rafmacalaba/datause-encoder-data huggingface dataset:
migrants; and (iii) per student education expenditure<sup>varies widely according to the city of residence and the head</sup> of household's occupation. Cities with higher public<sup>school repetition rates have a higher incidence of private</sup> school enrollments.

_Source:_ Psacharopoulos, Ariera and Mattson 1997 Findings of a study jointly commissioned<sup>by the World Bank and UNICEF, and</sup> conducted by Bray (1996), revealed<sup>that poor households in nine East Asian countries</sup> (Cambodia, China, Indonesia, Lao People's<sup>Democratic Republic, Mongolia, Myanmar, the</sup> Philippines, Thailand and Vietnam) already<sup>contribute a significant share of the total cost of</sup> public education. In these countries,<sup>household expenditure on education forms a significant</sup> share of household income. Given high<sup>inequality and inequity in income distribution, the</sup> poorest households' burden is much greater<sup>than that borne by the wealthiest households.</sup> Findings of the 1996 World Bank-UNICEF study<sup>also revealed that in some of the South-East</sup> Asian countries mentioned above, household<sup>contributions to education are greater than</sup> government contributions. In China, for<sup>example, where over 70 percent of the population</sup> resides in rural areas and where wages are about<sup>half of those in urban areas, parents and village</sup> communities have financed primary education<sup>through cash and/or in-kind contributions for</sup> both teachers' salaries and construction of school<sup>buildings. In some countries, awareness of</sup> such demands on household income discourages<sup>parents from enrolling their children in school.</sup> Governments and donors have been accused<sup>of showing little awareness of the magnitude of the</sup> burden to households.

All this goes to show that there is a growing<sup>market for education which is characterized</sup> by numerous factors pertaining to excess<sup>demand, differentiated demand, and consumer</sup> willingness to pay for educational services. A<sup>comparative analysis of five developing countries</sup> - Colombia, the Dominican Republic, the<sup>Philippines, Tanzania and Thailand (Jimenez and</sup> Lockheed 1995) - revealed that (1) although<sup>students in private schools came from more</sup> privileged families than those in public<sup>schools, on average, there was a significant overlap</sup> between the two groups; (2) with student<sup>background and selection bias held constant, students</sup> in private schools out-performed students in public<sup>schools on a variety of achievement tests; (3)</sup> unit costs of private schools were lower than<sup>those of public schools; and (4) private schools</sup> 9
# **Table 7:  Prevalence of uncertain deals discourages employment growth**

Dependent <u>variable: 3 year growth rate of permanent employees</u>

||(1)|(2)|
|---|---|---|
|Cell average meeting|0.014***||
|time with officials|(0.003)||
|Cell Std. Dev. meeting|‐0.007**||
|time with officials|(0.003)||
|Cell average pays||0.006|
|bribes||(0.008)|
|Cell Std. Dev. pays||‐0.010*|
|bribes||(0.006)|
|Age: medium|‐0.078***|‐0.074***|
||(0.012)|(0.013)|
|Age: old|‐0.167***|‐0.161***|
||(0.014)|(0.016)|
|Medium|‐0.027**|‐0.032***|
||(0.011)|(0.012)|
|Large|‐0.065***|‐0.070***|
||(0.016)|(0.018)|
|Very large|‐0.099***|‐0.147***|
||(0.021)|(0.028)|
|Foreign|0.012|0.006|
||(0.012)|(0.013)|
|Government|‐0.081***|‐0.094***|
||(0.020)|(0.029)|
|Large city|‐0.005|0.001|
||(0.015)|(0.022)|
|Small city|0.004|0.022|
||(0.035)|(0.036)|
|Constant|0.116|0.305***|
||(0.107)|(0.042)|
|Country, sector|||
|dummies|Yes|Yes|
|Observations|13097|10419|
|R‐squared|0.077|0.066|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The first result is that spending time with officials is indeed associated with better firm outcomes. In areas where firms are able to make deals, growth is higher. To the 37
Banks have been the main providers of investment advice in France. The Anglo certified financial advisor model remains underdeveloped.

