rafmacalaba/datause-encoder-data huggingface dataset:
the impacts of temporary cash transfers on household consumption. In this literature, a large body of work has leveraged random and quasi-random income shocks to estimate short run impacts in richer countries (Jappelli & Pistaferri, 2010) and developing countries (Haushofer & Shapiro, 2016). More recent work in Norway has taken advantage of high frequency household balance sheet data to estimate dynamic impacts leveraging natural experimental variation in cash transfers (Fagereng et al., 2019). Aggregating across experimental UCT studies meets the data requirements to produce comparable dynamic estimates for developing countries. These impacts can also be interpreted as intertemporal marginal propensities to consume, key parameters for disciplining models of intertemporal optimization subject to constraints (Auclert et al., 2018). Second, we contribute to a deep literature that investigates the role of poverty traps and implements empirical tests (Carter & Barrett, 2006; Kraay & McKenzie, 2014; Balboni et al., 2020). Complementarily, recent work leveraging experimental variation has consistently found large UCT and TUP programs increase household consumption 4 or more years after initial transfers, suggesting these programs enable households to escape from poverty (Bandiera et al., 2017; Blattman et al., 2020). Through a metaänalytic lens, we provide a relatively powered test of the relationship between intervention size and the impacts of cash transfers on household consumption over time. In addition, we theoretically link this relationship to the existence of poverty traps. Third, we contribute to a growing literature in economics that uses metaänalysis to aggregate estimated program impacts across studies (Baird et al., 2014; Burke et al., 2015; Meager, 2019; Vivalt, 2020). We do so by providing a template for future “metabenchmarking” of the cost-effectiveness of one intervention (here, the cost-effectiveness of either large UCT or TUP at increasing household consumption) against UCT.

In summary, we present a nuanced view of the impacts of intervention size on cost effective sustained poverty reduction in the context of temporary cash transfers and their theoretical underpinnings. We produce evidence that increasing intervention intensity, through increased transfer size, reduces cost effectiveness in the short and medium run. In our model, 7
averse as studies suggest (Binswanger 1980, Brauw and Eozenou 2014), then a large expected return may be needed in order to offset the disutility from negative returns. Indeed, coffee farmers have been shown to reduce input use in Uganda as a result of expected risk in prices and weather (Hill 2008). In the presence of risk aversion and uncertain returns, returns to using fertilizer in maize and matooke production, even when assuming high quality inputs, may not be enough to encourage adoption.

<!-- Start of picture text --> Figure 3.6: Returns to authentic inputs across major crops<br>1. Returns to fertilizer and improved seeds for 2. Returns to fertilizer and improved seeds for<br>maize in the Eastern region beans in the Western region<br>200% 400%<br>150%<br>300%<br>100%<br>200%<br>50%<br>0% 100%<br>0 50 100<br>‐50% Price percentile<br>Price percentile 0%<br>‐100% 0 50 100<br>Fertilizer and hybrid seeds Fertilizer and improved seeds<br>Fertilizer Fertilizer<br>Improved seeds<br>Hybrid seeds<br>3. Returns to fertilizer for matooke in the 4. Returns to fertilizer for Robusta coffee in the<br>Central region Western region<br>200% 200%<br>150% 150%<br>100% 100%<br>50% 50%<br>0% 0%<br>0 50 100 0 50 100<br>‐50% ‐50%<br>Price percentile Price percentile<br>‐100%<br>‐100%<br>Fertilizer Fertilizer<br><!-- End of picture text --> Source: 1: see Figure 3.5, 2‐4: staff calculations using yield responsiveness and cost of inputs estimated in the OFRA Response Function Database (2017) from the University of Nebraska. Wholesale prices from UBOS, 2000 ‐ 2012.

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2 KENYA AND CHINA’S TRADE RELATIONSHIP duction, a sector that is 20 percent of all formal manufacturing employment in Kenya. Clothing enterprises employ mainly women, so a weaker industry worsens gender equality.

