rafmacalaba/datause-encoder-data huggingface dataset:
distortions and find jobs, the success stories may just crowd out other jobseekers, and it would be better for governments to concentrate their policy efforts in addressing the distortions directly.

In the absence of these government-introduced distortions, the simplest introductory model of labor supply and labor demand, in which the market works to equilibrate the supply and demand for jobs through changes in the wage is actually not a bad approximation for many types of labor in less-skilled jobs in urban labor markets in developing countries. Many firms have no trouble filling the job openings that they have. For example, Groh et al. (2015) conducted a panel survey of employers in Jordan to track how long it took firms to fill vacancies. Most vacancies are filled fast, with only 6 percent requiring more than two weeks. In a field experiment conducted in Sri Lanka, De Mel et al. (2019) find that only 12 percent of microenterprises said they found it hard to find the right worker for routine, physical jobs. Many governments are particularly interested in boosting the workforce in formal jobs in large manufacturing firms. But several studies have found these manufacturing firms do not appear to be that discerning about who they hire. They find workers very quickly and many workers voluntarily quit these jobs – suggesting that these jobs are neither that hard to fill, nor so rationed and sought after that no one leaves these jobs after securing such a position (for example, Blattman and Dercon 2018).<sup>4</sup> So then what are some better reasons for government involvement in the provision of job training and search assistance? There are three main reasons why the private market allocation of labor may be inefficient or inequitable, thus providing a potential justification for the government to get involved. First, search and matching frictions are likely to exist for some jobs, and they not only inhibit individual jobseekers from finding work, but can slow down or prevent the reallocation of workers across sectors and geography that is such an integral part of structural transformation. For example, China’s rapid growth involved its urban population growing from 100 million in 1980 to over 500 million today. This enormous reallocation involves workers needing to learn about job opportunities in another place, and potentially also requires them to learn different skills. The government may be able to speed up this transition process through appropriate training and job search policies. Matching frictions can also mean inefficiently high rates of job turnover, which

> 4 Of course this high turnover can have costs for both firms and workers, and better matches can result in higher productivity. But many firms do not appear to view these costs as high enough to invest heavily in reducing turnover.

7
# TABLE 6

# OUTSTANDING BANK CREDIT: TOTAL ECONOMY, AGRICULTURE, INDUSTRY (Selected years, end of year balances in real values)

||In Pe|rcent of|1969 credit|In percent of|total credit|
|---|---|---|---|---|---|
|Year|total<br>credit|credit<br>agricult|to<br>credit to<br>ure<br>industry<br>...|credit to<br>agriculture<br>------------------------------|credit to<br>-<br>industry<br>-------------------------__---|
|1970|138|136|<br>124|10|32|
|1974|236|209|<br>219|9|33|
|1979|351|335|<br>327|10|33|
|1984|427|491|<br>352|12|29|
|19137|535|655|<br>402|13|27|

SoLrce: Annual Bank Statistics, various issues.

# TABLE 7

-

RATIO OF OUTSTANDING DEBT TO GROSS PRODUCT AND NET CAPITAL (Selected years, end of year data, real values)

|Year<br>----------------------------|total<br>credit,<br>% of gnp<br>-------------------------------------|credit to<br>agriculture,<br>7. of agricul-<br>tural product|credit to<br>agriculture<br>% of net|credit to<br>industry<br>capital<br>__--|
|---|---|---|---|---|
|1969|31|48|19|52|
|1974|48|76|35|69|
|1979|57|101|48|75|
|1984|57|177|67|70|
|1986|67|182|79|64|

---------------------------------------------------------------------

Source: Credit balances from Annual Bank Statistics, various issues. GNP and agricultural product from Statistical Abstract, various issues. Capital stock data from Buch (1984) and privately communicated updates

# TABLE 8 SHARE OF AGRICULTURAL CREDIT HELD BY MOSHAVIM AND SUPPLY COOPERATIVES

(Selected years, end of year data, current values) r

|Year|moshavim<br>percent|supply<br>cooperatives<br>percent|
|---|---|---|
|---------------------------|--------||
|1971|10|14|
|1974|6|23|
|1979|10|20|
|1984|15|26|
|1986|14|28|

____________..______________________ Source: Bank Credit, various issues, and Current Bank Statistics, Bank of Israel, Supervisor of Banks, Jerusalem.