Two of the insurers listed in table 3.2, while associated with banks, have partnership strategies and actively develop relationships with other financial institutions and adviser channels. These strategies reflect their origins, with Cardif working with nonBNPP banks and CNP operating through a formerly state-owned financial advisor network as well as its two quasi-public bank shareholders. Both Cardif and CNP have foreign operations, again working through partnerships, with Cardif actively following an international strategy.

Some of the challenges arising from elements of the insurance value chain that are not found in banking have been successfully addressed in France:

- Life risk underwriting has been partly dealt with by taking a group underwriting approach to products sold with lending products.

- Expert systems using socioeconomic and other nonintrusive indicators have also been developed.

- In more extreme cases, if an application for insurance has been declined by insurers after two medical examinations, it can be submitted to one of two reinsurance pools established to accept nonstandard risks. The original pool covers HIV-positive borrowers, and another more recent pool includes placements from insurers and reinsurers that are members of the two French insurance associations.

# **Non-Life Insurance Has Proved Less Effective**

The Bancassurance business model has proved to be less suited to non-Life insurance in France, with most gains having been made in the 1990s, when general agents gave up approximately 10 percent of market share to the banks (table 3.3).

**Table 3.3 French Non-Life Distribution**

**_<u>percent</u>_**

||General<br>Agents|Brokers|Salaried|Mutuals—<br>Direct|Bancassurance|Direct—<br>Internet<br>and others|
|---|---|---|---|---|---|---|
|2000|35|17|3|34|8|3|
|2010|34|18|2|33|11|2|

_Source:_ AXCO.

Credit Agricole and ACM account for more than 60 percent of non-Life Bancassurance, with household insurance being the most successful product. The postal service began to distribute retail insurances through its local offices in 2011 54
Table S. **Cot** of **Inland Transport** in Selected Countries, 1801-1960 (i US **mnts** per daort tonuzile)

||**United**<br>Kingdom|**Frnmco**|**United**<br> <br>**Stat**|**Ausualia**<br>**(Now**South Wales)|**India**|**Japa**|
|---|---|---|---|---|---|---|
|Road|||||||
|1800-20|18-29|-|30-70|-|-|-|
|1830s|13-22|5-11|12-17|-|12|-|
|**1860s**|-|-|-|13'|(s)b|-|
|Early l9th**centz**|**s-r**|24|(1.7-3.4)'|-|-|-|
|Rdaway|||||||
|1850|**(3-S)**|2.2'|(4.1)9|-|_|_|
|1880|2.4"|1.7|1.2|**4.1**|2.0'|-|
|1900|2.0|**1.3**|0.7|2.3|0.9|0.9|
|1920|2.2|1.2|1.1|1.4|0.8|1.0|
|1938|2.3|**1.7"**|1.0|**2.0**|1.0|0.7"|
|**1950**|2.2|2.1|1.3|1.6|1.0|0.8|
|1960|3.3|2.5|1.4|3.6|**1.1**|1.6"|

Note: Road and **canal figures a mosdly** approximate. Bracmts denote incomplete figures. Railway figues refer **to** averag receipts for goods carded by rail **and** are **fiom the** following **dates** onwards: **UK** 1820, **France 1850,** USA 1870, Australia (New South Wales) 1870, india 1900, **Japan** 1890.

1871 figure **'New** York **State** rilway. b 1861-2 fig=ue **for Cotton only** 1885 figure S **SeeWJ** acm -die _ACan_ route, 1830-50

**J 1874 fgure I** lncrase m 1938 **pary** accouted **for** by ew **method** of

Yorksire d **Iancasbie** railways for 1852-3 **193-7 fig** '1851 figure **A proximate** Source: Woodruff 1967, p.253.

In Asia, external finance was relatively small and unevenly distributed. Certain sparsely populated countries such as Malaya received large amounts of foreign investment per capita, fincing almost all of iheir infrastructure out of foreign fund. India, operating under the guaantee, received disportionate quantities of foreign investment. Most castes **had** a background unfavorable to financial involvement; hence Indians **provided** relatively little capital for these enterprises, concentratng instad in small-scale trading ventures.