**Some producers benefit from using Chinese intermediate goods in production** When local producers use intermediate goods, they can access goods unavailable locally to increase their productivity. Between 1990 and 2014, imports of intermediate and capital goods have grown 12.6 percent annually (figure 10). Over time, many domestic businesses have switched to cheaper Chinese goods. Figure 11 shows imports from top trading partners: since 1990, Kenya’s imports from China have increased 26.7 percent per year, and are now 21.9 percent of imports, replacing goods from other major sources. In 2014, only 6.9 percent of imports came from India and 4.5 percent came from South Africa, two former major sources of intermediate inputs.<sup>6</sup>

<!-- Start of picture text --> 10000<br>8000<br>6000<br>4000<br>2000<br>0<br>1990 1995 2000 2005 2010 2014<br>Year<br>Intermediate Goods imports (USD Millions)<br><!-- End of picture text --> Figure 10: Kenya’s imports of intermediate goods is rising quickly Source: UN Comtrade 2015 The top categories from China were processed industrial supplies, parts and accessories for transport equipment, capital goods, primary industrial supplies, and food and beverages for industry. Industry supplies were 77 percent, and transport equipment and capital goods were each ten percent of imports of intermediate goods. Imports of industrial supplies and transport equipment are high because of strong demand from the Standard Gauge Railway construction. In general, transport equipment contributes a large share to import growth, and the switch to Chinese imports cuts the cost of production.

> 6We use the Broad Economic Categories (BEC) classification for intermediate goods

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# **TABLE 2. ICT predictors**

|**Panel A: Firm characteristics**|Revenue|Labor<br>Productivity|Share<br>Unskilled|Foreign<br>Ownership|Part of<br>Group|
|---|---|---|---|---|---|
|ICT|0.088***|0.051***|‐0.58***|0.21***|0.22***|
||(0.0047)|(0.0084)|(0.10)|(0.029)|(0.025)|
|Observations|3,523|3,434|3,584|3,656|3,691|
|**Panel B: Characteristics of manager**|College|Graduate|Less than|Previous||
||Degree|Degree|50years old|Experience||
|ICT|0.15***|0.26***|0.065***|0.13***||
||(0.016)|(0.029)|(0.016)|(0.023)||
|Observations|3,691|3,691|3,691|3,675||

Correlation between investment in ICT and characteristics of firm and manager at the beginning of the sample period. Industry controls. Robust standard errors.

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# **The Water Sector**

The water sector review draws primarily upon data from utility surveys carried out by the Organization for Economic Cooperation and Development (OECD) and East Asia Pacific Region (EAP) Task Force in 2001-03 and now available through IB-NET. The original OECD country data sets are based on samples from utilities across the country, and the approach that has been taken by the OECD has been to multiply per capita averages for the sample by the urban population to arrive at a national average.

IB-NET otherwise known as the International Benchmarking Network for Water and Sanitation Utilities collects standard utilities performance indicators. The objective of IB-NET is to support access to comparative information that will help promote best practice among water supply and sanitation providers worldwide and eventually will provide consumers with access to high quality, and affordable water supply and sanitation services.

IB-NET sets forth a common set of data definitions and a minimum set of core indicators which, with some modifications and supplements from data sources from secondary sources, have been used for estimating hidden costs and presenting a picture of technical and financial performance of the water sector. The IBNET website is http://www.ib-net.org/.

A summary of data sources follows:

- Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyz Republic, Moldova, Russian Federation, Tajikistan (all 2001-2003) and Ukraine (2000-2001) use data collected by the OECD EAP Task Force (now available through: www.ib-net.org).

- Albanian data have been sourced from the National Water Association report, 2003.

- Data for Danube basin countries (Czech Republic, Hungary, Serbia and Montenegro, Romania, Slovak Republic, Croatia, Bulgaria, and Bosnia Herzegovina) have been sourced from UNDP/GEF (2004).

- Data for European Union accession countries have been sourced though ISPA reports (20002004).

- Data for Belarus have been sourced through the State of Environment report, Ministry of Environment, 2003.

