rafmacalaba/datause-encoder-data huggingface dataset:
**_9_**

# **_B. Economic'growth and its sources_**

This paper uses new and bettei'data on capital accumulation, productivity growth and private saving rates. This subsection describes our data on economic growth, capital per capita growth and three different measures of productivity growth.' _GROWTH_ equals the rate of real per capita **GDP** growth, where the underlying data are from the national accounts. For the pure cross-sectional data (where there is one observation per country for the period **_1960-1995),_** we compute _GROWTH_ for each country **by** running a least squares regression of the logarithm of real per capita **GDP** on a constant and a time trend. We use the estimated coefficient on the time trend as the growth rate. This procedure is more robust to differences in the serial correlation properties of the data than simply using the geometric rate of growth [Watson **1992].1** We do not use least squares growth rates for the panel data because the data are only over five-year periods. Instead, we calculate real per capita **GDP** growth as the geometric rate of growth for each of the seven five-year periods in the panel data.

The capital accumulation data are from a study **by** Levine and Orlov **(1998)** and improve significantly on figures from previous studies **by** using disaggregated data. Briefly, they construct capital stock figures, _K,_ from investment data, _I,_ and depreciation estimates, **8,** for five separate capital categories: machinery, transportation equipment, business construction, residential construction and other construction. These data are from revised Penn-World Tables **_(5.6)._** The capital stock number for each category, _i,_ is then computed using the following formula: K, 1 **=** K **it +** Iit **- 8i** ,K it. This perpetual inventory method requires the estimate of an initial capital stock Ko. Levine and Orlov **(1998)** use Harberger's **(1978)** suggestion for deriving a guess of the initial capital stock in **_1950,_**

> For a detailed description of sources and construction of the data see appendix B. **9** Using geometric growth rates yields virtually. identical results.
To our knowledge, this is the first firm-level data set providing detailed and rich information on both the market environment and contract features of supplier and customer “supply-chain” financing. For example, firms are surveyed on the percentage of sales and inputs financed with trade credit; the number of days offered (maturity); whether a discount is offered for early payment; and the number of days until payment is actually made /received. This allows us to look simultaneously at both sides of firm activity - the relation between a firm, its customers (product market), and its suppliers (input market) - and to precisely match these two sides. This unique perspective is crucial to highlight a direct and novel link between the supply and demand for trade credit.

The design of our analysis is straight-forward. First, we document the importance of trade credit as a competitive gesture. Specifically, firms that face stronger competition in the product market are more likely to extend trade credit and have a larger share of goods sold on credit. We then investigate how these firms finance the provision of trade credit and we provide empirical support for a matching story. We document that firms are likely to depend on their own receipt of trade credit to finance the extension of trade credit and to match maturity between contract terms of payables and receivables. We find very large and significant relationships between the decision to offer and the use of trade credit; the percentage of inputs and the percentage of sales financed by trade credit; the number of days extended to customers and the maturity received from suppliers; and whether the firm is offered a discount by its suppliers and offers a discount to its customers. Furthermore, we find that firms match the ex-post timing of payments, i.e. firms that receive payments early from customers are significantly more likely to remit early to suppliers, and viceversa.

3
Policy Research Working Paper 7398

# **Abstract**

The objective of this study is to assess the possibility of using Internet search keyword data for forecasting price series in Central America, focusing on Costa Rica, El Salvador, and Honduras. The Internet search data comes from Google Trends. The paper introduces these data and discusses some of the challenges inherent in working with it in the context of developing countries. A new index is introduced for consumer search behavior for these countries using Google Trends data covering a twoweek period during a single month. For each country, the study estimates one-step-ahead forecasts for several dozen price series for food and consumer goods categories. The study finds that the addition of the Internet search index improves forecasting over benchmark models in about 20 percent of the series. The paper discusses the reasons for the varied success and potential avenues for future research.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. 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 jsseabold@gmail.com.

_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
# **Data**

The main source of our data is the World Bank’s Enterprise Surveys. The data are collected using a standard and rigorous approach across countries. The surveys provide representative samples of each country’s private sector including manufacturing, construction, services and retail and wholesale trade.<sup>5</sup> Although Enterprise Surveys were conducted before 2006, we only include data collected after 2006. We do this because there was considerable heterogeneity across countries and regions in terms of sectors covered, questionnaire format, coverage of microenterprises, and sampling methodology before 2006. Moreover, the samples for surveys conducted before 2006 were not generally representative. Data from the Enterprise Surveys is supplemented with data from other sources. A complete list of variables and the sources of this data is shown in Table 1.

