## 02 Manufacturing Investment (incl. Fixed-Asset Investment Overview)

> Interfaces implemented in this chapter are listed below; uncovered indicators are noted under “To be added” at the end.

#### China urban fixed-asset investment

Interface: macro_china_gdzctz

Source URL: http://data.eastmoney.com/cjsj/gdzctz.html

Description: China urban fixed-asset investment; monthly data from 200802 to present

Limits: Returns all historical data in a single call

Input

| Name | Type | Description |
|-----|-----|-----|
| -   | -   | -   |

Output

| Name | Type | Description |
|-------|---------|----------|
| Month | object | - |
| Current month | float64 | Unit: 100 million yuan |
| YoY growth | float64 | Unit: % |
| MoM growth | float64 | Unit: % |
| YTD cumulative | float64 | Unit: 100 million yuan |

Example

```python
import meshare as ms

macro_china_gdzctz_df = ms.macro_china_gdzctz()
print(macro_china_gdzctz_df)
```

Sample data

```
        Month        Current month   YoY growth   MoM growth      YTD cumulative
0    Oct 2022  50047.00   4.27  -7.84  471459.00
1    Sep 2022  54306.00   6.66  14.83  421412.00
2    Aug 2022  47294.00   6.57  -2.25  367106.00
3    Jul 2022  48382.00   3.75 -26.10  319812.00
4    Jun 2022  65466.00   5.62  24.89  271430.00
..         ...       ...    ...    ...        ...
158  Jun 2008  18171.78  29.49  53.29   58435.98
159  May 2008  11854.13  25.44  17.45   40264.20
160  Apr 2008  10093.14  25.37  -1.01   28410.07
161  Mar 2008  10195.65  27.31    NaN   18316.94
162  Feb 2008       NaN    NaN    NaN    8121.29
```

#### Manufacturing investment

Interface: macro_china_manufacturing_invest

Source URL: https://data.stats.gov.cn/dg/website/page.html#/pc/national/monthData

Description: NBS monthly series “Fixed-asset investment growth by industry (2018–)”: cumulative YoY of manufacturing fixed-asset investment. Detailed public releases are mainly growth rates; there is generally no matching absolute-value series.

Limits: Returns all monthly data within the specified time range in a single call

Input

| Name | Type | Description |
|----|----|----|
| start | str | Start month YYYYMM; default `201801` (this table starts from 2018) |
| end | str | End month YYYYMM; default `203612` |

Output

| Name | Type | Description |
|----|----|----|
| Month | object | e.g. `2026-06` |
| Month name | object | e.g. `Jun 2026` |
| Manufacturing FAI cumulative YoY | float64 | Unit: % |

Example

```python
import meshare as ms

df = ms.macro_china_manufacturing_invest()
print(df.tail())

df = ms.macro_china_manufacturing_invest(start="202001", end="202606")
print(df)
```

#### Enterprise boom and entrepreneur confidence indices

Interface: macro_china_enterprise_boom_index

Source URL: http://data.eastmoney.com/cjsj/qyjqzs.html

Description: China enterprise boom index and entrepreneur confidence index; from 2005 Q1 to present

Limits: Returns all historical data in a single call

Input

| Name | Type | Description |
|-----|-----|-----|
| -   | -   | -   |

Output

| Name | Type | Description |
|------------|---------|---------|
| Quarter | object | Date |
| Enterprise boom index-Index | float64 | - |
| Enterprise boom index-YoY | float64 | Unit: % |
| Enterprise boom index-QoQ | float64 | Unit: % |
| Entrepreneur confidence-Index | float64 | - |
| Entrepreneur confidence-YoY | float64 | Unit: % |
| Entrepreneur confidence-QoQ | float64 | Unit: % |

Example

```python
import meshare as ms

macro_china_enterprise_boom_index_df = ms.macro_china_enterprise_boom_index()
print(macro_china_enterprise_boom_index_df)
```

Sample data

```
           Quarter  Enterprise boom index-Index  Enterprise boom index-YoY  ...  Entrepreneur confidence-Index  Entrepreneur confidence-YoY  Entrepreneur confidence-QoQ
0   2022 Q2     101.80       1.80  ...         NaN         NaN         NaN
1   2022 Q1     112.70      12.70  ...         NaN         NaN         NaN
2   2021 Q4     119.20      19.20  ...         NaN         NaN         NaN
3   2021 Q3     119.20      19.20  ...      120.90       20.90       -5.10
4   2021 Q2     123.80      23.80  ...      126.00       26.00       -1.80
..        ...        ...        ...  ...         ...         ...         ...
65  2006 Q1     131.50      31.50  ...      133.10       33.10        7.70
66  2005 Q4     131.70      31.70  ...      125.40       25.40       -2.20
67  2005 Q3     132.00      32.00  ...      127.60       27.60       -0.90
68  2005 Q2     131.71      31.71  ...      128.50       28.50       -7.35
69  2005 Q1     132.46      32.46  ...      135.85       35.85        5.04
```