33
# **5. Estimated impacts**

Table 1 gives mean income and consumption and the poverty rates for 1996, 2000 and 2004/05. The poverty line of 808 yuan per person per year in 1995 prices (corresponding to the $1 a day line used by Ravallion and Chen, 2005, at 1993 purchasing power parity) as well as poverty lines above and below this figure. We see that the income gains in SWP villages between 1996 and 2000 were larger than among non-SWP villages, but that this reverses between 2000 and 2004/05. Ten years after its commencement, the SWP does not appear to have allowed the selected poor villages to catch up with the rest of these (poor) counties.

Table 1 suggests that SWP had little or no impact on income and consumption. However, before accepting that conclusion we need to probe more deeply into the potential sources of bias described in the previous section. We begin with selection bias due to non-random placement of the SWP. At the end of this section we test for bias due to contaminating spillover effects.

- _5.1 Probits for selection into the SWP_

Table 2 gives probits for whether a village was selected for SWP, as used to estimate the propensity scores. The variables were chosen to reflect the selection criteria used by the project staff (based on our interviews at the time).

We find that project villages tend to be in more hilly/mountainous areas, are less likely to have electricity, less likely to have a school in the village or nearby, though more likely to have a health clinic within the village relative to nearby.<sup>22</sup> The SWP villages also tend to have larger populations, with lower mean income in 1995 (from the village-level data), lower mean consumption in 1995 (from the household survey) and more land per capita. The latter characteristic probably reflects lower population density and lower land quality in the project villages. In most respects, the results of Table 2 suggest that the SWP villages tend to be poorer than other villages within the project counties, consistently with Table 1.

Using the propensity scores based on Table 2 to re-weight the data we were able to obtain a close balancing of the characteristics of the two samples (including in the

> 22 Remote villages are more likely to have a very basic health clinic, to compensate for the inaccessibility to more comprehensive township facilities.

18
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42. Harrington, L. J. & Otto, F. E. L. Reconciling theory with the reality of African heatwaves. _Nat. Clim. Change_ **10** , 796–798 (2020).

43. Editorial. Ending hunger: science must stop neglecting smallholder farmers. _Nature_ **586** , 336–336 (2020).

44. Derbile, E. K., Chirawurah, D. & Naab, F. X. Vulnerability of smallholder agriculture to environmental change in North-Western Ghana and implications for development planning. _Clim. Dev._ **14** , 39–51 (2022).

45. Chandra, A., McNamara, K. E., Dargusch, P., Caspe, A. M. & Dalabajan, D. Gendered vulnerabilities of smallholder farmers to climate change in conflict-prone areas: A case study from Mindanao, Philippines. _J. Rural Stud._ **50** , 45–59 (2017).

46. Harvey, C. A. _et al._ Extreme vulnerability of smallholder farmers to agricultural risks and climate change in Madagascar. _Philos. Trans. R. Soc. B Biol. Sci._ **369** , 20130089 (2014).

47. Zeleke, T., Beyene, F., Deressa, T., Yousuf, J. & Kebede, T. Vulnerability of smallholder farmers to climate change-induced shocks in East Hararghe Zone, Ethiopia. _Sustainability_ **13** , 2162 (2021).

48. Marulanda, M. C., Cardona, O. D. & Barbat, A. H. Revealing the socioeconomic impact of small disasters in Colombia using the DesInventar database. _Disasters_ **34** , 552–570 (2010).

49. Gourlay, S., Kilic, T., Martuscelli, A., Wollburg, P. & Zezza, A. Viewpoint: High-frequency phone surveys on COVID-19: Good practices, open questions. _Food Policy_ **105** , 102153 (2021).

50. Carletto, G. & Banerjee, R. Strengthening Disaster Resilience: A Microdata Perspective. in _Capitalism, Global Change and Sustainable Development_ (ed. Paganetto, L.) 87–96 (Springer International Publishing, 2020). doi:10.1007/978-3-030-46143-0_6.

51. Carletto, G., Chen, H., Kılıç, T. & Perucci, F. Positioning household surveys for the next decade. _Stat._

- _J. IAOS_ **38** , 1–24 (2022).

