rafmacalaba/data-use-mentions-extended huggingface dataset:
usually not the focus in the existing literature relying on a linear estimating equation. When the education production function exhibits diminishing returns to financial investment and complementarity between parents’ direct impact and financial investment as proposed by Becker et al. (2015, 2018), the CEF can be concave or convex depending on the strength of complementarity. With quadratic CEF, parental gender bias affects both relative and absolute mobility.

We take advantage of rich household survey data from China and India to test the above ideas. The data sets used (IHDS for India and CFPS for China) do not suffer from sample truncation due to coresidency restrictions common in household surveys in developing countries. Our estimates are thus free of severe truncation bias reported recently by Emran et al. (2018) because of coresidency restrictions. The evidence shows interesting cross-country and rural-urban differences. The CEF is concave in India irrespective of gender and location. In contrast, the CEF is convex for sons, but linear for daughters in urban China. In rural China, the CEF is linear for both sons and daughters. The evidence on functional form suggests that children face diminishing returns India, and girls in rural India face diminishing returns both in financial investment and parental direct inputs to schooling. The convexity observed in urban China for sons supports the complementarity hypothesis of Becker et al. (2018).

Girls face lower relative and absolute mobility in India when the father has low education, but there is gender convergence at the right tail of the parental schooling distribution. The relative magnitudes of the estimated parameters of the investment and mobility equations across gender when combined with the evidence of higher returns to education for girls help us sort out possible explanations for the observed pattern of mobility. The evidence on rural and urban India is consistent with the hypothesis that parents systematically underestimate the academic ability of girls, and girls also face significant bias in school. The same mechanisms can also explain the evidence in urban China, but, in rural China, the evidence indicates constraints in the school as
The estimation is performed in two steps. First, the authors obtain an estimate of TFP by estimating the production function for each manufacturing industry. They amend the Olley-Pakes (1996) procedure by controlling for sub-industry-specific demand and price shocks following De Loecker (2011).

Second, following Arnold et al. (2011), they assume that firms that use services more intensively benefit more from services liberalization. The authors start with the EBRD indices of services reform which they aggregate into four sectors: telecommunications; transportation; finance and insurance; and other business related services.

They then construct an index of services liberalization that is firm-specific, reflecting the variation in firm-level intensity of usage of various services inputs, i.e., where _servlibit_ is the firm-specific index of services liberalization, _aijt_ is the share of input sourced from the services sub-sector _j_ in the total input for a firm _i_ at time _t_ , and _index jt_ is the EBRD measure of liberalization in the service sub-sector _j_ at time _t_ .

Then the estimated _TFP_ is further regressed on their firm-specific index of services liberalization and several other variables, including a firm-specific effect to control for the _within firm effect_ of liberalization on productivity.

# **Impact of Services Liberalization on Firm Level Productivity in Eastern Europe and**

# **Central Asia by Bogdan Klishchuk and Valentin Zelenyuk**

Bogdan Klishchuk and Valentin Zelenyuk examine the impact of services sector liberalization on the labor productivity of firms in 21 Transition countries. Their study employs data on 19,912 firms in Eastern Europe and Central Asia with an average of 4 observations per 12
now as large as its rural population. Extreme poverty remains a predominantly rural phenomenon, with some 75% of those who subsist on expenditures below $1-a-day still residing in rural areas in 2002, even when higher cost of living in urban areas is taken into account.<sup>4</sup> But urban poverty has been falling more slowly than rural poverty – in part because urbanization has been a

> 2 A classic study by Bates (1981) documented the use of price regulation and marketing boards in Ghana, Nigeria and Zambia to extract surplus from farmers to the benefit of urban food consumers. 3 Ravallion et al. (2007) produced arguably the first global poverty statistics that cover the majority of the world’s population and disaggregate between urban and rural areas. They estimate that the urban share of the world’s extreme poor in 1993 was 19%. 4 See Ravallion et al. (2007).
0.102|0.103|
|Endline control mean|0.4|48|0.5|22|0.3|82|
|Additional controls|No|Yes|No|Yes|No|Yes|
|R-squared|0.034|0.219|0.012|0.155|0.048|0.221|
|Observations|2,334|2,334|2,334|2,334|2,334|2,334|

_Source:_ Authors’ analyses based on baseline and endline survey data.