26
Policy Research Working Paper 6793

# **Abstract**

Using a new, large data set on quarterly reserve requirements for the period 1970–2011, this paper provides new evidence on the use of reserve requirements as a countercyclical macroprudential tool in developing countries. The appeal of reserve requirements lies in the pro-cyclical behavior of the exchange rate over the business cycle in developing countries. This enormously complicates the use of interest rates as a countercyclical instrument (because of its effect on the exchange rate) and calls for a second instrument. The paper suggests that conflicts may arise between the microprudential and macroprudential policy stances.

This paper is a product of the Operations and Strategy Unit, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at tcordella@worldbank.org, cvegh1@jhu.edu, pablo.federico@blackrock.com and GVuletin@brookings.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. 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._ Produced by the Research Support Team
- Hall, Bronwyn H, Adam B Jaffe, and Manuel Trajtenberg. The NBER patent citation data file: Lessons, insights and methodological tools. 2001.

- Han, Pengfei, Wei Jiang, and Danqing Mei. “Mapping US-China technology decoupling, innovation, and firm performance”. _Innovation, and Firm Performance_ , vol. 2, 2020.

- Holmes, Thomas J, Ellen R McGrattan, and Edward C Prescott. “Quid pro quo: Technology capital transfers for market access in China”. _The Review of Economic Studies_ , vol. 82, no. 3, 2015, pp. 1154–93.

- Hu, Albert Guangzhou, and Gary H Jefferson. “A great wall of patents: What is behind China’s recent patent explosion?” _Journal of Development Economics_ , vol. 90, no. 1, 2009, pp. 57–68.

- Jaffe, Adam B, Manuel Trajtenberg, and Rebecca Henderson. “Geographic localization of knowledge spillovers as evidenced by patent citations”. _the Quarterly journal of Economics_ , vol. 108, no. 3, 1993, pp. 577–98.

- Jeon, Yongbok, Byung Il Park, and Pervez N Ghauri. “Foreign direct investment spillover effects in China: Are they different across industries with different technological levels?” _China Economic Review_ , vol. 26, 2013, pp. 105–17.

- Jia, Ruixue, Margaret E Roberts, Ye Wang, and Eddie Yang. _The impact of US-China tensions on US science_ . National Bureau of Economic Research, 2022.

- Jiang, Kun, Wolfgang Keller, Larry D. Qiu, and William Ridley. “International joint ventures and internal technology transfer vs. external technology spillovers: Evidence from China”. _Journal of International Economics_ , vol. 150, 2024, p. 103939.

- Jinji, Naoto, and Shunya Ozawa. _Impact of Technological Decoupling between the United States and China on Trade and Welfare_ . Research Institute of Economy, Trade / Industry (RIETI), 2024.

- Kahn-Lang, Ariella, and Kevin Lang. “The Promise and Pitfalls of Differences-in-Differences: Reflections on 16 and Pregnant and Other Applications”. _Journal of Business & Eco-_

