rafmacalaba/datause-encoder-data huggingface dataset:
**Table 7B.  Loan Size, Business Owners**

OLS

|||Log of Am<br>|ount Request|ed||Log of Am<br>|ount Approve|d|
|---|---|---|---|---|---|---|---|---|
||Busin|ess Owners|Males|Females|Busin|ess Owners|Males|Females|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Female Brochure|0.093<br>(0.057)|0.08<br>(0.050)|0.135**<br>(0.052)|-0.061<br>(0.129)|0.027<br>(0.032)|0.011<br>(0.033)|0.002<br>(0.030)|-0.068<br>(0.081)|
|Offered Business Training||-0.032|-0.058|-0.021||0.054**|0.026|0.032|
|||(0.046)|(0.047)|(0.145)||(0.027)|(0.026)|(0.098)|
|Female||0.089||||-0.094|||
|||(0.082)||||(0.064)|||
|Age||0.013|0.017|-0.024||0.007|0.015**|-0.006|
|||(0.009)|(0.011)|(0.021)||(0.005)|(0.006)|(0.023)|
|Age^2||-0.000*|-0.000*|0.000||0.000|-0.000**|0.000|
|||(0.000)|(0.000)|(0.000)||(0.000)|(0.000)|(0.000)|
|High Decision-making power (Yes=1)||0.025|0.095**|-0.022||0.029|-0.007|0.094|
|||(0.038)|(0.038)|(0.091)||(0.030)|(0.024)|(0.070)|
|Own Education||0.022***|0.020***|0.027||0.012***|0.013***|0.012|
|||(0.005)|(0.005)|(0.017)||(0.004)|(0.004)|(0.010)|
|Digit Span Recall||-0.014|-0.019*|0.003||-0.009|-0.009|0.001|
|||(0.011)|(0.010)|(0.034)||(0.006)|(0.007)|(0.020)|
|Risk Tolerance||0.002|0.001|0.014||-0.001|0.000|-0.003|
|||(0.005)|(0.005)|(0.017)||(0.005)|(0.006)|(0.011)|
|Yrs. Of Education of Spouse||0.001|0.007|-0.007||0.002|-0.005|0.013|
|||(0.005)|(0.005)|(0.013)||(0.006)|(0.006)|(0.010)|
|Number of children under 9||0.004|0.008|-0.032||0.001|-0.004|-0.011|
|||(0.011)|(0.011)|(0.037)||(0.007)|(0.007)|(0.019)|
|Land||0.005***<br>(0.001)|0.005***<br>(0.002)|0.005<br>(0.005)||0.002<br>(0.001)|0.000<br>(0.001)|0.004<br>(0.003)|
|Member of a Mixed Group (Yes=1)||0.138|0.314|-0.293||-0.007|-0.256***|0.030|
|||(0.167)|(0.213)|(0.293)||(0.239)|(0.086)|(0.331)|
|Mean Dependent Variable|10.92|10.92|10.92|10.92|10.69|10.69|10.69|10.69|
|N. Observations|459|459|356|103|355|355|278|77|
|R-squared|0.23|0.30|0.34|0.42|0.28|0.35|0.29|0.58|

Note: The dependent variable is log of loan amount requested (columns 1-4) and log of loan amount approved (columns 5-8). The sample includes loan applicants. All regressions include branch fixed effects and are estimated using OLS methods. Standard errors are clustered at the borrower group level.The following symbols *, * * and ** * denote significance at the 10 , 5 , and 1 percent level, respectively. See Appendix B for definition of variables.
Two interesting insights can be gleaned for the results presented:

- i. The possible range of Africa-wide climate change impacts on streamflow increases significantly between 2050 and 2100. The range in 2050 is from a decrease of 15% to an increase of 5% above the 1961–1990 base for a range of 20%. For 2100 the range is from a decrease of 19% to an increase of 14% for a range of 33%.

- ii. For southern Africa for all four scenarios almost all countries except South Africa experience significant reduction in streamflow. And even for South Africa the increases under the two high scenarios are modest at under 10%.

To provide some additional insights into the change in precipitation, temperature and streamflow over the ten decades from 2001 to 2100 for the ten scenarios for each of the 49 African Nations analyses, 174 figures were prepared, 12 of which were randomly selected and are presented in Appendix A.

