rafmacalaba/data-use-mentions-extended huggingface dataset:
Similar to other national multi-topic surveys under the LSMS program, the LSMS+ supported surveys collected detailed data on the same range of individual and household socioeconomic and demographic outcomes critical for understanding poverty and welfare, with the same questionnaire structure, flow of modules and question wording. This allows for cross-country comparisons in the paper alongside the within-country analysis.

# 2.2 LSMS+ methodological approach to collecting individual-level data on assets

Survey modules on ownership of and rights to assets should be premised on the basic idea that assets can often be owned by multiple individuals, and in other cases only one individual. To account for these scenarios, a key component of the LSMS+ approach is to conduct intra-household and private interviews with all household members aged 18 and older on their personal ownership of and rights to assets (see Hasanbasri et al., 2021, for a detailed discussion of the survey methodology as well).<sup>5</sup> Following the UN guidelines, this involved first obtaining a roster of assets, in each asset class, belonging to household members, and then separately interviewing all adult household members, feeding a common roster of 5 As part of the Malawi Integrated Household Panel Survey 2016, the first LSMS+ supported survey, up to 4 adults were randomly selected for interview, following the approach used in MEXA in Uganda. This covered 99 percent of adults in the survey sample. Interviews prioritized the selection of the head of household, and his or her spouse, if working in a couple household, and the rest of the respondents were selected at random. By the second LSMS+ supported survey in Tanzania, the survey team decided it was feasible to interview all adults aged 18 and older. Tanzania, Ethiopia and Cambodia LSMS+ asset modules cover all adult household members 18 and older.

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increased from roughly 20 to nearly 60 percent. Further progress has been achieved in evaluation and debt reporting, domestic borrowing and the governance of guarantees, and debt administration and data security ( **Figure 25** ).

**Figure 25. Percentage of countries meeting minimum DeMPA requirements**

<!-- Start of picture text --> Legal framework<br>80<br>Debt Records Managerial Structure<br>60<br>Segregation of Duties, Staff<br>Debt Management Strategy<br>Capacity and BCP<br>40<br>Debt Administration and Data 20 Evaluation and Reporting<br>Security<br>0<br>Cash Flow Forecasting and Audit<br>Cash Balance Management<br>Loan Guarantees, On lending Coordination with Fiscal<br>Derivatives Policy<br>Coordination with Monetary<br>External Borrowing<br>Policy<br>Domestic Borrowing<br>1st DeMPA Most recent DeMPA<br><!-- End of picture text --> _Notes. The Debt Management Performance Assessment (DeMPA) is a tool used for assessing public debt management performance through a comprehensive set of indicators. Source: World Bank; based on DeMPAs in 22 countries._

# _Strengthening debt management practices_

In order to improve the risk and maturity profile of government debt portfolios, public debt managers have implemented policies to foster the development of local currency bond markets. This allows governments to issue debt in their own currency, thus reducing their dependence on external funding and exposure to exchange rate risk. Second, governments have raised the average life of their portfolio by issuing long-term, fixed-rate instruments —thus, reducing the exposure to interest rate fluctuations and reducing risks of refinancing. More recently, frontier markets in the region have been able to issue 30-year sovereign bonds (Nigeria, Angola and Ghana). Third, sound macroeconomic policies and financial reforms may allow governments to diversify their funding sources. Achieving investment grade —as a result of these policies
and Paraguay, there does appear to be a positive link for establishments of 50 of more employees, while in Peru the data show that establishments must employ at least 100 workers before a significant relationship between management practices and labor productivity can be detected.

We make several distinct contributions to the literature on management practices and productivity. Firstly, while analyses of establishments’ management practices have been studied in many countries across the world, in only two of the countries that we study (Argentina and Colombia) can we find evidence of previous research on management practices, and so we are shedding new light on the role of management in five previously unstudied contexts. Secondly, in addition to information on management practices, our data contain detailed information on establishment characteristics, on performance metrics and on the business environment. We can exploit these rich data to more accurately estimate the relationship between management practices and establishments’ outcomes, and to test in which contexts management matters most. Finally, the surveys from which our data come are designed to be 5
the parameter 0 _< δ ≤_ 1.<sup>19</sup> Column 1 reports the baseline effects of conflict exposure on sales and total costs (corresponding to columns 4 and 6 in Table 1, respectively). Columns 2–11 show that the estimated coefficient on conflict exposure remains negative across all values of _δ ∈_ (0 _,_ 1). Column 12 reports the value of _δ_<sup>ˆ</sup> required to drive the estimated effect to zero. In both cases, _δ_<sup>ˆ</sup> is negative and large in absolute value, implying that selection on unobservables would need to be substantial and of the opposite sign to the selection on the observables for the effect to be nullified by the bias. These results suggest that our main findings are unlikely to be driven by selection bias from unobserved factors.