31
|Zheng et al.<br>(2019)|Changsha,<br>China|April 29th,<br>2013–<br>April 29th,<br>2015|Hourly air quality from the<br>National Environmental<br>Protection Agency|Difference-in-<br>differences|Following the opening of<br>the first subway line, there<br>is an 18 percent reduction<br>in CO in areas close to<br>subway stations.|
|---|---|---|---|---|---|
|Li et al. (2019)|Beijing,<br>China|2008-2016|Density of subway network,<br>opening dates and the<br>locations of subway lines,<br>daily weather variables<br>(average temperature,<br>average relative humidity,<br>precipitation, and binary<br>variables indicating rain,<br>snow, storm, and fog),<br>hourly wind direction, daily<br>air quality of 27 monitors|Using the<br>predetermined planning<br>map as an IV|Rapid subway expansion<br>in Beijing represents only<br>a small fraction of the<br>overall construction and<br>operating costs; the benefit<br>from congestion relief is<br>much larger|
|Anas &<br>Timilsina<br>(2009)|Beijing|2000,<br>2005|Core versus periphery land,<br>housing and densities,<br>Percentage distribution by<br>mode of commuting and<br>residence-workplace<br>location, Average travel<br>times and distances by<br>mode of travel and<br>commuting arrangement|Calibration and<br>Simulation|Increasing bus services in<br>the city center would<br>reduce overall CO2<br>emissions, and that<br>expanding suburban roads<br>would increase them.|
|Lalive et al.<br>(2013)|Germany|1994-<br>2004|Frequency of service<br>defined as the number of<br>passenger trains per year on<br>a line, county-level data on<br>road traffic accidents, air<br>pollution, infant mortality<br>and travel modes|Difference-in-<br>differences|Increasing rail services<br>reduce emissions|

45
respect to telecom investments increased from 0.29 in countries without private participation in telecom to 0.48 in countries where private participation was allowed in the telecom sector. Similar results were obtained for sanitation, albeit at a lower level of significance. However, there seems to be no impact of PPI in the other three sectors, despite a relatively large proportion of PPI projects devoted to these subsectors.

# _Income level_

Countries with higher income levels have better rates of access to infrastructure services. Moreover, rich countries have higher levels of accumulated capital stock and savings rates (Cass, 1965; Koopmans, 1965), technology (Romer, 1986), and human capital (Lucas, 1988) to channel towards better provision of infrastructure services. Additionally, richer countries tend use resources spent on infrastructure more effectively (Hulten, 1996) to deliver better infrastructure service quality (Calderon and Serven, 2004) that is closely related to higher competitiveness (Global Competitiveness Report, various years) and higher rates of economic growth. Based on this intuition, we investigate whether the contribution of infrastructure to growth is systematically greater in richer countries than in poorer ones. We therefore expect to find evidence of differential impact of infrastructure investments on a country’s growth depending upon countries’ income levels.

Table 7 shows that an increase in countries’ income levels (i.e., from low to medium to high levels) is associated with a greater positive and significant impact of telecom, sanitation and water infrastructure on growth. Specifically, the elasticity of per capita GDP with respect to telecom increases from 0.37 for low-income countries to 0.54 in medium incomes countries and 0.91 in high-income countries. Similar results are obtained for sanitation. There was no significant growth effect associated with water infrastructure in low-income countries. However, the growth effect of water infrastructure differed between medium and high income countries, with an elasticity of growth with respect to water of 0.94 in high income countries, more than four times time higher than the elasticity in medium income countries (0.23). The policy implication emerging from these results is that growth-related results arising from expansion of stocks of telecom and sanitation infrastructure may be expected to increase as the country’s income level rises. In the water sector however, it appears that a certain threshold of income needs to be crossed (i.e., the transition from low to medium income) before the benefits of increasing water sector infrastructure begin to increase as the country’s income level rises further.

22
**Figure 3: Percentage change in female labor force participation rate, 1994-2004**

<!-- Start of picture text --> 5<br>0<br><!-- End of picture text --> <!-- Start of picture text -->
-10<br>ECA<br><!-- End of picture text -->

<!-- Start of picture text --> East Asia<br><!-- End of picture text --> Source: Key Indicators of the Labor Market, 4th edition.