# **4. Concluding remarks**

In Phase I, the following monthly time series have been derived for the years 1961–1990 for each of the politically delineated districts in the 11 countries of the study:

- Runoff (mm)

- Relative soil storage (0-1)

- Potential evapotranspiration (mm)

- Actual evapotranspiration (mm)

- Temperature (<sup>o</sup> C)

- Precipitation (mm)

- Streamflow (m<sup>3</sup> )

22
the suspension of public services (e.g., banking, telecommunication, electricity, and transport services). Specifically, we find that the outbreak of the civil war has dramatically reduced households’ access and ability to buy food while also significantly disrupting livelihoods of households in conflict affected areas. Non-farm and wage related activities appear to be the most affected while farm activities were more resilient than other activities.<sup>8</sup> Similarly, economic activities in urban areas were much more affected than those in rural areas. This is partly because urban areas and main roads connecting towns continued to be heavily militarized even during periods of relative lull in high intensity active war. These suggest that the ultimate impacts of the violent conflict, in addition to curtailment of livelihoods, were likely mediated through disruptions in the functioning of markets and supply chains in urban areas. Tigray has continued to be disconnected from the rest of the country for several months after the last survey round, and it is likely that these impacts have deepened in subsequent months. Our findings support the urgent calls for large-scale humanitarian assistance that were made even at early stages of the conflict.

The rest of the paper is structured as follows. Section 2 describes the geographic and political context of this study and data used. Section 3 summarizes trends in welfare outcomes. Section 4 outlines the empirical strategy while Section 5 presents our results and discussion. In Section 6, we provide concluding remarks as well as general discussion of the value – and shortcomings – of high-frequency phone surveys for monitoring large-scale conflicts in other settings in the future, including the preparatory work that would be required to enable mobilization of such efforts.

# **2. Context and Data**

## **2.1 Context**

For over two decades, Ethiopia has been one of the world’s fastest-growing economies and often nicknamed as the “hub of stability” in a volatile Horn of Africa region.<sup>9</sup> This changed drastically in early November 2020 when war broke out in Tigray, costing thousands of lives, inflicting massive humanitarian disasters and infrastructural damages. Initially, the war involved the Tigray regional forces, and an alliance of the federal army, regional forces from neighboring Amhara

> 8 It is important to note that farmers in many instances continued to prepare their land when the rainy season approached in April to June 2021 often at a risk to their lives.

> 9 Example, see the Atlantic Council, November 2021 (https://www.atlanticcouncil.org/contentseries/fastthinking/fast-thinking-ethiopia-is-on-the-brink/).

6
|**Table**|**1: Mean**|**Per Capit**|**%**<br>**a Real Consum**|**ption**|**%**|
|---|---|---|---|---|---|
|**Jordanian Dinars**|**2002**|**2010**|**change**<br>**(02-10)**|**2008**|**change**<br>**(08-10)**|
|Urban|1,403|1,765|25.7|1,596|10.5|
|Rural|1,070|1,402|31.0|1,170|19.9|
|**Governorates**||||||
|Amman|1,679|2,054|22.4|1,859|10.5|
|Balqa|1,198|1,480|23.5|1,294|14.3|
|Zarqa|1,015|1,550|52.7|1,322|17.3|
|Madaba|1,262|1,482|17.5|1,198|23.7|
|Irbid|1,178|1,484|25.9|1,364|8.7|
|Mafraq|937|1,298|38.5|1,068|21.5|
|Jarash|1,102|1,641|48.9|1,260|30.3|
|Ajlun|1,064|1,228|15.4|1,260|-2.6|
|Karak|1,262|1,667|32.1|1,283|30.0|
|Tafiela|1,143|1,364|19.4|1,178|15.8|
|Ma'an|987|1,211|22.7|1,185|2.2|
|Aqaba|1,196|1,411|18.1|1,424|-0.8|
|**Consumption Quintiles**||||||
|Lowest quintile|516|707|37.0|650|8.8|
|Q2|789|1,044|32.3|955|9.3|
|Q3|1,054|1,368|29.8|1,239|10.4|
|Q4|1,444|1,838|27.3|1,640|12.0|
|Highest quintile|2,844|3,551|24.8|3,107<br>0|14.3|
|Kingdom<br>Source: World Bank Staff Calcula|1,330<br>tions using Off|1,702<br>icial Househ|28.0<br>old Income and Expen|1,518<br>diture Surveys.|12.1|

Despite popular belief, welfare growth has been pro-poor in Jordan. Looking at the quintile distribution in Table 1, the summary statistics indicate that the increase in per capita real consumption was the highest for the bottom two quintiles compared to the upper ones<sup>17</sup> . This suggests that the richest Jordanians benefited relatively less from the growth in the Kingdom’s overall welfare. This result is upheld when examining the growth incidence curve of Figure 1. Indeed the downward slope of the curve supports the above claim and graphically highlights the relative welfare gains made by the poorest Jordanians between 2002 and 2010. This indicates that the economic growth of the past decade in Jordan not only reduced poverty, but also contributed to the decline in inequality and managed, to a certain extent, in reducing the welfare gap among Jordanians.