One final potential concern with our results is a possible violation of the stable unit treatment value assumption (SUTVA), which requires that a firm’s response depends solely on its own level of conflict exposure, and not on the exposure of other firms. In our setting, the estimated effect of conflict exposure on firm _i_ could be biased if firms in other areas—linked to firm _i_ through supplier or buyer relationships—are also affected by conflict events. In such cases, conflict would exert both a direct effect —through firm _i_ ’s own exposure— and an indirect effect —through network linkages. To address this concern, we check whether our results change when accounting for production and consumption network spillovers in the regression. Ideally, this analysis would require firmto-firm transaction data for each country. However, such data are not available for the vast majority of countries in our sample. As an alternative, we rely on OECD inputoutput tables by country and use the WBES identification of the main product sold (ISIC 4D) to construct a product–country–specific matrix of input-output relationships.<sup>20<
16 Figure 4: Trade restrictive NTMs faced by EU exporters, by type of measure <!-- Start of picture text --> Subsidies Competition Issues Government<br>3% 2% Procurement<br>Standards and Other<br>10%<br>Technical<br>Non Tariff Barriers<br>Requirements<br>3%<br>15%<br>Other Non‐Tariff<br>Measures<br>7%<br>Quantitative<br>Restrictions and<br>Related Measures<br>8%<br>Sanitary and<br>Phytosanitary Registration,<br>measures Documentation,<br>37% Customs Procedures<br>15%<br><!-- End of picture text --> Source: Authors's calculations based on EU Market Access database.

A difficult question to answer concerns the supposed proliferation over time of NTMs. There is little doubt that from the perspective of exporters, non-tariff measures are an increasing concern, particularly at times of economic crisis. Figure 5, which again is based on barriers reported to the EU Commission, shows this increase over time, with a peak around the global financial crisis. Official data, however, are not sufficiently detailed to subject this question to a systematic investigation. For instance, SPS and TBT notifications at the WTO show an increase up to the early 2000s and a flattening of the number of notifications afterwards (Figure 6, left panels). One suspects that these data may reflect lack of reporting rather than the absence of new measures, as governments may face incentives to under-report NTMs at times of economic crisis. Data on specific trade concerns partially address this problem, but still the evidence concerning a surge in NTMs is weak. The right panels in Figure 6 show a marked increase in new and cumulative (i.e. new minus resolved) concerns for technical barriers, however the number of new and cumulative SPS concerns appear relatively stable in recent years
(2020) for Kigali city in Rwanda). High property prices in EMDEs are often attributed to prohibitively high building costs due to the need to import materials, the shortage of local skills, and the absence of financial mechanisms that allow for materials to be bought in bulk (e.g., Gardner & Pienaar 2019). While unique in their efforts to shed some light on the housing market in emerging economies, these studies provide only a snapshot of the housing market of one country (or a handful of countries) – often with a regional focus or a focus on the biggest cities.

# 2.3 Big Data Approaches and Machine Learning for Private Property Price Estimation

In addition to traditional approaches of data collection of private property prices discussed in the previous section, in recent years and with the gaining momentum of big data and machine learning in economics, more studies started to gather property price data from online listing websites. While less than a decade ago, most private properties were listed for sale in local newspapers or with private realtors, today, much of the listing activity has moved to websites concentrating on housing advertisements.

Analyses that draw on property price data collected from listing websites allow for fine-grained spatial and temporal assessments of the entire housing market. Further, big data approaches to private property prices will enable one to investigate a particular housing market in more detail or add distributional aspects to the mostly averaged house prices made available by indices discussed in Section 2.1.

A predecessor of web scraping approaches to collect property price data includes Kim’s (2007) study on Vietnam. The author manually collated over 5,000 observations on property prices and property attributes drawing on classified advertisements in Vietnam’s most prominent newspaper. Applying a hedonic price model, Kim assesses the price differences between Hanoi and Ho Chi Minh City to investigate the impact of social norms on property prices. Over time and with the increased penetration of property listing websites, private property price collection efforts have transitioned to online listings where data
denar loans with FX clauses (the so-called FX indexed loans). The conditions are identical for both types of loans. Banks can extend these loans only if

1. the client is classified as ―A‖ or ―B‖<sup>19</sup> client by the bank and by the banking system (as reported within the NBRM’s Credit Registry<sup>20</sup> ), or

2. the claim is covered with first-rated collateral.<sup>21</sup> In addition to these requirements, the decision requires banks to have written policies and procedures for the management of the induced credit risk. These internal acts should include, inter alia:

1. criteria for assessment of the (mis)match of clients’ FX assets and liabilities,

2. FX exposure limits, and 3. regular stress testing of the FX risk (at least on annual basis).

These requirements are not intended to limit FX lending but to enhance its quality, thus reducing the induced credit risk. As a result, the deterioration of the quality of the overall credit portfolio experienced in the previous years was less apparent within the FX credit portfolio. In the past two years (end of 2010 compared to end of 2008), the share of A‖ and ―B‖ credit exposures to households and enterprises in the total credit exposure decreased by 2.9 percent,<sup>22</sup> while the share of FX credit exposures to households and enterprises classified in these risk categories decreased by 1.5 percent.