# _Barriers to female labor force participation_

What are the barriers to women engaging in paid market work, whether it be formal or informal work?<sup>8</sup> Numerous empirical studies find that women’s probability of working for pay increases with age (up to the mid 40s, at least in Latin America), urban residence, and increased schooling; it declines with family responsibilities (proxied by the number of children living at home) and family income and wealth (Duryea et al., 2004). These studies point to the importance of fertility and women’s reproductive role (and lack of child care) as a factor limiting work for pay. When women and men are questioned directly about reasons for not participating in the labor market, child care responsibilities are a much more frequent response for women than for men. In the Kyrgyz Republic, for example but not atypically, 24.8 percent of all women reported that “housekeeping, taking care of children, sick persons or the elderly” kept them from working outside the home, but only 1.5 percent of men reported this reason (Morrison and Lamana, 2006).

The importance of education in explaining women’s work for pay means that, in countries and regions where women still have lower educational attainment than men, women’s labor force participation will be lower than it otherwise would have been.

Another important barrier to women working for pay in many low-income countries is the time burden imposed by domestic tasks, especially the collection of water and firewood. A study of rural areas in Burkina Faso, Uganda and Zambia found that potential time savings from locating a potable water source within 400 meters of all households would result in time savings ranging from 125 to 664 hours per household per year (Barwell, 1996). More recent estimates show that women spend 273 hours per year gathering water in Benin (1998) and 164 in Madagascar

> 8 The emphasis is on remunerative work—rather than subsistence production, unpaid work in family enterprises, or volunteer work—because of the evidence that increased women’s control over resources is fundamental in reducing household poverty and improving children’s health and education outcomes.

11
# **3. Facilitating market transparency**

<mark>Agricultural product markets in many developing countries are often poorly integrated (Bardhan 1989, Banerjee and Munshi 2004). High search costs have tended to lower competition and create an inefficient allocation of goods across markets. Moreover, high and volatile food prices lead to severe negative consequences for the welfare of the poor. Agricultural supply chains are often dominated by various intermediaries with substantial market power. While intermediaries deliver critical services to rural producers, they are also often exploitative and there can be large efficiency gains from their removal (Besley and Burgess 2000). One source of market power lies in the fact that middlemen are better informed about market conditions, especially the prices further down the supply chain. This raises the possibility that better access to market information can increase the prices that farmers receive from middlemen, thereby increasing their income and helping them to make better production decisions. Improving the efficiency of agricultural markets is therefore a priority.</mark> <mark>When the internet took off in the mid-1990s, it was often claimed that it would improve price transparency, cut out middlemen, and make markets more efficient. Indeed, rapid adoption of digital technologies has dramatically reduced the search costs incurred by farmers and traders, and hence overcome an important constraint in the context of limited infrastructure. In Robert Jensen’s ‘classic’ study of Kerala sardine fishermen and wholesalers, new mobile phone service dramatically reduced price dispersion and waste, increasing welfare for producers and consumers (Figure 3). While the perishability of fish, in this case, led to unique arbitrage opportunities, positive effects have also been shown for other commodities such as cereals and cash crops using a variety of communication platforms such as Esoko in Ghana (Niyarko et al 2013), e-choupals in India (Goyal 2010), telecenters in Peru (Beuermann et al 2012), as well as in studies on Niger grain traders (Aker 2010) and Philippine farmers (Labonne and Chase 2009).</mark> 10
data are not available.<sup>5</sup> We offer two types of validation: imputation-based estimates for 2010 against the true rate in this year using only the HEIS, and imputation-based estimates for 2008 and 2010 against the true rates combining both the HEIS and the LFS. Validation results show that our imputed poverty estimates are close to the true rates based on the actual consumption data, with the former falling within the 95 percent confidence intervals of the latter. Indeed, in quite a few cases, our estimates are within one standard error of the true rates. Putting the true rates for the two years where consumption data are available together with the imputation-based estimates for the remaining years, estimation results point to a steadily decreasing trend in poverty over time for Jordan during the period 2008-2013.

This paper consists of five sections. A review of recent studies in economics and statistics is provided in the next section. This is followed in Section III by the theoretical framework, estimation procedures, and empirical application for imputation using surveys of the same design. Section IV extends this framework to imputation for surveys of different designs and then provides empirical illustrations. Section V concludes.