_Note:_ OLS regressions of household heads' knowledge scores, ranging from 0 to 1, and calculated based on the number of correct answers relative to the total number of questions in a respective domain. Additional control variables are age, gender, education (in completed years), whether respondent had access to off-farm work or a nonfarm family business, whether hh adopted the ISFM quickwin package at baseline, whether hh has a cell phone and radio, number of times hh had contact with an extension agent, whether it has participated in agricultural training, number of local organizations involved, and walking distance to nearest farmer training center. Two region dummies for Oromia and Amhara included. One-sided equality tests of T1 and T2 are F-tests. Robust standard errors in parentheses, clustered at the mws level. Significance levels indicated as following: *** p<0.01, ** p<0.05, * p<0.1.

33
the use of high-quality services, liberalizing the service sector, removing various types of policy barriers related to services and strengthening the institutional framework might allow India and other developing countries to strengthen their GVC participation and profit from the associated economic benefits.

Figure 1: GVC Participation and Economy-wide Productivity (2015-2016) <!-- Start of picture text --> GVC participation and Industry productivity<br>GVC participation and Services productivity<br>Coefficient 2.312; R2 0.09; **<br>Coefficient 2.486; R2 0.14; ***<br>SAU IRL SAU SGP<br>USA CHE IRL<br>CHE NOR SGP JPNAUSGBRESPGRCITASWEHKGFRADEUNORFINDNKAUTNLD BEL MLT<br>USA ISRJPNAUSGBRESPGRCITAISLSWEFRADEU FINDNKAUTNLDKORBELMYS HRVTURISR ISLLVAPRTROUCYPPOLLTUESTKORSVNMYSCZEHUN SVK<br>CRI HRVTURCOLCHNIDNPERRUSLVAKAZHKGZAFMARPRTCHLROUPHLCYPMEXTHAPOLLTUESTBGRSVN CZEHUN SVKMLT CRI BRACOLINDCHN PERRUSKAZZAFMARCHLTUNMEXTHA BGR<br>IDN PHL<br>BRA TUN<br>IND<br>ARG<br>ARG<br>.2 .3 .4 .5 .6 .7 .2 .3 .4 .5 .6 .7<br>GVC participation (2015) GVC participation (2015)<br>6<br>5<br>4<br>3<br>2<br>5<br>4<br>3<br>2<br><!-- End of picture text --> Source: OECD TiVA; WDI; Author calculations<sup>1</sup> Despite the recognition of the importance of services, studies on the relationship of the use of service inputs, the depth of GVC integration and firm productivity are few in number. Our study adds to the empirical evidence on GVC integration, service input use and firm performance using detailed firm-level data from India for the period 1990-2017. The study classifies the depth of GVC participation following Veugelers et al (2013) and Gereffi and Fernances-Stark (2016) and looks at different type of service inputs, i.e., the composition of services as
|
|Mixed-home cnty||-0.015|-0.015||0.001|0.001|
|(both treated & control)||(0.015)|(0.015)||(0.018)|(0.018)|
|Treated*Mixed-home cnty||-0.051*|-0.050*||-0.049|-0.050|
|||(0.027)|(0.027)||(0.032)|(0.032)|
|No. of Observations|4,587|4,587|4,587|2,915|2,915|2,915|
|R-squared|0.524|0.525|0.525|0.536|0.536|0.537|

Source: RUMiC Migrant Survey 2015 and 2016, RUMiC Information Intervention Field Experiment, 2016. Notes: All regressions are using the Model 3 specification (see note to Table 2). Robust standard errors clustered at workplace of household head are in parentheses. Significance codes: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1.

36
large degree of autonomy at the state or provincial level, there is a risk of state-specific studies that use non-compatible measures of performance. In addition, having international comparators will strengthen the value of this intra-national measurement.