- <u>Prevention of high credit growth risk and reduction of the credit risk arising from certain credit exposures</u>

In a favorable macroeconomic climate (2004–2007), the banking system has experienced a substantial credit growth, reaching an annual growth rate of 39 percent at the end of 2007 (or an average annual growth rate for the three years of 27 percent). Even though these growth rates were mainly a
--|
|wage|7.2|9.6|12.1|14.0|17.2|13.6|
|NF self emp income|1.7|0.2|0.5|1.6|1.7|1.3|
|**_Formal_**|**_8.9_**|**_9.9_**|**_12.7_**|**_15.6_**|**_18.9_**|**_14.9_**|
|wage|4.3|4.0|3.2|2.3|1.5|2.6|
|Agric income|58.7|56.4|52.6|46.2|44.9|49.4|
|NF self emp income|19.1|21.9|25.1|26.1|25.5|24.5|
|**_Informal_**|**_82.1_**|**_82.4_**|**_80.9_**|**_74.6_**|**_71.9_**_-_|**_76.6_**|
|**_Other_**|**_9.0_**|**_7.8_**|**_6.5_**|**_9.8_**|**_9.2_**|**_8.7_**|
|All|100|100|100|100|100|100|

_Source: GLSS, 1987/88_ In non-farm agricultural household enterprises a large share is in urban areas and two main sectors are trading and manufacturing. The latter was analyzed using RPED data, while the former is least understood. It is clear that in trading there is low capital involved and very seldom hired
. In turn, this is consistent with low rates of firm exit, which typically occur when there are high exit costs. See López-García and Puente (2006).

> 4 When comparing this low employment generation with the Enterprise Survey data for Ecuador—which includes firms of all sizes—the results are not particularly different. Firms which had 50 employees or less in 2007 have on average added less than one new employee per year, virtually the same rate as all of Latin America (LAC Enterprise Surveys, 2010).

5
The shares of non-empty corridors and regular corridors are similar across the key subsets used in the analysis.

Likewise, BLAs are also relatively rare in the data. About 1.6 percent of corridors have a BLA signed between them, with the share lower for African origin countries (0.80 percent) and higher for GCC destinations (3.4 percent). BLAs are more common in regular corridors with close to 9 percent of the regular corridors having a BLA. Similar pattern holds across the various sub-samples used in the analysis.

Of the 261 million migrants in our combined dataset in 2020, 82 million (31 percent) are in corridors with a BLA. The proportion of migration accounted by corridors with a BLA is

> 4To address the issues of countries and names that changed over time, we use the same principles and coding applied in the migration data (Ozden<sup> ̈</sup> et al., 2011).

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Figure 1. Share of Female Ownership and Management in Firms integrating into GVCs <!-- Start of picture text --> 100%<br>90% 64% 64% 73% 74% 87% 89% 87% 88%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>36% 36%<br>20% 27% 26%<br>10%<br>0% 13% 11% 13% 12%<br>GVC1 GVC2 GVC3 GVC4 GVC1 GVC2 GVC3 GVC4<br>(a) Female Ownership (b) Female Top Manager<br>Yes No<br><!-- End of picture text --> Source: Constructed by the authors using the WBES.

Note: GVC1 refers to firms that export and import simultaneously, GVC2 = GVC1 + international certification, GVC3= GVC1+ share of its capital owned by a foreign firm, GVC4 combines the four criteria altogether.

When we look at the relationship between female participation, firm size, and GVC integration, two remarks are worthwhile. First, for large firms and the strict definition of GVC, females (whether they are owners or managers) tend to be under-represented. Second, generally, for less restrictive definitions of GVC, we do not observe significant differences between small, medium, and large firms when it comes to female ownership and management. As for regional differences, Figure 3 shows that females are doing better in terms of ownership compared to management when it comes to GVC integration. Indeed, for all GVC definitions in different regions, the share of firms that are part of GVCs and that are owned by females is greater than the one of firms managed by females with a slightly lower figures for the most restrictive definition (GVC4) as it is shown in Figure 2. In addition, for the most restrictive definition, EAP followed by SAR
and how much is simply the result of a more favorable global environment? This paper attempts to shed some light on this issue.

The paper addresses the following questions. First, what are the monetary policy challenges in low-income countries? Second, how has inflation in LICs evolved? Third, how well anchored are inflation expectations in LICs? Finally, what country characteristics have been associated with stronger anchoring?

