rafmacalaba/data-use-mentions-extended huggingface dataset:
between 1960 and 1964 with primary schooling or less would form one cohort. The household head is defined as the person recognized as the head by the other household members and is generally male. McKenzie (2003) shows that there is no significant change in who is the head for individuals aged 25 to 49, even during the peso crisis in 1995.

A potential concern with the panel data is that more economically mobile households may move, and so the panel will be a selected sample of less mobile households. In order to ensure that the pseudo-panel does not suffer from the same problem, we construct our pseudo-panel using only the households who are in their first wave of the interview. As a result, we use just over 20 percent of the households available in any given cross-section, since the remaining households are those which are being re-interviewed. We restrict the sample further to cohorts with more than 100 observations in a given wave in order to be able to apply the asymptotic theory developed above which relies on a large number of observations per cohort. Approximately 9 percent of cohort-period observations have fewer than 100 households, and including these additional observations does not qualitatively affect our results. After these restrictions, we are left with a pseudo-panel over 58 quarters with 842 cohort-quarter observations.

# 5 Results

Panel A of Table 1 provides the estimates of the coefficient on quarterly lagged log income from a variety of different estimation methods. Column 1 provides the panel data OLS estimate, 0.668, which is significantly less than unity and suggests substantial mobility within a quarterly period. Adding cohort dummy variables in Column 2 lowers the coefficient estimate further to 0.598. Columns 3 and 4 provide the panel data instrumental variables estimates. As a labor force survey, the ENEU contains few of the variables commonly used in the literature as instruments. We use the education of the household head, and an asset index constructed as the first principal component from questions on the 17
of this paper is organized as follows. The next section reviews the data used for the analysis. Section III turns to the methodology. It discusses the estimation method and the process used to select the set of predictor variables, and examines the results of four alternative specifications to the model. Section IV compares the preferred model’s predictions for 2014-15 to the usual method used to project poverty forward, and investigates which specific variables are accounting for the change in average welfare predicted by the model. Section V shows that the estimated poverty rate implies a reasonable elasticity of poverty reduction with respect to growth, verifies that implied poverty reduction is greater in fast-growing states, and examines the model’s predictions at higher poverty lines. Section VI briefly describes how the 2014-15 estimates were used to generate poverty estimates for 2015, and Section VII concludes.

> 14According to the Economic Survey 2017-18 of the Ministry of Finance in India, agriculture employs over half of Indian workers but only contributes 17 to 18 percent of India’s GDP.

> 15The 2015 estimates can be viewed at http://iresearch.worldbank.org/PovcalNet/povDuplicateWB.aspx. See http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx for further details.

6
period of our analysis. We take data from UNCTAD’s _TRAINS_ dataset on NTMs applied by 34 OECD countries in 2015.

We restrict the analysis to the most common groups of applied NTMs in Indonesia, i.e. SPS, TBT and INSP. We focus in particular on SPS to identify possible “virtuous” NTMs, for two reasons. First, SPS measures address threats to the health and safety of consumers stemming from high-risk products, including food and medical products. The legitimacy of such first-order policy objectives may be more difficult to challenge than that of other NTM categories. Second, the application of SPS on agricultural products and drugs makes them a less suitable instrument to protect the manufacturing sector than 7
12<br>5 10<br>4 8<br>3 6<br>2 4<br>1 2<br>0 0<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>International internet bandwidth<br><!-- End of picture text --> <!-- Start of picture text --> b. Selected Central African countries, 2008<br>14 250<br>12<br>200<br>10<br>150<br>8<br>6<br>100<br>4<br>50<br>2<br>0 0<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>International internet bandwidth<br><!-- End of picture text --> Source: AICD database.

38
In those more export oriented firms the percentage of adjustment is larger both in the creation and destruction sides for blue collar labor, implying lower adjustment costs. Firms more export oriented, when their desired blue collar employment level is below the actual one, they adjust a higher share of the gap than firms oriented to the domestic market. The causality may go in the opposite direction. In order to be able to export, firms need to be more efficient, and therefore need to have more ability to adjust factor inputs.

There does not seem to be a clear significant pattern between export oriented and domestic market oriented firms with respect to capital and white collar adjustment functions.

# **5. Conclusions**

This paper intends to use micro data to improve our understanding of the effects of policy measures on the adjustment of factors of production. On the one hand, the paper finds evidence supporting a number of regularities that the previous literature on adjustment functions has highlighted.

Our investigation confirms that aggregate investment and job creation might be seen as the result of lumpy and discontinuous microeconomic decisions. Individual adjustment constraints depart significantly from the constraints implicit in the quadratic adjustment cost model. There are several sources of irreversibilities (technological, market-induced, increasing returns in the adjustment technology). The evidence provided seems to confirm a pattern that has important nonlinear features, hence consistent with such constraints. This impacts the use of all factors of production, particularly employment.