20
Table 5 provides evidence on the sources of (new) funds for non-financial Indian firms during the 14-year period of 1991-2004, based on the _Prowess_ database of the Centre for Monitoring the Indian Economy (CMIE). The firm categories (among the firms in the non-state sectors) are not always mutually exclusive but they show the breakdown between listed and unlisted firms, and, in the small-scale sector, between manufacturing (SSI) and services (SSSBE) sectors.<sup>21</sup> For all firms in the state and non-state sectors, the most important source of funds is “internal sources” (e.g., retained earnings) accounting for over one-third of all sources. However, the most striking finding from the table is the importance of short-term financing through trade credits, other current liabilities and provisions, reflected in the “Others” category. Across all firm categories short-term sources on an average accounted for close to 30% of all sources of funds. The importance of this source increases dramatically in the small scale segment accounting for over half and almost two-thirds of all financing for the SSI and SSSBE sectors, respectively. Since many firms in the SSSBE sector are engaged in wholesale and retail trade and with current liabilities playing an important role in financing and business activities, this finding is perhaps not too surprising.

The overall marginal debt-equity ratio, with debt equaling the sum of “debt” and “borrowing from banks or intermediaries” (part of this category may be short-term in nature) comes out to be approximately 1.5. Firms in the two small-scale sectors have negligible debt in their marginal capital structure. Firms in the state sector have the highest marginal leverage (over 1.8), followed by listed companies, unlisted companies, and small scale sector firms in that (expected) order. While there has been considerable temporal variation in the relative importance of individual sources of financing in the last decade and a half, the overall patterns noted above remain unaltered. Overall, the results shown in Table 5 are largely consistent with the findings of Love et al. (2004), who find

> 21 The numbers in the table are _flow_ variables. For a certain type of firm, the percentages reported in the table are obtained by first calculating the percentage of total _new_ funds in a given year for each funding source and then by taking the average from 1990 through 2004. CMIE is a Mumbai-based economic and business information and research organization.

19
came before the placebo ones, while for the other half, this order was reversed. For the placebo questions (Table D3), we find no effect of ordering, other than for the farm work question in Malawi. However, the effect goes contrary to the expected direction as the placebo effect of SI comes about when the placebos come later (i.e., practice does not help). That said, we do not wish to make much of this lone coefficient, as the placebo effect of SI is the same for “placebos first” and for “IPV first” in all other cases. For the IPV questions, we report coefficients in (Table D4), and find that IPV reporting increases for sexual IPV if the placebos come first. Overall, for Malawi, the effect of SI on the probability of answering a question “yes” is about 4 percentage points if the IPV questions come first, but 6 percentage points if the placebos come first ( _p-_ value for difference = 0.217). We find no significant effect of the ordering in Liberia either. This finding is consistent with the possibility that survey fatigue causes measurement error to increase, though it is also possible that the increase in IPV is real and that women became more familiar with the module over the course of the survey. We leave a further investigation of this channel to future work.

# **5 Conclusion**

In this paper, we test the efficacy of ACASI versus FTFI in eliciting truthful responses regarding IPV from women respondents in the context of a cash transfer experiment in rural Liberia and Malawi. Our results suggest that women do not understand ACASI, as evidenced by the fact that 1/3 of women incorrectly answer basic screening questions and that, even among those who pass, we observe a strong ACASI effect on innocuous placebo questions. This lack of understanding will tend to _increase_ IPV reporting, since the rate of IPV is much less than 50%. And indeed, we do find a striking increase in reported IPV in one country (Malawi). However, this result is likely entirely spurious. This is deeply concerning because measurement error goes in the same direction as destigmatization, and so what looks like a decrease in stigma could be purely fictional. SI could therefore give very misleading results.

Our results, combined with our read of the literature, suggest that there may be greater benefit from having well-trained, empathetic enumerators than from SI in the context of measuring IPV. For example, in a natural experiment in Serbia, respondents of a WHO-run IPV survey ended up getting randomly assigned to either a previously inexperienced but well-trained enumerator 15
# **Figure 2. Prevalence of Price Controls: LICs vs Other EMDEs**

- **B. Economies with price controls by sub-region**

## **A. Price controls in LICs and other EMDEs**

<!-- Start of picture text --> Percent of economies<br>LICs Other EMDEs LICs turned MICs<br>100<br>80<br>60<br>40<br>20<br>0<br>materials Energy Food and Beverages Others<br>Construction<br><!-- End of picture text --> <!-- Start of picture text --> Percent of economies LICs Average<br>100<br>80<br>60<br>40<br>20<br>0<br>Construction Energy Food and Others<br>Materials Beverages<br>EAPECALACMNASARSSA EAPECALACMNASARSSA EAPECALACMNASARSSA EAPECALACMNASARSSA<br><!-- End of picture text --> Source: World Bank; World Trade Organization.