In this paper, the question of the degree of anchoring of medium-term inflation expectations in LICs is tackled by estimating a novel heterogeneous panel structural vector autoregression (SVAR) model.<sup>6</sup> The model examines the extent to which core inflation in LICs has remained stable in the face of a variety of external shocks, including shocks to global core, energy, and food price inflation, and other shocks transmitted to the domestic economy through exchange rate fluctuations. The assessment is made based on the degree of sensitivity of domestic core inflation, which is determined by the degree of anchoring of inflation expectations, to external shocks. The estimation is based on a monthly panel data set that covers 104 countries (25 advanced economies and 79 EMDEs, including 18 LICs) for 1970M2-2016M12. The data set contains at least 36 months of continuous data for each country, for six variables—headline consumer price index (CPI), food CPI, energy CPI, core CPI, nominal effective exchange rate (NEER), and rainfall. Differences across income groups and subgroups in LICs in the extent to which domestic core inflation performance has been insulated from international factors are analyzed in terms of country characteristics, institutional factors, and policy regimes under a simple ordinary least squares regression framework.

The paper’s principal conclusions are as follows.

LICs, like other EMDEs, have experienced higher average levels and volatility of headline inflation than have advanced economies. However, the level and volatility of headline inflation have declined in all three country groups over the past two decades. The fall in inflation volatility in LICs is largely accounted for
2|
|Area reg. syst. m<sup>2</sup>||||1,926|2,009|
|No. reg. sales annually|0.06|0.04|0.08|0.36|0.40|
|<br>No. reg. mortgages annually|0.11|0.02|0.14|0.20|0.24|
|Share of built-up reg. area|NA|0.06|0.09|0.14|0.12|
|Population density indiv/km<sup>2</sup>|794|1,011|1,170|1,282|1,375|

_Sources:_ LAA land administration database from 2011 and digitization of paper records, 1981–2011 for parcels, transfers, and mortgages registered. WSFE for built area; and gridded population of the world v4 (CIESIN) for population density.

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out of the profits generated by the increasing returns sector<sup>4</sup> . This model suggests that a large internal market may provide a path to development, even in the absence of trade. The middle class, which determines the size of this market, is the result of an initial wealth shock _and_ an initial relatively equitable distribution of firm ownership.

# **3 Empirical Strategy**

Bringing a highly stylized model to the data is challenging. As noted earlier, we know that the model is “wrong.” Accordingly, we do not attempt to test it against alternative hypotheses. Instead, our empirics are very much in the spirit of Leamer and Levinsohn (1995) who in their Chapter for the Handbook of International Economics encourage empirical researchers to “‘estimate, not test.” We use an empirical model inspired by the theoretical framework described in the previous section to estimate the threshold market size required to achieve sustained poverty reduction, and answer the questions posed in the Introduction related to the role of domestic and international market size in development. In line with this thinking, the empirical model includes only variables suggested by the theoretical framework.

Before we present the empirical model in detail, a couple of remarks are in order. First, the theoretical model is static in nature, but makes statements about an inherently dynamic process, the process of development. The estimation utilizes cross-country panel data for countries in which extreme poverty still exists at the beginning of the sample period.<sup>5</sup> The specifics of the empirical model will be explained in the next section, but we note here that identification of the parameters relies heavily on cross-country variation, as most of the key variables in the analysis (population, various measures of market size) move only slowly over time. However, they do move (otherwise there would be no development, i.e., poverty reduction), so that the time dimension of the data is essential for identification. Given the
and its interaction with the female-headed household dummy. Thus, our results are not fully robust to modifications in functional form. However, use of levels remains our preferred specification because of our interest in changes in the right tail of the distribution. Our qualitative work revealed that better roads triggered productive changes in agriculture, such as the increased use of machinery and switch towards more commercially lucrative crops. However, these changes take time and happened in the households that were more likely to invest in improved production processes and planting new crops. However, these types of changes may substantially increase production rather than trigger change in the margin. Consequently, we would like our estimates to be sensitive to the changes in the right tail of the distribution, which are likely to be subdued when IHS transformation is used.

# 6. Conclusion

Although accurately estimating the magnitude of global investment in transport is fraught with challenges, the estimates that exist point to a very large number—on the order of magnitude of US$1 trillion annually. Recent studies demonstrate that such investments play an important role in improving development outcomes ranging from crop diversification (Shamdasani, 2016) to higher educational enrollment (Garz

> 20 Our administrative data do not allow us to distinguish between activities carried out on sealed versus unsealed roads and the baseline conditions of roads, two data points which would narrow the range of life spans, we could reasonably expect. They also do not allow us to distinguish between upgrading and rehabilitation, but as we note in footnote 15, the impact of this is not as large given the short time frame of this study.

> 21 Results are available upon request.

20