## _Econometric methodology_

Before presenting results, it is useful to note several important issues when using cross-country firm-level data. The first issue is whether to use weights or not. In the empirical analysis, we will focus on unweighted regressions, that is, using the “model approach” (Ayyagari and others, 2011; Cameron and Trivedi, 2005). We do this for several practical reasons. First, although the Enterprise Surveys use a standard methodology for sampling and weighting (World Bank, 2007b), the quality of the sampling frames varies significantly across countries. Although in most cases the initial sample frame is constructed based on lists collected by government agencies, the quality and coverage of these lists can vary significantly. In Kenya, for example, the firm conducting the surveys had to combine lists from Kenya National Bureau of Statistics, the Kenya Association of Manufacturers, the Kenya National Chamber of Commerce, the Kenya Private Sector Alliance, and from the Federation of Kenya Employers to obtain a satisfactory sampling frame (World Bank, 2007a). Finally, in some countries including Botswana, since no lists were available, the sample frame was constructed through block enumeration (World Bank, 2006). Second, we want to use variation in agglomeration and business environment variables across countries and regions to identify the characteristics that affect firm growth throughout the

> 5 See http:\\www.enterprisesurveys.org for a more complete description of the data.

9
To measure _σ_ we employ the estimations and the sectoral classification proposed by Broda and Weinstein[13]. The authors extend the seminal work by Feenstra[21], and use data on U.S imports during the period 1990-2001 to estimate the elasticity of substitution between two products under a monopolistic competitive framework. They obtain estimations of such parameter for each 3-digit SITC (Rev.3) sectors, and they classify sectors in three categories: low- _σ_ , medium- _σ_ , and high- _σ_ . Low- _σ_ sectors are below the 33rd percentile in the distribution of the estimated _σ_ . Medium- _σ_ sectors are between the 33rd percentile and the 66th percentile. High- _σ_ sectors are above the 66th percentile.

# **Imitation**

To measure _m_ we employ information about the impact of imitation on innovators’ performance. The survey allows firms to classify such impact as follows: strongly significant, significant, or irrelevant. We define _m_ as the proportion of firms that in a particular sector receive a strongly significant negative impact on sales due to imitation.

# **4.1.2 Data analysis**

Before turning to the estimations, we look to the data and we analyze if there is preliminary evidence in support of our model. Because Proposition 1 and Corollary 1 are crucial to derive our results, we start the analysis exploring them. Recall that Corollary 1 shows that in equilibrium the following condition is Figure 1 shows the relationship the data describe. In the y-axis we measure a proxy for the left hand side of equation (19), which is defined as the average of the ratio of the sales of any firm to that of the leading supplier, adjusted by the power of _σ−_ 1. In the x-axis we measure the right hand side, assuming _γ_ = 1. The line represents the pattern we should observe according to the theory. The scatter plot shows the relationship the data describe. Interestingly, both graphs display similar patterns.

14
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the model’s results – for example, a finding that gains for unskilled labor are larger than those for skilled labor – should be reliable.

Major advantages of this type of model are that it represents the whole economy in a consistent and theoretically sound framework and that the structural features of the country investigated strongly influence the final results. Table 3 shows these features for Nicaragua in terms of sectoral shares of gross production, imports, exports and private demand; the middle panel details, for each sector, the U.S. weight in total trade; the right panel shows Nicaraguan tariffs against the U.S. and other partners and the U.S. tariffs against Nicaraguan products. For convenience, the bottom panel of the table reports measurements for aggregate macro sectors, although the model’s actual 28 sectors are shown in the top panel. In commenting on the results of the policy simulations, we will refer to data in this table.

The initial import protection, both in its level and sectoral variability, is among the key elements determining the outcome of the simulated trade reforms. Three key features are highlighted by the tariff data: 1) the overall trade-weighted protection rate is rather low, 2) its dispersion is high with a clear bias against agricultural imports, and 3) tariffs against the U.S. are generally above the trade-weighted average of tariffs against the Rest of the World.

Table 3 also shows that domestic Nicaraguan agricultural producers may be facing strong competition from U.S. imports, which are 41% of total imports of agricultural commodities. It is likely that a liberalization of U.S. imports, which reduces an anti-agricultural import bias, would lead to an increase of competition in the agricultural sectors with a potential initial negative shock for households strongly dependent on farming incomes. Clearly, the level of sector aggregation used in the model may exacerbate this potentially negative outcome. It may be that at finer sectoral levels, one finds that imports and domestic products are complements rather than substitutes. For example, lower feed costs could stimulate livestock and poultry productions. However, agricultural products are normally fairly homogeneous, and thus substitutable, and the risk of negative impacts should not be completely ruled out.

