnam, ADB (2009) estimated that households saved roughly US$68 each year using biogas by substituting biomass, coal or kerosene fuels. The report also finds that women in northern Vietnam also saved on avearge1.8 hours a day by using biogas. Christiaensen and Heltberg (2012) find that use of biogas among smallholder farmers in rural China lead to decline in fuelwood and crop residues use for cooking, less time spent by women in collecting fuelwood, improvement in respiratory health and saving in fertilizers. In western Kenya, Djedje (2009) finds that both private and commercial users of ICS were able to reduce the cost of fuels (by using less fuelwood) and time for cooking. The study finds that commercial users of ICS were able to save Euro 1.1 - Euro 6.6 per day. Based on Expenditure and Consumption Survey in Lao People’s Democratic Republic, national average time spent by women collecting fuelwood have fallen from 18 minutes in 2003 to about 12 minutes in 2008 mainly due to shift from fuelwood to charcoal for cooking in urban areas (ASTAE, 2013b). However, time spent by women collecting fuelwood in the villages is significantly higher, in the range of 1-3 hours per day. Although time savings and the opportunity cost of time are important, Jeuland and Pattanayak (2012), however, suggest that the private net benefits of ICS are more likely negative because the ways in which users change behaviors lead to no change or net increases in time spent cooking or preparing fuels resulting reduced health benefits.

In the case of costs associated ICS and modern fuels, WHO (2006b) finds that fuel-, stove- and program- costs are some of the main cooking system intervention costs. For instance, 25
<!-- Start of picture text --> Number of DSM woredas<br>250<br>200<br>.<br>3150<br>|<br>5100<br>>. ._t lhl<br>2011 2012 2013 2014 2015 2016 2017 2018 2019<br>= Maize TM Wheat<br><!-- End of picture text -->

<!-- Start of picture text --> Total seed supply by DSM<br>250<br>2 200<br>§<br>.g 150<br>5<br>2 100<br>S<br>o- - LEO<br>2011 2012 2013 2014 2015 2016 2017 2018 2019<br>= Maize TM Wheat<br><!-- End of picture text -->
which we refer to as a province for the rest of this paper.<sup>14</sup> Adverse weather events are typically spatially correlated, and even expected long-run changes in weather patterns, in Southern Africa and elsewhere, are expected to occur at broad spatial scales (e.g. Shongwe et al. (2009)). This choice is consistent with interpreting our shocks as being those that affect the entire local economy. Second we use Normalized Difference Vegetation Indices (NDVI) — averaged at the province x month level — to determine harvest cycles for each province. Third, we use rainfall estimates, averaged for each province, to estimate sequences of dry spells. In the rest of this section we describe our methodology and results in more detail.

# A. _Estimation of Harvest Cycles_

The NDVI data were collected by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the NASA polar orbiting satellite Terra from February 2000 to April 2019. MODIS provided an NDVI estimate every eight days at an approximately 250 meter ground sample (pixel) resolution across the entire study area. To isolate the NDVI to those areas where are the time series was ‘masked’ signal only crops expected by the Croplands and Cropland/Natural Vegetation Mosaic categories found within the IGBP land cover classification (Loveland et al. (2000)). Next, to reduce potential pixel-level noise (both in time and space) and simplify the modeling efforts, the measurements were aggregated to monthly values at a 5 kilometer grid cell size. And finally, the values were spatially and temporally averaged further to each province x month.

We identify harvest cycles — more specifically growing months, the month that a harvest starts, and the lean season — in the following algorithmic manner.<sup>15</sup> First, we calculate the percentage change in monthly NDVI. A month is labeled as a growing month if the percent increase is greater than 20 percent. Second, the start of the harvest season is defined as the first month in which the NDVI falls by at least 5 percent. Third, the lean season is defined as the three months preceding the start of the harvest season.

For a graphical illustration of the use of monthly NDVI averages to estimate harvest cycles, please refer to Figures A-1a to A-1e in the appendix. There are subtle differences in harvest cycle patterns even within a country. For example, in Mozambique (Figure A-1b), the South-Western province of Manica has a slightly earlier harvest cycle than the NorthEastern province of Nampula. In Manica, the average November NDVI is greater than

> 14These are districts in Malawi, regions in Tanzania, and provinces in Mozambique, Zambia and Zimbabwe.

> 15Our purpose is to standardize these definitions so that they can be used to build an automated (i.e. rulesbased) definition of a drought shock. This is a prototype; in practice we could use NDVI data at more disaggregate temporal frequencies, allow for differences in harvest cycles at more refined spatial scales, and incorporate other data (e.g. soil moisture) into this algorithm.

8
# **Table A2. Description of Explanatory Variables**

|Variable|Source|Description|
|---|---|---|
|Common border|CEPII<sup>a</sup>|Dummy equal to 1 if a country pair share a land border|
|Distance|CEPII<sup>a</sup>|Measure of geodesic distance between country pair's main<br>cities|
|Common language|CEPII<sup>a</sup>|Dummy equal to 1 if a country pair shares a common<br>official language|
|Former colony|CEPII<sup>a</sup>|Dummy equal to 1 if a country pair share a colonial history|
|OPSW bilateral stock|OPSW (2010)|Total migrant stock recorded between origin i and<br>destination j|
|Some English|CIA World<br>Factbook<sup>b</sup>|Dummy equal to 1 if a destination country speaks some<br>English|
|GDP per capita|Penn World Tables<sup>c</sup>|Per capita income of the destination country in PPP|
|Total fertility|World Development<br>Indicators|Total fertility rate (in log) in the destination country|
|Skill destination workforce|DLM (2009)|Share of the destination country workforce that are tertiary<br>educated (by gender)|
|Total labor force|DLM (2009)|Population aged 25 and over in the destination country (by<br>gender)|
|Labor force participation|World Development<br>Indicators|Labor force participation rate in the destination country (by<br>gender)|
|Military service dummy|Own calculation|Dummy equal to 1 if military service is compulsory in the<br>destination country|
|Polygamy dummy|Own calculation|Dummy equal to 1 if polygamy is legally or socially<br>accepted in the destination country|
|GCC dummy|Own calculation|Dummy equal to 1 if a destination country belongs to GCC|

Notes: a See: http://www.cepii.fr/anglaisgraph/bdd/distances.htm. see Clair et al. (2004). b See: https://www.cia.gov/library/publications/the-world-factbook. c See: http://pwt.econ.upenn.edu.