# **Better Measurement Makes Impact Evaluation Possible**

Ultimately, we want to know not only how effective governance and service delivery are, but also what to do to improve them. This requires evaluating the impact of different interventions, at each stage along the results chain. Good measurement of governance and service delivery are essential to tracing these impacts and identifying where the intervention is or is not working.

Consider, as an example, the case of an intervention aimed at transferring more managerial authority to the school level. In this case, the intervention is intended to improve such elements of service delivery as teaching quality and teacher attendance, with the ultimate goal of improving student outcomes. Better measurement and evaluation are necessary all along this envisioned results chain. Of all of these elements, several indicators of student outcomes— enrollment, persistence, and completion—are probably measured most regularly and accurately, but even in these cases we often must rely on household surveys to supplement the administrative data. Student learning is typically poorly measured, and so improving 6
# **1. Introduction**

Understanding informality—a prominent feature of most developing economies—and its economic implications is of paramount importance for poverty-reducing and welfare-enhancing policies. Indeed, informal businesses absorb a substantial share of the unskilled labor force. Nearly 70 percent of the labor force in emerging markets and developing economies (EMDEs) work informally (International Labour Organization 2018). Moreover, small start-ups often use the informal sector as a stepping stone towards formalization (Nguimkeu 2014). Burdensome regulations (De Soto 1989), exit strategies (Farrell 2004), and survival motives (La Porta and Shleifer 2014) can shape differently the informal or shadow economy through its intensive (workers) and extensive (firms) margins (Ulyssea 2018). Despite the pervasiveness of informality in developing countries, empirical evidence on several key issues pertaining to the informal economy remain to be explored, in part due to data limitations. The paper attempts to fill this gap in the literature by analyzing the labor productivity of informal firms vis-à-vis formal firms and assessing the relationship between competition from informal firms and formal firms’ productivity. Our results, based on firm-level survey data from a large cross-section of countries, show that labor productivity is substantially lower for informal firms compared to formal firms, and that the competition from informal firms is negatively related with the labor productivity of formal firms. On average, the labor productivity of informal firms is about one-fourth of that of formal firms. Moreover, the labor productivity of formal firms that face competition from informal firms is about 75 percent of the average labor productivity of formal firms that do not experience informal competition.

The productivity differential between formal and informal firms is a well-established stylized fact (Loayza and Rigolini 2006; Oviedo 2009). Available evidence suggests that compared 2
power, and _perceiving a sense of control_ is a central and definitional requirement of empowerment (Kabeer, 1999). This sentiment also permeates the feminist movement. For example, Gloria Steinem wrote, “power can be taken, but not given. The process of the taking is empowerment in itself” (Steinem 2012, p.577).

This dimensionality of taking versus being given power is markedly missing in most empirical work on women’s empowerment. Indeed, most of the literature demonstrating the importance of women’s empowerment for the well-being of women and their children focuses on women’s reports of their own decision-making power. While women’s reports of their own decision-making should not be discounted in and of itself as a measure of empowerment (Kabeer, 1994), the dimensionality of and contention over this power may shed further light on its meaning and deserves more empirical consideration, including the potential positive and negative outcomes in this process.

The empirical literature that looks beyond women’s reports of their own decision-making power focuses primarily on scenarios of uncontested power, where the husband and wife agree that women can make decisions. Using the 2001 Nepal DHS, for example, Allendorf (2007) finds that maternal and child health care outcomes can improve significantly when spouses agree that the wife is the main decision-maker. Story and Burgard (2012) also use the 2007 Bangladesh DHS and find that antenatal care use and skilled delivery care improve when couples agree that decisionmaking is joint and worsen when both agree the husband is solely responsible, or if both disagree. Poutvaara and Schwefer (2018) in Indonesia find that female labor supply and contraception use are higher when both partners perceive female decision-making power in these domains. A recent 6
.3175|
|•|Tertiary education|-1.6217|-1.5366|0.0341|0.0307|
|_Health_||1.3802|1.5309|2.7075|3.6565|

_Source: Authors’ calculations; ESS2018/19 data._ _Note:_ The marginal contributions—redistributive and poverty reduction effects—are in percentage points. A positive marginal contribution means it reduces inequality and poverty; a negative means that it increases inequality and poverty.