The existence of adjustment costs implies that the desired levels of white and blue collar employment and capital often deviate from the observed ones. In our data these deviations imply that the yearly gaps might be above 10%. To have an idea of the importance of the adjustment costs, it is useful to consider that for the whole period the average output gap is of 2%.

On the other hand, the paper intended to assess the effects of protection and trade liberalization on firms’ adjustment process. The constraints arising from the adjustment cost functions may become an important part of the
2<sup>nd</sup> stage of development; second, whether the progressive transition to the 3<sup>rd</sup> stage – the specialization in high-tech industries – of the costal region leads to a diffusion of the labor intensive activities to the inland provinces. We study whether the labor intensive industries move inland from the coast when looking at the country as a whole; and we study whether these industries move within the region when looking at the coastal region alone. Of course, the economic concentration may depend on a number of factors. The study presented here is subject to data constraints. The objective of this analysis is to provide a first step in understanding the complexities of the spatial disparities of economic concentration in different stages of development in a broader context.

The study is structured as follows: In section 2, we study the polarized development in China, and describe how the gradual open-door policies and the inflow of FDI have led to a strong concentration of the activities and an upgrade of the production structure in the coastal provinces. In section 3, we empirically examine the determination of the geographic concentration of different industries using panel data at the provincial level of the period 1988-97. The conclusions are presented in section 4.

# **2. POLARIZED DEVELOPMENT IN CHINA**

# **2.1. Polarization related to openness and foreign direct investment**

Since the early 1980s, the government of China has gradually extended the open-door policies from the coast to the inland (see map 1). In 1980, the first Special Economic Zones (SEZ) were established in four cities (Shenzhen, Zhuhai, Shantou, and Xiamen) in the coastal provinces Guangdong and Fujian, hometowns of many oversea Chinese investors, to attract FDI.<sup>4</sup> In order to scale up the success to the entire coastal region, the open-door policies were further extended to 14 coastal cities in 1984, to the Delta of Yangtze, the Delta of the Pearl
# **1.  Introduction**

Protected-area strategies seek to conserve biodiversity in moist tropical forests by restricting infrastructure development in legally-demarcated zones. However, conflicts can arise when forested areas have significant agricultural potential. Within conservation areas, regulatory enforcement may be weakened by dominant economic interests. And some areas with high biodiversity value may be left unprotected so that development can proceed. By implication, both development and conservation may be hindered by a dichotomous policy regime that treats large areas as either completely protected or completely open for development. In reality, valuable economic and ecological resources have non-uniform, overlapping spatial distributions. Policy makers should therefore be equipped to weigh context-specific trade-offs between development and conservation objectives. This paper seeks to contribute by focusing on the trade-offs associated with road improvement in tropical forests. For three illustrative country cases in Latin America (Bolivia), Asia (Myanmar) and Africa (Cameroon), we develop and apply a high-resolution spatial model of road improvement impacts that includes both ecological risks and the economics of forest clearing. Our empirical work makes exclusive use of globallyavailable databases to ensure its applicability to all moist tropical forest countries.

The remainder of the paper is organized as follows. Section 2 reviews prior empirical research on the economics of forest clearing. In Section 3, we motivate our exercise with a theoretical economic model of road improvement and deforestation. Section 4 describes our spatially-formatted database, while Section 5 specifies and estimates a deforestation model that incorporates the impact of road improvement. In Section 6, we explore the implications of the results for our three cases. Section 7 develops an empirical framework for estimating the ecological impacts of road improvement, while Section 8 applies the framework to an assessment 2
The potential for extensions and applications of this work is large. Section 8 has discussed various avenues for future extensions and applications and suggested a number of options, including the use of the estimated quarterly poverty series for further cross-section and panel econometric work, forecasting and simulation of policy reforms and economic shocks. These are all possible extensions of the work presented in this paper that can substantially increase the toolkit of the welfare economist.