- Data for Poland (2000-03) and Ukraine (2002-03) have been sourced through national experts on the basis of reports from national municipal water authorities.

- The Bank’s World Development Indicators Database (2004) is the source for GDP, Exchange Rate, and Price Deflators.

- Data from various environmental and water sector reports have been used to supplement primary data.

# _Data Availability_

Data have been collated for sixteen countries<sup>11</sup> that have been classified into groups based on a combination of geographic and economic criteria. The data set covers the four-year period 2000-03, but in

> 11 For the analysis of hidden costs, 11 of the 16 countries have data available for 3-4 years.

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# **TABLE A5. Total Employment. ICT intensity**

||F<br>|E<br>|FE‐<br>|2SLS<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|ICT|8.52***|8.67***|15.7|15.7|
||(2.55)|(2.45)|(24.3)|(24.3)|
|ICT|‐1.02|‐0.14|‐88.4**|‐88.4**|
||(2.09)|(2.04)|(34.9)|(34.9)|
|ICT * High Growth|20.4***|18.8***|184***|184***|
||(4.55)|(4.33)|(22.1)|(22.1)|
|Observations|3,481|3,481|3,481|3,481|

Analogous to Table 15. Regressor: average firm investment in ICT over firm sales during sample period.

45
its workforce as the main dependent variables. Fourth, we match these data with administrative information on the enforcement of labor market regulations, captured by the municipal incidence of labor inspections (as published by the Brazilian Ministry of Labor).

Our empirical strategy to estimate the effect of digital technologies on employment and the demand for skills relies on substantial variation across municipalities and over time. The fact that the internet rollout varied across municipalities and over time makes it possible to control for municipality-specific and time-specific effects. We interact this main supply-side technology variable with industry-specific information on the demand for information and communications technology, relying on data from the _Current Population Survey_ computer supplement, which asks workers whether they use a computer at their job. Industries that use information technology intensively and that are located in cities that have early access to internet services are likely early adopters of digital technologies. Therefore, the identification of the effect of technology uses across-municipality differences in internet services provision over time for industries that differentially use information technology.

Our work significantly contributes to a growing literature. While we are not the first to investigate the impact of digital technology on employment and the task content of occupations using micro data<sup>3</sup> , we are among the first papers to explore this question for an emerging economy.<sup>4</sup> Furthermore, to our knowledge we are the first paper allowing technology to impact industries differently depending on their exposure to labor market regulatory enforcement. Contrary to previous studies that rely on cross-country or acrossstate variation in existing _de jure_ labor regulations<sup>5</sup> , we explore the fact that Brazilian employers are exposed to varying degrees of _de facto_ labor regulations, measured by the number of Ministry of Labor inspections per establishment in the city and broad sector.

> 3 See, for example, Goos, Manning, and Salomons (2009), Böckerman, et al. (2016), and Akerman, et al. (2015) for research on industrialized countries.

> 4 To our knowledge, the only other paper assessing the impact of technology adoption on employment and the skill content of jobs in a developing country context is Almeida, Fernandes, and Violaz (2017). However, unlike us, their work focuses on the medium-term impact of the adoption of an advanced software within firms. Unlike the Brazilian data, where there is detailed information on the occupational disaggregation within firms, the Chilean data are less detailed.

> 5 As examples, please see Besley and Burgess (2004) and Autor, Kerr, and Kugler (2007),

4
Table 3: Sample actual and offered payments, acceptance rates

||Mean|sd|Max|min|
|---|---|---|---|---|
|a. Leaders|||||
|Approx. payment per ha|412|98|700|280|
|First offer in referendum|326|1678|770|80|
|Actual value - referendum value|86|163|590|-440|
|Voted yes to frst offer|0.72|0.45|1|0|
|Obs|862||||
|b. Households|||||
|Approx. payment per ha|412|98|700|280|
|First offer in referendum|329|171|770|80|
|Actual payment - referendum offer|84|165|590|-440|
|Voted yes to frst offer|0.79|0.41|1|0|
|Obs|8413||||

Values from leader (panel a.) and household (panel b.) survey.

of acceptance was somewhat lower for those leaders who were offered a contract at a value less than the one that they were supposed to receive; in these cases, 64% of the leaders voted yes, and 78% of households. This rate decreased to 59% among leaders of beneficiary communities, but remained high, at 76%, among beneficiary community households.