19
_Africa’s Macroeconomic Story_

# **IV. Macroeconomic Policy over the Historical Period**

This section of the paper examines how macroeconomic policies in the region have evolved over the past few decades by focusing on key macroeconomic variables—debt burdens, fiscal balances, inflation rates and exchange rates.

# _Debt burdens have come down to more manageable levels_

External debt increased from under 15 percent of GDP in the early 1970s to 23 percent at the end of the decade, just slightly higher than that for all developing countries (Figure 12, left panel). The debt burden rose dramatically throughout the 1980s and into the early 1990s, peaking at just over 75 percent of GDP in 1994, almost 40 percentage points above the level for all developing countries. In 1994, debt burdens exceeded 100 percent of GDP in half of African countries (represented by the median in Figure 12, right panel) and exceeded 160 percent in one quarter (the upper quartile). The sharp drop in the early 2000s was largely due to the HIPC/MDRI debt reduction initiatives.<sup>11</sup>

# **Figure 12: External Debt in Developing Countries, 1971-2013**

_Percent of GDP_ <!-- Start of picture text --> 75 150<br>50 100<br>25 50<br>0 0<br>1970 1980 1990 2000 2010 1971 1981 1991 2001 2011<br>Africa Region Median Upper quartile<br>All developing countries Africa Region<br><!-- End of picture text --> 11 Debt relief provided by the HIPC/MDRI initiatives are recorded in the external debt statistics when countries reach the completion point, the timing of which has varied significantly across the region. Uganda reached its completion point in (2000), followed by Mozambique and Tanzania (2001), Burkina Faso and Mauritania (2002), Benin and Mali (2003), Ghana, Ethiopia, Madagascar, Niger, and Senegal (2004), Rwanda and Zambia (2005), Cameroon, Malawi and Sierra Leone (2006), The Gambia and Sao Tome and Principe (2007), Burundi and CAR (2009), Liberia, Guinea-Bissau, Dem. Rep. of Congo, Rep. of Congo, and Togo (2010), Cote d’Ivoire and Guinea (2012), and Comoros (2013).

21
illustrate how each can be translated into a rationale for some sort of population policy. We note that in terms of policy as it affects the poor, the implications of the two different approaches are virtually identical. In Section III we discuss the implications of these two concerns for the structure and content of programs to reduce fertility -- in this context we review education, health and family planning programs; effects of family planning on women's welfare; expansion of contraceptive choice; and the use of incentives to reduce fertility. We conclude with an assessment of the importance to the future success of fertility reduction efforts of taking a fundameiLtally redistributive approach to population policy, -- an approach in which one segment of society (the relatively rich) offers another segment (the relatively poor) additional resources -- irr:spective of the specific rationale for the policy.

# **Section II.** The **Externalitv** and Income Distribution AvDroaghes

# Neqative Environmental Externalities

Negative externalities to childbearing arise if there is a divergence between the private and social costs of children, i.e., if one couple's childbearing decisions imposes costs on other families. Z/ In a recent paper Lee (1991) sets out a framework for considering empirically such externalities. He concludes (with great caution -- he notes<sup>that the</sup> numbers are rough and the exercise is experimental) that externalities are not so great nor always in the expected direction -- they are not always negative for developing countries. However, as Lee himself points out,<sup>he</sup> does not include any estimate to account for externalities arising from

- 7/ In the World Bank's 1984 World Development Report, this divergence between private and social costs provided an important justification for government programs to reduce fertility.

3
|Sisben eligible|1,363,200|
|---|---|
|Poor|1,059,720|
|Indigent|385,274|

A more detailed look at the data, however, yields a more complicated picture. Table 2 shows the distribution of all age-eligible individuals according to their eligibility by the Sisben criterion and their poverty status. The shares represent weighted averages of each category over all survey years. While table 1 shows that the total number of age-eligible elderly with low Sisben scores is about equal to the number of elderly living in conditions of poverty, table 2 shows that these two groups overlap only partially. Over 35 percent of the poor are not considered eligible according to their Sisben scores. The same is true of almost one-third of the indigent elderly.