# **II. Review of Missing Data Imputation Methods in Recent Studies**

The idea of imputing missing household consumption has existed in various forms in the economic literature, but there was an upsurge of interest in the 2000s. Except for the survey-tosurvey imputation on India by Deaton and Drèze (2002) and Tarozzi (2007), earlier work on poverty based on imputations largely focuses on survey to census imputation and includes a study on Ecuador by Hentschel et al (2000), which is followed by a formalization of the approach in Elbers, Lanjouw, and Lanjouw (ELL) (2003).<sup>6</sup> While a consumption survey collects

> 5 While a more general and widely used statistical term “model-based” exists which can include the term “imputation-based”, we prefer to use the latter to emphasize the more specific imputation nature of our estimates. We also use the terms “imputation” and “prediction” interchangeably in this paper.

> 6 An earlier study by Ravallion (1996) proposes using time series data consisting of aggregated agricultural wages and outputs to forecast poverty rates in India. Another method to track poverty over time constructs an index for

7
||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|VARIABLES||Height-for-|age z-score||
||(0.073)|(0.073)|(0.073)|(0.073)|
|Improved water|-0.021|-0.030|-0.035|-0.026|
||(0.048)|(0.048)|(0.048)|(0.048)|
|Improved sanitation|0.083***|0.085***|0.080***|0.083***|
||(0.023)|(0.023)|(0.023)|(0.023)|
|Currently breastfeeding|<br>-0.165***|<br>-0.167***|<br>-0.163***|<br>-0.166***|
||(0.030)|(0.030)|(0.030)|(0.030)|
|Wealth index 2|<br>0.111***|<br>0.118***|<br>0.112***|<br>0.116***|
||(0.033)|(0.032)|(0.033)|(0.032)|
|Wealth index 3|0.135***|0.149***|0.138***|0.143***|
||(0.034)|(0.033)|(0.033)|(0.033)|
|Wealth index 4|0.241***|0.261***|0.239***|0.250***|
||(0.037)|(0.036)|(0.036)|(0.036)|
|Wealth index 5 (Wealthiest)|0.449***|0.476***|0.444***|0.458***|
||(0.045)|(0.042)|(0.044)|(0.043)|
|Rural|-0.029|-0.024|-0.019|-0.022|
||(0.040)|(0.040)|(0.040)|(0.040)|
|Rajshahi|0.046|0.049|0.046|0.051|
||(0.038)|(0.038)|(0.038)|(0.038)|
|Khulna|0.211***|0.204***|0.200***|0.207***|
||(0.045)|(0.045)|(0.045)|(0.045)|
|Barisal|-0.075|-0.077|-0.085*|-0.081*|
||(0.047)|(0.047)|(0.047)|(0.047)|
|Dhaka|0.036|0.040|0.022|0.034|
||(0.034)|(0.034)|(0.034)|(0.034)|
|Sylhet|-0.157***|-0.151***|-0.161***|-0.156***|
||(0.042)|(0.042)|(0.043)|(0.042)|
|Chittagong|0.018|0.023|0.015|0.012|
||(0.037)|(0.037)|(0.037)|(0.037)|
|Season 2|0.048**|0.043*|0.039|0.036|
||(0.024)|(0.025)|(0.025)|(0.025)|
|Season 3|-0.020|-0.018|-0.019|-0.017|
||(0.026)|(0.026)|(0.026)|(0.026)|
|Constant|<br>-0.827***|<br>-0.768***|<br>-0.802***|<br>-0.817***|
||(0.108)|(0.113)|(0.109)|(0.108)|
|Observations|10,144|10,144|10,144|10,144|
|R-squared|0.134|0.134|0.134|0.134|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 HFIAS Score is household food insecurity access scale, child DDC is the dietary diversity score constructed for each child, FCS is the food consumption score and MDDS is the mother’s dietary diversity score.

49