With regards to the second offer price, we did encounter switching consistent with a change in the level. For those who voted yes to the first contract price offered, and hence received a lower price for the second question, 27% said that they would not take the lower offer. On the other hand, among the 244 respondents in the leader survey who said no to the first offered price, 83% said yes to the second (see Appendix Tables A7 and A8). The fact that many individuals did change their answer suggests that they were engaged with the interviewer and understood the question.

Table 4 supports the assumption that leader votes are representative of community preferences, which indicates that they are acting as community representatives. The table shows the distribution of calculated median voter prices, both for the full sample and for the sample for which there 24
according to the Ferson and Khang (2002) and Kaminsky et al. (2004) measures, but even in this case the hypothesis of contrarian trading cannot be rejected for only four percent of the PFAs. Among the measures, the Grinblatt et al. (1995) measure is the one that results in more rejections of the hypotheses of no-momentum or contrarian trading, followed by the Kaminsky et al. (2004) and the Ferson and Khang (2002), respectively.

Columns (4) to (6) present the momentum statistics based on contemporaneous returns. There are two aspects of these results that are worth highlighting. First, the hypothesis of momentum trading in domestic equity cannot be rejected only for the Grinblatt et al. (1995) measure, while the other two measures do not allow us to reject the hypothesis of contrarian trading, which is consistent with the results from the regression approach reported in Table 12. Second, there is evidence of significant contemporaneous momentum trading for mortgage bonds and government bonds, which could be driven by reverse causality because of the importance of PFAs in the market for these assets.<sup>57</sup> However, as in the regressions presented in Table 12, there is also evidence of contemporaneous momentum trading for quotas of foreign investment and mutual funds, which is unlikely to be driven by endogeneity and suggests that at least part of the contemporaneous evidence of momentum trading in other asset classes is indeed related to momentum trading within the current month.

Although in principle the momentum strategies followed by PFAs have the potential of destabilizing capital markets and increasing price volatility, this does not seem to be happening in Chilean capital markets in general, as can be seen in Table 14 that shows the results of regressing an asset’s return on the lagged fraction of PFAs buying that asset for all domestic assets traded in secondary markets. The only asset class in which past trading affects future prices is government bonds, which is somewhat surprising considering the tradability of these assets (as measured by their overall market turnover ratio) but not considering the importance of PFAs in this market.

# **_5.4. Does Momemtum Explain Herding?_**

As mentioned above, momentum strategies are one form of herding behavior. If all funds buy assets with high past returns they will all tend to be on the same side of the

> 57 Around 100 and 60 percent of the amount outstanding in each of these asset classes are in the hands of PFAs, respectively, according to the Asociación Gremial de Administradoras de Fondos de Pensiones (2007).

32
current level of the benchmark is used to calculate the cap, but some countries also use average past values. For example, in Guatemala the cap on interest rates charged on credit cards is calculated as twice the median moving annual interest rate of the banking system for the previous 12 months.

**Figure 7: Application of interest rate caps**

<!-- Start of picture text --> Type Methodology Benchmark<br>40 20 25<br>20<br>30 15<br>15<br>20 10<br>10<br>10 5 5<br>0 0 0<br>Absolute Cap Relative Cap Fixed spread Multiple of Policy rate Average<br>benchmark market rate<br>Lower middle income<br>Lower middle income Low income<br>Low income<br><!-- End of picture text --> _Source:_ Authors’ calculation The type of benchmark used for calculating the level of relative interest rate caps is usually either the _central bank’s policy rate_ or an _average market rate_ . The use of central bank rates is less prevalent in our sample. Only six countries, including Kenya, Nepal, Nigeria and South Africa, cap lending rates based on the level of the policy rate. In contrast, 20 countries use some form of (weighted) average market interest rate to determine the level of the benchmark. For example, in Colombia the cap is calculated as 1.5 times the current average banking sector rate; the Reserve Bank of India sets the cap for regulated MFIs at 2.75 times the average base rate of five large commercial banks<sup>11</sup> ; in the Kyrgyz Republic the relative cap is calculated as the weighted average interest rate on loans issued by banks and MFIs plus 15%; and in Paraguay interest rates applied on credit cards may not be higher than three times the current average deposit rate as published monthly by the Central Bank.