PSNP reduces income inequality by 0.37 pp for women and 0.31 pp for men. It also reduces the poverty headcount by 0.9 pp for women and 0.6 pp for men. The effectiveness of PSNP transfers in improving welfare—reducing inequality and poverty—is in line with the findings of PSNP impact evaluation studies (Gilligan, Hoddinott, and Taffesse 2009; Hill et al. 2017). The positive distribution impacts of PSNP might also be attributable to the program’s targeting (Coll-Black et al. 2013).

22
<sup>8</sup>

# _Socioeconomic factors_

We use local estimates of deprivation scores, a proxy for poverty, calculated by the Poverty Global Practice at the World Bank. These calculations were done in small areas for the whole country, providing helpful information for comparisons within counties in Kenya. The methodology used and leveraged the "Small Area Estimation of Poverty under Structural Change" by Lange, Pape, & Putz, 2018. Poverty is estimated for each sub-location in two stages by estimating household consumption using census data.<sup>9</sup> In the first one, a consumption model is developed using survey data<sup>10</sup> but including non-variable household characteristics from the census. In the second stage, the estimated parameters are applied to census data, providing estimates for welfare at the sub-location level. In Kenya, 7,110 sub-locations were used in the analysis; on average, each had 120 households. The measure of poverty is included in the analysis as a control and proxy for the facility's resources.

- 3.2. Key Variables and Summary Statistics

We first merge the SDI data set and the Master List of facilities which results in a subset of 2,578 facilities. This means that 15% of the 2018 SDI facilities could not be found in Kenya's Master List of facilities. This final sample was complemented with information on road, electricity, and cellphone internet access, with additional data described in the previous subsection.<sup>11</sup>

> 7 D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D.
rate are not comparable in _either_ levels or changes. In this section we create, as best as possible, a consistent series of poverty rates using our own estimates from the various data sets which we have access to and by adjusting the estimates from different sources where we do not have the raw data.

All of the estimates must start from a consistent base. This needs to take into account that the economy was growing from February 1996 at least through the middle of 1997.14 This additional income would have likely reduced poverty, so that the poverty rate just before the crisis is not simply the _level_ of poverty in February 1996, but the level reached accounting for poverty reduction from February 1996 to the beginning of the crisis.

Second, the estimates must use common method of computing changes over time, in particular how the poverty line is inflated. We attempt to adjust all the estimates so that the poverty line deflator used is consistent with that using a food share of 0.8 (as in method II above).

The databases from which we calculate our own estimates from household survey data, chronologically, are:

- Susenas February 1996 (consumption module with sample of 65,000 HH),

- 100 Village Survey May 1997 (12,000 HH),

- 100 Village Survey August 1998 (12,000 HH),

- Mini Susenas December 1L998 (10,000 HH),

- 100 Village Survey December 1998 (12,000 HH),

- 4 February 1996 is the last Susenas consumption module available before the crisis.

23
handle firm level data from loan applications, as they are the primary data collectors. Financial regulators collect these data from institutions and aggregate them. In this respect, there seems to be a need to harmonize SME definitions within each country even though such a task might be unfeasible across countries. In the long run, encouraging financial institutions to collect and maintain information on the number of employees and sale volumes in their systems will

> 9 Additional data were used from the following sources: UNDP country studies, MSME Country Indicators (WBG/IFC), and central bank websites.

8
(1998) for reviews on the damaging consequences of corruption in an economy. Most recently, Ayyagari, Demirguc-Kunt, and Maksimovic (2010) use data on bribe payments across 25,000 firms in 57 countries and show that corruption is indeed a tax on innovation as suggested by Murphy, Shleifer, and Vishny (1994). They find that innovating firms pay more bribes to government officials than non-innovating firms and that innovating firms that pay bribes do not receive better services than firms that do not bribe.