**Table 2: Comparison of Sisben Score and Poverty Status,** **<u>percent</u>**

|_Eligibility_|_Nonpoor_|_Poor_|_Nonindigent_|_Indigent_|_Total_|
|---|---|---|---|---|---|
|Noneligible by Sisben score|52.73|10.60|60.22|3.11|63.33|
|<br>Eligible by Sisben score|17.71|18.96|28.62|8.06|36.67|
|<br>Total|70.44|29.56|88.83|11.17||

The most important question is how the eligibility criteria translate into actual receipt of the benefit. Table 3 shows the share of individuals who report living in a household where at least one person receives the benefit. We present weighted averages from the 2013 round of the ENCV. As can be seen, coverage levels are fairly low in each of the target groups. Even among individuals who are older than 65 years of age, only slightly over 50 percent of the indigent and of persons living in households that are both poor and in Sisben groups 1 or 2 receive the benefit. Among all other groups, coverage is significantly lower.

**Table 3: Share of Beneficiaries according to ENCV 2013, percent**

||_Age eligible_|_60 or older_|_65 or older_|
|---|---|---|---|
|Sisben eligible|32.44|37.20|40.69|
|Poor|31.85|39.91|47.88|
|Indigent|36.72|44.83|54.65|
|Sisben eligible or poor|28.69|34.80|39.92|
|Sisben eligible andpoor|40.84|46.79|52.04|

A different way to address this question is shown in table 4. Here, we look at the share of program beneficiaries within each group in 2013. As becomes clear, almost 30 percent of the beneficiaries did not fulfill the Sisben requirement, and almost 40 percent are not poor in monetary terms. While only 10
# **4.3.3 Interacting linkages with affiliates of pioneer firms**

The results above show no significant effect for firms affiliated with pioneer firms. This lack of significance is evident both in the baseline regression (Table 8) and when controlling for distance (Table 9). To further understand this null affiliate estimate, we also interact all other linkage variables with an affiliate dummy. Consistent with earlier results, all linkage effects lose significance when interacted with the affiliate dummy, as shown in Table 14. This further solidifies the evidence that it is linkages outside the firm boundary that are important for the spread of new technologies.

Table 14. Single linkage interacted with affiliate dummy

||Dep variable|: Normalized|share of techn|ology-related|job adverts|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Pioneer|4.630***|3.883***|3.908***|3.923***|3.878***|
||(0.398)|(0.774)|(0.775)|(0.776)|(0.775)|
|Pioneer_×_years since emergence|-0.202***|-0.185**|-0.187***|-0.188***|-0.185***|
||(0.036)|(0.072)|(0.072)|(0.072)|(0.072)|
|**Linkages of pioneer frms**||||||
|Buyer||1.731***|||1.416***|
|||(0.291)|||(0.311)|
|Buyer_×_affliate||1.855|||0.297|
|||(1.693)|||(2.337)|
|Supplier|||0.545***||-0.027|
||||(0.156)||(0.161)|
|Supplier_×_affliate|||3.222*||2.765|
||||(1.809)||(2.041)|
|Innovation partner||||1.517***|1.031***|
|||||(0.258)|(0.273)|
|Innovation partner_×_affliate||||1.632|-0.439|
|||||(1.594)|(2.234)|
|**Linkages of pioneer frms over time**||||||
|Buyer_×_years since emergence||-0.048|||-0.051*|
|||(0.029)|||(0.030)|
|Buyer_×_years since emergence_×_affliate||-0.069|||0.044|
|||(0.137)|||(0.179)|
|Supplier_×_years since emergence|||0.003||0.031*|
||||(0.017)||(0.018)|
|Supplier_×_years since emergence_×_affliate|||-0.189||-0.205|
||||(0.156)||(0.163)|
|Innovation partner_×_years since emergence||||-0.030|-0.017|
|||||(0.026)|(0.026)|
|Innovation partner_×_years since emergence_×_affliate||||-0.092|0.018|
|||||(0.129)|(0.169)|
|Technology FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Firms FE|Yes|Yes|Yes|Yes|Yes|
|Adjusted R-squared|0.041|0.041|0.041|0.041|0.041|
|Observations|10,072,709|10,072,709|10,072,709|10,072,709|10,072,709|

Notes: Standard errors clustered at the firm level in parentheses.The normalized share is capped at the 99<sup>_th_</sup> percentile of non-zero observations. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 31
**Figure 29: Mineral and Non-Mineral Exports by Type of Region, 2011, Percent**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50% Non‐mineral Exports<br>40%<br>Mineral Exports<br>30%<br>20%<br>10%<br>0%<br>Leading Middle‐range Lagging RUSSIAN<br>Regions Regions FEDERATION<br><!-- End of picture text --> _Source_ : Rosstat.

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