# **References**

Christiaensen, L, Lanjouw, P., Luoto, J and Stifel, D. (2012) Small area estimation-based prediction methods to track poverty: validation and applications, Journal of Economic Inequality, 10, 267-297 Deaton, A. (2008): Price trends in India and their implications for measuring poverty. Econ. Polit.Weekly, 7, 3729–3748 Deaton, A., Kozel, V. (2005) Data and dogma: the great Indian poverty debate. World Bank Research Observer, 20 (2), 177–199 Douidich, M. and Ezzrari, A. (2008) Dynamique de la pauvreté au Maroc 1985-2007, Haut Commissariat au Plan, Morocco Elbers, C., Lanjouw, J.O., Lanjouw, P. (2002) Micro-level estimation of welfare. Policy Research Working Paper 2911. World Bank, Washington DC (2002) Elbers, C., Lanjouw, J.O., Lanjouw, P. (2003) Micro-level estimation of poverty and inequality. Econometrica, 71(1), 355–364 Elbers, C., Lanjouw, J.O. and Lanjouw, P. (2005) Imputed Welfare Estimates in Regression Analysis, Journal of Economic geography, 5, pp.101-118 Fujii, T. and R. van der Weide (2012), Two-Sample Cross-Tabulation, mimeo, The World Bank Fujii, T. and R. van der Weide (2013), Cost-effective estimation of the population mean using prediction estimators, mimeo, The World Bank HCP (2000-2010) National
increased trust in management and overall employee retention—by reducing the cost of staff absenteeism and turnover, they would increase business performance by increasing productivity (IFC, 2019). At a global level, it has been shown that the adoption of HRM policies is positively correlated with business development, organizational performance and firm productivity (Bloom, Kretschmer and Van Reenen, 2011, Jones, Kalmi and Kauhanen, 2006, Arthur and Cook, 2004 and De Kok and Uhlaner, 2001), including for smaller businesses (<100 employees) (Sels, De Winne, Delmotte, 2006). Shafeek (2016), studies the impact of HR policies in SMEs (ranging from micro-firms, <10 employees to large firms with over 250 employees), in the context of Saud Arabia. He finds that the adoption of these practices positively affected SME performance regardless of size. Naz et al., (2016) and Yordanova, (2011), in their studies in Pakistan and Bulgaria, provide further evidence supporting this finding.

Overall HRM policies are conducive to business growth, improved productivity, employee retention, and a reduced gender gap due to higher female employee retention, even when looking at small firms. In our study, we are going to evaluate whether the adoption of informal employee practices has a similar impact on different measures of business development and access to finance. Further, given that evidence points to formal family-friendly policies being beneficial in reducing gender barriers, we proceed to test whether a similar story can be told in our context.

> 16 A survey conducted in Tanzania indicates that micro and small enterprises perceived training in bookkeeping, financial services, marketing and sales promotion, training in costing and pricing and transport services as the most needed competitiveness improving services for their businesses. BDS market survey conducted by Swisscontact 2003.

> 17 Improving the competitiveness of SMEs in developing countries. The Role of Finance to Enhance Enterprise Development, UNCTAD, 2001.

9
# **I. Introduction and background**

Phone interviewing opens new possibilities for empirical research in social sciences. It greatly reduces the costs of conducting surveys because all communication occurs without the face-to-face interaction customary in traditional surveys. With the outbreak of the COVID-19 pandemic limiting face-to-face interviews, phone surveys are more prevalent among academic institutions, survey companies, and individual researchers for individual-level and household-level data collection. To track households’ living conditions on a timely basis during a pandemic, the World Bank launched the COVID-19 High-Frequency Phone Surveys (HFPS), which have been carried out in around 80 countries since March 2020. These surveys allow policy makers to monitor a wide variety of socioeconomic indicators in a timely and frequent manner.

Phone surveys have shortcomings. First, it is difficult to monitor poverty using the COVID-19 HFPS. The COVID-19 HFPS does not collect consumption or income data, which are necessary for measuring poverty and inequality under the traditional poverty monitoring approach. It is time-consuming, costly, and complex to collect such data. An interviewer must ask households a number of complex questions regarding their recent consumption, expenditures, and detailed income components. Such an interview requires at least 30 minutes and up to two hours, even by well-trained and qualified interviewers. It is also challenging to administer the interview in developing countries where telephone connections are not always stable enough to complete such a long interview.

_A solution to Challenge 1: SWIFT as a rapid poverty monitoring tool_ In March 2020, the World Bank launched a pilot for the use of a rapid poverty monitoring tool, Survey of Wellbeing via Instant, Frequent Tracking (SWIFT), to estimate monetary poverty using the COVID-19 HFPS. SWIFT adds 10 to 15 simple questions to the COVID-19 HFPS questionnaire, and these additional questions take 3 to 5 minutes to ask. Based on households’ responses to these questions, the SWIFT method can be adopted to impute poverty and inequality rates among the population of interest.