An alternate second mode of government intervention is through connections with the firms. There has been a large literature analyzing the benefits and disadvantages of political connections. One strand of this literature focuses on identifying connections between individual firms and government officials and shows how these connections increase firm value (e.g. Fisman, 2001; Johnson and Mitton, 2003; Faccio, 2004; Goldman, Rocholl and So, 2009). Faccio, Masulis, and McConnell (2006) find that politically connected firms are significantly more likely to be bailed out than similar non-connected firms suggesting that these firms receive financial assistance during periods of financial distress. Fan, Wong, and Zhang (2007) find that Chinese firms with politically connected CEOs underperform those without politically connected CEOs in terms of stock returns, earnings growth, sales growth and return on sales.

Finally, a third strand of the literature focuses on how political favors influence financing. Dinc (2004) provides cross-country bank-level evidence in 22 emerging markets of politically motivated lending at government owned banks. He shows that government owned banks increase their lending in election years relative to private banks. Other studies have taken a detailed single-country approach to analyze the consequences of government lending. Khwaja and Mian (2005) use loan-level data from Pakistan and find that government banks differentially favor politically connected firms by providing greater access to credit - firms that are politically connected borrow 45% more but also have 50% higher default rates. Sapienza (2004) also finds that Italian public banks charge lower interest rates
While these results by themselves may not rule out a demand-side channel, they do substantially constrain the routes through which it can work. Fathers' education could not stimulate demand for children's education (since boys' schools have no eect); primary schooling for mothers could not be enough to stimulate demand; mothers' schooling must therefore have a non-linear eect on the demand for children's education.

# Price Margin

Table V provides further evidence for a supply channel by examining the price margin. Recall, in sharp contrast to a demand-channel, a supply channel would suggest that GSS construction would lead to a fall in private school teacher (i.e. skilled women) wages. We compare the average (log) teacher salary in private schools in villages with and without a GSS using data from the private school census. Column (1) presents the OLS results in the sample of villages for which we have teacher wage data. We include PC FEs in all specications. The results are large and signicant: Private schools in villages with a GSS report a 27 percent lower average (teaching) wage.

Columns (2) through (5) correct for selection into the wage sample. Columns (2) and (3) present results using Heckman's selection model, and Columns (4) and (5) use the control function approach (see Appendix I). In both approaches, identication is based on the non-linearity of the selection equation (see Duo [2001] as an example). Augmenting the instrument set with potential candidates that are correlated to the probability of having a private school but uncorrelated to the wage-bill can help with the identication and the eciency of the estimator. Following Downes and Greenstein (1996), we propose using the that have both. An alternate strategy is to check whether there is a dierence in the existence of a private school based on years of exposure to a GSS. Here,
# **Figure 15. Sample Means by Usage of Credit Products**

<!-- Start of picture text --> Sample means by Usage of Credit products<br>No acces to financing<br>1<br>Access to financing<br>0.87<br>0.9<br>0.78<br>0.8<br>0.68<br>0.7 0.64<br>0.6<br>0.5<br>0.41<br>0.4<br>0.3<br>0.3 0.24<br>0.2<br>0.2 0.15 0.17<br>0.1<br>0<br>Operating Income Any Investment IK Retained Earnings Retained Earnings<br>to sales Variables (Investment) (Working Capital)<br>Companies which have overdaft or line of creadit or loan<br>Sample Mean<br><!-- End of picture text --> Source: ICA data The usage of credit products is not significantly related to our proxy for cash flow, but, as shown above, it clearly is an important determinant of investment. While our results cannot be interpreted in a casual manner, they are suggestive of the positive impact increased usage of financial products might have on stimulating investment into productive assets. While we cannot test for this directly, these results are also consistent with a possibility that firms without access to external finance will need to accumulate financial savings before they can make investment.

Firms that claim they have no demand for loans have higher cash flows. This may explain why these firms claim that they have no demand – their cash flows are adequate for their financing needs. They do not make any more investment relative to firms that have demand for more loans.

Firms that applied and were rejected for loans have lower cash flows. This might