A.B. EMDEs = emerging markets and developing economies; LICs = low-income countries. Sample includes 21 low-income countries and 79 other EMDEs, which include 23 low-income countries turned middle-income countries. A. Unweighted averages. Listed price control policies are retrieved from the latest (2003-19) country Trade Policy Review publication.

B. Listed price control policies are retrieved from the latest (2003-19) country Trade Policy Review publication. Unweighted averages. EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, SSA = Sub-Saharan Africa.

Individual goods and services most frequently subject to controls vary noticeably across advanced economies and EMDEs, but little across LICs and other EMDEs (Figure 3.A-C). Whereas controls in advanced economies are most frequently imposed on basic utilities such as electricity and water, and telecommunication and postal services, controls in EMDEs are much more frequently applied to petroleum products such as gasoline and liquified petroleum gas, as well as food products including flour, bread, sugar and rice. Less prevalent goods and services subject to controls include various pharmaceutical products, services such as the provision of medical care, public sanitation and transportation, as well as construction materials such as cement, reinforcing bars, and metal sheets.

**Figure 3. Goods most frequently subject to price controls**

<!-- Start of picture text --> A. Goods most frequently subject B. Goods most frequently subject C. Goods most frequently subject<br>to price controls in advanced to price controls in EMDEs other to price controls in LICs<br>economies than LICs Percent of economies<br>Percent of economies Percent of economies 80<br>80 80<br>60<br>60 60<br>40<br>40 40 20<br>20 20 0<br>0 Electricity Telecomm Postal services Water Petroleum products Medicine 0 Petroleum products Electricity Water Cereal Products Sugar Rice Petroleum products Electricity Water Cereal products Sugar Rice<br><!-- End of picture text --> Source: World Bank; World Trade Organization. A.B.C. Listed price control policies are retrieved from the latest (2003-19) country Trade Policy Review publication.

5
31 _teachers_ . Stanford, CA: Stanford Center for Opportunity Policy in Education. Retrieved from http://www.oup.hu/howhigh_doug.pdf. Accessed December 16<sup>th</sup> , 2016.

Darling-Hammond, L., Wei, R. C., Andree, A., Richardson, N., & Orphanos, S. (2009). _Professional learning in the learning profession._ Washington, DC: National Staff Development Council. Retrieved from http://www.ostrc.org/docs/document_library/ppd/Professionalism/Professional%20Learning%20i n%20the%20Learning%20Profession.pdf. Accessed December 16<sup>th</sup> , 2016.

Desimone, L. M. (2009). Improving impact studies of teachers’ professional development: Toward better conceptualizations and measures. _Educational Researcher_ , _38_ (3), 181-199.

Evans, D. K., & Popova, A. (2016). What really works to improve learning in developing countries? An analysis of divergent findings in systematic reviews. _World Bank Research Observer_ . Doi:10.1093/wbro/lkw004 Fisher, R. A. (1925). _Statistical Methods for Research Workers_ , 1st ed. Edinburgh: Oliver and Boyd Ltd. Fisher, R. A. (1935). _The Design of Experiments._ Sixth edition. Edinburgh: Oliver and Boyd, Ltd, 1951. Fryer, Jr, R. G. (2017). <mark>The production of human capital in developed countries: Evidence from 196</mark> <mark>randomized field experiments. In</mark> _<mark>Handbook of Economic Field Experiments, 2</mark>_ <mark>: 95-322.</mark> Gersten, R., Taylor, M. J., Keys, T.D., Rolfhus, E., & Newman-Gonchar, R. (2014). _Summary of research on the effectiveness of math professional development approaches._ Washington, DC: Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, U.S. Department of Education; and Regional Educational Laboratory Southeast at Florida State University. Retrieved from http://files.eric.ed.gov/fulltext/ED544681.pdf. Accessed December 16<sup>th</sup> , 2016.

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