SWIFT was chosen among many poverty projection methodologies because it has been thoroughly tested and reviewed
Last but not least, formality is positively associated with better access to formal sources of financing at start-up. Columns 3-4 of Table 2 show that, controlling for other factors, more formal firms are more likely to have received some portion of their initial capital from formal sources, which are defined as private and public banks and micro-finance organizations.<sup>20</sup> The EMES data on access to credit shows that formal sources of financing tend to offer credit on better terms, which in turn positively affects the profitability of firms who are able to access these sources. Clearly, these results fall short of establishing causality between formality and access to finance, because we do not observe the formality status of firms at start-up.<sup>21</sup> However, as shown in Figure 10, firms who complied with regulatory requirements from the start do have a higher likelihood of having benefitted from formal sources of financing; on the other hand, due to a relatively small number of firms who actually obtained credit from formal sources, the standard errors are too large for the differences to be statistically significant. Therefore, the evidence on a positive link between formality and improved access to credit in Table 2 and Figure 10 is suggestive, but not conclusive.

**Figure 10: Formal financing and time of registration**

<!-- Start of picture text --> 0.30 0.30<br>0.20 0.20<br>0.10 0.10<br>0.00 0.00<br>Reg. from start <1 yr 1-2 yrs 3-4 yrs >4 yrs Never reg. Reg. from start <1 yr 1-2 yrs 3-4 yrs >4 yrs Never reg.<br>Time between start-up and registering for a municipal license Time between start-up and registering for a RUC<br>Likelihood of obtaining finance from formal sources Likelihood of obtaining finance from formal sources<br><!-- End of picture text -
is 7.2 percentage points lower. In terms of EBITDA to revenues and EBITDA to assets, SOEs that do not receive subsidies have 34.9 and 7.1 percentage points worse ratios, respectively. These ratios are significantly different from both private firms and SOEs that receive subsidies. In terms of economic significance, the coefficients are extremely large relative to the means of these variables—SOEs with a coefficient that is two times the mean of the sample for EBITDA to revenues and one time the mean for EBITDA to assets. If we view these coefficients as basic measures of efficiency, SOEs are in a different league of inefficiency from private infrastructure firms.

These results are surprising, because one would expect that receiving subsidies would generate moral hazard and thus incentives to perform worse than SOEs that do not receive subsidies. Receiving subsidies, usually tied to QFOs, opens the door for SOE managers to request support for losses from other aspects of their operation. What our findings suggest is that SOEs that receive subsidies are probably subject to more attention and perhaps monitoring. As the Ministry of Finance must compensate those firms for losses related to QFOs, its technocrats may pay closer attention to the rest of the operation. As a result, these SOEs may end up performing better than SOEs that do not receive subsidies.

# **6 Conclusions**

This paper uses the World Bank Infrastructure SOEs Database to document the underperformance of SOEs in the database vis-à-vis similar private ones. It highlights some of the factors that may be driving underperformance, such as overstaffing. It also provides evidence that ownership matters by comparing SOEs that are entirely owned by the state with SOEs that are partially privatized and with private firms.

The analysis reveals that SOEs are large and accumulate significant liabilities, which, together with their weak performance, creates significant fiscal risk. We document that subsidies often mask the underperformance of infrastructure SOEs. Netting out those fiscal transfers reveals even weaker performance. The data on subsidies also show a dangerous pattern of fiscal dependence. The soft
data indicate large information frictions in the acquisition and processing of information about macroeconomic factors, such as inflation (Armantier et al., 2016; Malmendier and Nagel, 2016; Cavallo, Cruces, and Perez-Truglia, 2016, 2017), home prices (Armona, Fuster, and Zafar, 2019; Fuster et al., 2020; Bottan and Perez-Truglia, 2020b), and GDP growth (Roth and Wohlfart, 2020). Growing evidence shows how these expectations are formed. Less is known, however, about whether individuals actually factor macroeconomic expectations into their consumption decisions (Bachmann, Berg, and Sims, 2015; Armantier et al., 2015; Coibion et al., 2019; D’Acunto et al., 2019; Bottan and Perez-Truglia, 2020b). For example, Bachmann, Berg, and Sims (2015) shows that inflation expectations and survey measures of readiness to spend are not positively correlated. And Coibion et al. (2019) present evidence from a survey experiments that, if anything, inflation expectations have a negative impact on durable spending.

We contribute to this literature in two ways. We are the first to measure the impact of expectations on actual, rather than self-reported, consumer behavior using detailed administrative data on credit card spending. This exercise overcomes several limitations specific to survey data, such as measurement error or experimenter demand effects. Indeed, we provide direct evidence that the concerns about survey data should be taken seriously, as we document a weak correlation between self-reported spending plans and the corresponding actual spending decisions. Second, we add to this line of research by studying exchange rate expectations. In recent years, inflation rates have been low and stable in most of the world and are thus arguably of little importance for most households. In contrast, large fluctuations in the nominal exchange continue to be commonplace around the world and have meaningful economic consequences for many households (Gouvea, 2020; Cravino and Levchenko, 2017). Despite the economic importance of exchange rates, there is little