While quantitative indicators support this claim, it is frequently reported that the population’s overall perception has been of dissatisfaction with the country’s economic performance. This is often fueled by non-economic parameters related to accountability,

> 17 Two considerations should be made when examining these percentage changes: (i) the low nominal base from which the poorest quintile consumption depart, (ii) the downward bias that is potentially depicted in the upper quintile sample as the richest households often under report their consumption and have lower response rates in expenditure surveys.

7
**Table 6. Production, sales, and exports by automakers with assembly plants in Mexico, 2004-2007**

||Domestic<br>production|Domestic<br>production<br>soldlocally|Imports|Exports|Exports to US<br>and Canada|Total<br>domestic<br>sales|
|---|---|---|---|---|---|---|
|GM|1,884,730|385,665|585,989|1,499,065|1,483,965|971,654|
|Nissan|1,550,563|726,829|184,209|823,734|669,167|911,038|
|Chrysler|1,282,670|20,785|475,948|1,261,885|1,185,608|496,733|
|Volkswagen|1,282,314|261,979|329,356|1,020,335|568,750|591,335|
|Ford|909,480|165,007|527,052|744,473|730,110|692,059|
|Honda|89,753|29,734|133,309|60,019|52,713|163,043|
|Toyota|65,458|0|185,490|65,458|42,360|185,490|
|Total|7,064,968|1,589,999|2,421,353|5,474,969|4,732,673|4,011,352|
||%<br>of<br>domestic<br>sales<br>produced<br>locally|Share of local<br>production<br>exported|Share<br>of<br>exports<br>to<br>US<br>and<br>Canada|Exports<br>CAGR04-07|Production<br>CAGR04-07|Domestic<br>sales<br>CAGR<br>04-<br>07|
|GM|40%|80%|99%|1%|-1%|-3%|
|Nissan|80%|53%|81%|34%|16%|-2%|
|Chrysler|4%|98%|94%|-7%|-6%|4%|
|Volkswagen|44%|80%|56%|27%|22%|-7%|
|Ford|24%|82%|98%|54%|41%|-5%|
|Honda|18%|67%|88%|3%|-3%|18%|
|Toyota*|0%*|100%*|100%*|NA*|NA*|40%|
|Total|40%|77%|86%|14%|10%|-1%|

*Toyota began production in Mexico in 2006; production data are for 2006 and 2007 only.  Figures for domestic sales are for 2004-2007.

**Source:** Associacion Mexicana de la Industria Automotriz (AMIA)

Clearly, the fate of an industry in a small, regionally embedded country like Mexico is tied to factors that lie largely outside the control of the state or of local firms. Ironically, the flagging prospects of the Big 3 automakers have created more risks for Mexico and Canada than it has for the United States.<sup>13</sup> These companies, even though they are based in the United States, have been more important in driving investment and industrial upgrading in Mexico than Asian firms have. Japanese and Korean automakers, with the exception of Nissan, have concentrated their North American investments within the United States (and to a lesser extent, Canada) for political reasons, while the Big 3, when they have made new North American investments at all, have sought to cut costs in North America by building and planning new capacity in Mexico. Now, with the crisis, we believe that the future of this most recent investment wave must be called into question by the severe crisis that has currently overtaken the Big 3.

> 13 In 2005 the automotive assembly and parts sectors accounted for 1.05% of Canada’s total private sector employment and 1.07% of Mexico’s, but only .77% of the United States’ (based on calculation using ILO, U.S.Bureau of Labor Statistics; INEGI, and AMDA data).

19
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- _of financial economics_ , 1993, _33_ (1), 3–56.