> 11 See Section 5 for more details on India.

16
(asinhM,.,1) (asinhM,.,1) it = Miami; + », P pt >, P pt », p(Dp i) PePRE-79 PePOST-79 PePRE-79 + >, p(Dp i) + Eit PePOST-79 it Pp P 80-82 <u>non — repatriate unemployed,g¢6i</u> f (repatriatey 68 non — repatriate labor force,9¢68,i 1968,i 1968,i 1968,i)
<!-- Start of picture text --> g 0.10 —<br>2 0.08<br>7)<br>8 0.06 =<br>a 0.04 = $12,9081%<br>2 ow $2,92512% Seei .<br>“2 0,00 e ,<br>o 6 e e .. e<br>9 56% e ba > e<br>Ria& -0.02 e e ces5 9 $23,309.<br>0.04 Te<br>i) e<br>v “"e o<br>006<br>= “e ° 11%<br>3-008 e 8<br>is 0.10 $1,624= |<br>-0.06 -0.04 -0.02 0.00 0.02 0.04 0.06<br>Per Capita Income Yearly Convergence Rate<br>@ Latin America<br><!-- End of picture text -->

<!-- Start of picture text --> g ae 57%<br>= 0.08<br>“a ‘ eee China<br>% 0.06 a<br>0.04x ° e % ee,50 te48ge<br>KS} e o o°ee ‘a o %<br>is s1.946 9” e % Mo9."* toe $2,763<br>et ang 6° Qe<br>TM 3°. e<br>8 a @ e e e<br>3g -0.02 “oo eeoad8 ° ee Se<br>Y 6.04 $18,268<br>7m<br>9°<br>g 006 50%<br>De<br>2 -0.08 $3,722<br>S 0.10 a<br>-0.04 -0.02 0.00 0.02 0.04 0.06 0.08 0.10<br>Per Capita Income Yearly Convergence Rate<br>@ Latin America<br><!-- End of picture text -->
<!-- Start of picture text --> Figure 14 Education of first-time migrants Figure 15 Age of first-time migrants<br>(number of years of schooling)<br>year 1st episode year 1st episode<br> all males all males<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>12 32<br>11 30<br>10 28<br>avg education years avg age at migration<br>9 26<br>8 24<br><!-- End of picture text --> As expected, a larger share of temporary migrants originated in rural areas, mostly from the Central region closer to the Greek borders (see Figures 16 and 17). Although to different degrees, all areas experienced an increase in outflows in the periods after the opening of the borders and the collapse of the pyramid scheme. This last peak also coincides with the 1998 regularization in Greece, which may account for part of the increase in 1997-98. In fact, we observed that most of the 1998 flow originated in the rural areas of the Central region, the primary reservoir of migration to Greece.

<!-- Start of picture text --> Figure 16 Flow of first-time temporary migrants Figure 17 Flow of first-time temporary migrant<br>by location of original household by region of residence of original househo<br>year 1st episode year 1st episode<br>Tirana Other urban Rural Tirana Coastal Central Mountain<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>20000<br>15000<br>10000<br>10000<br>5000<br># of first-time temporary migrants # of first-time temporary migrants<br>0 0<br><!-- End of picture text --> The flow figures, considering both the timing of temporary departures and returns, are cumulated in Figure 18 to estimate the stock of temporary migrants abroad in each year. As expected, the stock of temporary migrants abroad in a given year sharply increased in correspondence of the two major episodes of migration ‘outbreak’ in the early 1990s and post-1996, and have leveled off over the past few years.

12