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25
The transaction cost motive assumes that there are certain fixed costs to placing an order or that there are economies of scale in ordering in large batches. When faced with uncertain demand as in the stockout model, firms follow an (S,s) strategy. As soon as the inventory falls below s, the firm places an order of a lot size equal to S-s so that the inventory level for each firm fluctuates between s and S. In determining the optimal lot size, firms weigh inventory holding costs against savings from large orders. According to Mosser [1991], retail inventories are usually managed by an (S,s) rule, as evidenced by its presence in textbooks on purchasing, retailing, and merchandising as well as in trade journals and business reviews which describe implementations of the (S,s) rule using computers.7 Poor infrastructure would affect raw materials inventories either through the stockout or transaction cost motive. According to the stockout model, poor infrastructure could increase the time it takes for a shipment to arrive. When a firm finds itself running low on raw materials due to a sudden increase in demand for its finished products, it places an order to replenish its supplies. Since the delivery time is longer, the firm must maintain a larger reserve for this contingency. Alternatively, poor infrastructure makes delivery times more uncertain and firms hold a reserve for the contingency that the delivery takes longer than average. By the transaction cost model, poor infrastructure would increase the fixed cost of each shipment, making small frequent shipments costly. This case might occur, for example, if poor infrastructure resulted in a lack of third party logistics providers who could efficiently handle small shipments. The extent of informatics technology and telecommunications development in any given country can also

> 7The transaction cost model predicts that large firms would hold less inventory than small firms when inventory is measured as a fraction of sales. Intuitively, a large firm can place orders in batches to capture economies of scale without spacing its orders far apart. Our analysis of firm level inventory data for several countries in Latin America did not find significant differences between inventory holdings of large and small firms.

8
_one-to-one mapping between equations and endogenous variables_<sup>8</sup> . Such a map is a function of model closure, which in turn depends on assumptions made about the functioning of the economy.

Table 2.2. Baseline Data

||**Activity**|**Commodity**|**Household**|**Rest of World**|**Total**|
|---|---|---|---|---|---|
|**Activity**||75||25|100|
|**Commodity**|||100||100|
|**Household**|100|||0|100|
|**Rest of World**||25|||25|
|**Total**|100|100|100|25||

Data Source: de Melo and Robinson (1989: 59) The following assumptions will help determine some exogenous variables. The small country assumption implies that both the world price of exports (πe) and imports (πm) are exogenous. If we choose the aggregate consumption good as _numéraire_ , then its price (Pq) can be set exogenously equal to unity. Full employment of primary factors of production means that real output is fixed. Therefore Xs is taken to be exogenous. Furthermore, we assume that nominal exchange rate adjusts to bring the trade account into balance. This assumption makes the balance of trade (Sf) an exogenous variable. **Box 2.1** shows the core model in EViews. The model consists of 10 equations and 10 endogenous variables. The two equilibrium conditions 2.11 and 2.12 are handled implicitly by letting a single variable stand for both supply and demand. Finally, it can be shown that the three equilibrium conditions are not linearly independent by Walras’ Law. Therefore any one of those can be left out of the system of equation describing the model.

We now present the entire program designed to set up the model, produce the baseline solution and simulate structural adjustment to external shocks, namely an increase in foreign transfers and a deterioration in the terms of trade. The data base is presented in Table 2.2. The program includes four basic components. The first sets up

> 8 A model is termed square when the number of endogenous variables is equal to the number of independent equations.

11
Policy Research Working Paper 5191

# **Abstract**

This paper provides a systematic, empirical assessment of the impact of infrastructure quality on the total factor productivity (TFP) of African manufacturing firms. This measure is understood to include quality in the provision of customs clearance, energy, water, sanitation, transportation, telecommunications, and information and communications technology (ICT). Microeconometric techniques to investment climate surveys (ICSs) of 26 African countries are carried out in different years during the period 2002–6, making country-specific evaluations of the impact of investment climate (IC) quality on aggregate TFP, average TFP, and allocative efficiency. For each country the impact is evaluated based on 10 different productivity measures. Results are robust once controlled for observable fixed effects (red tape, corruption and crime, finance, innovation and labor skills, etc.) obtained from the ICSs. African countries are ranked according to several indices: per capita income, ease of doing business, firm perceptions of growth bottlenecks, and the concept of demeaned productivity (Olley and Pakes 1996). The countries are divided into two blocks: high-incomegrowth and low-income-growth. Infrastructure quality has a low impact on TFP in countries of the first block and a high (negative) impact in countries of the second. There is significant heterogeneity in the individual infrastructure elements affecting countries from both blocks. Poor-quality electricity provision affects mainly poor countries, whereas problems dealing with customs while importing or exporting affects mainly fastergrowing countries. Losses from transport interruptions affect mainly slower-growing countries. Water outages affect mainly slower-growing countries. There is also some heterogeneity among countries in the infrastructure determinants of the allocative efficiency of African firms.

This paper—a product of the Sustainable Development Department, Latin American and the Caribbean Region—is part of a larger effort in the department to advocate and support the development and improvement of infrastructure services. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jguasch@worldbank.org.

_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